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n.inflate}(e);for(let t=0;t{const e=Date.now();this.chain().where((t=>{const n=t.meta.updated||t.meta.created,r=e-n;return this._ttl.agee&&e[t]?e[t]:null),this)[path[path.length-1]]=e},enumerable:!1,configurable:!0})}return data}clear({removeIndices:e=!1}={}){if(this._data=[],this._idIndex=[],this._cached=null,this._maxId=0,this._dynamicViews=[],this._dirty=!0,!0===e)this._rangedIndexes={},this._constraints={unique:{}};else{for(let e in this._rangedIndexes)this.ensureRangedIndex(e,this._rangedIndexes[e].indexTypeName,this._rangedIndexes[e].comparatorName);const e=Object.keys(this._constraints.unique);for(let i=0;i{this._constraints.unique[e].update(o.$loki,o[e])})),this._data[r]=o,this._lokimap[e.$loki]=o;for(let e=0;e{e in r&&this._constraints.unique[e].remove(r.$loki)}));for(let e=0;e>1;if(e="number"==typeof e?e:parseInt(e,10),isNaN(e))throw new TypeError("Passed id is not an integer");for(;data[r]>1,data[o]n&&(t.value=n,t.index=this._data[i].$loki)}return t}maxRecord(e){const t={index:0,value:0};if(0===this._data.length)return t.index=null,t.value=null,t;t.index=this._data[0].$loki,t.value=parseFloat(this._data[0][e]);for(let i=1;i!isNaN(e)))}avg(e){return k(this.extractNumerical(e))}stdDev(e){return function(e){const t=k(e),n=k(e.map((e=>{const n=e-t;return n*n})));return Math.sqrt(n)}(this.extractNumerical(e))}mode(e){const t={},data=this.extractNumerical(e);let n=data[0],r=-1/0;for(let i=0;ir&&(n=e,r=t[e])}return n}median(e){const t=this.extractNumerical(e);t.sort(((a,b)=>a-b));const n=Math.floor(t.length/2);return t.length%2?t[n]:(t[n-1]+t[n])/2}}},function(e,t,n){"use strict";class r{constructor(e,t){this.nodes={},this.apex=null,this.name=e,this.comparator=t}backup(){let e=new r(this.name,this.comparator);return e.nodes=JSON.parse(JSON.stringify(this.nodes)),e.apex=this.apex,e}restore(e){this.name=e.name,this.comparator=e.comparator,this.nodes=JSON.parse(JSON.stringify(e.nodes)),this.apex=e.apex}insert(e,t){if(e<=0)throw new Error("avl index ids are required to be numbers greater than zero");let n=this.nodes[e]={id:e,value:t,parent:null,balance:0,height:0,left:null,right:null,siblings:[]};this.apex?this.insertNode(this.nodes[this.apex],n):this.apex=e}insertNode(e,t){switch(this.comparator(t.value,e.value)){case 0:e.siblings.push(t.id),t.parent=e.id;break;case 1:e.right?(this.insertNode(this.nodes[e.right],t),this.updateBalance(e)):(e.right=t.id,t.parent=e.id,this.updateBalance(e));break;case-1:e.left?(this.insertNode(this.nodes[e.left],t),this.updateBalance(e)):(e.left=t.id,t.parent=e.id,this.updateBalance(e));break;default:throw new Error("Invalid comparator result")}if(e.balance<-1){if(null===e.left)throw new Error("insertNode.balance() : left child should not be null");this.nodes[e.left].balance<=0?this.leftLeftCase(e):this.leftRightCase(e)}if(e.balance>1){if(null===e.right)throw new Error("insertNode.balance() : right child should not be null");this.nodes[e.right].balance>=0?this.rightRightCase(e):this.rightLeftCase(e)}return e.height}updateBalance(e){let t=e.left?this.nodes[e.left].height:-1,hr=e.right?this.nodes[e.right].height:-1;e.height=t>hr?1+t:1+hr,e.balance=hr-t}leftLeftCase(e){return this.rotateRight(e)}leftRightCase(e){if(!e.left)throw new Error("leftRightCase: left child not set");return e.left=this.rotateLeft(this.nodes[e.left]).id,this.rotateRight(e)}rightRightCase(e){return this.rotateLeft(e)}rightLeftCase(e){if(!e.right)throw new Error("rightLeftCase: right child not set");return e.right=this.rotateRight(this.nodes[e.right]).id,this.rotateLeft(e)}rotateLeft(e){if(!e.right)throw new Error("rotateLeft: right child was unavailable.");let t=e.parent?this.nodes[e.parent]:null,n=this.nodes[e.right];if(e.right=n.left,e.right&&(this.nodes[e.right].parent=e.id),n.left=e.id,n.parent=e.parent,e.parent=n.id,t)if(t.left===e.id)t.left=n.id;else{if(t.right!==e.id)throw new Error("rotateLeft() : attempt to remap parent back to child failed... not found");t.right=n.id}else{if(this.apex!==e.id)throw new Error("rightRotate expecting parentless node to be apex");this.apex=n.id}return this.updateBalance(e),this.updateBalance(n),n}rotateRight(e){if(!e.left)throw new Error("rotateRight : left child unavailable");let t=e.parent?this.nodes[e.parent]:null,n=this.nodes[e.left];if(e.left=n.right,n.right&&(this.nodes[n.right].parent=e.id),n.right=e.id,n.parent=e.parent,e.parent=n.id,t)t.left===e.id?t.left=n.id:t.right=n.id;else{if(this.apex!==e.id)throw new Error("rightRotate expecting parentless node to be apex");this.apex=n.id}return this.updateBalance(e),this.updateBalance(n),n}getValuesAsTree(e){return null===this.apex?null:{id:(e=e||this.nodes[this.apex]).id,val:e.value,siblings:e.siblings,balance:e.balance,height:e.height,left:e.left?this.getValuesAsTree(this.nodes[e.left]):null,right:e.right?this.getValuesAsTree(this.nodes[e.right]):null}}update(e,t){let n=this.nodes[e];0!==this.comparator(n.value,t)&&(this.remove(e),this.insert(e,t))}remove(e){if(!this.apex)throw new Error("remove() : attempting remove when tree has no apex");this.removeNode(this.nodes[this.apex],e)}removeNode(e,t){if(!this.nodes[t])throw new Error("removeNode: attempting to remove a node which is not in hashmap");let n=this.nodes[t].value;switch(this.comparator(n,e.value)){case 0:if(e.siblings.length>0){if(e.id===t){let n=e.siblings.shift(),r=this.nodes[n];r.parent=e.parent,this.updateChildLink(e.parent,t,n),e.left&&(this.nodes[e.left].parent=n),e.right&&(this.nodes[e.right].parent=n),r.left=e.left,r.right=e.right,r.siblings=e.siblings,r.height=e.height,r.balance=e.balance,this.apex===t&&(this.apex=n);for(let e of r.siblings)this.nodes[e].parent=n;return void delete this.nodes[t]}{let n=e.siblings.indexOf(t);if(-1===n)throw new Error("Unable to remove sibling from parented sibling");return e.siblings.splice(n,1),void delete this.nodes[t]}}return e.left||e.right?e.left&&e.right?void this.promoteSuccessor(e):(e.left&&(this.promoteChild(e,this.nodes[e.left]),this.apex===t&&(this.apex=e.left)),void(e.right&&(this.promoteChild(e,this.nodes[e.right]),this.apex===t&&(this.apex=e.right)))):(this.updateChildLink(e.parent,e.id,null),delete this.nodes[t],void(t===this.apex&&(this.apex=null)));case 1:if(!e.right)throw new Error("removeNode: Unable to find value in tree");this.removeNode(this.nodes[e.right],t);break;case-1:if(!e.left)throw new Error("removeNode: Unable to find value in tree");this.removeNode(this.nodes[e.left],t)}if(this.updateBalance(e),e.balance<-1){if(null===e.left)throw new Error("insertNode.balance() : left child should not be null");this.nodes[e.left].balance<=0?this.leftLeftCase(e):this.leftRightCase(e)}if(e.balance>1){if(null===e.right)throw new Error("insertNode.balance() : right child should not be null");this.nodes[e.right].balance>=0?this.rightRightCase(e):this.rightLeftCase(e)}}updateChildLink(e,t,n){if(null===e)return;let r=this.nodes[e];r.left===t?r.left=n:r.right===t&&(r.right=n)}promoteChild(e,t){let n=e.parent;if(n){let r=this.nodes[n];r.left===e.id?r.left=t.id:r.right===e.id&&(r.right=t.id)}t.parent=n,delete this.nodes[e.id]}promoteSuccessor(e){let t=e.id;if(!e.right||!e.left)throw new Error("promoteSuccessor() : node to replace does not have two children");let n,r,o,l=null;if(e.balance<0){let t=this.nodes[e.left];l=this.findGreaterLeaf(t),n=l.id,r=l.value,o=l.siblings,l.siblings=[],this.removeNode(t,n)}else{let t=this.nodes[e.right];l=this.findLesserLeaf(t),n=l.id,r=l.value,o=l.siblings,l.siblings=[],this.removeNode(t,n)}if(e.parent){let p=this.nodes[e.parent];p.left===t&&(p.left=n),p.right===t&&(p.right=n)}e.left&&(this.nodes[e.left].parent=n),e.right&&(this.nodes[e.right].parent=n),e.id=n,e.value=r,e.siblings=o,this.nodes[n]=e,delete this.nodes[t],this.apex===t&&(this.apex=n),this.updateBalance(e)}findGreaterLeaf(e){if(!e.right)return e;let t=this.findGreaterLeaf(this.nodes[e.right]);return t||e}findLesserLeaf(e){if(!e.left)return e;let t=this.findLesserLeaf(this.nodes[e.left]);return t||e}rangeRequest(e){if(!this.apex)return[];if(!e)return this.collateIds(this.nodes[this.apex]);if("$eq"===e.op){let t=this.locate(this.nodes[this.apex],e.val);return null===t?[]:t.siblings.length?[t.id,...t.siblings]:[t.id]}return this.collateRequest(this.nodes[this.apex],e)}collateRequest(e,t){let n=[];if("$eq"===t.op)throw new Error("collateRequest does not support $eq range request");let r=this.comparator(e.value,t.val),o=0;if("$between"===t.op){if(null===t.high||void 0===t.high)throw new Error("collateRequest: $between request missing high range value");o=this.comparator(e.value,t.high)}if(e.left)switch(t.op){case"$lt":case"$lte":n=this.collateRequest(this.nodes[e.left],t);break;case"$gt":case"$gte":case"$between":1===r&&(n=this.collateRequest(this.nodes[e.left],t))}if(t)switch(t.op){case"$lt":-1===r&&(n.push(e.id),n.push(...e.siblings));break;case"$lte":-1!==r&&0!==r||(n.push(e.id),n.push(...e.siblings));break;case"$gt":1===r&&(n.push(e.id),n.push(...e.siblings));break;case"$gte":1!==r&&0!==r||(n.push(e.id),n.push(...e.siblings));break;case"$between":r>=0&&o<=0&&(n.push(e.id),n.push(...e.siblings))}else n.push(e.id),n.push(...e.siblings);if(e.right)if(t)switch(t.op){case"$lt":case"$lte":-1===r&&n.push(...this.collateRequest(this.nodes[e.right],t));break;case"$gt":case"$gte":n.push(...this.collateRequest(this.nodes[e.right],t));break;case"$between":-1===o&&n.push(...this.collateRequest(this.nodes[e.right],t))}else n.push(...this.collateRequest(this.nodes[e.right],t));return n}collateIds(e){let t=[];return e?(e.left&&(t=this.collateIds(this.nodes[e.left])),t.push(e.id),t.push(...e.siblings),e.right&&t.push(...this.collateIds(this.nodes[e.right])),t):[]}locate(e,t){for(;null!==e;)switch(this.comparator(t,e.value)){case 0:return e;case 1:if(!e.right)return null;e=this.nodes[e.right];break;case-1:if(!e.left)return null;e=this.nodes[e.left]}return null}validateIndex(){if(!this.apex)return 0===Object.keys(this.nodes).length;if(null!==this.nodes[this.apex].parent)return!1;let e=this.collateIds(this.nodes[this.apex]),t=Object.keys(this.nodes).length;if(e.length!==t)return!1;if(1===e.length)return null===this.nodes[e[0]].parent&&null===this.nodes[e[0]].left&&null===this.nodes[e[0]].right;for(let i=0;i0)for(let t of e.siblings)if(this.nodes[t].parent!==e.id)return!1;if(e.left){if(this.nodes[e.left].parent!==e.id)return!1;if(!this.validateNode(this.nodes[e.left]))return!1}if(e.right){if(this.nodes[e.right].parent!==e.id)return!1;if(!this.validateNode(this.nodes[e.right]))return!1}return!0}}n.d(t,"a",(function(){return o}));let o={avl:(e,t)=>new r(e,t)}},function(e,t,n){"use strict";n.d(t,"a",(function(){return r}));class r{constructor(){this._events={},this._asyncListeners=!1}on(e,t){let n;return Array.isArray(e)?(e.forEach((e=>{this.on(e,t)})),t):(n=this._events[e],n||(n=this._events[e]=[]),n.push(t),t)}emit(e,...data){e&&this._events[e]&&this._events[e].forEach((e=>{this._asyncListeners?setTimeout((()=>{e(...data)}),1):e(...data)}))}addListener(e,t){return this.on(e,t)}removeListener(e,t){if(Array.isArray(e)&&e.forEach((e=>{this.removeListener(e,t)})),this._events[e]){const n=this._events[e];n.splice(n.indexOf(t),1)}}}},function(e,t,n){"use strict";(function(e){n.d(t,"a",(function(){return _}));var r=n(5),o=n(3),l=n(1),h=n(0),c=n(4),d=n(2);function f(){if(void 0!==e&&(e.android||e.NSObject))return"NATIVESCRIPT";if(void 0!==e&&"[object process]"==={}.toString.call(e.process))return e.window,"NODEJS";if(void 0!==document)return-1===document.URL.indexOf("http://")&&-1===document.URL.indexOf("https://")?"CORDOVA":"BROWSER";if(void 0!==window&&"[object Window]"==={}.toString.call(window))return"BROWSER";throw SyntaxError("Unknown environment...")}class _ extends r.a{constructor(e="loki.db",t={}){if(super(),this.databaseVersion=1.5,this.engineVersion=1.5,this._persistenceMethod=null,this._persistenceAdapter=null,this._throttledSaves=!0,this._throttledSaveRunning=null,this._throttledSavePending=null,this._autosave=!1,this._autosaveInterval=5e3,this._autosaveRunning=!1,this._autosaveHandler=Promise.resolve(),this.filename=e,this._collections=[],({serializationMethod:this._serializationMethod="normal",destructureDelimiter:this._destructureDelimiter="$<\n",env:this._env=f()}=t),this._events={init:[],loaded:[],flushChanges:[],close:[],changes:[],warning:[]},t.comparatorMap)for(let e in t.comparatorMap)h.a[e]=t.comparatorMap[e];if(t.rangedIndexFactoryMap)for(let e in t.rangedIndexFactoryMap)c.a[e]=t.rangedIndexFactoryMap[e];if(t.lokiOperatorPackageMap)for(let e in t.lokiOperatorPackageMap)d.a[e]=t.lokiOperatorPackageMap[e];this.on("init",this.clearChanges)}initializePersistence(e={}){let t=this._autosaveDisable();({autosave:this._autosave=!1,autosaveInterval:this._autosaveInterval=5e3,persistenceMethod:this._persistenceMethod,throttledSaves:this._throttledSaves=!0}=e);const n={NODEJS:["fs-storage"],BROWSER:["local-storage","indexed-storage"],CORDOVA:["local-storage","indexed-storage"],MEMORY:["memory-storage"]},r={"fs-storage":l.a.FSStorage,"local-storage":l.a.LocalStorage,"indexed-storage":l.a.IndexedStorage,"memory-storage":l.a.MemoryStorage};if(void 0!==this._persistenceMethod){if("function"!=typeof r[this._persistenceMethod])throw Error("Unknown persistence method.");this._persistenceAdapter=new r[this._persistenceMethod]}if(void 0!==e.adapter&&(this._persistenceMethod="adapter",this._persistenceAdapter=e.adapter),null===this._persistenceAdapter){let e=n[this._env];if(e)for(let i=0;ithis._loadDatabase(e.inflate,!0)))),t.then((()=>{this._autosaveEnable()}))}copy(e={}){const t=new _(this.filename,{env:this._env});if(t.loadJSONObject(this,{retainDirtyFlags:!0}),e.removeNonSerializable){t._persistenceAdapter=null;for(let e=0;e=0)return this.serializeCollection({delimited:e.delimited,delimiter:e.delimiter,collectionIndex:e.partition});let t=new _(this.filename);t.loadJSONObject(this);for(let e=0;eo&&(c=!0):r._collections[l]._data.push(JSON.parse(n[h])),n[h++]=null;return r}deserializeCollection(e,t={}){void 0===t.partitioned&&(t.partitioned=!1),void 0===t.delimited&&(t.delimited=!0),void 0===t.delimiter&&(t.delimiter=this._destructureDelimiter);let n=[];t.delimited?(n=e.split(t.delimiter),n.pop()):n=e;for(let e=0;ethis._autosaveDirty()?this.saveDatabase():Promise.resolve())):Promise.resolve().then((()=>{this.emit("close")}))}generateChangesNotification(e){let t=[];const n=e||this._collections.map((e=>e.name));return this._collections.forEach((e=>{-1!==n.indexOf(e.name)&&(t=t.concat(e.getChanges()))})),t}serializeChanges(e){return JSON.stringify(this.generateChangesNotification(e))}clearChanges(){this._collections.forEach((e=>{e.flushChanges&&e.flushChanges()}))}throttledSaveDrain(e={}){const t=(new Date).getTime();return this._throttledSaves?(void 0===e.recursiveWait&&(e.recursiveWait=!0),void 0===e.recursiveWaitLimit&&(e.recursiveWaitLimit=!1),void 0===e.recursiveWaitLimitDuration&&(e.recursiveWaitLimitDuration=2e3),void 0===e.started&&(e.started=new Date),this._throttledSaves&&null!==this._throttledSaveRunning?e.recursiveWait?Promise.resolve(Promise.all([this._throttledSaveRunning,this._throttledSavePending])).then((()=>null!==this._throttledSaveRunning||null!==this._throttledSavePending?e.recursiveWaitLimit&&t-e.started.getTime()>e.recursiveWaitLimitDuration?Promise.reject({}):this.throttledSaveDrain(e):Promise.resolve())):Promise.resolve(this._throttledSaveRunning):Promise.resolve()):Promise.resolve()}_loadDatabase(e={},t=!1){return null===this._persistenceAdapter?Promise.reject(new Error("persistenceAdapter not configured")):Promise.resolve(this._persistenceAdapter.loadDatabase(this.filename)).then((t=>{if("string"==typeof t)this.loadJSON(t,e),this.emit("load",this);else{if("object"!=typeof t||null===t||t instanceof Error)throw t;this.loadJSONObject(t,e),this.emit("load",this)}})).catch((e=>{if(e instanceof Error)throw e;if(null!=e)throw new TypeError("The persistence adapter did not load a serialized DB string or object.");if(!t)throw new Error("Database not found.")}))}loadDatabase(e={}){return this._throttledSaves?this.throttledSaveDrain(e).then((()=>(this._throttledSaveRunning=this._loadDatabase(e).then((()=>{this._throttledSaveRunning=null})),this._throttledSaveRunning)),(()=>{throw new Error("Unable to pause save throttling long enough to read database")})):this._loadDatabase(e)}_saveDatabase(){return null===this._persistenceAdapter?Promise.reject(new Error("persistenceAdapter not configured")):"reference"===this._persistenceAdapter.mode&&"function"==typeof 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this._persistent&&(this._resultData=this._resultSet.data()),void(this._sortFunction||this._sortCriteria||this._sortCriteriaSimple?this._queueSortPhase():this._queueRebuildEvent());const n=this._resultSet._filteredRows,r=t?-1:n.indexOf(+e),o=n.length,l=new v(this._collection);let filter;l._filteredRows=[e],l._filterInitialized=!0;for(let e=0,t=this._filterPipeline.length;ee&&t[n]--}mapReduce(e,t){try{return t(this.data().map(e))}catch(e){throw e}}}var x=n(4),S=n(1);function k(e){return e.reduce(((a,b)=>a+b),0)/e.length}function I(e,path,t,n=0){if(void 0===e)return!1;if(n+1===path.length)return t.push(e[path[n]]),!1;const r=e[path[n]];if(Array.isArray(r)){for(let i=0;i0)throw new Error("disableMeta option cannot be passed as true when ttl is enabled")}if(this.name=e,this._unindexedSortComparator=t.unindexedSortComparator||"js",this._defaultLokiOperatorPackage=t.defaultLokiOperatorPackage||"js",void 0!==t.unique&&(Array.isArray(t.unique)||(t.unique=[t.unique]),t.unique.forEach((e=>{this._constraints.unique[e]=new o(e)}))),void 0!==S.a.FullTextSearch?this._fullTextSearch=void 0!==t.fullTextSearch?new S.a.FullTextSearch(t.fullTextSearch):null:this._fullTextSearch=null,this._transactional=void 0!==t.transactional&&t.transactional,this._cloneObjects=void 0!==t.clone&&t.clone,this._asyncListeners=void 0!==t.asyncListeners&&t.asyncListeners,this._disableMeta=void 0!==t.disableMeta&&t.disableMeta,this._disableChangesApi=void 0===t.disableChangesApi||t.disableChangesApi,this._disableDeltaChangesApi=void 0===t.disableDeltaChangesApi||t.disableDeltaChangesApi,this._cloneMethod=void 0!==t.cloneMethod?t.cloneMethod:"deep",this._disableChangesApi&&(this._disableDeltaChangesApi=!0),this._serializableIndexes=void 0===t.serializableIndexes||t.serializableIndexes,null!=t.nestedProperties)for(let i=0;i{for(let n in e)t[n]=e[n]});return data=>{const t=new n.proto;return e(data,t),t}}return n.inflate}(e);for(let t=0;t{const e=Date.now();this.chain().where((t=>{const n=t.meta.updated||t.meta.created,r=e-n;return this._ttl.agee&&e[t]?e[t]:null),this)[path[path.length-1]]=e},enumerable:!1,configurable:!0})}return data}clear({removeIndices:e=!1}={}){if(this._data=[],this._idIndex=[],this._cached=null,this._maxId=0,this._dynamicViews=[],this._dirty=!0,!0===e)this._rangedIndexes={},this._constraints={unique:{}};else{for(let e in this._rangedIndexes)this.ensureRangedIndex(e,this._rangedIndexes[e].indexTypeName,this._rangedIndexes[e].comparatorName);const e=Object.keys(this._constraints.unique);for(let i=0;i{this._constraints.unique[e].update(o.$loki,o[e])})),this._data[r]=o,this._lokimap[e.$loki]=o;for(let e=0;e{e in r&&this._constraints.unique[e].remove(r.$loki)}));for(let e=0;e>1;if(e="number"==typeof e?e:parseInt(e,10),isNaN(e))throw new TypeError("Passed id is not an integer");for(;data[r]>1,data[o]n&&(t.value=n,t.index=this._data[i].$loki)}return t}maxRecord(e){const t={index:0,value:0};if(0===this._data.length)return t.index=null,t.value=null,t;t.index=this._data[0].$loki,t.value=parseFloat(this._data[0][e]);for(let i=1;i!isNaN(e)))}avg(e){return k(this.extractNumerical(e))}stdDev(e){return function(e){const t=k(e),n=k(e.map((e=>{const n=e-t;return n*n})));return Math.sqrt(n)}(this.extractNumerical(e))}mode(e){const t={},data=this.extractNumerical(e);let n=data[0],r=-1/0;for(let i=0;ir&&(n=e,r=t[e])}return n}median(e){const t=this.extractNumerical(e);t.sort(((a,b)=>a-b));const n=Math.floor(t.length/2);return t.length%2?t[n]:(t[n-1]+t[n])/2}}},function(e,t,n){"use strict";class r{constructor(e,t){this.nodes={},this.apex=null,this.name=e,this.comparator=t}backup(){let e=new r(this.name,this.comparator);return e.nodes=JSON.parse(JSON.stringify(this.nodes)),e.apex=this.apex,e}restore(e){this.name=e.name,this.comparator=e.comparator,this.nodes=JSON.parse(JSON.stringify(e.nodes)),this.apex=e.apex}insert(e,t){if(e<=0)throw new Error("avl index ids are required to be numbers greater than zero");let n=this.nodes[e]={id:e,value:t,parent:null,balance:0,height:0,left:null,right:null,siblings:[]};this.apex?this.insertNode(this.nodes[this.apex],n):this.apex=e}insertNode(e,t){switch(this.comparator(t.value,e.value)){case 0:e.siblings.push(t.id),t.parent=e.id;break;case 1:e.right?(this.insertNode(this.nodes[e.right],t),this.updateBalance(e)):(e.right=t.id,t.parent=e.id,this.updateBalance(e));break;case-1:e.left?(this.insertNode(this.nodes[e.left],t),this.updateBalance(e)):(e.left=t.id,t.parent=e.id,this.updateBalance(e));break;default:throw new Error("Invalid comparator result")}if(e.balance<-1){if(null===e.left)throw new Error("insertNode.balance() : left child should not be null");this.nodes[e.left].balance<=0?this.leftLeftCase(e):this.leftRightCase(e)}if(e.balance>1){if(null===e.right)throw new Error("insertNode.balance() : right child should not be null");this.nodes[e.right].balance>=0?this.rightRightCase(e):this.rightLeftCase(e)}return e.height}updateBalance(e){let t=e.left?this.nodes[e.left].height:-1,hr=e.right?this.nodes[e.right].height:-1;e.height=t>hr?1+t:1+hr,e.balance=hr-t}leftLeftCase(e){return this.rotateRight(e)}leftRightCase(e){if(!e.left)throw new Error("leftRightCase: left child not set");return e.left=this.rotateLeft(this.nodes[e.left]).id,this.rotateRight(e)}rightRightCase(e){return this.rotateLeft(e)}rightLeftCase(e){if(!e.right)throw new Error("rightLeftCase: right child not set");return e.right=this.rotateRight(this.nodes[e.right]).id,this.rotateLeft(e)}rotateLeft(e){if(!e.right)throw new Error("rotateLeft: right child was unavailable.");let t=e.parent?this.nodes[e.parent]:null,n=this.nodes[e.right];if(e.right=n.left,e.right&&(this.nodes[e.right].parent=e.id),n.left=e.id,n.parent=e.parent,e.parent=n.id,t)if(t.left===e.id)t.left=n.id;else{if(t.right!==e.id)throw new Error("rotateLeft() : attempt to remap parent back to child failed... not found");t.right=n.id}else{if(this.apex!==e.id)throw new Error("rightRotate expecting parentless node to be apex");this.apex=n.id}return this.updateBalance(e),this.updateBalance(n),n}rotateRight(e){if(!e.left)throw new Error("rotateRight : left child unavailable");let t=e.parent?this.nodes[e.parent]:null,n=this.nodes[e.left];if(e.left=n.right,n.right&&(this.nodes[n.right].parent=e.id),n.right=e.id,n.parent=e.parent,e.parent=n.id,t)t.left===e.id?t.left=n.id:t.right=n.id;else{if(this.apex!==e.id)throw new Error("rightRotate expecting parentless node to be apex");this.apex=n.id}return this.updateBalance(e),this.updateBalance(n),n}getValuesAsTree(e){return null===this.apex?null:{id:(e=e||this.nodes[this.apex]).id,val:e.value,siblings:e.siblings,balance:e.balance,height:e.height,left:e.left?this.getValuesAsTree(this.nodes[e.left]):null,right:e.right?this.getValuesAsTree(this.nodes[e.right]):null}}update(e,t){let n=this.nodes[e];0!==this.comparator(n.value,t)&&(this.remove(e),this.insert(e,t))}remove(e){if(!this.apex)throw new Error("remove() : attempting remove when tree has no apex");this.removeNode(this.nodes[this.apex],e)}removeNode(e,t){if(!this.nodes[t])throw new Error("removeNode: attempting to remove a node which is not in hashmap");let n=this.nodes[t].value;switch(this.comparator(n,e.value)){case 0:if(e.siblings.length>0){if(e.id===t){let n=e.siblings.shift(),r=this.nodes[n];r.parent=e.parent,this.updateChildLink(e.parent,t,n),e.left&&(this.nodes[e.left].parent=n),e.right&&(this.nodes[e.right].parent=n),r.left=e.left,r.right=e.right,r.siblings=e.siblings,r.height=e.height,r.balance=e.balance,this.apex===t&&(this.apex=n);for(let e of r.siblings)this.nodes[e].parent=n;return void delete this.nodes[t]}{let n=e.siblings.indexOf(t);if(-1===n)throw new Error("Unable to remove sibling from parented sibling");return e.siblings.splice(n,1),void delete this.nodes[t]}}return e.left||e.right?e.left&&e.right?void this.promoteSuccessor(e):(e.left&&(this.promoteChild(e,this.nodes[e.left]),this.apex===t&&(this.apex=e.left)),void(e.right&&(this.promoteChild(e,this.nodes[e.right]),this.apex===t&&(this.apex=e.right)))):(this.updateChildLink(e.parent,e.id,null),delete this.nodes[t],void(t===this.apex&&(this.apex=null)));case 1:if(!e.right)throw new Error("removeNode: Unable to find value in tree");this.removeNode(this.nodes[e.right],t);break;case-1:if(!e.left)throw new Error("removeNode: Unable to find value in tree");this.removeNode(this.nodes[e.left],t)}if(this.updateBalance(e),e.balance<-1){if(null===e.left)throw new Error("insertNode.balance() : left child should not be null");this.nodes[e.left].balance<=0?this.leftLeftCase(e):this.leftRightCase(e)}if(e.balance>1){if(null===e.right)throw new Error("insertNode.balance() : right child should not be null");this.nodes[e.right].balance>=0?this.rightRightCase(e):this.rightLeftCase(e)}}updateChildLink(e,t,n){if(null===e)return;let r=this.nodes[e];r.left===t?r.left=n:r.right===t&&(r.right=n)}promoteChild(e,t){let n=e.parent;if(n){let r=this.nodes[n];r.left===e.id?r.left=t.id:r.right===e.id&&(r.right=t.id)}t.parent=n,delete this.nodes[e.id]}promoteSuccessor(e){let t=e.id;if(!e.right||!e.left)throw new Error("promoteSuccessor() : node to replace does not have two children");let n,r,o,l=null;if(e.balance<0){let t=this.nodes[e.left];l=this.findGreaterLeaf(t),n=l.id,r=l.value,o=l.siblings,l.siblings=[],this.removeNode(t,n)}else{let t=this.nodes[e.right];l=this.findLesserLeaf(t),n=l.id,r=l.value,o=l.siblings,l.siblings=[],this.removeNode(t,n)}if(e.parent){let p=this.nodes[e.parent];p.left===t&&(p.left=n),p.right===t&&(p.right=n)}e.left&&(this.nodes[e.left].parent=n),e.right&&(this.nodes[e.right].parent=n),e.id=n,e.value=r,e.siblings=o,this.nodes[n]=e,delete this.nodes[t],this.apex===t&&(this.apex=n),this.updateBalance(e)}findGreaterLeaf(e){if(!e.right)return e;let t=this.findGreaterLeaf(this.nodes[e.right]);return t||e}findLesserLeaf(e){if(!e.left)return e;let t=this.findLesserLeaf(this.nodes[e.left]);return t||e}rangeRequest(e){if(!this.apex)return[];if(!e)return this.collateIds(this.nodes[this.apex]);if("$eq"===e.op){let t=this.locate(this.nodes[this.apex],e.val);return null===t?[]:t.siblings.length?[t.id,...t.siblings]:[t.id]}return this.collateRequest(this.nodes[this.apex],e)}collateRequest(e,t){let n=[];if("$eq"===t.op)throw new Error("collateRequest does not support $eq range request");let r=this.comparator(e.value,t.val),o=0;if("$between"===t.op){if(null===t.high||void 0===t.high)throw new Error("collateRequest: $between request missing high range value");o=this.comparator(e.value,t.high)}if(e.left)switch(t.op){case"$lt":case"$lte":n=this.collateRequest(this.nodes[e.left],t);break;case"$gt":case"$gte":case"$between":1===r&&(n=this.collateRequest(this.nodes[e.left],t))}if(t)switch(t.op){case"$lt":-1===r&&(n.push(e.id),n.push(...e.siblings));break;case"$lte":-1!==r&&0!==r||(n.push(e.id),n.push(...e.siblings));break;case"$gt":1===r&&(n.push(e.id),n.push(...e.siblings));break;case"$gte":1!==r&&0!==r||(n.push(e.id),n.push(...e.siblings));break;case"$between":r>=0&&o<=0&&(n.push(e.id),n.push(...e.siblings))}else n.push(e.id),n.push(...e.siblings);if(e.right)if(t)switch(t.op){case"$lt":case"$lte":-1===r&&n.push(...this.collateRequest(this.nodes[e.right],t));break;case"$gt":case"$gte":n.push(...this.collateRequest(this.nodes[e.right],t));break;case"$between":-1===o&&n.push(...this.collateRequest(this.nodes[e.right],t))}else n.push(...this.collateRequest(this.nodes[e.right],t));return n}collateIds(e){let t=[];return e?(e.left&&(t=this.collateIds(this.nodes[e.left])),t.push(e.id),t.push(...e.siblings),e.right&&t.push(...this.collateIds(this.nodes[e.right])),t):[]}locate(e,t){for(;null!==e;)switch(this.comparator(t,e.value)){case 0:return e;case 1:if(!e.right)return null;e=this.nodes[e.right];break;case-1:if(!e.left)return null;e=this.nodes[e.left]}return null}validateIndex(){if(!this.apex)return 0===Object.keys(this.nodes).length;if(null!==this.nodes[this.apex].parent)return!1;let e=this.collateIds(this.nodes[this.apex]),t=Object.keys(this.nodes).length;if(e.length!==t)return!1;if(1===e.length)return null===this.nodes[e[0]].parent&&null===this.nodes[e[0]].left&&null===this.nodes[e[0]].right;for(let i=0;i0)for(let t of e.siblings)if(this.nodes[t].parent!==e.id)return!1;if(e.left){if(this.nodes[e.left].parent!==e.id)return!1;if(!this.validateNode(this.nodes[e.left]))return!1}if(e.right){if(this.nodes[e.right].parent!==e.id)return!1;if(!this.validateNode(this.nodes[e.right]))return!1}return!0}}n.d(t,"a",(function(){return o}));let o={avl:(e,t)=>new r(e,t)}},function(e,t,n){"use strict";n.d(t,"a",(function(){return r}));class r{constructor(){this._events={},this._asyncListeners=!1}on(e,t){let n;return Array.isArray(e)?(e.forEach((e=>{this.on(e,t)})),t):(n=this._events[e],n||(n=this._events[e]=[]),n.push(t),t)}emit(e,...data){e&&this._events[e]&&this._events[e].forEach((e=>{this._asyncListeners?setTimeout((()=>{e(...data)}),1):e(...data)}))}addListener(e,t){return this.on(e,t)}removeListener(e,t){if(Array.isArray(e)&&e.forEach((e=>{this.removeListener(e,t)})),this._events[e]){const n=this._events[e];n.splice(n.indexOf(t),1)}}}},function(e,t,n){"use strict";(function(e){n.d(t,"a",(function(){return _}));var r=n(5),o=n(3),l=n(1),h=n(0),c=n(4),d=n(2);function f(){if(void 0!==e&&(e.android||e.NSObject))return"NATIVESCRIPT";if(void 0!==e&&"[object process]"==={}.toString.call(e.process))return e.window,"NODEJS";if(void 0!==document)return-1===document.URL.indexOf("http://")&&-1===document.URL.indexOf("https://")?"CORDOVA":"BROWSER";if(void 0!==window&&"[object Window]"==={}.toString.call(window))return"BROWSER";throw SyntaxError("Unknown environment...")}class _ extends r.a{constructor(e="loki.db",t={}){if(super(),this.databaseVersion=1.5,this.engineVersion=1.5,this._persistenceMethod=null,this._persistenceAdapter=null,this._throttledSaves=!0,this._throttledSaveRunning=null,this._throttledSavePending=null,this._autosave=!1,this._autosaveInterval=5e3,this._autosaveRunning=!1,this._autosaveHandler=Promise.resolve(),this.filename=e,this._collections=[],({serializationMethod:this._serializationMethod="normal",destructureDelimiter:this._destructureDelimiter="$<\n",env:this._env=f()}=t),this._events={init:[],loaded:[],flushChanges:[],close:[],changes:[],warning:[]},t.comparatorMap)for(let e in t.comparatorMap)h.a[e]=t.comparatorMap[e];if(t.rangedIndexFactoryMap)for(let e in t.rangedIndexFactoryMap)c.a[e]=t.rangedIndexFactoryMap[e];if(t.lokiOperatorPackageMap)for(let e in t.lokiOperatorPackageMap)d.a[e]=t.lokiOperatorPackageMap[e];this.on("init",this.clearChanges)}initializePersistence(e={}){let t=this._autosaveDisable();({autosave:this._autosave=!1,autosaveInterval:this._autosaveInterval=5e3,persistenceMethod:this._persistenceMethod,throttledSaves:this._throttledSaves=!0}=e);const n={NODEJS:["fs-storage"],BROWSER:["local-storage","indexed-storage"],CORDOVA:["local-storage","indexed-storage"],MEMORY:["memory-storage"]},r={"fs-storage":l.a.FSStorage,"local-storage":l.a.LocalStorage,"indexed-storage":l.a.IndexedStorage,"memory-storage":l.a.MemoryStorage};if(void 0!==this._persistenceMethod){if("function"!=typeof r[this._persistenceMethod])throw Error("Unknown persistence method.");this._persistenceAdapter=new r[this._persistenceMethod]}if(void 0!==e.adapter&&(this._persistenceMethod="adapter",this._persistenceAdapter=e.adapter),null===this._persistenceAdapter){let e=n[this._env];if(e)for(let i=0;ithis._loadDatabase(e.inflate,!0)))),t.then((()=>{this._autosaveEnable()}))}copy(e={}){const t=new _(this.filename,{env:this._env});if(t.loadJSONObject(this,{retainDirtyFlags:!0}),e.removeNonSerializable){t._persistenceAdapter=null;for(let e=0;e=0)return this.serializeCollection({delimited:e.delimited,delimiter:e.delimiter,collectionIndex:e.partition});let t=new _(this.filename);t.loadJSONObject(this);for(let e=0;eo&&(c=!0):r._collections[l]._data.push(JSON.parse(n[h])),n[h++]=null;return r}deserializeCollection(e,t={}){void 0===t.partitioned&&(t.partitioned=!1),void 0===t.delimited&&(t.delimited=!0),void 0===t.delimiter&&(t.delimiter=this._destructureDelimiter);let n=[];t.delimited?(n=e.split(t.delimiter),n.pop()):n=e;for(let e=0;ethis._autosaveDirty()?this.saveDatabase():Promise.resolve())):Promise.resolve().then((()=>{this.emit("close")}))}generateChangesNotification(e){let t=[];const n=e||this._collections.map((e=>e.name));return this._collections.forEach((e=>{-1!==n.indexOf(e.name)&&(t=t.concat(e.getChanges()))})),t}serializeChanges(e){return JSON.stringify(this.generateChangesNotification(e))}clearChanges(){this._collections.forEach((e=>{e.flushChanges&&e.flushChanges()}))}throttledSaveDrain(e={}){const t=(new Date).getTime();return this._throttledSaves?(void 0===e.recursiveWait&&(e.recursiveWait=!0),void 0===e.recursiveWaitLimit&&(e.recursiveWaitLimit=!1),void 0===e.recursiveWaitLimitDuration&&(e.recursiveWaitLimitDuration=2e3),void 0===e.started&&(e.started=new Date),this._throttledSaves&&null!==this._throttledSaveRunning?e.recursiveWait?Promise.resolve(Promise.all([this._throttledSaveRunning,this._throttledSavePending])).then((()=>null!==this._throttledSaveRunning||null!==this._throttledSavePending?e.recursiveWaitLimit&&t-e.started.getTime()>e.recursiveWaitLimitDuration?Promise.reject({}):this.throttledSaveDrain(e):Promise.resolve())):Promise.resolve(this._throttledSaveRunning):Promise.resolve()):Promise.resolve()}_loadDatabase(e={},t=!1){return null===this._persistenceAdapter?Promise.reject(new 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We annotate these studies with a single EFO term."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Some common examples include:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Standard case-control studies comparing individuals with a disease or phenotypic characteristic, versus controls individuals without that trait."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-sg-trait-binary.png","alt":"A case-control study with a single binary trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Quantitative studies looking at a single measurement"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-sg-trait-quant.png","alt":"A quantitative study with a single continuous trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In these examples, any reported variants are clearly and straightforwardly associated with that single trait."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"b-multi-trait-studies"},"children":[{"type":"element","tag":"a","props":{"href":"#b-multi-trait-studies","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"B. Multi-trait studies"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In about 10% of studies, there are multiple traits of interest that are analysed simultaneously. We annotate these studies with multiple EFO terms separated by a comma, indicating that any significant variants reported in the study are associated with both traits in some way."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Some common examples include:"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Studies comparing individuals with two comorbid diseases (or other traits), versus control individuals who have neither disease. In the reported trait we write “Disease 1 "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"and"}]},{"type":"text","value":" Disease 2”. In the trait we list multiple EFO terms separated by a comma."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-multi-trait-and.png","alt":"A case-control study where cases have two co-occuring traits"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Studies comparing individuals with either of two diseases (or other traits), versus control individuals who have neither disease. Often these are two traits that are hypothesised to have some common underlying genetic factors (pleiotropy). In the reported trait we write “Disease 1 "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"or"}]},{"type":"text","value":" Disease 2”. In the trait we list multiple EFO terms separated by a comma."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-multi-trait-or.png","alt":"A case-control study where cases may have either of two distinct traits"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In these examples, any significant variants reported are associated with either or both of the annotated traits. If a user is searching for associations with either trait term, then these results will come up in their search."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"c-studies-with-a-background-trait"},"children":[{"type":"element","tag":"a","props":{"href":"#c-studies-with-a-background-trait","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"C. Studies with a background trait"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Finally, about 6% of studies analyse only one main trait of interest, but in the context of a background trait that is shared by all of the participants in the study."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Some examples include:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Studies comparing cases and controls of one disease, but only within a sample of people who have another disease. In the reported trait we write “Disease 1 "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" Disease 2”. In the past, we listed multiple EFO terms in the trait column, as for scenario B."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-bg-trait-binary.png","alt":"A case-control study for one binary trait within a second background trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Quantitative studies analysing a measurement in the context of a disease. In the reported trait we write “Measurement "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" Disease”. In the past, we listed multiple EFO terms in the trait column, as for scenario B."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-bg-trait-quant.png","alt":"A quantitative study for one continuous trait within a second background trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In these examples, any reported variants are associated with the main trait, but not with the background trait."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"room-for-improvement"},"children":[{"type":"element","tag":"a","props":{"href":"#room-for-improvement","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Room for improvement"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In the past, we annotated background trait studies (scenario C) in the same way as we have for multi-trait studies (scenario B): with multiple EFO terms listed together in the trait field, separated by commas."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This had some benefits:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We were able to indicate that the study has something to do with the background trait - e.g. a particular association with allergic rhinitis may only hold true in asthmatics."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Users could search for associations with a trait, as well as associations that are found in the context of that trait as a background characteristic. Both kinds of associations might be relevant to users working in a particular field."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"However, there were some are also some disadvantages:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"It was not possible to tell that a particular EFO term was a background trait, without looking at the reported trait field. This complicated analysis particularly when accessing the data programmatically."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"When searching for a trait term (e.g. asthma), there was no easy way to distinguish which studies were direct associations with asthma, and which have asthma as the background trait, without reading the reported trait for each one."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The overall study and association numbers provided for some traits could be misleading since they included studies and associations where the trait of interest was a background trait."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"changes-to-trait-annotation"},"children":[{"type":"element","tag":"a","props":{"href":"#changes-to-trait-annotation","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Changes to trait annotation"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"To make our trait annotations more informative, we have added an additional background trait field to the GWAS Catalog database."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We have moved all EFO terms related to background traits to this new field, and removed them from the original trait field:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/annotation-change.png","alt":"We have moved EFO terms for background traits to a separate field"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"changes-to-the-website"},"children":[{"type":"element","tag":"a","props":{"href":"#changes-to-the-website","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Changes to the website"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We have also updated our web interface in order to display the restructured background trait information."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"data-tables"},"children":[{"type":"element","tag":"a","props":{"href":"#data-tables","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Data tables"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"New columns have been added to the Associations and Studies tables to clearly indicate the main and background traits."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Associations:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/new-assoc-table.png","alt":"Separate main and background traits displayed in Associations table"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Studies:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/new-study-table.png","alt":"Separate main and background traits displayed in Studies table"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The improved tables will be displayed on all Publication, Study, Trait, Variant, Gene and Region pages."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"study-pages"},"children":[{"type":"element","tag":"a","props":{"href":"#study-pages","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Study pages"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Each Study page also displays the main and background traits in separate fields in the Study Information panel.\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/study-info-panel.png","alt":"Main and background traits displayed in the Study Information panel"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"trait-pages"},"children":[{"type":"element","tag":"a","props":{"href":"#trait-pages","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Trait pages"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The Trait page has been updated so that background trait studies and associations are no longer shown by default. For example, the asthma page only shows associations with asthma itself, and not associations with others traits in asthma.\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/asthma-no-bg.png","alt":"Asthma Trait page showing associations with asthma only"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"Shows associations with:"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"asthma"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"del","props":{},"children":[{"type":"text","value":"[other traits] "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" asthma"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"However, users can select the box to include background trait data if they wish:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/asthma-with-bg.png","alt":"Asthma Trait page showing associations with asthma AND with other traits in a background of asthma."},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"Shows associations with:"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"asthma"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"[other traits] "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" asthma"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Some trait terms may only be used in the GWAS Catalog to annotate background traits. These terms will continue to have their own Trait page, but no associations or studies will be displayed under the default view. For example, autoimmune pancreatitis type 1 (EFO_1000780) currently appears in the Catalog only as a background trait for one study (“Lachrymal/Salivary gland lesion in type 1 autoimmune pancreatitis”), so no associations with autoimmune pancreatitis type 1 are displayed by default:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/no-records.png","alt":"A Trait page with no main trait associations"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The association plot on each Trait page will now display only main trait associations by default, but background trait data can be added by selecting the box:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/assoc-plot-change.png","alt":"Association plot with with or without background trait data"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"downloads"},"children":[{"type":"element","tag":"a","props":{"href":"#downloads","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Downloads"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In "},{"type":"element","tag":"a","props":{"href":"http://www.ebi.ac.uk/gwas/docs/file-downloads","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the full Catalog downloads"}]},{"type":"text","value":", the MAPPED_TRAIT and MAPPED_TRAIT_URI columns will now only show EFO terms for main traits. Background trait terms have been removed from these columns."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In the newest version of the download (v1.0.3), new columns have been added for the background trait: MAPPED BACKGROUND TRAIT and MAPPED BACKGROUND TRAIT URI. This is not included in earlier versions of the download."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"api"},"children":[{"type":"element","tag":"a","props":{"href":"#api","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"API"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For users of the GWAS Catalog API, searching for “associationByEfoTrait” will now return only main trait associations."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"There is currently no background trait field available in the API, but we plan to add this feature in the future."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"expected-scope-of-the-changes"},"children":[{"type":"element","tag":"a","props":{"href":"#expected-scope-of-the-changes","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Expected scope of the changes"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We reviewed all of the studies in the Catalog and identified just over 1000 studies (about 6% of the total) with background traits that have now been moved to the new field. These studies contain around 10,000 associations (about 4% of all associations in the Catalog) which have also been reannotated."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The changes affect some trait terms more than others. Here are some of the terms that will see the greatest change in the number of annotated Associations:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/most-affected-traits.png","alt":"Graph of selected trait terms affected by this change"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"questions-and-feedback"},"children":[{"type":"element","tag":"a","props":{"href":"#questions-and-feedback","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Questions and feedback"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"If you have any questions or comments about this change, please contact us as "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":"."}]}]},"text":"\nEach study in the GWAS Catalog investigates the association between variants in the human genome and a particular trait or phenotypic characteristic.\n\nFor each study, we annotate the trait in two ways:\n\n1. A reported trait that reflects the author’s description of the disease or phenotypic characteristic under investigation. This is a free text description and sometimes different studies might use a different wording to describe similar traits or to capture more nuanced distinctions. Reported traits can also include multiple component traits, depending on the study design.\n\n2. One or more trait terms from the Experimental Factor Ontology (EFO) that represent phenotypic characteristics in a more standardised way. These terms make studies on similar traits easier for users to find and compare. For multifaceted traits, each component is represented by a separate term.\n\n\n\n## Annotating different study types \n\n### A. Single-trait studies\n\nThe vast majority (>80%) of studies in the GWAS Catalog only analyse a single trait. We annotate these studies with a single EFO term.\n\nSome common examples include:\n\n1. Standard case-control studies comparing individuals with a disease or phenotypic characteristic, versus controls individuals without that trait.\n\n\n2. Quantitative studies looking at a single measurement\n\n\n\nIn these examples, any reported variants are clearly and straightforwardly associated with that single trait.\n\n### B. Multi-trait studies\n\nIn about 10% of studies, there are multiple traits of interest that are analysed simultaneously. We annotate these studies with multiple EFO terms separated by a comma, indicating that any significant variants reported in the study are associated with both traits in some way.\n\n**Some common examples include:**\n\n1. Studies comparing individuals with two comorbid diseases (or other traits), versus control individuals who have neither disease. In the reported trait we write “Disease 1 _and_ Disease 2”. In the trait we list multiple EFO terms separated by a comma.\n\n\n2. Studies comparing individuals with either of two diseases (or other traits), versus control individuals who have neither disease. Often these are two traits that are hypothesised to have some common underlying genetic factors (pleiotropy). In the reported trait we write “Disease 1 _or_ Disease 2”. In the trait we list multiple EFO terms separated by a comma.\n\n\nIn these examples, any significant variants reported are associated with either or both of the annotated traits. If a user is searching for associations with either trait term, then these results will come up in their search.\n\n### C. Studies with a background trait\n\nFinally, about 6% of studies analyse only one main trait of interest, but in the context of a background trait that is shared by all of the participants in the study.\n\nSome examples include:\n\n1. Studies comparing cases and controls of one disease, but only within a sample of people who have another disease. In the reported trait we write “Disease 1 _in_ Disease 2”. In the past, we listed multiple EFO terms in the trait column, as for scenario B.\n\n\n2. Quantitative studies analysing a measurement in the context of a disease. In the reported trait we write “Measurement _in_ Disease”. In the past, we listed multiple EFO terms in the trait column, as for scenario B.\n\n\nIn these examples, any reported variants are associated with the main trait, but not with the background trait.\n\n## Room for improvement\n\nIn the past, we annotated background trait studies (scenario C) in the same way as we have for multi-trait studies (scenario B): with multiple EFO terms listed together in the trait field, separated by commas.\n\nThis had some benefits:\n\n* We were able to indicate that the study has something to do with the background trait - e.g. a particular association with allergic rhinitis may only hold true in asthmatics.\n\n* Users could search for associations with a trait, as well as associations that are found in the context of that trait as a background characteristic. Both kinds of associations might be relevant to users working in a particular field.\n\nHowever, there were some are also some disadvantages:\n\n* It was not possible to tell that a particular EFO term was a background trait, without looking at the reported trait field. This complicated analysis particularly when accessing the data programmatically.\n\n* When searching for a trait term (e.g. asthma), there was no easy way to distinguish which studies were direct associations with asthma, and which have asthma as the background trait, without reading the reported trait for each one.\n\n* The overall study and association numbers provided for some traits could be misleading since they included studies and associations where the trait of interest was a background trait.\n\n## Changes to trait annotation\n\nTo make our trait annotations more informative, we have added an additional background trait field to the GWAS Catalog database.\n\nWe have moved all EFO terms related to background traits to this new field, and removed them from the original trait field:\n\n\n## Changes to the website\n\nWe have also updated our web interface in order to display the restructured background trait information.\n\n### Data tables\n\nNew columns have been added to the Associations and Studies tables to clearly indicate the main and background traits.\n\nAssociations:\n\n\nStudies:\n\n\nThe improved tables will be displayed on all Publication, Study, Trait, Variant, Gene and Region pages.\n\n### Study pages\n\nEach Study page also displays the main and background traits in separate fields in the Study Information panel.\n\n\n### Trait pages\n\nThe Trait page has been updated so that background trait studies and associations are no longer shown by default. For example, the asthma page only shows associations with asthma itself, and not associations with others traits in asthma.\n\n\n_Shows associations with:_\n* asthma\n* ~[other traits] _in_ asthma~\n\nHowever, users can select the box to include background trait data if they wish:\n\n\n_Shows associations with:_\n* asthma\n* [other traits] _in_ asthma\n\nSome trait terms may only be used in the GWAS Catalog to annotate background traits. These terms will continue to have their own Trait page, but no associations or studies will be displayed under the default view. For example, autoimmune pancreatitis type 1 (EFO_1000780) currently appears in the Catalog only as a background trait for one study (“Lachrymal/Salivary gland lesion in type 1 autoimmune pancreatitis”), so no associations with autoimmune pancreatitis type 1 are displayed by default:\n\n\nThe association plot on each Trait page will now display only main trait associations by default, but background trait data can be added by selecting the box:\n\n\n### Downloads\n\nIn [the full Catalog downloads](http://www.ebi.ac.uk/gwas/docs/file-downloads), the MAPPED_TRAIT and MAPPED_TRAIT_URI columns will now only show EFO terms for main traits. Background trait terms have been removed from these columns.\n\nIn the newest version of the download (v1.0.3), new columns have been added for the background trait: MAPPED BACKGROUND TRAIT and MAPPED BACKGROUND TRAIT URI. This is not included in earlier versions of the download.\n\n### API\n\nFor users of the GWAS Catalog API, searching for “associationByEfoTrait” will now return only main trait associations.\n\nThere is currently no background trait field available in the API, but we plan to add this feature in the future.\n\n## Expected scope of the changes\n\nWe reviewed all of the studies in the Catalog and identified just over 1000 studies (about 6% of the total) with background traits that have now been moved to the new field. These studies contain around 10,000 associations (about 4% of all associations in the Catalog) which have also been reannotated.\n\nThe changes affect some trait terms more than others. Here are some of the terms that will see the greatest change in the number of annotated Associations:\n\n\n## Questions and feedback\n\nIf you have any questions or comments about this change, please contact us as gwas-info@ebi.ac.uk.","dir":"/blog","path":"/blog/background-trait-update","extension":".md","createdAt":"2024-03-11T15:03:22.553Z","updatedAt":"2024-03-11T15:03:22.553Z","meta":{"version":0,"revision":0,"created":1710171149360},"$loki":1},{"slug":"everything-you-need-to-know-about-the-new-gwas-catalog","description":"Genetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation.","title":"Everything you need to know about the new GWAS Catalog","author":"Annalisa Buniello","date":"Jun 13, 2018","img":"blog-1.jpg","toc":[{"id":"new-and-expanded-gwas-catalog-to-improve-target-identification","depth":3,"text":"New and expanded GWAS Catalog to improve target identification"},{"id":"selecting-and-curating-non-genome-wide-array-studies","depth":3,"text":"Selecting and curating non-genome-wide array studies"},{"id":"modifying-the-gwas-catalog-user-interface","depth":3,"text":"Modifying the GWAS Catalog user interface"},{"id":"stay-tuned","depth":3,"text":"Stay tuned!"}],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Genetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The NHGRI-EBI GWAS Catalog, an awesome public resource of human genotype-phenotype associations from published GWAS analyses, is one of the data sources for manually curated genetic evidence for the Open Targets Platform. The GWAS Catalog data is widely used to prioritise new candidate loci, predict disease risk and understand disease mechanisms, with a number of users and applications that is always increasing."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/FINAL_USERS_withOT-2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"new-and-expanded-gwas-catalog-to-improve-target-identification"},"children":[{"type":"element","tag":"a","props":{"href":"#new-and-expanded-gwas-catalog-to-improve-target-identification","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"New and expanded GWAS Catalog to improve target identification"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"I am happy to announce an expansion of the GWAS Catalog scientific scope to host non genome-wide studies. During its first 10 years of life, in fact, the GWAS Catalog has only included array studies with genome-wide coverage, therefore curation of exome and targeted array studies were outside its scientific scope. These data are however valuable to Open Targets’ scientific goals of target identification and prioritisation, so much so they decided to invest in a new project aiming to expand the scope of the Catalog to include targeted arrays, such as MetaboChip, ImmunoChip, and Exome array. The new GWAS will now allow variants associated with immunologic, metabolic and oncologic phenotypes to be included in the Open Targets Platform for the expansion of target identification and selection in these traits."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"selecting-and-curating-non-genome-wide-array-studies"},"children":[{"type":"element","tag":"a","props":{"href":"#selecting-and-curating-non-genome-wide-array-studies","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Selecting and curating non-genome-wide array studies"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"As a first step, Open Targets selected almost 60 publications for inclusion in the GWAS Catalog. I then extracted a total of 120 new independent association studies and 823 SNP-trait associations from these publications. These are impressive numbers, and give you an idea of how many studies and SNP-trait associations (and potential targets) we were missing by not including targeted array studies in the GWAS Catalog!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The final step for me to do was to map all these traits, diseases and phenotypes to EFO terms, the ontology used by\n"},{"type":"element","tag":"a","props":{"href":"https://docs.targetvalidation.org/faq/describing-diseases","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Open Targets to describe its diseases"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"modifying-the-gwas-catalog-user-interface"},"children":[{"type":"element","tag":"a","props":{"href":"#modifying-the-gwas-catalog-user-interface","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Modifying the GWAS Catalog user interface"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS Catalog team have changed the way the user interface works to support searching, displaying, filtering and download of targeted and exome array studies. "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/search?query=celiac%20disease","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Try it out using \"celiac disease\""}]},{"type":"text","value":", for example. The interface will now give you:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Drop-down filter by genotyping technology to help you focus on your favourite array"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/filter1-5.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Small \"target\" icon (designed for us by the EMBL-EBI media team. Thanks for that!) in the study accession number column for all targeted array studies prioritised by Open Targets"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/target-1.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"New \"genotyping technology\" shown for study, e.g. genome-wide genotyping array, exome-wide genotyping array or targeted genotyping array with an optional field for additional array information, for example ImmunoChip or MetaboChip"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/gen-tech-1.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"stay-tuned"},"children":[{"type":"element","tag":"a","props":{"href":"#stay-tuned","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Stay tuned!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This is an important milestone for Open Targets and the GWAS Catalog, and a lot more is yet to come. The next step for the collaboration between GWAS and Open Targets will be to develop a comprehensive database of all available GWAS full summary statistics stored in a common format and harmonised across studies to enable searching, easy comparison and downstream analysis. This effort will generate more comprehensive new raw genetic data accessible by the entire research community through the GWAS Catalog, the Open Targets Platform and additional resources the Open Targets team are working on."}]}]},"text":"\nGenetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation.\n\nThe NHGRI-EBI GWAS Catalog, an awesome public resource of human genotype-phenotype associations from published GWAS analyses, is one of the data sources for manually curated genetic evidence for the Open Targets Platform. The GWAS Catalog data is widely used to prioritise new candidate loci, predict disease risk and understand disease mechanisms, with a number of users and applications that is always increasing.\n\n\n\n### New and expanded GWAS Catalog to improve target identification\n\nI am happy to announce an expansion of the GWAS Catalog scientific scope to host non genome-wide studies. During its first 10 years of life, in fact, the GWAS Catalog has only included array studies with genome-wide coverage, therefore curation of exome and targeted array studies were outside its scientific scope. These data are however valuable to Open Targets’ scientific goals of target identification and prioritisation, so much so they decided to invest in a new project aiming to expand the scope of the Catalog to include targeted arrays, such as MetaboChip, ImmunoChip, and Exome array. The new GWAS will now allow variants associated with immunologic, metabolic and oncologic phenotypes to be included in the Open Targets Platform for the expansion of target identification and selection in these traits.\n\n### Selecting and curating non-genome-wide array studies\n\nAs a first step, Open Targets selected almost 60 publications for inclusion in the GWAS Catalog. I then extracted a total of 120 new independent association studies and 823 SNP-trait associations from these publications. These are impressive numbers, and give you an idea of how many studies and SNP-trait associations (and potential targets) we were missing by not including targeted array studies in the GWAS Catalog!\n\nThe final step for me to do was to map all these traits, diseases and phenotypes to EFO terms, the ontology used by \n[Open Targets to describe its diseases](https://docs.targetvalidation.org/faq/describing-diseases)\n\n### Modifying the GWAS Catalog user interface\n\nThe GWAS Catalog team have changed the way the user interface works to support searching, displaying, filtering and download of targeted and exome array studies. [Try it out using \"celiac disease\"](https://www.ebi.ac.uk/gwas/search?query=celiac%20disease), for example. The interface will now give you:\n\n- **Drop-down filter by genotyping technology to help you focus on your favourite array**\n\n\n\n- **Small \"target\" icon (designed for us by the EMBL-EBI media team. Thanks for that!) in the study accession number column for all targeted array studies prioritised by Open Targets**\n\n\n\n- **New \"genotyping technology\" shown for study, e.g. genome-wide genotyping array, exome-wide genotyping array or targeted genotyping array with an optional field for additional array information, for example ImmunoChip or MetaboChip**\n\n\n\n### Stay tuned!\n\nThis is an important milestone for Open Targets and the GWAS Catalog, and a lot more is yet to come. The next step for the collaboration between GWAS and Open Targets will be to develop a comprehensive database of all available GWAS full summary statistics stored in a common format and harmonised across studies to enable searching, easy comparison and downstream analysis. This effort will generate more comprehensive new raw genetic data accessible by the entire research community through the GWAS Catalog, the Open Targets Platform and additional resources the Open Targets team are working on.\n","dir":"/blog","path":"/blog/everything-you-need-to-know-about-the-new-gwas-catalog","extension":".md","createdAt":"2024-03-11T15:03:22.554Z","updatedAt":"2024-03-11T15:03:22.554Z","meta":{"version":0,"revision":0,"created":1710171149376},"$loki":2},{"slug":"cohorts-now-available","description":"We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics.","title":"Cohorts now available in the GWAS Catalog","author":"GWAS Catalog team","date":"Nov 14, 2023","img":"blog-1.img","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"There are two new studies "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/file-downloads","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"downloads"}]},{"type":"text","value":" named as follows:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"gwas-catalog-studies_ontology-annotated_v1.0.2.1.tsv"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"gwas-catalog-download-studies-v1.0.3.1.tsv"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Versions 1.0.2 (gwas-catalog-studies_ontology-annotated.tsv) and 1.0.3 (gwas-catalog-download-studies-v1.0.3.tsv) of the studies downloads will be deprecated and cease to be generated on 1 December 2023."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The COHORT field describes the discovery stage (genome-wide) cohorts used in each study. Since we started piloting curation of cohort information in 2020, over 700 different discovery cohorts have been described in GWAS publications."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Cohort abbreviations from discovery stage GWAS either are extracted from the literature or supplied by sumitters, to match a predefined list which we share with the PGS Catalog. The initial list of common cohorts used in genetics studies that seeded these annotations is from Mills & Rahal, Communications Biology (2019), and has been expanded during our pilot phase. A full list of the abbreviations and corresponding full cohort names in use is available to download separately ("},{"type":"element","tag":"a","props":{"href":"https://ftp.ebi.ac.uk/pub/databases/spot/pgs/metadata/pgs_all_metadata_cohorts.csv","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"https://ftp.ebi.ac.uk/pub/databases/spot/pgs/metadata/pgs_all_metadata_cohorts.csv"}]},{"type":"text","value":"). We share this list with the PGS Catalog, so you may find cohorts listed there that are not currently associated with a GWAS Catalog study. It's important to remember we only extract cohorts when they have been clearly and unambiguously named by authors, so a cohort may have been used in other publications which don't have annotation.\nWhere a sample cohort in the literature was not already in the predefined list at the time of curation, or was ambiguously described, “other” will appear in our studies download file. Where a sample in the literature had no cohort reported, “NR” will appear in our studies download file. Consortium names are extracted if the component cohorts are not provided, or there are too many component cohorts to curate. Empty cohort fields appear for studies curated before the pilot work to extract this information began (~2020).\nWe also accept submissions of unpublished data and these are listed in separate download files. Cohorts appearing in the unpublished download files are yet to undergo in-house curation and therefore may not exactly match against the predefined list."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Cohort data annotations for published studies are also now available via the REST API, accessible via the /studies and /studies/{accessionId} endpoints. The field is called "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"cohort"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"As well as cohort data, the new studies downloads also contain clear annotation of which studies have full genomewide summary statistics available for download in the column FULL SUMMARY STATISTICS with the ftp location available in the adjacent column SUMMARY STATS LOCATION."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We hope you make great use of these data! Questions? Feedback? Contact "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]}]}]},"text":"We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics. \n\nThere are two new studies [downloads](https://www.ebi.ac.uk/gwas/docs/file-downloads) named as follows:\n- gwas-catalog-studies_ontology-annotated_v1.0.2.1.tsv\n- gwas-catalog-download-studies-v1.0.3.1.tsv\n\nVersions 1.0.2 (gwas-catalog-studies_ontology-annotated.tsv) and 1.0.3 (gwas-catalog-download-studies-v1.0.3.tsv) of the studies downloads will be deprecated and cease to be generated on 1 December 2023. \n\nThe COHORT field describes the discovery stage (genome-wide) cohorts used in each study. Since we started piloting curation of cohort information in 2020, over 700 different discovery cohorts have been described in GWAS publications. \n\nCohort abbreviations from discovery stage GWAS either are extracted from the literature or supplied by sumitters, to match a predefined list which we share with the PGS Catalog. The initial list of common cohorts used in genetics studies that seeded these annotations is from Mills & Rahal, Communications Biology (2019), and has been expanded during our pilot phase. A full list of the abbreviations and corresponding full cohort names in use is available to download separately (https://ftp.ebi.ac.uk/pub/databases/spot/pgs/metadata/pgs_all_metadata_cohorts.csv). We share this list with the PGS Catalog, so you may find cohorts listed there that are not currently associated with a GWAS Catalog study. It's important to remember we only extract cohorts when they have been clearly and unambiguously named by authors, so a cohort may have been used in other publications which don't have annotation. \nWhere a sample cohort in the literature was not already in the predefined list at the time of curation, or was ambiguously described, “other” will appear in our studies download file. Where a sample in the literature had no cohort reported, “NR” will appear in our studies download file. Consortium names are extracted if the component cohorts are not provided, or there are too many component cohorts to curate. Empty cohort fields appear for studies curated before the pilot work to extract this information began (~2020).\nWe also accept submissions of unpublished data and these are listed in separate download files. Cohorts appearing in the unpublished download files are yet to undergo in-house curation and therefore may not exactly match against the predefined list.\n\nCohort data annotations for published studies are also now available via the REST API, accessible via the /studies and /studies/{accessionId} endpoints. The field is called *cohort*. \n\nAs well as cohort data, the new studies downloads also contain clear annotation of which studies have full genomewide summary statistics available for download in the column FULL SUMMARY STATISTICS with the ftp location available in the adjacent column SUMMARY STATS LOCATION. \n\nWe hope you make great use of these data! Questions? Feedback? Contact gwas-info@ebi.ac.uk\n","dir":"/blog","path":"/blog/cohorts-now-available","extension":".md","createdAt":"2024-03-11T15:03:22.554Z","updatedAt":"2024-03-11T15:03:22.554Z","meta":{"version":0,"revision":0,"created":1710171149385},"$loki":3},{"slug":"gwas-ui-labs-release","description":"We are pleased to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing at www.ebi.ac.uk/gwas/labs. Here is a guide to the main differences you will see.","title":"GWAS Catalog web search updates - help us with testing","author":"GWAS Catalog team","date":"5 May 2023","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"h1","props":{"id":"new-gwas-web-search-interface-ready-for-testing"},"children":[{"type":"element","tag":"a","props":{"href":"#new-gwas-web-search-interface-ready-for-testing","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"New GWAS web search interface ready for testing"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We are excited to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing on our "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/labs","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"labs site"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This is a guide to the main differences you will see."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-labs-release/SlideA.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We’ve also changed the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-labs-release/SlideB.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Studies with full summary statistics available for download are now easier to find in their own tab:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-labs-release/SlideC.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Please try out the new site and let us know what you like, what you don’t like, if you find any bugs or if any of your favourite features are missing. You can send feedback via "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":" or report bugs directly via "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-ui/issues","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GitHub"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Lastly, note that links to the GWAS Diagram have been disabled in the labs site. We are working on the next generation of the Diagram and will share this with you soon!"}]}]},"text":"\n# New GWAS web search interface ready for testing\n\nWe are excited to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing on our [labs site](https://www.ebi.ac.uk/gwas/labs).\n\nThis is a guide to the main differences you will see.\n\nThe biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect! \n\n\n\nWe’ve also changed the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest: \n\n\n\nStudies with full summary statistics available for download are now easier to find in their own tab: \n\n\n\nPlease try out the new site and let us know what you like, what you don’t like, if you find any bugs or if any of your favourite features are missing. You can send feedback via gwas-info@ebi.ac.uk or report bugs directly via [GitHub](https://github.com/EBISPOT/gwas-ui/issues)\n\nLastly, note that links to the GWAS Diagram have been disabled in the labs site. We are working on the next generation of the Diagram and will share this with you soon!\n","dir":"/blog","path":"/blog/gwas-ui-labs-release","extension":".md","createdAt":"2024-03-11T15:03:22.555Z","updatedAt":"2024-03-11T15:03:22.555Z","meta":{"version":0,"revision":0,"created":1710171149391},"$loki":4},{"slug":"new-standard-for-gwas-summary-statistics","description":"Today we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, GWAS-SSF. The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a recent analysis of 327 summary statistics","title":"A new standard for GWAS summary statistics","author":"Laura Harris","date":"July 20, 2022","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Today we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v1","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS-SSF"}]},{"type":"text","value":". The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a "},{"type":"element","tag":"a","props":{"href":"https://academic.oup.com/bioinformatics/article/37/23/4593/6380562","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"recent analysis"}]},{"type":"text","value":" of 327 summary statistics files finding over 100 unique formats. We were about to launch the GWAS Catalog author submission system and needed to enforce a standard format as QC on submissions, and to ensure that submitted data could be passed through our harmonisation pipeline. So we quickly developed a "},{"type":"element","tag":"a","props":{"href":"https://pubmed.ncbi.nlm.nih.gov/30445434/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"minimal standard format"}]},{"type":"text","value":" based on the most commonly included fields, with variant ID/genomic location and p-value as mandatory fields, and standard headers for other fields such as beta and CI. This was enough for our goal of getting data into the GWAS Catalog. But we heard many consumers of the data complain that datasets with the minimum mandatory fields did not meet their requirements, for example to use the data for Mendelian randomisation or to generate polygenic scores. We needed to encourage our submitters to share their data more fully. A second issue is that while the GWAS Catalog curates metadata from the literature, there is no standard for GWAS metadata reporting, resulting in missing data that limits re-use of the summary statistics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We started on a process of consulting with the community that began with our workshop on summary statistics data and sharing in 2020. This was a wide ranging discussion over 2 days that you can read all about in our "},{"type":"element","tag":"a","props":{"href":"https://www.sciencedirect.com/science/article/pii/S2666979X21000045","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"workshop proceedings"}]},{"type":"text","value":". At the end of that process we had an outline of some mandatory fields for data and metadata, but refining these and ensuring they met all the major use cases would take a more focused discussion. Our working group on summary statistics data content and format, chaired by Ines Barroso, held 3 meetings over the course of 2021. We held a parallel working group to discuss diversity and privacy in data sharing (to be reported elsewhere) and incorporated their recommendations (for example on the sharing of allele frequency data) into our decision making process. Both the original workshop and the working group were designed to represent a wide range of stakeholders - from industry and academia, developers of other resources & tools, data generators and data consumers, research groups primarily performing wet-lab research and high-throughput bioinformatics users. Our discussions focused on the need to enforce key mandatory fields to maximise the reuse of the data, but also to recognise that setting the bar too high could discourage data sharing. Similarly, data need to be made accessible in a way that suits the high-throughput user who might be piping data in bulk into a computational pipeline, but also users without advanced bioinformatics skills who might be downloading a single file to investigate a locus of interest. We heard that preparing data for submission to databases can be a huge drain for under-resourced labs, so needs to be as straightforward and user-friendly as possible, and that interoperability between different databases (such as "},{"type":"element","tag":"a","props":{"href":"https://www.ncbi.nlm.nih.gov/gap/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"dbGaP"}]},{"type":"text","value":", "},{"type":"element","tag":"a","props":{"href":"http://www.ebi.ac.uk/gwas","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the GWAS Catalog"}]},{"type":"text","value":" and "},{"type":"element","tag":"a","props":{"href":"https://gwas.mrcieu.ac.uk/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"OpenGWAS"}]},{"type":"text","value":") is important to reduce the workload for data generators. You can read the outcome of our discussions in our "},{"type":"element","tag":"a","props":{"href":"https://biohackrxiv.org/7zxht","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"new manuscript"}]},{"type":"text","value":". Behind the scenes, we are making the necessary changes to the GWAS Catalog infrastructure to validate new submissions according to the new standard and where possible, make all our existing summary statistics available in the new format. We are working on tools (together with PLINK and others) to make data preparation and formatting as easy as possible. In the meantime, we welcome feedback from the wider community on the standard and will collate this at the end of August prior to release of the new infrastructure."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS-SSF is suitable for array-based GWAS and single variant seqGWAS. As data sharing has become more widely accepted, there is appetite from the genomics community to share full p value datasets from gene-based analyses of sequencing data and CNVs. Whilst we don’t have a standard format for these yet, by gathering data we hope to go through a similar process to work towards a standard format. Please share your data, and let us know your thoughts!"}]}]},"text":"\nToday we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, [GWAS-SSF](https://www.biorxiv.org/content/10.1101/2022.07.15.500230v1). The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a [recent analysis](https://academic.oup.com/bioinformatics/article/37/23/4593/6380562) of 327 summary statistics files finding over 100 unique formats. We were about to launch the GWAS Catalog author submission system and needed to enforce a standard format as QC on submissions, and to ensure that submitted data could be passed through our harmonisation pipeline. So we quickly developed a [minimal standard format](https://pubmed.ncbi.nlm.nih.gov/30445434/) based on the most commonly included fields, with variant ID/genomic location and p-value as mandatory fields, and standard headers for other fields such as beta and CI. This was enough for our goal of getting data into the GWAS Catalog. But we heard many consumers of the data complain that datasets with the minimum mandatory fields did not meet their requirements, for example to use the data for Mendelian randomisation or to generate polygenic scores. We needed to encourage our submitters to share their data more fully. A second issue is that while the GWAS Catalog curates metadata from the literature, there is no standard for GWAS metadata reporting, resulting in missing data that limits re-use of the summary statistics.\n\nWe started on a process of consulting with the community that began with our workshop on summary statistics data and sharing in 2020. This was a wide ranging discussion over 2 days that you can read all about in our [workshop proceedings](https://www.sciencedirect.com/science/article/pii/S2666979X21000045). At the end of that process we had an outline of some mandatory fields for data and metadata, but refining these and ensuring they met all the major use cases would take a more focused discussion. Our working group on summary statistics data content and format, chaired by Ines Barroso, held 3 meetings over the course of 2021. We held a parallel working group to discuss diversity and privacy in data sharing (to be reported elsewhere) and incorporated their recommendations (for example on the sharing of allele frequency data) into our decision making process. Both the original workshop and the working group were designed to represent a wide range of stakeholders - from industry and academia, developers of other resources & tools, data generators and data consumers, research groups primarily performing wet-lab research and high-throughput bioinformatics users. Our discussions focused on the need to enforce key mandatory fields to maximise the reuse of the data, but also to recognise that setting the bar too high could discourage data sharing. Similarly, data need to be made accessible in a way that suits the high-throughput user who might be piping data in bulk into a computational pipeline, but also users without advanced bioinformatics skills who might be downloading a single file to investigate a locus of interest. We heard that preparing data for submission to databases can be a huge drain for under-resourced labs, so needs to be as straightforward and user-friendly as possible, and that interoperability between different databases (such as [dbGaP](https://www.ncbi.nlm.nih.gov/gap/), [the GWAS Catalog](http://www.ebi.ac.uk/gwas) and [OpenGWAS](https://gwas.mrcieu.ac.uk/)) is important to reduce the workload for data generators. You can read the outcome of our discussions in our [new manuscript](https://biohackrxiv.org/7zxht). Behind the scenes, we are making the necessary changes to the GWAS Catalog infrastructure to validate new submissions according to the new standard and where possible, make all our existing summary statistics available in the new format. We are working on tools (together with PLINK and others) to make data preparation and formatting as easy as possible. In the meantime, we welcome feedback from the wider community on the standard and will collate this at the end of August prior to release of the new infrastructure.\n\nThe GWAS-SSF is suitable for array-based GWAS and single variant seqGWAS. As data sharing has become more widely accepted, there is appetite from the genomics community to share full p value datasets from gene-based analyses of sequencing data and CNVs. Whilst we don’t have a standard format for these yet, by gathering data we hope to go through a similar process to work towards a standard format. Please share your data, and let us know your thoughts!\n","dir":"/blog","path":"/blog/new-standard-for-gwas-summary-statistics","extension":".md","createdAt":"2024-03-11T15:03:22.556Z","updatedAt":"2024-03-11T15:03:22.557Z","meta":{"version":0,"revision":0,"created":1710171149411},"$loki":5},{"slug":"gwas-ssf-release","description":"Next month the GWAS Catalog is switching to a NEW summary statistics format. Find out more about what this means for submitters and consumers of summary statistics","title":"New submissions standard - release date","author":"GWAS Catalog team","date":"22 March 2023","img":"blog-1.jpg","toc":[{"id":"the-new-summary-statistics-format-is-coming---what-you-need-to-know","depth":2,"text":"The new summary statistics format is coming - what you need to know"},{"id":"what-does-this-mean-for-you","depth":2,"text":"What does this mean for you?"},{"id":"frequently-asked-questions","depth":2,"text":"Frequently asked questions"},{"id":"for-submitters","depth":3,"text":"For submitters:"},{"id":"for-consumers","depth":3,"text":"For consumers:"}],"body":{"type":"root","children":[{"type":"element","tag":"h2","props":{"id":"the-new-summary-statistics-format-is-coming---what-you-need-to-know"},"children":[{"type":"element","tag":"a","props":{"href":"#the-new-summary-statistics-format-is-coming---what-you-need-to-know","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"The new summary statistics format is coming - what you need to know"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"From 12th April we are switching to our NEW summary statistics format, GWAS-SSF, an overview of which is described in our "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v2","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"manuscript"}]},{"type":"text","value":" and "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-summary-statistics-standard","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"github repository"}]},{"type":"text","value":". There will be a short period of submission system downtime while we switch over to the new pipelines."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"what-does-this-mean-for-you"},"children":[{"type":"element","tag":"a","props":{"href":"#what-does-this-mean-for-you","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"What does this mean for you?"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"If you are a SUBMITTER of summary statistics, you will need to submit additional mandatory information, including effect size, allele frequency, and genomic location for each variant, and columns must be presented in a specific order. You are also encouraged to provide additional structured data (e.g. reference allele, imputation info score) and metadata (e.g. ancestry method, sex of participants) which will increase the usability of your data to others, and hopefully get you "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.09.27.509657v2","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"more citations"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"If you are a CONSUMER of summary statistics, you will start to see changes to the way we make these available. All newly submitted summary statistics will include the additional fields described "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-summary-statistics-standard","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"here"}]},{"type":"text","value":", with metadata available in a text file (.yaml) stored in the same ftp location as the data file, as well as its existing availability via the GWAS Catalog website and downloads. Over the coming months, we will generate metadata files for all existing summary statistics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"frequently-asked-questions"},"children":[{"type":"element","tag":"a","props":{"href":"#frequently-asked-questions","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Frequently asked questions"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"for-submitters"},"children":[{"type":"element","tag":"a","props":{"href":"#for-submitters","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"For submitters:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I’ve already started a submission and prepared my files in the old format. What should I do?"}]},{"type":"text","value":"\nYou can submit files in the old format until April 11th. If you are unable to complete your submission before this deadline, please contact us via "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-subs@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-subs@ebi.ac.uk"}]},{"type":"text","value":" for assistance."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I submitted summary statistics in the old format a while back and they are under embargo or in pre-published state. Do I need to change anything?"}]},{"type":"text","value":"\nNo. Your data will be made available in the old format (upon journal publication, if embargoed), and the metadata file will be prepared from the information you submitted."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I want to start preparing my submission now, but won’t be ready to submit till after April 12th. How can I find instructions on how to prepare the new format?"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Detailed documentation on the new format is available as follows:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/gwas-ssf/summary-statistics-format","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Summary statistics data file preparation"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics-plus-metadata","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Submission instructions for summary statistics and sample metadata"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Submission instructions for summary statistics only"}]},{"type":"text","value":" (for publications with metadata already available in the GWAS Catalog)."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"An example new-style metadata template is available to download "},{"type":"element","tag":"a","props":{"href":"http://ftp.ebi.ac.uk/pub/databases/gwas/sumstats_and_meta_submission_template_v1_11.xlsx","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"here"}]},{"type":"text","value":"\nA recorded "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/training/events/data-submission-genome-wide-association-studies-gwas-catalog/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"webinar"}]},{"type":"text","value":" is available, describing the submission process and the new format."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Are there tools available to help me prepare data in GWAS-SSF format?"}]},{"type":"text","value":"\nYou can find some useful tools to format your files and validate them prior to upload "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-sumstats-tools","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"here"}]},{"type":"text","value":"\nPLINK 2.0 will shortly include an option to generate output in the accepted format, using the option --gwas-ssf."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I made a submission in the old format but would like to update it to include the new fields. Can I do this?"}]},{"type":"text","value":"\nAt the moment our update process is manual and we don’t have capacity to update large numbers of files unless there is an error. We plan to improve this process in the future."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"for-consumers"},"children":[{"type":"element","tag":"a","props":{"href":"#for-consumers","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"For consumers:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"How can I tell if summary statistics are in the old or new format?"}]},{"type":"text","value":"\nThis is indicated in the metadata file. If a metadata file exists, and the field named “fileType” has the value “GWAS-SSF v1.0”, then the file is in the new GWAS-SSF format. Other values in this field indicate that it is not in the new GWAS-SSF format, and may be lacking mandatory fields. If no metadata file exists, the summary statistics are in the old format, or a non-standard format."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"How can I combine or compare new and old format summary statistics?"}]},{"type":"text","value":"\nData fields included in the old format (chromosome, base_pair_location, effect_allele, other_allele, effect_allele_frequency, odds_ratio, ci_lower, ci_upper, beta, standard_error) are the same in the new format. There are two exceptions: variant_id and p_value. In the old format, variant_id contained rsIDs, whereas in the new format rsID has a separate column rsid, and variant_ID contains a variant identifier in the format chr_bp_ref_alt. In the old format, p_value was a mandatory field and could only contain p-values between 0 and 1. In the new format, p-values may be either presented in the p_value field OR as negative log10 p-values, with the header neg_log_10_p_value, but always in column 7."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"More questions?"}]},{"type":"text","value":" Get in touch: contact "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":" for general queries or "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-subs@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-subs@ebi.ac.uk"}]},{"type":"text","value":" for submission support"}]}]},"text":"\n## The new summary statistics format is coming - what you need to know\n\nFrom 12th April we are switching to our NEW summary statistics format, GWAS-SSF, an overview of which is described in our [manuscript](https://www.biorxiv.org/content/10.1101/2022.07.15.500230v2) and [github repository](https://github.com/EBISPOT/gwas-summary-statistics-standard). There will be a short period of submission system downtime while we switch over to the new pipelines.\n\n## What does this mean for you?\n\nIf you are a SUBMITTER of summary statistics, you will need to submit additional mandatory information, including effect size, allele frequency, and genomic location for each variant, and columns must be presented in a specific order. You are also encouraged to provide additional structured data (e.g. reference allele, imputation info score) and metadata (e.g. ancestry method, sex of participants) which will increase the usability of your data to others, and hopefully get you [more citations](https://www.biorxiv.org/content/10.1101/2022.09.27.509657v2).\n\nIf you are a CONSUMER of summary statistics, you will start to see changes to the way we make these available. All newly submitted summary statistics will include the additional fields described [here](https://github.com/EBISPOT/gwas-summary-statistics-standard), with metadata available in a text file (.yaml) stored in the same ftp location as the data file, as well as its existing availability via the GWAS Catalog website and downloads. Over the coming months, we will generate metadata files for all existing summary statistics.\n\n## Frequently asked questions\n\n### For submitters:\n\n**I’ve already started a submission and prepared my files in the old format. What should I do?**\nYou can submit files in the old format until April 11th. If you are unable to complete your submission before this deadline, please contact us via gwas-subs@ebi.ac.uk for assistance.\n\n**I submitted summary statistics in the old format a while back and they are under embargo or in pre-published state. Do I need to change anything?**\nNo. Your data will be made available in the old format (upon journal publication, if embargoed), and the metadata file will be prepared from the information you submitted.\n\n**I want to start preparing my submission now, but won’t be ready to submit till after April 12th. How can I find instructions on how to prepare the new format?**\n\nDetailed documentation on the new format is available as follows:\n- [Summary statistics data file preparation](https://www.ebi.ac.uk/gwas/docs/gwas-ssf/summary-statistics-format)\n- [Submission instructions for summary statistics and sample metadata](https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics-plus-metadata)\n- [Submission instructions for summary statistics only](https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics) (for publications with metadata already available in the GWAS Catalog).\n\nAn example new-style metadata template is available to download [here](http://ftp.ebi.ac.uk/pub/databases/gwas/sumstats_and_meta_submission_template_v1_11.xlsx)\nA recorded [webinar](https://www.ebi.ac.uk/training/events/data-submission-genome-wide-association-studies-gwas-catalog/) is available, describing the submission process and the new format.\n\n**Are there tools available to help me prepare data in GWAS-SSF format?**\nYou can find some useful tools to format your files and validate them prior to upload [here]( https://github.com/EBISPOT/gwas-sumstats-tools)\nPLINK 2.0 will shortly include an option to generate output in the accepted format, using the option --gwas-ssf.\n\n**I made a submission in the old format but would like to update it to include the new fields. Can I do this?**\nAt the moment our update process is manual and we don’t have capacity to update large numbers of files unless there is an error. We plan to improve this process in the future.\n\n### For consumers:\n\n**How can I tell if summary statistics are in the old or new format?**\nThis is indicated in the metadata file. If a metadata file exists, and the field named “fileType” has the value “GWAS-SSF v1.0”, then the file is in the new GWAS-SSF format. Other values in this field indicate that it is not in the new GWAS-SSF format, and may be lacking mandatory fields. If no metadata file exists, the summary statistics are in the old format, or a non-standard format.\n\n**How can I combine or compare new and old format summary statistics?**\nData fields included in the old format (chromosome, base_pair_location, effect_allele, other_allele, effect_allele_frequency, odds_ratio, ci_lower, ci_upper, beta, standard_error) are the same in the new format. There are two exceptions: variant_id and p_value. In the old format, variant_id contained rsIDs, whereas in the new format rsID has a separate column rsid, and variant_ID contains a variant identifier in the format chr_bp_ref_alt. In the old format, p_value was a mandatory field and could only contain p-values between 0 and 1. In the new format, p-values may be either presented in the p_value field OR as negative log10 p-values, with the header neg_log_10_p_value, but always in column 7.\n\n\n**More questions?** Get in touch: contact [gwas-info@ebi.ac.uk](mailto:gwas-info@ebi.ac.uk) for general queries or [gwas-subs@ebi.ac.uk](mailto:gwas-subs@ebi.ac.uk) for submission support\n","dir":"/blog","path":"/blog/gwas-ssf-release","extension":".md","createdAt":"2024-03-11T15:03:22.554Z","updatedAt":"2024-03-11T15:03:22.555Z","meta":{"version":0,"revision":0,"created":1710171149429},"$loki":6},{"slug":"gwas-ui-production-release-2023","description":"We are pleased to announce the release of a new version of the GWAS Catalog web search interface, which is now in production. Here is a guide to the main differences you will see.","title":"GWAS Catalog web search - new release!","author":"GWAS Catalog team","date":"12 July 2023","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"h1","props":{"id":"new-gwas-web-search-interface"},"children":[{"type":"element","tag":"a","props":{"href":"#new-gwas-web-search-interface","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"New GWAS web search interface"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We are excited to announce the full production release of the new GWAS Catalog web search interface."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideA.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We’ve also main some small changes to the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideB.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Interested to find studies with full genomewide summary statistics available for download? They now have their own tab, making it easier to see when summary statistics are available:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideC.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Interested to find the study with the biggest sample size for your trait of interest? Just use the sort option on the Discovery sample number columnn, which now sorts by the total number of samples in the Discovery stage, irrespective of ancestry groups:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideD.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Hope you enjoy using the new features! Please report any bugs to "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":" or via "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-ui/issues","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GitHub"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Lastly, watch this space for the next generation of the GWAS Diagram - we're working on it!"}]}]},"text":"\n# New GWAS web search interface\n\nWe are excited to announce the full production release of the new GWAS Catalog web search interface. \n\nThe biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect! \n\n\n\nWe’ve also main some small changes to the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest: \n\n\n\nInterested to find studies with full genomewide summary statistics available for download? They now have their own tab, making it easier to see when summary statistics are available: \n\n\n\nInterested to find the study with the biggest sample size for your trait of interest? Just use the sort option on the Discovery sample number columnn, which now sorts by the total number of samples in the Discovery stage, irrespective of ancestry groups: \n\n\n\nHope you enjoy using the new features! Please report any bugs to gwas-info@ebi.ac.uk or via [GitHub](https://github.com/EBISPOT/gwas-ui/issues)\n\nLastly, watch this space for the next generation of the GWAS Diagram - we're working on it!\n","dir":"/blog","path":"/blog/gwas-ui-production-release-2023","extension":".md","createdAt":"2024-03-11T15:03:22.556Z","updatedAt":"2024-03-11T15:03:22.556Z","meta":{"version":0,"revision":0,"created":1710171149437},"$loki":7},{"slug":"streamlining-GWAS-Summary-Statistics-Data-Interpretation-with-Improved-YAML-Metadata","description":"Having clear and accessible metadata is essential for enhancing data interpretation and ensuring its reusability. In the case of Genome-Wide Association Studies (GWAS), having a standardized and easy-to-understand format for documenting study metadata is crucial. In the GWAS Catalog, metadata associated with full genome-wide summary statistics files is accessible via multiple routes - searchable in the main Catalog via the website and REST API,","title":"Streamlining GWAS Summary Statistics Data Interpretation with Improved YAML Metadata","author":"Yue Ji and Laura Harris","date":"Feburary 22, 2024","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Having clear and accessible metadata is essential for enhancing data interpretation and ensuring its reusability. In the case of Genome-Wide Association Studies (GWAS), having a standardized and easy-to-understand format for documenting study metadata is crucial. In the GWAS Catalog, metadata associated with full genome-wide summary statistics files is accessible via multiple routes - searchable in the main Catalog via the website and REST API, and additionally via a text file in YAML format, contained in the same directory as the data file."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We have identified key fields that should be included in the metadata for a comprehensive understanding, interpretation, and subsequent reuse of GWAS studies. Mandatory fields document essential study details and optional fields cater to specific study designs (such as case/control counts in case-control studies), and provide supplementary analysis information. You can find detailed information about these fields in Table 1 and in our recent preprint (Hayhurst et al 2023 "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v4","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v4"}]},{"type":"text","value":")."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In our latest release, we've introduced several updates to the GWAS metadata YAML file format. The YAML format, known it's user-friendliness, provides a combination of structured information and ease of use for humans and computers. The latest changes aim to enhance the human readability of the files with a clearer, more unambiguous, and user-friendly experience without compromising the file's compatibility with automated systems. Here’s an overview of the significant enhancements:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Reordering and Grouping of Fields:"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"The fields within the YAML files have been reorganized and grouped by related topics, each marked with a group name starting with '#' (group names are listed in Table 1). This organization helps users to quickly locate specific fields by group name, such as '# Sample Information', making the file more navigable for humans. Importantly, this structural refinement does not affect the way machines parse and interpret the data."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Clear Indentation for Hierarchical Relationships:"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Indentation has been introduced to visually represent the relationships between different data fields. This visual aid improves the readability of the files, making it easier for users to discern the levels of data organization and relationships between various fields. In the meantime, this indentation does not impact the machine processing of the file."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":3},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Renaming 'data_last_modified' to 'date_metadata_last_modified':"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"To provide clearer context, the 'data_last_modified' field has been renamed to 'date_metadata_last_modified'. This change emphasizes that the timestamp refers to the last modification date of the metadata, not the summary statistics data. It aids users in tracking any changes or updates in the metadata over time."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":4},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Addition of 'Ancestry Category' Field:"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"We have introduced a new 'ancestry category' field in addition to the existing 'ancestry' field. This new feature provides a broad overview of the ancestry category that best describes the sample, allowing for a more generalized view of the sample's ancestry. This new field also ensures consistency and facilitates comparative analysis across different studies, in line with the rest of the Catalog."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"These updates reflect our commitment to improving the user experience while ensuring the YAML files maintain their structural integrity for computer readability. Our focus continues to be on making GWAS metadata more accessible, interpretable, and useful for researchers."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Table 1. Metadata field definitions"}]},{"type":"text","value":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"},{"type":"element","tag":"table","props":{},"children":[{"type":"element","tag":"thead","props":{},"children":[{"type":"element","tag":"tr","props":{},"children":[{"type":"element","tag":"th","props":{},"children":[{"type":"text","value":"Field"}]},{"type":"element","tag":"th","props":{},"children":[{"type":"text","value":"Description"}]},{"type":"element","tag":"th","props":{},"children":[{"type":"text","value":"Data type 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cohorts."}]}]},{"type":"element","tag":"tr","props":{},"children":[{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"gwas_catalog_api"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"GWAS catalog REST API link"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Text 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datasets"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"false"}]}]},{"type":"element","tag":"tr","props":{},"children":[{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"is_sorted"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Flag whether the file is sorted by genomic location"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Boolean"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Yes"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"false"}]}]},{"type":"element","tag":"tr","props":{},"children":[{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"harmonisation_reference"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"The genome reference file used for harmonising the summary statistics file"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Text string"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"No"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"ftp://ftp.ensembl.org/pub/release-104/fasta/homo_sapiens/dna/"}]}]},{"type":"element","tag":"tr","props":{},"children":[{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"## Questions and feedback"}]},{"type":"element","tag":"td","props":{},"children":[]},{"type":"element","tag":"td","props":{},"children":[]},{"type":"element","tag":"td","props":{},"children":[]},{"type":"element","tag":"td","props":{},"children":[]}]}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Questions or comments about this change? Please contact us as "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":"."}]}]},"text":"\nHaving clear and accessible metadata is essential for enhancing data interpretation and ensuring its reusability. In the case of Genome-Wide Association Studies (GWAS), having a standardized and easy-to-understand format for documenting study metadata is crucial. In the GWAS Catalog, metadata associated with full genome-wide summary statistics files is accessible via multiple routes - searchable in the main Catalog via the website and REST API, and additionally via a text file in YAML format, contained in the same directory as the data file. \n\nWe have identified key fields that should be included in the metadata for a comprehensive understanding, interpretation, and subsequent reuse of GWAS studies. Mandatory fields document essential study details and optional fields cater to specific study designs (such as case/control counts in case-control studies), and provide supplementary analysis information. You can find detailed information about these fields in Table 1 and in our recent preprint (Hayhurst et al 2023 https://www.biorxiv.org/content/10.1101/2022.07.15.500230v4).\n\nIn our latest release, we've introduced several updates to the GWAS metadata YAML file format. The YAML format, known it's user-friendliness, provides a combination of structured information and ease of use for humans and computers. The latest changes aim to enhance the human readability of the files with a clearer, more unambiguous, and user-friendly experience without compromising the file's compatibility with automated systems. Here’s an overview of the significant enhancements:\n\n1. Reordering and Grouping of Fields:\n* The fields within the YAML files have been reorganized and grouped by related topics, each marked with a group name starting with '#' (group names are listed in Table 1). This organization helps users to quickly locate specific fields by group name, such as '# Sample Information', making the file more navigable for humans. Importantly, this structural refinement does not affect the way machines parse and interpret the data.\n\n2. Clear Indentation for Hierarchical Relationships:\n* Indentation has been introduced to visually represent the relationships between different data fields. This visual aid improves the readability of the files, making it easier for users to discern the levels of data organization and relationships between various fields. In the meantime, this indentation does not impact the machine processing of the file.\n\n3. Renaming 'data_last_modified' to 'date_metadata_last_modified':\n* To provide clearer context, the 'data_last_modified' field has been renamed to 'date_metadata_last_modified'. This change emphasizes that the timestamp refers to the last modification date of the metadata, not the summary statistics data. It aids users in tracking any changes or updates in the metadata over time.\n \n4. Addition of 'Ancestry Category' Field:\n* We have introduced a new 'ancestry category' field in addition to the existing 'ancestry' field. This new feature provides a broad overview of the ancestry category that best describes the sample, allowing for a more generalized view of the sample's ancestry. This new field also ensures consistency and facilitates comparative analysis across different studies, in line with the rest of the Catalog. \n\nThese updates reflect our commitment to improving the user experience while ensuring the YAML files maintain their structural integrity for computer readability. Our focus continues to be on making GWAS metadata more accessible, interpretable, and useful for researchers.\n\n\nTable 1. Metadata field definitions\n| Field | Description | Data type and values | Mandatory | Example |\n| -------------------------------- | ---------------------------------------------------------------------------------- | ------------------------------------------------------- | ----------------------------------- | -------------------------------------------------------------------------------------------------------------------- |\n| \\# Study meta-data |\n| gwas_id | GWAS Catalog accession ID | Text string | Yes | GCST90244057 |\n| author_notes | Additional information about this study from the author | Text string | No | File contains GWAS summary statistics from a meta-analysis of NMR metabolic traits in up to 33 cohorts. |\n| gwas_catalog_api | GWAS catalog REST API link | Text string | Yes | [https://www.ebi.ac.uk/gwas/rest/api/studies/GCST90244057](https://www.ebi.ac.uk/gwas/rest/api/studies/GCST90244057) |\n| date_metadata_last_modified | The latest date that metadata YAML file was modified | date | Yes | 2023-11-28 |\n| \\# Trait Information |\n| trait_description | Author reported trait description | Text string (multiple possible) | Yes | Body mass index |\n| ontology_mapping | Short form ontology terms describing the trait | Text string (multiple possible) | No | EFO_0004918 |\n| \\# Genotyping Information |\n| genome_assembly | Genome assembly for the summary statistics. | GRCh/NCBI/UCSC value | Yes | GRCh37 |\n| coordinate_system | Coordinate system used for the summary statistics | Text String (1-based or 0-based) | No | 1-based |\n| genotyping_technology | Method(s) used to genotype variants in the discovery stage. | Text string (multiple possible) | Yes | Genome-wide genotyping array |\n| imputation_panel | Panel used for imputation | Text string | No | HRC + UK10K |\n| imputation_software | Software used for imputation | Text string | No | SHAPEIT3 + IMPUTE4 |\n| \\# Sample Information |\n| sample_ancestry_category | Broad ancestry category that best describes the sample. | Text string | Yes | European |\n| sample_ancestry | The most detailed ancestry descriptor(s) for the sample. | Text string (multiple possible) | Yes | \\- Finnish
- British |\n| sample_size | Sample size | Integer | Yes | 27006 |\n| ancestry_method | Method used to determine sample ancestry e.g. self-reported/genetically determined | Text string (multiple possible) | No | self-reported |\n| case_control_study | Flag whether the study is a case-control study | Boolean | No (default is false) | true |\n| case_count | Number of cases for case/control study | Integer | No, unless caseControlStudy is true | 27006 |\n| control_count | Number of controls for case/control study | Integer | No, unless caseControlStudy is true | 27006 |\n| sex | To indicate a sex-stratified analysis | M (for male), F (for female), combined or NR if unknown | No | combined |\n| \\# Summary Statistic information |\n| data_file_name | The name of the summary statistics file | Text string | Yes | GCST90244057_buildGRCh37.tsv |\n| file_type | The format of the summary statistics file | \"GWAS-SSF v1.0\", \"pre-GWAS-SSF\", \"non-GWAS-SSF\" | Yes | GWAS-SSF v1.0 |\n| data_file_md5sum | The md5 checksum of the summary statistics file. | Text string | Yes | 0ec56396f89edcc21a3d5a25a6fa993d |\n| analysis_software | Software and version used for the association analysis | Text string (multiple possible) | Yes if p-values of 0 given | REGENIE |\n| adjusted_covariates | Any covariates the GWAS is adjusted for | Text string (multiple possible) | No | sex |\n| minor_allele_freq_lower_limit | Lowest possible effect allele frequency | Numeric | No | 0.0003 |\n| \\# Harmonization status |\n| is_harmonised | Description of harmonisation codes | Text string | Only given in harmonised datasets | false |\n| is_sorted | Flag whether the file is sorted by genomic location | Boolean | Yes | false |\n| harmonisation_reference | The genome reference file used for harmonising the summary statistics file | Text string | No | ftp://ftp.ensembl.org/pub/release-104/fasta/homo_sapiens/dna/ |\n## Questions and feedback\n\nQuestions or comments about this change? Please contact us as gwas-info@ebi.ac.uk.\n","dir":"/blog","path":"/blog/streamlining-GWAS-Summary-Statistics-Data-Interpretation-with-Improved-YAML-Metadata","extension":".md","createdAt":"2024-03-11T15:03:22.558Z","updatedAt":"2024-03-11T15:03:22.558Z","meta":{"version":0,"revision":0,"created":1710171149478},"$loki":8},{"slug":"open-sharing-of-cancer-summary-statistics","description":"The NHGRI-EBI GWAS Catalog is a central resource for the Genome Wide Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations. For more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable)","title":"Sharing is caring, why we need more freely available cancer GWAS summary statistics","author":"Annalisa Buniello","date":"March 11, 2021","img":"blog-1.jpg","toc":[{"id":"the-gwas-catalog-summary-statistics-repository","depth":3,"text":"The GWAS Catalog summary statistics repository"},{"id":"why-are-few-open-access-cancer-gwas-summary-statistics-available","depth":3,"text":"Why are few open access cancer GWAS summary statistics available?"},{"id":"how-can-we-promote-sharing","depth":3,"text":"How can we promote sharing?"}],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"NHGRI-EBI GWAS Catalog"}]},{"type":"text","value":" is a central resource for the Genome Wides Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable), while serving as a starting point for investigations to identify causal variants, calculate disease risk, understand disease mechanisms and establish targets for novel therapies."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Here we aim to illuminate progress that has been made to incorporate GWAS summary statistics into the GWAS Catalog, and reach out to the cancer genetics community in particular to promote submission of summary statistics for GWAS studies in cancer cohorts."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"the-gwas-catalog-summary-statistics-repository"},"children":[{"type":"element","tag":"a","props":{"href":"#the-gwas-catalog-summary-statistics-repository","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"The GWAS Catalog summary statistics repository"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Three years ago, the GWAS Catalog and Open Targets started to work together to expand the Catalog’s scientific scope to include full p-value summary statistics (aggregate p-values and association data for every variant analysed in a genome-wide association study) in addition to the manually curated top associations."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Summary statistics provide more detailed GWAS results than a lead SNP table, which allows other scientists to better use the data for sophisticated downstream analysis, whilst maintaining patient privacy. For example, summary statistics are integrated into the Open Targets Genetics Portal, where they are used to narrow down candidate loci and prioritise new drug targets through PheWAS, colocalisation analysis, fine mapping and mendelian randomisation"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"[GWAS-catalog-statistics-1.png"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS Catalog is now one of the largest, most visited and most frequently updated resources of freely available GWAS summary statistics. The repository includes summary statistics from 3,623 independent analyses (from 447 publications), accounting for a total of more than 22,000 datasets from a wide variety of traits. The Catalog users can easily access and download summary statistics from the GWAS Catalog "},{"type":"element","tag":"a","props":{"href":"ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/"},"children":[{"type":"text","value":"FTP site"}]},{"type":"text","value":" or via a dedicated "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/summary-statistics/docs/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"summary statistics API"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The increasing trend to share these datasets is reflected in the availability of summary statistics in the GWAS Catalog over time. There was also a significant increase in data downloads of summary statistics in 2020 compared to the previous year."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-Fig-1a-V2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-Fig-1b-V2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"why-are-few-open-access-cancer-gwas-summary-statistics-available"},"children":[{"type":"element","tag":"a","props":{"href":"#why-are-few-open-access-cancer-gwas-summary-statistics-available","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Why are few open access cancer GWAS summary statistics available?"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This expansion of the GWAS Catalog summary statistics repository, while promising, required a considerable outreach effort from the Catalog data team and the main stakeholders, including Open Targets. Interestingly, the rate of summary statistics data sharing noticeably differs among different genetics cohorts and research groups, with the lowest submission rate in cancer genetics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-FIg-2a-new-data.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For example 33% of epilepsy and 21% of diabetes summary statistics are publicly available, compared to only 7.5% for cancer. The trend slightly differs amongst different cancer groups, and we have observed a more positive trend for the few papers published in the last two years."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-2b-c-1.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In a recent "},{"type":"element","tag":"a","props":{"href":"https://twitter.com/GWASCatalog/status/1365359253175881742","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Twitter poll"}]},{"type":"text","value":", we asked the community to tell us what they thought was the most important reason for the low rate of cancer summary statistics submission."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The results suggest that the main barriers to the sharing of summary statistics are:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"div","props":{"className":["nuxt-content-highlight"]},"children":[{"type":"element","tag":"pre","props":{"className":["language-text","line-numbers"]},"children":[{"type":"element","tag":"code","props":{},"children":[{"type":"text","value":"1. Results are usually embargoed for use in future research,\n2. Data privacy issues (e.g. patient confidentiality agreement), and\n3. Lack of awareness/knowledge on the appropriate data repository.\n"}]}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We believe it is necessary for the genetics community to have a more comprehensive and transparent discussion about such barriers, starting from the privacy issues. As highlighted in the recently revised "},{"type":"element","tag":"a","props":{"href":"https://osp.od.nih.gov/2018/11/01/provide-access-gsr/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"NIH genomic data sharing (GDS) policy"}]},{"type":"text","value":", summary statistics do not include individual-level information and can empower researchers to determine which genomic variants potentially contribute to a disease"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"how-can-we-promote-sharing"},"children":[{"type":"element","tag":"a","props":{"href":"#how-can-we-promote-sharing","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"How can we promote sharing?"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"As a part of a wider plan to identify and remove barriers to data sharing among different research communities, we have launched a dedicated public engagement campaign to motivate and encourage the genetics community to make their summary statistics publicly available – and make the difference in the field. In particular, following up from a pressing request from our users and stakeholders, we hope to understand the reasons why so few summary statistics are available for cancer studies. Open sharing of cancer GWAS data will lead the way to new, improved therapies and shed some light on the molecular mechanisms involved in such complex disease."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-quote.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We call on researchers, journals, funders and charities to support our cause and spread our message. The Nature Journals Group are already proactively supporting an open access policy for their published cancer GWAS data. We hope to get more journals on board, including the top cancer journals."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In an aim to facilitate data sharing and interoperability, the GWAS Catalog team has recently released a "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/deposition","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"web-based deposition interface"}]},{"type":"text","value":" to support scalable author submission of summary statistics and metadata from published and pre-published (submitted at the time of journal submission upon request from the reviewers) GWAS. The datasets are submitted in a standard format and harmonised against the latest genome build and forward strand. This facilitates downstream analysis and integration into resources like the Open Targets Genetics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Please submit your summary statistics to the GWAS Catalog."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"What do you think are the most important barriers to the open sharing of summary statistics in cancer studies? Please let us know your thoughts by completing "},{"type":"element","tag":"a","props":{"href":"https://docs.google.com/forms/d/e/1FAIpQLSfuAO-cZWdIm8aF0140QXsZOxVlw7gLKj1a__KZ3aF4CUNamQ/viewform?usp=sf_link","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"this short survey"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"You can also reach out to us via "},{"type":"element","tag":"a","props":{"href":"https://twitter.com/GWASCatalog","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Twitter (@GWASCatalog)"}]},{"type":"text","value":" or "},{"type":"element","tag":"a","props":{"href":"gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"email (gwas-info@ebi.ac.uk)"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Together, we can make the difference."}]}]},"text":"\nThe [NHGRI-EBI GWAS Catalog](https://www.ebi.ac.uk/gwas/) is a central resource for the Genome Wides Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations.\n\nFor more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable), while serving as a starting point for investigations to identify causal variants, calculate disease risk, understand disease mechanisms and establish targets for novel therapies.\n\nHere we aim to illuminate progress that has been made to incorporate GWAS summary statistics into the GWAS Catalog, and reach out to the cancer genetics community in particular to promote submission of summary statistics for GWAS studies in cancer cohorts.\n\n### The GWAS Catalog summary statistics repository\n\nThree years ago, the GWAS Catalog and Open Targets started to work together to expand the Catalog’s scientific scope to include full p-value summary statistics (aggregate p-values and association data for every variant analysed in a genome-wide association study) in addition to the manually curated top associations.\n\nSummary statistics provide more detailed GWAS results than a lead SNP table, which allows other scientists to better use the data for sophisticated downstream analysis, whilst maintaining patient privacy. For example, summary statistics are integrated into the Open Targets Genetics Portal, where they are used to narrow down candidate loci and prioritise new drug targets through PheWAS, colocalisation analysis, fine mapping and mendelian randomisation\n\n[GWAS-catalog-statistics-1.png\n\nThe GWAS Catalog is now one of the largest, most visited and most frequently updated resources of freely available GWAS summary statistics. The repository includes summary statistics from 3,623 independent analyses (from 447 publications), accounting for a total of more than 22,000 datasets from a wide variety of traits. The Catalog users can easily access and download summary statistics from the GWAS Catalog [FTP site](ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/) or via a dedicated [summary statistics API](https://www.ebi.ac.uk/gwas/summary-statistics/docs/).\n\nThe increasing trend to share these datasets is reflected in the availability of summary statistics in the GWAS Catalog over time. There was also a significant increase in data downloads of summary statistics in 2020 compared to the previous year.\n\n\n\n\n\n### Why are few open access cancer GWAS summary statistics available?\n\nThis expansion of the GWAS Catalog summary statistics repository, while promising, required a considerable outreach effort from the Catalog data team and the main stakeholders, including Open Targets. Interestingly, the rate of summary statistics data sharing noticeably differs among different genetics cohorts and research groups, with the lowest submission rate in cancer genetics.\n\n\n\nFor example 33% of epilepsy and 21% of diabetes summary statistics are publicly available, compared to only 7.5% for cancer. The trend slightly differs amongst different cancer groups, and we have observed a more positive trend for the few papers published in the last two years.\n\n\n\n\nIn a recent [Twitter poll](https://twitter.com/GWASCatalog/status/1365359253175881742), we asked the community to tell us what they thought was the most important reason for the low rate of cancer summary statistics submission.\n\nThe results suggest that the main barriers to the sharing of summary statistics are:\n\n 1. Results are usually embargoed for use in future research,\n 2. Data privacy issues (e.g. patient confidentiality agreement), and\n 3. Lack of awareness/knowledge on the appropriate data repository.\n\nWe believe it is necessary for the genetics community to have a more comprehensive and transparent discussion about such barriers, starting from the privacy issues. As highlighted in the recently revised [NIH genomic data sharing (GDS) policy](https://osp.od.nih.gov/2018/11/01/provide-access-gsr/), summary statistics do not include individual-level information and can empower researchers to determine which genomic variants potentially contribute to a disease\n\n### How can we promote sharing?\n\nAs a part of a wider plan to identify and remove barriers to data sharing among different research communities, we have launched a dedicated public engagement campaign to motivate and encourage the genetics community to make their summary statistics publicly available – and make the difference in the field. In particular, following up from a pressing request from our users and stakeholders, we hope to understand the reasons why so few summary statistics are available for cancer studies. Open sharing of cancer GWAS data will lead the way to new, improved therapies and shed some light on the molecular mechanisms involved in such complex disease.\n\n\n\nWe call on researchers, journals, funders and charities to support our cause and spread our message. The Nature Journals Group are already proactively supporting an open access policy for their published cancer GWAS data. We hope to get more journals on board, including the top cancer journals.\n\nIn an aim to facilitate data sharing and interoperability, the GWAS Catalog team has recently released a [web-based deposition interface](https://www.ebi.ac.uk/gwas/deposition) to support scalable author submission of summary statistics and metadata from published and pre-published (submitted at the time of journal submission upon request from the reviewers) GWAS. The datasets are submitted in a standard format and harmonised against the latest genome build and forward strand. This facilitates downstream analysis and integration into resources like the Open Targets Genetics.\n\nPlease submit your summary statistics to the GWAS Catalog.\n\nWhat do you think are the most important barriers to the open sharing of summary statistics in cancer studies? Please let us know your thoughts by completing [this short survey](https://docs.google.com/forms/d/e/1FAIpQLSfuAO-cZWdIm8aF0140QXsZOxVlw7gLKj1a__KZ3aF4CUNamQ/viewform?usp=sf_link).\n\nYou can also reach out to us via [Twitter (@GWASCatalog)](https://twitter.com/GWASCatalog) or [email (gwas-info@ebi.ac.uk)](gwas-info@ebi.ac.uk).\n\nTogether, we can make the difference.\n","dir":"/blog","path":"/blog/open-sharing-of-cancer-summary-statistics","extension":".md","createdAt":"2024-03-11T15:03:22.557Z","updatedAt":"2024-03-11T15:03:22.558Z","meta":{"version":0,"revision":0,"created":1710171149496},"$loki":9},{"slug":"ten-years-of-the-gwas-catalog-past-present-and-future","description":"This year marks a special anniversary for the GWAS Catalog, as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers","title":"Ten years of the GWAS Catalog – Past, present and future","author":"Laura Harris (GWAS Catalog) and Astrid Gall (Ensembl Outreach)","date":"Jun 28, 2018","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This year marks a special anniversary for the "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS Catalog"}]},{"type":"text","value":", as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers and clinicians without them spending a lot of time undertaking regular systematic reviews of the literature. This is where we can help you!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The project is a collaboration between "},{"type":"element","tag":"a","props":{"href":"https://www.genome.gov/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the National Human Genome Research Institute (NHGRI)"}]},{"type":"text","value":" in Bethesda, Maryland, USA, and the European Molecular Biology Laboratory – "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"European Bioinformatics Institute (EMBL-EBI)"}]},{"type":"text","value":" in Hinxton, UK, where the majority of our curators and all of our software developers are based"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"ten-years-of-the-gwas-catalog-past-present-and-future/GWAS-catalog_EBI_group-768x576.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"At EMBL-EBI, we share the building with "},{"type":"element","tag":"a","props":{"href":"https://www.ensembl.org/index.html","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the Ensembl team"}]},{"type":"text","value":" and work very closely with them – which is very handy for both teams!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Our curators extract genotype-phenotype associations from all published GWAS studies, along with study information to allow you to interpret the data accurately. We make the data structured, searchable and visualisable, annotate it and integrate it with other resources including Ensembl. Each association with a standard variant identifier (rsID) enters our automated Ensembl mapping pipeline which provides the genomic annotation, i.e. the location on the chromosome and cytogenetic band as well as the mapped genes. We use this pipeline to check for errors as part of our quality control and display the information in our interface."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Our colleagues at Ensembl benefit from the GWAS Catalog as they import variant and phenotype data from it. "},{"type":"element","tag":"a","props":{"href":"http://www.ensembl.org/info/genome/variation/species/sources_documentation.html","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"More than 74,000 human variants associated with phenotype data from the GWAS catalog were included in the latest Ensembl release (e92)"}]},{"type":"text","value":"; and the number is increasing continuously!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"[video]"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Over the last ten years, the GWAS Catalog has grown, from a single published GWAS on age-related macular degeneration, into the primary source of disease-related associations with genetic variants. Watch how the number of associations has increased over time in this video!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Today, the GWAS Catalog contains over 3,400 publications and over 62,000 unique SNP-trait associations. More than 10,000 visitors from around the globe access the GWAS Catalog per month. People using the resource have a wide range of goals – they include researchers wanting to narrow down or prioritise candidate loci, scientists investigating disease mechanisms, clinicians aiming to predict disease risk, pharma industry professionals improving the drug discovery process and anyone wanting to get the latest statistics on disease knowledge or summary data from particular populations of individuals."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS landscape has evolved over these ten years, with new developments in study design and genotyping technologies. More and more publications on GWAS come out each month. Often a publication contains more than one GWAS – sometimes there are over a thousand in a single paper! The number of individuals studied in a GWAS is also increasing, and so is the number of variants assayed, with bigger arrays and better imputation methods."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The nature of the traits studied is evolving too. Researchers are still publishing GWAS on major common diseases such as type 2 diabetes and breast cancer. However, these studies are now being published alongside studies on ever more specific traits. For example, "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/search?query=schizophrenia","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"searching the GWAS Catalog for “schizophrenia”"}]},{"type":"text","value":" brings up more than 50 different traits related to this disorder. These include sub-phenotypes such as specific symptoms in schizophrenia, endophenotypes such as brain imaging measurements, multi-trait analyses such as “schizophrenia and bipolar disorder”, drug response measurements as well as analyses of the interaction between genetic associations and environmental factors."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"All of this means more data to curate. We are adapting our infrastructure constantly to improve curation efficiency so that we can keep up with the rate of data generation. At the same time, we need to maintain the accuracy of the resource and make sure it contains the most relevant and up-to-date research results."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"ten-years-of-the-gwas-catalog-past-present-and-future/fig2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Highlights over the last ten years include releasing a new "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS Catalog website"}]},{"type":"text","value":" in 2015, "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/ontology","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"mapping curated trait descriptions to ontology terms"}]},{"type":"text","value":" to enable enriched ontology-driven search capabilities, improving the interactive "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/diagram","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS Catalog diagram"}]},{"type":"text","value":" and "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/ancestry","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"capturing ancestry more completely"}]},{"type":"text","value":". Accurate characterisation of ancestry is essential to interpret human genomics data. Recently, we have developed a new framework for describing detailed ancestry information and recommendations for reporting ancestry systematically [(Morales et al 2018 Genome Biology 19:2)].("},{"type":"element","tag":"a","props":{"href":"https://europepmc.org/abstract/MED/29448949","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"https://europepmc.org/abstract/MED/29448949"}]},{"type":"text","value":")"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"So what will the future hold for the GWAS Catalog? We have just started hosting "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/downloads/summary-statistics","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"summary statistics for studies in the GWAS Catalog"}]},{"type":"text","value":" with support from the scientific community (the files contain the full set of p values for every SNP on the array used). In the future we plan to integrate these datasets with structured meta-data to make it even easier to access GWAS results. We also aim to expand our scope to include other types of association analyses, including targeted arrays and sequencing-based genotyping, which enable deeper interrogation of diseases of interest and inclusion of less common variants."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"With these developments come many challenges, including how best to represent the increasing complexity of study designs, trait architecture and statistical analyses. But with the help of the authors of publications in the GWAS Catalog, the scientific community using our resource and our colleagues at Ensembl, we are excited about what the next ten years of GWAS will hold!"}]}]},"text":"\nThis year marks a special anniversary for the [GWAS Catalog](https://www.ebi.ac.uk/gwas/), as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers and clinicians without them spending a lot of time undertaking regular systematic reviews of the literature. This is where we can help you!\n\nThe project is a collaboration between [the National Human Genome Research Institute (NHGRI)](https://www.genome.gov/) in Bethesda, Maryland, USA, and the European Molecular Biology Laboratory – [European Bioinformatics Institute (EMBL-EBI)](https://www.ebi.ac.uk/) in Hinxton, UK, where the majority of our curators and all of our software developers are based\n\n\n\n\nAt EMBL-EBI, we share the building with [the Ensembl team](https://www.ensembl.org/index.html) and work very closely with them – which is very handy for both teams!\n\n\nOur curators extract genotype-phenotype associations from all published GWAS studies, along with study information to allow you to interpret the data accurately. We make the data structured, searchable and visualisable, annotate it and integrate it with other resources including Ensembl. Each association with a standard variant identifier (rsID) enters our automated Ensembl mapping pipeline which provides the genomic annotation, i.e. the location on the chromosome and cytogenetic band as well as the mapped genes. We use this pipeline to check for errors as part of our quality control and display the information in our interface.\n\nOur colleagues at Ensembl benefit from the GWAS Catalog as they import variant and phenotype data from it. [More than 74,000 human variants associated with phenotype data from the GWAS catalog were included in the latest Ensembl release (e92)](http://www.ensembl.org/info/genome/variation/species/sources_documentation.html); and the number is increasing continuously!\n\n[video]\n\nOver the last ten years, the GWAS Catalog has grown, from a single published GWAS on age-related macular degeneration, into the primary source of disease-related associations with genetic variants. Watch how the number of associations has increased over time in this video!\n\n\nToday, the GWAS Catalog contains over 3,400 publications and over 62,000 unique SNP-trait associations. More than 10,000 visitors from around the globe access the GWAS Catalog per month. People using the resource have a wide range of goals – they include researchers wanting to narrow down or prioritise candidate loci, scientists investigating disease mechanisms, clinicians aiming to predict disease risk, pharma industry professionals improving the drug discovery process and anyone wanting to get the latest statistics on disease knowledge or summary data from particular populations of individuals.\n\nThe GWAS landscape has evolved over these ten years, with new developments in study design and genotyping technologies. More and more publications on GWAS come out each month. Often a publication contains more than one GWAS – sometimes there are over a thousand in a single paper! The number of individuals studied in a GWAS is also increasing, and so is the number of variants assayed, with bigger arrays and better imputation methods.\n\nThe nature of the traits studied is evolving too. Researchers are still publishing GWAS on major common diseases such as type 2 diabetes and breast cancer. However, these studies are now being published alongside studies on ever more specific traits. For example, [searching the GWAS Catalog for “schizophrenia”](https://www.ebi.ac.uk/gwas/search?query=schizophrenia) brings up more than 50 different traits related to this disorder. These include sub-phenotypes such as specific symptoms in schizophrenia, endophenotypes such as brain imaging measurements, multi-trait analyses such as “schizophrenia and bipolar disorder”, drug response measurements as well as analyses of the interaction between genetic associations and environmental factors.\n\nAll of this means more data to curate. We are adapting our infrastructure constantly to improve curation efficiency so that we can keep up with the rate of data generation. At the same time, we need to maintain the accuracy of the resource and make sure it contains the most relevant and up-to-date research results.\n\n\n\nHighlights over the last ten years include releasing a new [GWAS Catalog website](https://www.ebi.ac.uk/gwas/) in 2015, [mapping curated trait descriptions to ontology terms](https://www.ebi.ac.uk/gwas/docs/ontology) to enable enriched ontology-driven search capabilities, improving the interactive [GWAS Catalog diagram](https://www.ebi.ac.uk/gwas/diagram) and [capturing ancestry more completely](https://www.ebi.ac.uk/gwas/ancestry). Accurate characterisation of ancestry is essential to interpret human genomics data. Recently, we have developed a new framework for describing detailed ancestry information and recommendations for reporting ancestry systematically [(Morales et al 2018 Genome Biology 19:2)].(https://europepmc.org/abstract/MED/29448949)\n\nSo what will the future hold for the GWAS Catalog? We have just started hosting [summary statistics for studies in the GWAS Catalog](https://www.ebi.ac.uk/gwas/downloads/summary-statistics) with support from the scientific community (the files contain the full set of p values for every SNP on the array used). In the future we plan to integrate these datasets with structured meta-data to make it even easier to access GWAS results. We also aim to expand our scope to include other types of association analyses, including targeted arrays and sequencing-based genotyping, which enable deeper interrogation of diseases of interest and inclusion of less common variants.\n\nWith these developments come many challenges, including how best to represent the increasing complexity of study designs, trait architecture and statistical analyses. But with the help of the authors of publications in the GWAS Catalog, the scientific community using our resource and our colleagues at Ensembl, we are excited about what the next ten years of GWAS will 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For each study, we annotate the trait in two ways, a reported trait that reflects the author’s description of the disease or phenotypic characteristic under investigation. This is a free text description and sometimes.","title":"Trait annotation in the GWAS Catalog","author":"Elliot Sollis","date":"August 3, 2021","img":"blog-1.jpg","toc":[{"id":"annotating-different-study-types","depth":2,"text":"Annotating different study types"},{"id":"a-single-trait-studies","depth":3,"text":"A. Single-trait studies"},{"id":"b-multi-trait-studies","depth":3,"text":"B. Multi-trait studies"},{"id":"c-studies-with-a-background-trait","depth":3,"text":"C. Studies with a background trait"},{"id":"room-for-improvement","depth":2,"text":"Room for improvement"},{"id":"changes-to-trait-annotation","depth":2,"text":"Changes to trait annotation"},{"id":"changes-to-the-website","depth":2,"text":"Changes to the website"},{"id":"data-tables","depth":3,"text":"Data tables"},{"id":"study-pages","depth":3,"text":"Study pages"},{"id":"trait-pages","depth":3,"text":"Trait pages"},{"id":"downloads","depth":3,"text":"Downloads"},{"id":"api","depth":3,"text":"API"},{"id":"expected-scope-of-the-changes","depth":2,"text":"Expected scope of the changes"},{"id":"questions-and-feedback","depth":2,"text":"Questions and feedback"}],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Each study in the GWAS Catalog investigates the association between variants in the human genome and a particular trait or phenotypic characteristic."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For each study, we annotate the trait in two ways:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"A reported trait that reflects the author’s description of the disease or phenotypic characteristic under investigation. This is a free text description and sometimes different studies might use a different wording to describe similar traits or to capture more nuanced distinctions. Reported traits can also include multiple component traits, depending on the study design."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"One or more trait terms from the Experimental Factor Ontology (EFO) that represent phenotypic characteristics in a more standardised way. These terms make studies on similar traits easier for users to find and compare. For multifaceted traits, each component is represented by a separate term."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/rep-trait-example.png","alt":"Reported traits mapped to EFO terms"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"annotating-different-study-types"},"children":[{"type":"element","tag":"a","props":{"href":"#annotating-different-study-types","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Annotating different study types"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"a-single-trait-studies"},"children":[{"type":"element","tag":"a","props":{"href":"#a-single-trait-studies","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"A. Single-trait studies"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The vast majority (>80%) of studies in the GWAS Catalog only analyse a single trait. We annotate these studies with a single EFO term."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Some common examples include:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Standard case-control studies comparing individuals with a disease or phenotypic characteristic, versus controls individuals without that trait."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-sg-trait-binary.png","alt":"A case-control study with a single binary trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Quantitative studies looking at a single measurement"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-sg-trait-quant.png","alt":"A quantitative study with a single continuous trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In these examples, any reported variants are clearly and straightforwardly associated with that single trait."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"b-multi-trait-studies"},"children":[{"type":"element","tag":"a","props":{"href":"#b-multi-trait-studies","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"B. Multi-trait studies"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In about 10% of studies, there are multiple traits of interest that are analysed simultaneously. We annotate these studies with multiple EFO terms separated by a comma, indicating that any significant variants reported in the study are associated with both traits in some way."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Some common examples include:"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Studies comparing individuals with two comorbid diseases (or other traits), versus control individuals who have neither disease. In the reported trait we write “Disease 1 "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"and"}]},{"type":"text","value":" Disease 2”. In the trait we list multiple EFO terms separated by a comma."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-multi-trait-and.png","alt":"A case-control study where cases have two co-occuring traits"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Studies comparing individuals with either of two diseases (or other traits), versus control individuals who have neither disease. Often these are two traits that are hypothesised to have some common underlying genetic factors (pleiotropy). In the reported trait we write “Disease 1 "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"or"}]},{"type":"text","value":" Disease 2”. In the trait we list multiple EFO terms separated by a comma."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-multi-trait-or.png","alt":"A case-control study where cases may have either of two distinct traits"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In these examples, any significant variants reported are associated with either or both of the annotated traits. If a user is searching for associations with either trait term, then these results will come up in their search."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"c-studies-with-a-background-trait"},"children":[{"type":"element","tag":"a","props":{"href":"#c-studies-with-a-background-trait","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"C. Studies with a background trait"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Finally, about 6% of studies analyse only one main trait of interest, but in the context of a background trait that is shared by all of the participants in the study."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Some examples include:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Studies comparing cases and controls of one disease, but only within a sample of people who have another disease. In the reported trait we write “Disease 1 "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" Disease 2”. In the past, we listed multiple EFO terms in the trait column, as for scenario B."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-bg-trait-binary.png","alt":"A case-control study for one binary trait within a second background trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Quantitative studies analysing a measurement in the context of a disease. In the reported trait we write “Measurement "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" Disease”. In the past, we listed multiple EFO terms in the trait column, as for scenario B."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-bg-trait-quant.png","alt":"A quantitative study for one continuous trait within a second background trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In these examples, any reported variants are associated with the main trait, but not with the background trait."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"room-for-improvement"},"children":[{"type":"element","tag":"a","props":{"href":"#room-for-improvement","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Room for improvement"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In the past, we annotated background trait studies (scenario C) in the same way as we have for multi-trait studies (scenario B): with multiple EFO terms listed together in the trait field, separated by commas."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This had some benefits:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We were able to indicate that the study has something to do with the background trait - e.g. a particular association with allergic rhinitis may only hold true in asthmatics."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Users could search for associations with a trait, as well as associations that are found in the context of that trait as a background characteristic. Both kinds of associations might be relevant to users working in a particular field."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"However, there were some are also some disadvantages:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"It was not possible to tell that a particular EFO term was a background trait, without looking at the reported trait field. This complicated analysis particularly when accessing the data programmatically."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"When searching for a trait term (e.g. asthma), there was no easy way to distinguish which studies were direct associations with asthma, and which have asthma as the background trait, without reading the reported trait for each one."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The overall study and association numbers provided for some traits could be misleading since they included studies and associations where the trait of interest was a background trait."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"changes-to-trait-annotation"},"children":[{"type":"element","tag":"a","props":{"href":"#changes-to-trait-annotation","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Changes to trait annotation"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"To make our trait annotations more informative, we have added an additional background trait field to the GWAS Catalog database."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We have moved all EFO terms related to background traits to this new field, and removed them from the original trait field:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/annotation-change.png","alt":"We have moved EFO terms for background traits to a separate field"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"changes-to-the-website"},"children":[{"type":"element","tag":"a","props":{"href":"#changes-to-the-website","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Changes to the website"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We have also updated our web interface in order to display the restructured background trait information."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"data-tables"},"children":[{"type":"element","tag":"a","props":{"href":"#data-tables","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Data tables"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"New columns have been added to the Associations and Studies tables to clearly indicate the main and background traits."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Associations:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/new-assoc-table.png","alt":"Separate main and background traits displayed in Associations table"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Studies:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/new-study-table.png","alt":"Separate main and background traits displayed in Studies table"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The improved tables will be displayed on all Publication, Study, Trait, Variant, Gene and Region pages."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"study-pages"},"children":[{"type":"element","tag":"a","props":{"href":"#study-pages","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Study pages"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Each Study page also displays the main and background traits in separate fields in the Study Information panel.\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/study-info-panel.png","alt":"Main and background traits displayed in the Study Information panel"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"trait-pages"},"children":[{"type":"element","tag":"a","props":{"href":"#trait-pages","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Trait pages"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The Trait page has been updated so that background trait studies and associations are no longer shown by default. For example, the asthma page only shows associations with asthma itself, and not associations with others traits in asthma.\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/asthma-no-bg.png","alt":"Asthma Trait page showing associations with asthma only"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"Shows associations with:"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"asthma"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"del","props":{},"children":[{"type":"text","value":"[other traits] "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" asthma"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"However, users can select the box to include background trait data if they wish:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/asthma-with-bg.png","alt":"Asthma Trait page showing associations with asthma AND with other traits in a background of asthma."},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"Shows associations with:"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"asthma"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"[other traits] "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" asthma"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Some trait terms may only be used in the GWAS Catalog to annotate background traits. These terms will continue to have their own Trait page, but no associations or studies will be displayed under the default view. For example, autoimmune pancreatitis type 1 (EFO_1000780) currently appears in the Catalog only as a background trait for one study (“Lachrymal/Salivary gland lesion in type 1 autoimmune pancreatitis”), so no associations with autoimmune pancreatitis type 1 are displayed by default:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/no-records.png","alt":"A Trait page with no main trait associations"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The association plot on each Trait page will now display only main trait associations by default, but background trait data can be added by selecting the box:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/assoc-plot-change.png","alt":"Association plot with with or without background trait data"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"downloads"},"children":[{"type":"element","tag":"a","props":{"href":"#downloads","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Downloads"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In "},{"type":"element","tag":"a","props":{"href":"http://www.ebi.ac.uk/gwas/docs/file-downloads","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the full Catalog downloads"}]},{"type":"text","value":", the MAPPED_TRAIT and MAPPED_TRAIT_URI columns will now only show EFO terms for main traits. Background trait terms have been removed from these columns."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In the newest version of the download (v1.0.3), new columns have been added for the background trait: MAPPED BACKGROUND TRAIT and MAPPED BACKGROUND TRAIT URI. This is not included in earlier versions of the download."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"api"},"children":[{"type":"element","tag":"a","props":{"href":"#api","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"API"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For users of the GWAS Catalog API, searching for “associationByEfoTrait” will now return only main trait associations."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"There is currently no background trait field available in the API, but we plan to add this feature in the future."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"expected-scope-of-the-changes"},"children":[{"type":"element","tag":"a","props":{"href":"#expected-scope-of-the-changes","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Expected scope of the changes"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We reviewed all of the studies in the Catalog and identified just over 1000 studies (about 6% of the total) with background traits that have now been moved to the new field. These studies contain around 10,000 associations (about 4% of all associations in the Catalog) which have also been reannotated."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The changes affect some trait terms more than others. Here are some of the terms that will see the greatest change in the number of annotated Associations:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/most-affected-traits.png","alt":"Graph of selected trait terms affected by this change"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"questions-and-feedback"},"children":[{"type":"element","tag":"a","props":{"href":"#questions-and-feedback","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Questions and feedback"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"If you have any questions or comments about this change, please contact us as "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":"."}]}]},"text":"\nEach study in the GWAS Catalog investigates the association between variants in the human genome and a particular trait or phenotypic characteristic.\n\nFor each study, we annotate the trait in two ways:\n\n1. A reported trait that reflects the author’s description of the disease or phenotypic characteristic under investigation. This is a free text description and sometimes different studies might use a different wording to describe similar traits or to capture more nuanced distinctions. Reported traits can also include multiple component traits, depending on the study design.\n\n2. One or more trait terms from the Experimental Factor Ontology (EFO) that represent phenotypic characteristics in a more standardised way. These terms make studies on similar traits easier for users to find and compare. For multifaceted traits, each component is represented by a separate term.\n\n\n\n## Annotating different study types \n\n### A. Single-trait studies\n\nThe vast majority (>80%) of studies in the GWAS Catalog only analyse a single trait. We annotate these studies with a single EFO term.\n\nSome common examples include:\n\n1. Standard case-control studies comparing individuals with a disease or phenotypic characteristic, versus controls individuals without that trait.\n\n\n2. Quantitative studies looking at a single measurement\n\n\n\nIn these examples, any reported variants are clearly and straightforwardly associated with that single trait.\n\n### B. Multi-trait studies\n\nIn about 10% of studies, there are multiple traits of interest that are analysed simultaneously. We annotate these studies with multiple EFO terms separated by a comma, indicating that any significant variants reported in the study are associated with both traits in some way.\n\n**Some common examples include:**\n\n1. Studies comparing individuals with two comorbid diseases (or other traits), versus control individuals who have neither disease. In the reported trait we write “Disease 1 _and_ Disease 2”. In the trait we list multiple EFO terms separated by a comma.\n\n\n2. Studies comparing individuals with either of two diseases (or other traits), versus control individuals who have neither disease. Often these are two traits that are hypothesised to have some common underlying genetic factors (pleiotropy). In the reported trait we write “Disease 1 _or_ Disease 2”. In the trait we list multiple EFO terms separated by a comma.\n\n\nIn these examples, any significant variants reported are associated with either or both of the annotated traits. If a user is searching for associations with either trait term, then these results will come up in their search.\n\n### C. Studies with a background trait\n\nFinally, about 6% of studies analyse only one main trait of interest, but in the context of a background trait that is shared by all of the participants in the study.\n\nSome examples include:\n\n1. Studies comparing cases and controls of one disease, but only within a sample of people who have another disease. In the reported trait we write “Disease 1 _in_ Disease 2”. In the past, we listed multiple EFO terms in the trait column, as for scenario B.\n\n\n2. Quantitative studies analysing a measurement in the context of a disease. In the reported trait we write “Measurement _in_ Disease”. In the past, we listed multiple EFO terms in the trait column, as for scenario B.\n\n\nIn these examples, any reported variants are associated with the main trait, but not with the background trait.\n\n## Room for improvement\n\nIn the past, we annotated background trait studies (scenario C) in the same way as we have for multi-trait studies (scenario B): with multiple EFO terms listed together in the trait field, separated by commas.\n\nThis had some benefits:\n\n* We were able to indicate that the study has something to do with the background trait - e.g. a particular association with allergic rhinitis may only hold true in asthmatics.\n\n* Users could search for associations with a trait, as well as associations that are found in the context of that trait as a background characteristic. Both kinds of associations might be relevant to users working in a particular field.\n\nHowever, there were some are also some disadvantages:\n\n* It was not possible to tell that a particular EFO term was a background trait, without looking at the reported trait field. This complicated analysis particularly when accessing the data programmatically.\n\n* When searching for a trait term (e.g. asthma), there was no easy way to distinguish which studies were direct associations with asthma, and which have asthma as the background trait, without reading the reported trait for each one.\n\n* The overall study and association numbers provided for some traits could be misleading since they included studies and associations where the trait of interest was a background trait.\n\n## Changes to trait annotation\n\nTo make our trait annotations more informative, we have added an additional background trait field to the GWAS Catalog database.\n\nWe have moved all EFO terms related to background traits to this new field, and removed them from the original trait field:\n\n\n## Changes to the website\n\nWe have also updated our web interface in order to display the restructured background trait information.\n\n### Data tables\n\nNew columns have been added to the Associations and Studies tables to clearly indicate the main and background traits.\n\nAssociations:\n\n\nStudies:\n\n\nThe improved tables will be displayed on all Publication, Study, Trait, Variant, Gene and Region pages.\n\n### Study pages\n\nEach Study page also displays the main and background traits in separate fields in the Study Information panel.\n\n\n### Trait pages\n\nThe Trait page has been updated so that background trait studies and associations are no longer shown by default. For example, the asthma page only shows associations with asthma itself, and not associations with others traits in asthma.\n\n\n_Shows associations with:_\n* asthma\n* ~[other traits] _in_ asthma~\n\nHowever, users can select the box to include background trait data if they wish:\n\n\n_Shows associations with:_\n* asthma\n* [other traits] _in_ asthma\n\nSome trait terms may only be used in the GWAS Catalog to annotate background traits. These terms will continue to have their own Trait page, but no associations or studies will be displayed under the default view. For example, autoimmune pancreatitis type 1 (EFO_1000780) currently appears in the Catalog only as a background trait for one study (“Lachrymal/Salivary gland lesion in type 1 autoimmune pancreatitis”), so no associations with autoimmune pancreatitis type 1 are displayed by default:\n\n\nThe association plot on each Trait page will now display only main trait associations by default, but background trait data can be added by selecting the box:\n\n\n### Downloads\n\nIn [the full Catalog downloads](http://www.ebi.ac.uk/gwas/docs/file-downloads), the MAPPED_TRAIT and MAPPED_TRAIT_URI columns will now only show EFO terms for main traits. Background trait terms have been removed from these columns.\n\nIn the newest version of the download (v1.0.3), new columns have been added for the background trait: MAPPED BACKGROUND TRAIT and MAPPED BACKGROUND TRAIT URI. This is not included in earlier versions of the download.\n\n### API\n\nFor users of the GWAS Catalog API, searching for “associationByEfoTrait” will now return only main trait associations.\n\nThere is currently no background trait field available in the API, but we plan to add this feature in the future.\n\n## Expected scope of the changes\n\nWe reviewed all of the studies in the Catalog and identified just over 1000 studies (about 6% of the total) with background traits that have now been moved to the new field. These studies contain around 10,000 associations (about 4% of all associations in the Catalog) which have also been reannotated.\n\nThe changes affect some trait terms more than others. Here are some of the terms that will see the greatest change in the number of annotated Associations:\n\n\n## Questions and feedback\n\nIf you have any questions or comments about this change, please contact us as gwas-info@ebi.ac.uk.","dir":"/blog","path":"/blog/background-trait-update","extension":".md","createdAt":"2024-03-11T15:03:22.553Z","updatedAt":"2024-03-11T15:03:22.553Z","meta":{"version":0,"revision":0,"created":1710171117041},"$loki":1},{"slug":"everything-you-need-to-know-about-the-new-gwas-catalog","description":"Genetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation.","title":"Everything you need to know about the new GWAS Catalog","author":"Annalisa Buniello","date":"Jun 13, 2018","img":"blog-1.jpg","toc":[{"id":"new-and-expanded-gwas-catalog-to-improve-target-identification","depth":3,"text":"New and expanded GWAS Catalog to improve target identification"},{"id":"selecting-and-curating-non-genome-wide-array-studies","depth":3,"text":"Selecting and curating non-genome-wide array studies"},{"id":"modifying-the-gwas-catalog-user-interface","depth":3,"text":"Modifying the GWAS Catalog user interface"},{"id":"stay-tuned","depth":3,"text":"Stay tuned!"}],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Genetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The NHGRI-EBI GWAS Catalog, an awesome public resource of human genotype-phenotype associations from published GWAS analyses, is one of the data sources for manually curated genetic evidence for the Open Targets Platform. The GWAS Catalog data is widely used to prioritise new candidate loci, predict disease risk and understand disease mechanisms, with a number of users and applications that is always increasing."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/FINAL_USERS_withOT-2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"new-and-expanded-gwas-catalog-to-improve-target-identification"},"children":[{"type":"element","tag":"a","props":{"href":"#new-and-expanded-gwas-catalog-to-improve-target-identification","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"New and expanded GWAS Catalog to improve target identification"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"I am happy to announce an expansion of the GWAS Catalog scientific scope to host non genome-wide studies. During its first 10 years of life, in fact, the GWAS Catalog has only included array studies with genome-wide coverage, therefore curation of exome and targeted array studies were outside its scientific scope. These data are however valuable to Open Targets’ scientific goals of target identification and prioritisation, so much so they decided to invest in a new project aiming to expand the scope of the Catalog to include targeted arrays, such as MetaboChip, ImmunoChip, and Exome array. The new GWAS will now allow variants associated with immunologic, metabolic and oncologic phenotypes to be included in the Open Targets Platform for the expansion of target identification and selection in these traits."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"selecting-and-curating-non-genome-wide-array-studies"},"children":[{"type":"element","tag":"a","props":{"href":"#selecting-and-curating-non-genome-wide-array-studies","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Selecting and curating non-genome-wide array studies"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"As a first step, Open Targets selected almost 60 publications for inclusion in the GWAS Catalog. I then extracted a total of 120 new independent association studies and 823 SNP-trait associations from these publications. These are impressive numbers, and give you an idea of how many studies and SNP-trait associations (and potential targets) we were missing by not including targeted array studies in the GWAS Catalog!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The final step for me to do was to map all these traits, diseases and phenotypes to EFO terms, the ontology used by\n"},{"type":"element","tag":"a","props":{"href":"https://docs.targetvalidation.org/faq/describing-diseases","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Open Targets to describe its diseases"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"modifying-the-gwas-catalog-user-interface"},"children":[{"type":"element","tag":"a","props":{"href":"#modifying-the-gwas-catalog-user-interface","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Modifying the GWAS Catalog user interface"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS Catalog team have changed the way the user interface works to support searching, displaying, filtering and download of targeted and exome array studies. "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/search?query=celiac%20disease","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Try it out using \"celiac disease\""}]},{"type":"text","value":", for example. The interface will now give you:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Drop-down filter by genotyping technology to help you focus on your favourite array"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/filter1-5.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Small \"target\" icon (designed for us by the EMBL-EBI media team. Thanks for that!) in the study accession number column for all targeted array studies prioritised by Open Targets"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/target-1.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"New \"genotyping technology\" shown for study, e.g. genome-wide genotyping array, exome-wide genotyping array or targeted genotyping array with an optional field for additional array information, for example ImmunoChip or MetaboChip"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/gen-tech-1.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"stay-tuned"},"children":[{"type":"element","tag":"a","props":{"href":"#stay-tuned","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Stay tuned!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This is an important milestone for Open Targets and the GWAS Catalog, and a lot more is yet to come. The next step for the collaboration between GWAS and Open Targets will be to develop a comprehensive database of all available GWAS full summary statistics stored in a common format and harmonised across studies to enable searching, easy comparison and downstream analysis. This effort will generate more comprehensive new raw genetic data accessible by the entire research community through the GWAS Catalog, the Open Targets Platform and additional resources the Open Targets team are working on."}]}]},"text":"\nGenetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation.\n\nThe NHGRI-EBI GWAS Catalog, an awesome public resource of human genotype-phenotype associations from published GWAS analyses, is one of the data sources for manually curated genetic evidence for the Open Targets Platform. The GWAS Catalog data is widely used to prioritise new candidate loci, predict disease risk and understand disease mechanisms, with a number of users and applications that is always increasing.\n\n\n\n### New and expanded GWAS Catalog to improve target identification\n\nI am happy to announce an expansion of the GWAS Catalog scientific scope to host non genome-wide studies. During its first 10 years of life, in fact, the GWAS Catalog has only included array studies with genome-wide coverage, therefore curation of exome and targeted array studies were outside its scientific scope. These data are however valuable to Open Targets’ scientific goals of target identification and prioritisation, so much so they decided to invest in a new project aiming to expand the scope of the Catalog to include targeted arrays, such as MetaboChip, ImmunoChip, and Exome array. The new GWAS will now allow variants associated with immunologic, metabolic and oncologic phenotypes to be included in the Open Targets Platform for the expansion of target identification and selection in these traits.\n\n### Selecting and curating non-genome-wide array studies\n\nAs a first step, Open Targets selected almost 60 publications for inclusion in the GWAS Catalog. I then extracted a total of 120 new independent association studies and 823 SNP-trait associations from these publications. These are impressive numbers, and give you an idea of how many studies and SNP-trait associations (and potential targets) we were missing by not including targeted array studies in the GWAS Catalog!\n\nThe final step for me to do was to map all these traits, diseases and phenotypes to EFO terms, the ontology used by \n[Open Targets to describe its diseases](https://docs.targetvalidation.org/faq/describing-diseases)\n\n### Modifying the GWAS Catalog user interface\n\nThe GWAS Catalog team have changed the way the user interface works to support searching, displaying, filtering and download of targeted and exome array studies. [Try it out using \"celiac disease\"](https://www.ebi.ac.uk/gwas/search?query=celiac%20disease), for example. The interface will now give you:\n\n- **Drop-down filter by genotyping technology to help you focus on your favourite array**\n\n\n\n- **Small \"target\" icon (designed for us by the EMBL-EBI media team. Thanks for that!) in the study accession number column for all targeted array studies prioritised by Open Targets**\n\n\n\n- **New \"genotyping technology\" shown for study, e.g. genome-wide genotyping array, exome-wide genotyping array or targeted genotyping array with an optional field for additional array information, for example ImmunoChip or MetaboChip**\n\n\n\n### Stay tuned!\n\nThis is an important milestone for Open Targets and the GWAS Catalog, and a lot more is yet to come. The next step for the collaboration between GWAS and Open Targets will be to develop a comprehensive database of all available GWAS full summary statistics stored in a common format and harmonised across studies to enable searching, easy comparison and downstream analysis. This effort will generate more comprehensive new raw genetic data accessible by the entire research community through the GWAS Catalog, the Open Targets Platform and additional resources the Open Targets team are working on.\n","dir":"/blog","path":"/blog/everything-you-need-to-know-about-the-new-gwas-catalog","extension":".md","createdAt":"2024-03-11T15:03:22.554Z","updatedAt":"2024-03-11T15:03:22.554Z","meta":{"version":0,"revision":0,"created":1710171117056},"$loki":2},{"slug":"cohorts-now-available","description":"We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics.","title":"Cohorts now available in the GWAS Catalog","author":"GWAS Catalog team","date":"Nov 14, 2023","img":"blog-1.img","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"There are two new studies "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/file-downloads","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"downloads"}]},{"type":"text","value":" named as follows:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"gwas-catalog-studies_ontology-annotated_v1.0.2.1.tsv"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"gwas-catalog-download-studies-v1.0.3.1.tsv"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Versions 1.0.2 (gwas-catalog-studies_ontology-annotated.tsv) and 1.0.3 (gwas-catalog-download-studies-v1.0.3.tsv) of the studies downloads will be deprecated and cease to be generated on 1 December 2023."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The COHORT field describes the discovery stage (genome-wide) cohorts used in each study. Since we started piloting curation of cohort information in 2020, over 700 different discovery cohorts have been described in GWAS publications."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Cohort abbreviations from discovery stage GWAS either are extracted from the literature or supplied by sumitters, to match a predefined list which we share with the PGS Catalog. The initial list of common cohorts used in genetics studies that seeded these annotations is from Mills & Rahal, Communications Biology (2019), and has been expanded during our pilot phase. A full list of the abbreviations and corresponding full cohort names in use is available to download separately ("},{"type":"element","tag":"a","props":{"href":"https://ftp.ebi.ac.uk/pub/databases/spot/pgs/metadata/pgs_all_metadata_cohorts.csv","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"https://ftp.ebi.ac.uk/pub/databases/spot/pgs/metadata/pgs_all_metadata_cohorts.csv"}]},{"type":"text","value":"). We share this list with the PGS Catalog, so you may find cohorts listed there that are not currently associated with a GWAS Catalog study. It's important to remember we only extract cohorts when they have been clearly and unambiguously named by authors, so a cohort may have been used in other publications which don't have annotation.\nWhere a sample cohort in the literature was not already in the predefined list at the time of curation, or was ambiguously described, “other” will appear in our studies download file. Where a sample in the literature had no cohort reported, “NR” will appear in our studies download file. Consortium names are extracted if the component cohorts are not provided, or there are too many component cohorts to curate. Empty cohort fields appear for studies curated before the pilot work to extract this information began (~2020).\nWe also accept submissions of unpublished data and these are listed in separate download files. Cohorts appearing in the unpublished download files are yet to undergo in-house curation and therefore may not exactly match against the predefined list."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Cohort data annotations for published studies are also now available via the REST API, accessible via the /studies and /studies/{accessionId} endpoints. The field is called "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"cohort"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"As well as cohort data, the new studies downloads also contain clear annotation of which studies have full genomewide summary statistics available for download in the column FULL SUMMARY STATISTICS with the ftp location available in the adjacent column SUMMARY STATS LOCATION."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We hope you make great use of these data! Questions? Feedback? Contact "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]}]}]},"text":"We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics. \n\nThere are two new studies [downloads](https://www.ebi.ac.uk/gwas/docs/file-downloads) named as follows:\n- gwas-catalog-studies_ontology-annotated_v1.0.2.1.tsv\n- gwas-catalog-download-studies-v1.0.3.1.tsv\n\nVersions 1.0.2 (gwas-catalog-studies_ontology-annotated.tsv) and 1.0.3 (gwas-catalog-download-studies-v1.0.3.tsv) of the studies downloads will be deprecated and cease to be generated on 1 December 2023. \n\nThe COHORT field describes the discovery stage (genome-wide) cohorts used in each study. Since we started piloting curation of cohort information in 2020, over 700 different discovery cohorts have been described in GWAS publications. \n\nCohort abbreviations from discovery stage GWAS either are extracted from the literature or supplied by sumitters, to match a predefined list which we share with the PGS Catalog. The initial list of common cohorts used in genetics studies that seeded these annotations is from Mills & Rahal, Communications Biology (2019), and has been expanded during our pilot phase. A full list of the abbreviations and corresponding full cohort names in use is available to download separately (https://ftp.ebi.ac.uk/pub/databases/spot/pgs/metadata/pgs_all_metadata_cohorts.csv). We share this list with the PGS Catalog, so you may find cohorts listed there that are not currently associated with a GWAS Catalog study. It's important to remember we only extract cohorts when they have been clearly and unambiguously named by authors, so a cohort may have been used in other publications which don't have annotation. \nWhere a sample cohort in the literature was not already in the predefined list at the time of curation, or was ambiguously described, “other” will appear in our studies download file. Where a sample in the literature had no cohort reported, “NR” will appear in our studies download file. Consortium names are extracted if the component cohorts are not provided, or there are too many component cohorts to curate. Empty cohort fields appear for studies curated before the pilot work to extract this information began (~2020).\nWe also accept submissions of unpublished data and these are listed in separate download files. Cohorts appearing in the unpublished download files are yet to undergo in-house curation and therefore may not exactly match against the predefined list.\n\nCohort data annotations for published studies are also now available via the REST API, accessible via the /studies and /studies/{accessionId} endpoints. The field is called *cohort*. \n\nAs well as cohort data, the new studies downloads also contain clear annotation of which studies have full genomewide summary statistics available for download in the column FULL SUMMARY STATISTICS with the ftp location available in the adjacent column SUMMARY STATS LOCATION. \n\nWe hope you make great use of these data! Questions? Feedback? Contact gwas-info@ebi.ac.uk\n","dir":"/blog","path":"/blog/cohorts-now-available","extension":".md","createdAt":"2024-03-11T15:03:22.554Z","updatedAt":"2024-03-11T15:03:22.554Z","meta":{"version":0,"revision":0,"created":1710171117065},"$loki":3},{"slug":"gwas-ssf-release","description":"Next month the GWAS Catalog is switching to a NEW summary statistics format. Find out more about what this means for submitters and consumers of summary statistics","title":"New submissions standard - release date","author":"GWAS Catalog team","date":"22 March 2023","img":"blog-1.jpg","toc":[{"id":"the-new-summary-statistics-format-is-coming---what-you-need-to-know","depth":2,"text":"The new summary statistics format is coming - what you need to know"},{"id":"what-does-this-mean-for-you","depth":2,"text":"What does this mean for you?"},{"id":"frequently-asked-questions","depth":2,"text":"Frequently asked questions"},{"id":"for-submitters","depth":3,"text":"For submitters:"},{"id":"for-consumers","depth":3,"text":"For consumers:"}],"body":{"type":"root","children":[{"type":"element","tag":"h2","props":{"id":"the-new-summary-statistics-format-is-coming---what-you-need-to-know"},"children":[{"type":"element","tag":"a","props":{"href":"#the-new-summary-statistics-format-is-coming---what-you-need-to-know","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"The new summary statistics format is coming - what you need to know"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"From 12th April we are switching to our NEW summary statistics format, GWAS-SSF, an overview of which is described in our "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v2","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"manuscript"}]},{"type":"text","value":" and "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-summary-statistics-standard","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"github repository"}]},{"type":"text","value":". There will be a short period of submission system downtime while we switch over to the new pipelines."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"what-does-this-mean-for-you"},"children":[{"type":"element","tag":"a","props":{"href":"#what-does-this-mean-for-you","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"What does this mean for you?"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"If you are a SUBMITTER of summary statistics, you will need to submit additional mandatory information, including effect size, allele frequency, and genomic location for each variant, and columns must be presented in a specific order. You are also encouraged to provide additional structured data (e.g. reference allele, imputation info score) and metadata (e.g. ancestry method, sex of participants) which will increase the usability of your data to others, and hopefully get you "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.09.27.509657v2","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"more citations"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"If you are a CONSUMER of summary statistics, you will start to see changes to the way we make these available. All newly submitted summary statistics will include the additional fields described "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-summary-statistics-standard","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"here"}]},{"type":"text","value":", with metadata available in a text file (.yaml) stored in the same ftp location as the data file, as well as its existing availability via the GWAS Catalog website and downloads. Over the coming months, we will generate metadata files for all existing summary statistics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"frequently-asked-questions"},"children":[{"type":"element","tag":"a","props":{"href":"#frequently-asked-questions","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Frequently asked questions"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"for-submitters"},"children":[{"type":"element","tag":"a","props":{"href":"#for-submitters","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"For submitters:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I’ve already started a submission and prepared my files in the old format. What should I do?"}]},{"type":"text","value":"\nYou can submit files in the old format until April 11th. If you are unable to complete your submission before this deadline, please contact us via "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-subs@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-subs@ebi.ac.uk"}]},{"type":"text","value":" for assistance."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I submitted summary statistics in the old format a while back and they are under embargo or in pre-published state. Do I need to change anything?"}]},{"type":"text","value":"\nNo. Your data will be made available in the old format (upon journal publication, if embargoed), and the metadata file will be prepared from the information you submitted."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I want to start preparing my submission now, but won’t be ready to submit till after April 12th. How can I find instructions on how to prepare the new format?"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Detailed documentation on the new format is available as follows:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/gwas-ssf/summary-statistics-format","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Summary statistics data file preparation"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics-plus-metadata","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Submission instructions for summary statistics and sample metadata"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Submission instructions for summary statistics only"}]},{"type":"text","value":" (for publications with metadata already available in the GWAS Catalog)."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"An example new-style metadata template is available to download "},{"type":"element","tag":"a","props":{"href":"http://ftp.ebi.ac.uk/pub/databases/gwas/sumstats_and_meta_submission_template_v1_11.xlsx","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"here"}]},{"type":"text","value":"\nA recorded "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/training/events/data-submission-genome-wide-association-studies-gwas-catalog/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"webinar"}]},{"type":"text","value":" is available, describing the submission process and the new format."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Are there tools available to help me prepare data in GWAS-SSF format?"}]},{"type":"text","value":"\nYou can find some useful tools to format your files and validate them prior to upload "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-sumstats-tools","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"here"}]},{"type":"text","value":"\nPLINK 2.0 will shortly include an option to generate output in the accepted format, using the option --gwas-ssf."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I made a submission in the old format but would like to update it to include the new fields. Can I do this?"}]},{"type":"text","value":"\nAt the moment our update process is manual and we don’t have capacity to update large numbers of files unless there is an error. We plan to improve this process in the future."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"for-consumers"},"children":[{"type":"element","tag":"a","props":{"href":"#for-consumers","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"For consumers:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"How can I tell if summary statistics are in the old or new format?"}]},{"type":"text","value":"\nThis is indicated in the metadata file. If a metadata file exists, and the field named “fileType” has the value “GWAS-SSF v1.0”, then the file is in the new GWAS-SSF format. Other values in this field indicate that it is not in the new GWAS-SSF format, and may be lacking mandatory fields. If no metadata file exists, the summary statistics are in the old format, or a non-standard format."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"How can I combine or compare new and old format summary statistics?"}]},{"type":"text","value":"\nData fields included in the old format (chromosome, base_pair_location, effect_allele, other_allele, effect_allele_frequency, odds_ratio, ci_lower, ci_upper, beta, standard_error) are the same in the new format. There are two exceptions: variant_id and p_value. In the old format, variant_id contained rsIDs, whereas in the new format rsID has a separate column rsid, and variant_ID contains a variant identifier in the format chr_bp_ref_alt. In the old format, p_value was a mandatory field and could only contain p-values between 0 and 1. In the new format, p-values may be either presented in the p_value field OR as negative log10 p-values, with the header neg_log_10_p_value, but always in column 7."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"More questions?"}]},{"type":"text","value":" Get in touch: contact "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":" for general queries or "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-subs@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-subs@ebi.ac.uk"}]},{"type":"text","value":" for submission support"}]}]},"text":"\n## The new summary statistics format is coming - what you need to know\n\nFrom 12th April we are switching to our NEW summary statistics format, GWAS-SSF, an overview of which is described in our [manuscript](https://www.biorxiv.org/content/10.1101/2022.07.15.500230v2) and [github repository](https://github.com/EBISPOT/gwas-summary-statistics-standard). There will be a short period of submission system downtime while we switch over to the new pipelines.\n\n## What does this mean for you?\n\nIf you are a SUBMITTER of summary statistics, you will need to submit additional mandatory information, including effect size, allele frequency, and genomic location for each variant, and columns must be presented in a specific order. You are also encouraged to provide additional structured data (e.g. reference allele, imputation info score) and metadata (e.g. ancestry method, sex of participants) which will increase the usability of your data to others, and hopefully get you [more citations](https://www.biorxiv.org/content/10.1101/2022.09.27.509657v2).\n\nIf you are a CONSUMER of summary statistics, you will start to see changes to the way we make these available. All newly submitted summary statistics will include the additional fields described [here](https://github.com/EBISPOT/gwas-summary-statistics-standard), with metadata available in a text file (.yaml) stored in the same ftp location as the data file, as well as its existing availability via the GWAS Catalog website and downloads. Over the coming months, we will generate metadata files for all existing summary statistics.\n\n## Frequently asked questions\n\n### For submitters:\n\n**I’ve already started a submission and prepared my files in the old format. What should I do?**\nYou can submit files in the old format until April 11th. If you are unable to complete your submission before this deadline, please contact us via gwas-subs@ebi.ac.uk for assistance.\n\n**I submitted summary statistics in the old format a while back and they are under embargo or in pre-published state. Do I need to change anything?**\nNo. Your data will be made available in the old format (upon journal publication, if embargoed), and the metadata file will be prepared from the information you submitted.\n\n**I want to start preparing my submission now, but won’t be ready to submit till after April 12th. How can I find instructions on how to prepare the new format?**\n\nDetailed documentation on the new format is available as follows:\n- [Summary statistics data file preparation](https://www.ebi.ac.uk/gwas/docs/gwas-ssf/summary-statistics-format)\n- [Submission instructions for summary statistics and sample metadata](https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics-plus-metadata)\n- [Submission instructions for summary statistics only](https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics) (for publications with metadata already available in the GWAS Catalog).\n\nAn example new-style metadata template is available to download [here](http://ftp.ebi.ac.uk/pub/databases/gwas/sumstats_and_meta_submission_template_v1_11.xlsx)\nA recorded [webinar](https://www.ebi.ac.uk/training/events/data-submission-genome-wide-association-studies-gwas-catalog/) is available, describing the submission process and the new format.\n\n**Are there tools available to help me prepare data in GWAS-SSF format?**\nYou can find some useful tools to format your files and validate them prior to upload [here]( https://github.com/EBISPOT/gwas-sumstats-tools)\nPLINK 2.0 will shortly include an option to generate output in the accepted format, using the option --gwas-ssf.\n\n**I made a submission in the old format but would like to update it to include the new fields. Can I do this?**\nAt the moment our update process is manual and we don’t have capacity to update large numbers of files unless there is an error. We plan to improve this process in the future.\n\n### For consumers:\n\n**How can I tell if summary statistics are in the old or new format?**\nThis is indicated in the metadata file. If a metadata file exists, and the field named “fileType” has the value “GWAS-SSF v1.0”, then the file is in the new GWAS-SSF format. Other values in this field indicate that it is not in the new GWAS-SSF format, and may be lacking mandatory fields. If no metadata file exists, the summary statistics are in the old format, or a non-standard format.\n\n**How can I combine or compare new and old format summary statistics?**\nData fields included in the old format (chromosome, base_pair_location, effect_allele, other_allele, effect_allele_frequency, odds_ratio, ci_lower, ci_upper, beta, standard_error) are the same in the new format. There are two exceptions: variant_id and p_value. In the old format, variant_id contained rsIDs, whereas in the new format rsID has a separate column rsid, and variant_ID contains a variant identifier in the format chr_bp_ref_alt. In the old format, p_value was a mandatory field and could only contain p-values between 0 and 1. In the new format, p-values may be either presented in the p_value field OR as negative log10 p-values, with the header neg_log_10_p_value, but always in column 7.\n\n\n**More questions?** Get in touch: contact [gwas-info@ebi.ac.uk](mailto:gwas-info@ebi.ac.uk) for general queries or [gwas-subs@ebi.ac.uk](mailto:gwas-subs@ebi.ac.uk) for submission support\n","dir":"/blog","path":"/blog/gwas-ssf-release","extension":".md","createdAt":"2024-03-11T15:03:22.554Z","updatedAt":"2024-03-11T15:03:22.555Z","meta":{"version":0,"revision":0,"created":1710171117080},"$loki":4},{"slug":"gwas-ui-labs-release","description":"We are pleased to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing at www.ebi.ac.uk/gwas/labs. Here is a guide to the main differences you will see.","title":"GWAS Catalog web search updates - help us with testing","author":"GWAS Catalog team","date":"5 May 2023","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"h1","props":{"id":"new-gwas-web-search-interface-ready-for-testing"},"children":[{"type":"element","tag":"a","props":{"href":"#new-gwas-web-search-interface-ready-for-testing","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"New GWAS web search interface ready for testing"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We are excited to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing on our "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/labs","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"labs site"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This is a guide to the main differences you will see."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-labs-release/SlideA.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We’ve also changed the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-labs-release/SlideB.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Studies with full summary statistics available for download are now easier to find in their own tab:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-labs-release/SlideC.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Please try out the new site and let us know what you like, what you don’t like, if you find any bugs or if any of your favourite features are missing. You can send feedback via "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":" or report bugs directly via "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-ui/issues","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GitHub"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Lastly, note that links to the GWAS Diagram have been disabled in the labs site. We are working on the next generation of the Diagram and will share this with you soon!"}]}]},"text":"\n# New GWAS web search interface ready for testing\n\nWe are excited to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing on our [labs site](https://www.ebi.ac.uk/gwas/labs).\n\nThis is a guide to the main differences you will see.\n\nThe biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect! \n\n\n\nWe’ve also changed the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest: \n\n\n\nStudies with full summary statistics available for download are now easier to find in their own tab: \n\n\n\nPlease try out the new site and let us know what you like, what you don’t like, if you find any bugs or if any of your favourite features are missing. You can send feedback via gwas-info@ebi.ac.uk or report bugs directly via [GitHub](https://github.com/EBISPOT/gwas-ui/issues)\n\nLastly, note that links to the GWAS Diagram have been disabled in the labs site. We are working on the next generation of the Diagram and will share this with you soon!\n","dir":"/blog","path":"/blog/gwas-ui-labs-release","extension":".md","createdAt":"2024-03-11T15:03:22.555Z","updatedAt":"2024-03-11T15:03:22.555Z","meta":{"version":0,"revision":0,"created":1710171117084},"$loki":5},{"slug":"new-standard-for-gwas-summary-statistics","description":"Today we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, GWAS-SSF. The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a recent analysis of 327 summary statistics","title":"A new standard for GWAS summary statistics","author":"Laura Harris","date":"July 20, 2022","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Today we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v1","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS-SSF"}]},{"type":"text","value":". The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a "},{"type":"element","tag":"a","props":{"href":"https://academic.oup.com/bioinformatics/article/37/23/4593/6380562","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"recent analysis"}]},{"type":"text","value":" of 327 summary statistics files finding over 100 unique formats. We were about to launch the GWAS Catalog author submission system and needed to enforce a standard format as QC on submissions, and to ensure that submitted data could be passed through our harmonisation pipeline. So we quickly developed a "},{"type":"element","tag":"a","props":{"href":"https://pubmed.ncbi.nlm.nih.gov/30445434/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"minimal standard format"}]},{"type":"text","value":" based on the most commonly included fields, with variant ID/genomic location and p-value as mandatory fields, and standard headers for other fields such as beta and CI. This was enough for our goal of getting data into the GWAS Catalog. But we heard many consumers of the data complain that datasets with the minimum mandatory fields did not meet their requirements, for example to use the data for Mendelian randomisation or to generate polygenic scores. We needed to encourage our submitters to share their data more fully. A second issue is that while the GWAS Catalog curates metadata from the literature, there is no standard for GWAS metadata reporting, resulting in missing data that limits re-use of the summary statistics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We started on a process of consulting with the community that began with our workshop on summary statistics data and sharing in 2020. This was a wide ranging discussion over 2 days that you can read all about in our "},{"type":"element","tag":"a","props":{"href":"https://www.sciencedirect.com/science/article/pii/S2666979X21000045","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"workshop proceedings"}]},{"type":"text","value":". At the end of that process we had an outline of some mandatory fields for data and metadata, but refining these and ensuring they met all the major use cases would take a more focused discussion. Our working group on summary statistics data content and format, chaired by Ines Barroso, held 3 meetings over the course of 2021. We held a parallel working group to discuss diversity and privacy in data sharing (to be reported elsewhere) and incorporated their recommendations (for example on the sharing of allele frequency data) into our decision making process. Both the original workshop and the working group were designed to represent a wide range of stakeholders - from industry and academia, developers of other resources & tools, data generators and data consumers, research groups primarily performing wet-lab research and high-throughput bioinformatics users. Our discussions focused on the need to enforce key mandatory fields to maximise the reuse of the data, but also to recognise that setting the bar too high could discourage data sharing. Similarly, data need to be made accessible in a way that suits the high-throughput user who might be piping data in bulk into a computational pipeline, but also users without advanced bioinformatics skills who might be downloading a single file to investigate a locus of interest. We heard that preparing data for submission to databases can be a huge drain for under-resourced labs, so needs to be as straightforward and user-friendly as possible, and that interoperability between different databases (such as "},{"type":"element","tag":"a","props":{"href":"https://www.ncbi.nlm.nih.gov/gap/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"dbGaP"}]},{"type":"text","value":", "},{"type":"element","tag":"a","props":{"href":"http://www.ebi.ac.uk/gwas","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the GWAS Catalog"}]},{"type":"text","value":" and "},{"type":"element","tag":"a","props":{"href":"https://gwas.mrcieu.ac.uk/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"OpenGWAS"}]},{"type":"text","value":") is important to reduce the workload for data generators. You can read the outcome of our discussions in our "},{"type":"element","tag":"a","props":{"href":"https://biohackrxiv.org/7zxht","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"new manuscript"}]},{"type":"text","value":". Behind the scenes, we are making the necessary changes to the GWAS Catalog infrastructure to validate new submissions according to the new standard and where possible, make all our existing summary statistics available in the new format. We are working on tools (together with PLINK and others) to make data preparation and formatting as easy as possible. In the meantime, we welcome feedback from the wider community on the standard and will collate this at the end of August prior to release of the new infrastructure."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS-SSF is suitable for array-based GWAS and single variant seqGWAS. As data sharing has become more widely accepted, there is appetite from the genomics community to share full p value datasets from gene-based analyses of sequencing data and CNVs. Whilst we don’t have a standard format for these yet, by gathering data we hope to go through a similar process to work towards a standard format. Please share your data, and let us know your thoughts!"}]}]},"text":"\nToday we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, [GWAS-SSF](https://www.biorxiv.org/content/10.1101/2022.07.15.500230v1). The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a [recent analysis](https://academic.oup.com/bioinformatics/article/37/23/4593/6380562) of 327 summary statistics files finding over 100 unique formats. We were about to launch the GWAS Catalog author submission system and needed to enforce a standard format as QC on submissions, and to ensure that submitted data could be passed through our harmonisation pipeline. So we quickly developed a [minimal standard format](https://pubmed.ncbi.nlm.nih.gov/30445434/) based on the most commonly included fields, with variant ID/genomic location and p-value as mandatory fields, and standard headers for other fields such as beta and CI. This was enough for our goal of getting data into the GWAS Catalog. But we heard many consumers of the data complain that datasets with the minimum mandatory fields did not meet their requirements, for example to use the data for Mendelian randomisation or to generate polygenic scores. We needed to encourage our submitters to share their data more fully. A second issue is that while the GWAS Catalog curates metadata from the literature, there is no standard for GWAS metadata reporting, resulting in missing data that limits re-use of the summary statistics.\n\nWe started on a process of consulting with the community that began with our workshop on summary statistics data and sharing in 2020. This was a wide ranging discussion over 2 days that you can read all about in our [workshop proceedings](https://www.sciencedirect.com/science/article/pii/S2666979X21000045). At the end of that process we had an outline of some mandatory fields for data and metadata, but refining these and ensuring they met all the major use cases would take a more focused discussion. Our working group on summary statistics data content and format, chaired by Ines Barroso, held 3 meetings over the course of 2021. We held a parallel working group to discuss diversity and privacy in data sharing (to be reported elsewhere) and incorporated their recommendations (for example on the sharing of allele frequency data) into our decision making process. Both the original workshop and the working group were designed to represent a wide range of stakeholders - from industry and academia, developers of other resources & tools, data generators and data consumers, research groups primarily performing wet-lab research and high-throughput bioinformatics users. Our discussions focused on the need to enforce key mandatory fields to maximise the reuse of the data, but also to recognise that setting the bar too high could discourage data sharing. Similarly, data need to be made accessible in a way that suits the high-throughput user who might be piping data in bulk into a computational pipeline, but also users without advanced bioinformatics skills who might be downloading a single file to investigate a locus of interest. We heard that preparing data for submission to databases can be a huge drain for under-resourced labs, so needs to be as straightforward and user-friendly as possible, and that interoperability between different databases (such as [dbGaP](https://www.ncbi.nlm.nih.gov/gap/), [the GWAS Catalog](http://www.ebi.ac.uk/gwas) and [OpenGWAS](https://gwas.mrcieu.ac.uk/)) is important to reduce the workload for data generators. You can read the outcome of our discussions in our [new manuscript](https://biohackrxiv.org/7zxht). Behind the scenes, we are making the necessary changes to the GWAS Catalog infrastructure to validate new submissions according to the new standard and where possible, make all our existing summary statistics available in the new format. We are working on tools (together with PLINK and others) to make data preparation and formatting as easy as possible. In the meantime, we welcome feedback from the wider community on the standard and will collate this at the end of August prior to release of the new infrastructure.\n\nThe GWAS-SSF is suitable for array-based GWAS and single variant seqGWAS. As data sharing has become more widely accepted, there is appetite from the genomics community to share full p value datasets from gene-based analyses of sequencing data and CNVs. Whilst we don’t have a standard format for these yet, by gathering data we hope to go through a similar process to work towards a standard format. Please share your data, and let us know your thoughts!\n","dir":"/blog","path":"/blog/new-standard-for-gwas-summary-statistics","extension":".md","createdAt":"2024-03-11T15:03:22.556Z","updatedAt":"2024-03-11T15:03:22.557Z","meta":{"version":0,"revision":0,"created":1710171117102},"$loki":6},{"slug":"gwas-ui-production-release-2023","description":"We are pleased to announce the release of a new version of the GWAS Catalog web search interface, which is now in production. Here is a guide to the main differences you will see.","title":"GWAS Catalog web search - new release!","author":"GWAS Catalog team","date":"12 July 2023","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"h1","props":{"id":"new-gwas-web-search-interface"},"children":[{"type":"element","tag":"a","props":{"href":"#new-gwas-web-search-interface","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"New GWAS web search interface"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We are excited to announce the full production release of the new GWAS Catalog web search interface."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideA.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We’ve also main some small changes to the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideB.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Interested to find studies with full genomewide summary statistics available for download? They now have their own tab, making it easier to see when summary statistics are available:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideC.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Interested to find the study with the biggest sample size for your trait of interest? Just use the sort option on the Discovery sample number columnn, which now sorts by the total number of samples in the Discovery stage, irrespective of ancestry groups:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideD.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Hope you enjoy using the new features! Please report any bugs to "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":" or via "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-ui/issues","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GitHub"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Lastly, watch this space for the next generation of the GWAS Diagram - we're working on it!"}]}]},"text":"\n# New GWAS web search interface\n\nWe are excited to announce the full production release of the new GWAS Catalog web search interface. \n\nThe biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect! \n\n\n\nWe’ve also main some small changes to the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest: \n\n\n\nInterested to find studies with full genomewide summary statistics available for download? They now have their own tab, making it easier to see when summary statistics are available: \n\n\n\nInterested to find the study with the biggest sample size for your trait of interest? Just use the sort option on the Discovery sample number columnn, which now sorts by the total number of samples in the Discovery stage, irrespective of ancestry groups: \n\n\n\nHope you enjoy using the new features! Please report any bugs to gwas-info@ebi.ac.uk or via [GitHub](https://github.com/EBISPOT/gwas-ui/issues)\n\nLastly, watch this space for the next generation of the GWAS Diagram - we're working on it!\n","dir":"/blog","path":"/blog/gwas-ui-production-release-2023","extension":".md","createdAt":"2024-03-11T15:03:22.556Z","updatedAt":"2024-03-11T15:03:22.556Z","meta":{"version":0,"revision":0,"created":1710171117108},"$loki":7},{"slug":"open-sharing-of-cancer-summary-statistics","description":"The NHGRI-EBI GWAS Catalog is a central resource for the Genome Wide Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations. For more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable)","title":"Sharing is caring, why we need more freely available cancer GWAS summary statistics","author":"Annalisa Buniello","date":"March 11, 2021","img":"blog-1.jpg","toc":[{"id":"the-gwas-catalog-summary-statistics-repository","depth":3,"text":"The GWAS Catalog summary statistics repository"},{"id":"why-are-few-open-access-cancer-gwas-summary-statistics-available","depth":3,"text":"Why are few open access cancer GWAS summary statistics available?"},{"id":"how-can-we-promote-sharing","depth":3,"text":"How can we promote sharing?"}],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"NHGRI-EBI GWAS Catalog"}]},{"type":"text","value":" is a central resource for the Genome Wides Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable), while serving as a starting point for investigations to identify causal variants, calculate disease risk, understand disease mechanisms and establish targets for novel therapies."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Here we aim to illuminate progress that has been made to incorporate GWAS summary statistics into the GWAS Catalog, and reach out to the cancer genetics community in particular to promote submission of summary statistics for GWAS studies in cancer cohorts."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"the-gwas-catalog-summary-statistics-repository"},"children":[{"type":"element","tag":"a","props":{"href":"#the-gwas-catalog-summary-statistics-repository","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"The GWAS Catalog summary statistics repository"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Three years ago, the GWAS Catalog and Open Targets started to work together to expand the Catalog’s scientific scope to include full p-value summary statistics (aggregate p-values and association data for every variant analysed in a genome-wide association study) in addition to the manually curated top associations."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Summary statistics provide more detailed GWAS results than a lead SNP table, which allows other scientists to better use the data for sophisticated downstream analysis, whilst maintaining patient privacy. For example, summary statistics are integrated into the Open Targets Genetics Portal, where they are used to narrow down candidate loci and prioritise new drug targets through PheWAS, colocalisation analysis, fine mapping and mendelian randomisation"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"[GWAS-catalog-statistics-1.png"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS Catalog is now one of the largest, most visited and most frequently updated resources of freely available GWAS summary statistics. The repository includes summary statistics from 3,623 independent analyses (from 447 publications), accounting for a total of more than 22,000 datasets from a wide variety of traits. The Catalog users can easily access and download summary statistics from the GWAS Catalog "},{"type":"element","tag":"a","props":{"href":"ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/"},"children":[{"type":"text","value":"FTP site"}]},{"type":"text","value":" or via a dedicated "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/summary-statistics/docs/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"summary statistics API"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The increasing trend to share these datasets is reflected in the availability of summary statistics in the GWAS Catalog over time. There was also a significant increase in data downloads of summary statistics in 2020 compared to the previous year."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-Fig-1a-V2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-Fig-1b-V2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"why-are-few-open-access-cancer-gwas-summary-statistics-available"},"children":[{"type":"element","tag":"a","props":{"href":"#why-are-few-open-access-cancer-gwas-summary-statistics-available","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Why are few open access cancer GWAS summary statistics available?"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This expansion of the GWAS Catalog summary statistics repository, while promising, required a considerable outreach effort from the Catalog data team and the main stakeholders, including Open Targets. Interestingly, the rate of summary statistics data sharing noticeably differs among different genetics cohorts and research groups, with the lowest submission rate in cancer genetics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-FIg-2a-new-data.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For example 33% of epilepsy and 21% of diabetes summary statistics are publicly available, compared to only 7.5% for cancer. The trend slightly differs amongst different cancer groups, and we have observed a more positive trend for the few papers published in the last two years."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-2b-c-1.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In a recent "},{"type":"element","tag":"a","props":{"href":"https://twitter.com/GWASCatalog/status/1365359253175881742","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Twitter poll"}]},{"type":"text","value":", we asked the community to tell us what they thought was the most important reason for the low rate of cancer summary statistics submission."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The results suggest that the main barriers to the sharing of summary statistics are:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"div","props":{"className":["nuxt-content-highlight"]},"children":[{"type":"element","tag":"pre","props":{"className":["language-text","line-numbers"]},"children":[{"type":"element","tag":"code","props":{},"children":[{"type":"text","value":"1. Results are usually embargoed for use in future research,\n2. Data privacy issues (e.g. patient confidentiality agreement), and\n3. Lack of awareness/knowledge on the appropriate data repository.\n"}]}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We believe it is necessary for the genetics community to have a more comprehensive and transparent discussion about such barriers, starting from the privacy issues. As highlighted in the recently revised "},{"type":"element","tag":"a","props":{"href":"https://osp.od.nih.gov/2018/11/01/provide-access-gsr/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"NIH genomic data sharing (GDS) policy"}]},{"type":"text","value":", summary statistics do not include individual-level information and can empower researchers to determine which genomic variants potentially contribute to a disease"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"how-can-we-promote-sharing"},"children":[{"type":"element","tag":"a","props":{"href":"#how-can-we-promote-sharing","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"How can we promote sharing?"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"As a part of a wider plan to identify and remove barriers to data sharing among different research communities, we have launched a dedicated public engagement campaign to motivate and encourage the genetics community to make their summary statistics publicly available – and make the difference in the field. In particular, following up from a pressing request from our users and stakeholders, we hope to understand the reasons why so few summary statistics are available for cancer studies. Open sharing of cancer GWAS data will lead the way to new, improved therapies and shed some light on the molecular mechanisms involved in such complex disease."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-quote.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We call on researchers, journals, funders and charities to support our cause and spread our message. The Nature Journals Group are already proactively supporting an open access policy for their published cancer GWAS data. We hope to get more journals on board, including the top cancer journals."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In an aim to facilitate data sharing and interoperability, the GWAS Catalog team has recently released a "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/deposition","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"web-based deposition interface"}]},{"type":"text","value":" to support scalable author submission of summary statistics and metadata from published and pre-published (submitted at the time of journal submission upon request from the reviewers) GWAS. The datasets are submitted in a standard format and harmonised against the latest genome build and forward strand. This facilitates downstream analysis and integration into resources like the Open Targets Genetics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Please submit your summary statistics to the GWAS Catalog."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"What do you think are the most important barriers to the open sharing of summary statistics in cancer studies? Please let us know your thoughts by completing "},{"type":"element","tag":"a","props":{"href":"https://docs.google.com/forms/d/e/1FAIpQLSfuAO-cZWdIm8aF0140QXsZOxVlw7gLKj1a__KZ3aF4CUNamQ/viewform?usp=sf_link","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"this short survey"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"You can also reach out to us via "},{"type":"element","tag":"a","props":{"href":"https://twitter.com/GWASCatalog","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Twitter (@GWASCatalog)"}]},{"type":"text","value":" or "},{"type":"element","tag":"a","props":{"href":"gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"email (gwas-info@ebi.ac.uk)"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Together, we can make the difference."}]}]},"text":"\nThe [NHGRI-EBI GWAS Catalog](https://www.ebi.ac.uk/gwas/) is a central resource for the Genome Wides Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations.\n\nFor more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable), while serving as a starting point for investigations to identify causal variants, calculate disease risk, understand disease mechanisms and establish targets for novel therapies.\n\nHere we aim to illuminate progress that has been made to incorporate GWAS summary statistics into the GWAS Catalog, and reach out to the cancer genetics community in particular to promote submission of summary statistics for GWAS studies in cancer cohorts.\n\n### The GWAS Catalog summary statistics repository\n\nThree years ago, the GWAS Catalog and Open Targets started to work together to expand the Catalog’s scientific scope to include full p-value summary statistics (aggregate p-values and association data for every variant analysed in a genome-wide association study) in addition to the manually curated top associations.\n\nSummary statistics provide more detailed GWAS results than a lead SNP table, which allows other scientists to better use the data for sophisticated downstream analysis, whilst maintaining patient privacy. For example, summary statistics are integrated into the Open Targets Genetics Portal, where they are used to narrow down candidate loci and prioritise new drug targets through PheWAS, colocalisation analysis, fine mapping and mendelian randomisation\n\n[GWAS-catalog-statistics-1.png\n\nThe GWAS Catalog is now one of the largest, most visited and most frequently updated resources of freely available GWAS summary statistics. The repository includes summary statistics from 3,623 independent analyses (from 447 publications), accounting for a total of more than 22,000 datasets from a wide variety of traits. The Catalog users can easily access and download summary statistics from the GWAS Catalog [FTP site](ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/) or via a dedicated [summary statistics API](https://www.ebi.ac.uk/gwas/summary-statistics/docs/).\n\nThe increasing trend to share these datasets is reflected in the availability of summary statistics in the GWAS Catalog over time. There was also a significant increase in data downloads of summary statistics in 2020 compared to the previous year.\n\n\n\n\n\n### Why are few open access cancer GWAS summary statistics available?\n\nThis expansion of the GWAS Catalog summary statistics repository, while promising, required a considerable outreach effort from the Catalog data team and the main stakeholders, including Open Targets. Interestingly, the rate of summary statistics data sharing noticeably differs among different genetics cohorts and research groups, with the lowest submission rate in cancer genetics.\n\n\n\nFor example 33% of epilepsy and 21% of diabetes summary statistics are publicly available, compared to only 7.5% for cancer. The trend slightly differs amongst different cancer groups, and we have observed a more positive trend for the few papers published in the last two years.\n\n\n\n\nIn a recent [Twitter poll](https://twitter.com/GWASCatalog/status/1365359253175881742), we asked the community to tell us what they thought was the most important reason for the low rate of cancer summary statistics submission.\n\nThe results suggest that the main barriers to the sharing of summary statistics are:\n\n 1. Results are usually embargoed for use in future research,\n 2. Data privacy issues (e.g. patient confidentiality agreement), and\n 3. Lack of awareness/knowledge on the appropriate data repository.\n\nWe believe it is necessary for the genetics community to have a more comprehensive and transparent discussion about such barriers, starting from the privacy issues. As highlighted in the recently revised [NIH genomic data sharing (GDS) policy](https://osp.od.nih.gov/2018/11/01/provide-access-gsr/), summary statistics do not include individual-level information and can empower researchers to determine which genomic variants potentially contribute to a disease\n\n### How can we promote sharing?\n\nAs a part of a wider plan to identify and remove barriers to data sharing among different research communities, we have launched a dedicated public engagement campaign to motivate and encourage the genetics community to make their summary statistics publicly available – and make the difference in the field. In particular, following up from a pressing request from our users and stakeholders, we hope to understand the reasons why so few summary statistics are available for cancer studies. Open sharing of cancer GWAS data will lead the way to new, improved therapies and shed some light on the molecular mechanisms involved in such complex disease.\n\n\n\nWe call on researchers, journals, funders and charities to support our cause and spread our message. The Nature Journals Group are already proactively supporting an open access policy for their published cancer GWAS data. We hope to get more journals on board, including the top cancer journals.\n\nIn an aim to facilitate data sharing and interoperability, the GWAS Catalog team has recently released a [web-based deposition interface](https://www.ebi.ac.uk/gwas/deposition) to support scalable author submission of summary statistics and metadata from published and pre-published (submitted at the time of journal submission upon request from the reviewers) GWAS. The datasets are submitted in a standard format and harmonised against the latest genome build and forward strand. This facilitates downstream analysis and integration into resources like the Open Targets Genetics.\n\nPlease submit your summary statistics to the GWAS Catalog.\n\nWhat do you think are the most important barriers to the open sharing of summary statistics in cancer studies? 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datasets"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"false"}]}]},{"type":"element","tag":"tr","props":{},"children":[{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"is_sorted"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Flag whether the file is sorted by genomic location"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Boolean"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Yes"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"false"}]}]},{"type":"element","tag":"tr","props":{},"children":[{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"harmonisation_reference"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"The genome reference file used for harmonising the summary statistics file"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Text string"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"No"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"ftp://ftp.ensembl.org/pub/release-104/fasta/homo_sapiens/dna/"}]}]},{"type":"element","tag":"tr","props":{},"children":[{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"## Questions and feedback"}]},{"type":"element","tag":"td","props":{},"children":[]},{"type":"element","tag":"td","props":{},"children":[]},{"type":"element","tag":"td","props":{},"children":[]},{"type":"element","tag":"td","props":{},"children":[]}]}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Questions or comments about this change? Please contact us as "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":"."}]}]},"text":"\nHaving clear and accessible metadata is essential for enhancing data interpretation and ensuring its reusability. In the case of Genome-Wide Association Studies (GWAS), having a standardized and easy-to-understand format for documenting study metadata is crucial. In the GWAS Catalog, metadata associated with full genome-wide summary statistics files is accessible via multiple routes - searchable in the main Catalog via the website and REST API, and additionally via a text file in YAML format, contained in the same directory as the data file. \n\nWe have identified key fields that should be included in the metadata for a comprehensive understanding, interpretation, and subsequent reuse of GWAS studies. Mandatory fields document essential study details and optional fields cater to specific study designs (such as case/control counts in case-control studies), and provide supplementary analysis information. You can find detailed information about these fields in Table 1 and in our recent preprint (Hayhurst et al 2023 https://www.biorxiv.org/content/10.1101/2022.07.15.500230v4).\n\nIn our latest release, we've introduced several updates to the GWAS metadata YAML file format. The YAML format, known it's user-friendliness, provides a combination of structured information and ease of use for humans and computers. The latest changes aim to enhance the human readability of the files with a clearer, more unambiguous, and user-friendly experience without compromising the file's compatibility with automated systems. Here’s an overview of the significant enhancements:\n\n1. Reordering and Grouping of Fields:\n* The fields within the YAML files have been reorganized and grouped by related topics, each marked with a group name starting with '#' (group names are listed in Table 1). This organization helps users to quickly locate specific fields by group name, such as '# Sample Information', making the file more navigable for humans. Importantly, this structural refinement does not affect the way machines parse and interpret the data.\n\n2. Clear Indentation for Hierarchical Relationships:\n* Indentation has been introduced to visually represent the relationships between different data fields. This visual aid improves the readability of the files, making it easier for users to discern the levels of data organization and relationships between various fields. In the meantime, this indentation does not impact the machine processing of the file.\n\n3. Renaming 'data_last_modified' to 'date_metadata_last_modified':\n* To provide clearer context, the 'data_last_modified' field has been renamed to 'date_metadata_last_modified'. This change emphasizes that the timestamp refers to the last modification date of the metadata, not the summary statistics data. It aids users in tracking any changes or updates in the metadata over time.\n \n4. Addition of 'Ancestry Category' Field:\n* We have introduced a new 'ancestry category' field in addition to the existing 'ancestry' field. This new feature provides a broad overview of the ancestry category that best describes the sample, allowing for a more generalized view of the sample's ancestry. This new field also ensures consistency and facilitates comparative analysis across different studies, in line with the rest of the Catalog. \n\nThese updates reflect our commitment to improving the user experience while ensuring the YAML files maintain their structural integrity for computer readability. Our focus continues to be on making GWAS metadata more accessible, interpretable, and useful for researchers.\n\n\nTable 1. Metadata field definitions\n| Field | Description | Data type and values | Mandatory | Example |\n| -------------------------------- | ---------------------------------------------------------------------------------- | ------------------------------------------------------- | ----------------------------------- | -------------------------------------------------------------------------------------------------------------------- |\n| \\# Study meta-data |\n| gwas_id | GWAS Catalog accession ID | Text string | Yes | GCST90244057 |\n| author_notes | Additional information about this study from the author | Text string | No | File contains GWAS summary statistics from a meta-analysis of NMR metabolic traits in up to 33 cohorts. |\n| gwas_catalog_api | GWAS catalog REST API link | Text string | Yes | [https://www.ebi.ac.uk/gwas/rest/api/studies/GCST90244057](https://www.ebi.ac.uk/gwas/rest/api/studies/GCST90244057) |\n| date_metadata_last_modified | The latest date that metadata YAML file was modified | date | Yes | 2023-11-28 |\n| \\# Trait Information |\n| trait_description | Author reported trait description | Text string (multiple possible) | Yes | Body mass index |\n| ontology_mapping | Short form ontology terms describing the trait | Text string (multiple possible) | No | EFO_0004918 |\n| \\# Genotyping Information |\n| genome_assembly | Genome assembly for the summary statistics. | GRCh/NCBI/UCSC value | Yes | GRCh37 |\n| coordinate_system | Coordinate system used for the summary statistics | Text String (1-based or 0-based) | No | 1-based |\n| genotyping_technology | Method(s) used to genotype variants in the discovery stage. | Text string (multiple possible) | Yes | Genome-wide genotyping array |\n| imputation_panel | Panel used for imputation | Text string | No | HRC + UK10K |\n| imputation_software | Software used for imputation | Text string | No | SHAPEIT3 + IMPUTE4 |\n| \\# Sample Information |\n| sample_ancestry_category | Broad ancestry category that best describes the sample. | Text string | Yes | European |\n| sample_ancestry | The most detailed ancestry descriptor(s) for the sample. | Text string (multiple possible) | Yes | \\- Finnish
- British |\n| sample_size | Sample size | Integer | Yes | 27006 |\n| ancestry_method | Method used to determine sample ancestry e.g. self-reported/genetically determined | Text string (multiple possible) | No | self-reported |\n| case_control_study | Flag whether the study is a case-control study | Boolean | No (default is false) | true |\n| case_count | Number of cases for case/control study | Integer | No, unless caseControlStudy is true | 27006 |\n| control_count | Number of controls for case/control study | Integer | No, unless caseControlStudy is true | 27006 |\n| sex | To indicate a sex-stratified analysis | M (for male), F (for female), combined or NR if unknown | No | combined |\n| \\# Summary Statistic information |\n| data_file_name | The name of the summary statistics file | Text string | Yes | GCST90244057_buildGRCh37.tsv |\n| file_type | The format of the summary statistics file | \"GWAS-SSF v1.0\", \"pre-GWAS-SSF\", \"non-GWAS-SSF\" | Yes | GWAS-SSF v1.0 |\n| data_file_md5sum | The md5 checksum of the summary statistics file. | Text string | Yes | 0ec56396f89edcc21a3d5a25a6fa993d |\n| analysis_software | Software and version used for the association analysis | Text string (multiple possible) | Yes if p-values of 0 given | REGENIE |\n| adjusted_covariates | Any covariates the GWAS is adjusted for | Text string (multiple possible) | No | sex |\n| minor_allele_freq_lower_limit | Lowest possible effect allele frequency | Numeric | No | 0.0003 |\n| \\# Harmonization status |\n| is_harmonised | Description of harmonisation codes | Text string | Only given in harmonised datasets | false |\n| is_sorted | Flag whether the file is sorted by genomic location | Boolean | Yes | false |\n| harmonisation_reference | The genome reference file used for harmonising the summary statistics file | Text string | No | ftp://ftp.ensembl.org/pub/release-104/fasta/homo_sapiens/dna/ |\n## Questions and feedback\n\nQuestions or comments about this change? Please contact us as gwas-info@ebi.ac.uk.\n","dir":"/blog","path":"/blog/streamlining-GWAS-Summary-Statistics-Data-Interpretation-with-Improved-YAML-Metadata","extension":".md","createdAt":"2024-03-11T15:03:22.558Z","updatedAt":"2024-03-11T15:03:22.558Z","meta":{"version":0,"revision":0,"created":1710171117158},"$loki":9},{"slug":"ten-years-of-the-gwas-catalog-past-present-and-future","description":"This year marks a special anniversary for the GWAS Catalog, as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers","title":"Ten years of the GWAS Catalog – Past, present and future","author":"Laura Harris (GWAS Catalog) and Astrid Gall (Ensembl Outreach)","date":"Jun 28, 2018","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This year marks a special anniversary for the "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS Catalog"}]},{"type":"text","value":", as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers and clinicians without them spending a lot of time undertaking regular systematic reviews of the literature. This is where we can help you!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The project is a collaboration between "},{"type":"element","tag":"a","props":{"href":"https://www.genome.gov/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the National Human Genome Research Institute (NHGRI)"}]},{"type":"text","value":" in Bethesda, Maryland, USA, and the European Molecular Biology Laboratory – "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"European Bioinformatics Institute (EMBL-EBI)"}]},{"type":"text","value":" in Hinxton, UK, where the majority of our curators and all of our software developers are based"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"ten-years-of-the-gwas-catalog-past-present-and-future/GWAS-catalog_EBI_group-768x576.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"At EMBL-EBI, we share the building with "},{"type":"element","tag":"a","props":{"href":"https://www.ensembl.org/index.html","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the Ensembl team"}]},{"type":"text","value":" and work very closely with them – which is very handy for both teams!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Our curators extract genotype-phenotype associations from all published GWAS studies, along with study information to allow you to interpret the data accurately. We make the data structured, searchable and visualisable, annotate it and integrate it with other resources including Ensembl. Each association with a standard variant identifier (rsID) enters our automated Ensembl mapping pipeline which provides the genomic annotation, i.e. the location on the chromosome and cytogenetic band as well as the mapped genes. We use this pipeline to check for errors as part of our quality control and display the information in our interface."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Our colleagues at Ensembl benefit from the GWAS Catalog as they import variant and phenotype data from it. "},{"type":"element","tag":"a","props":{"href":"http://www.ensembl.org/info/genome/variation/species/sources_documentation.html","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"More than 74,000 human variants associated with phenotype data from the GWAS catalog were included in the latest Ensembl release (e92)"}]},{"type":"text","value":"; and the number is increasing continuously!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"[video]"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Over the last ten years, the GWAS Catalog has grown, from a single published GWAS on age-related macular degeneration, into the primary source of disease-related associations with genetic variants. Watch how the number of associations has increased over time in this video!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Today, the GWAS Catalog contains over 3,400 publications and over 62,000 unique SNP-trait associations. More than 10,000 visitors from around the globe access the GWAS Catalog per month. People using the resource have a wide range of goals – they include researchers wanting to narrow down or prioritise candidate loci, scientists investigating disease mechanisms, clinicians aiming to predict disease risk, pharma industry professionals improving the drug discovery process and anyone wanting to get the latest statistics on disease knowledge or summary data from particular populations of individuals."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS landscape has evolved over these ten years, with new developments in study design and genotyping technologies. More and more publications on GWAS come out each month. Often a publication contains more than one GWAS – sometimes there are over a thousand in a single paper! The number of individuals studied in a GWAS is also increasing, and so is the number of variants assayed, with bigger arrays and better imputation methods."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The nature of the traits studied is evolving too. Researchers are still publishing GWAS on major common diseases such as type 2 diabetes and breast cancer. However, these studies are now being published alongside studies on ever more specific traits. For example, "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/search?query=schizophrenia","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"searching the GWAS Catalog for “schizophrenia”"}]},{"type":"text","value":" brings up more than 50 different traits related to this disorder. These include sub-phenotypes such as specific symptoms in schizophrenia, endophenotypes such as brain imaging measurements, multi-trait analyses such as “schizophrenia and bipolar disorder”, drug response measurements as well as analyses of the interaction between genetic associations and environmental factors."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"All of this means more data to curate. We are adapting our infrastructure constantly to improve curation efficiency so that we can keep up with the rate of data generation. At the same time, we need to maintain the accuracy of the resource and make sure it contains the most relevant and up-to-date research results."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"ten-years-of-the-gwas-catalog-past-present-and-future/fig2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Highlights over the last ten years include releasing a new "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS Catalog website"}]},{"type":"text","value":" in 2015, "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/ontology","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"mapping curated trait descriptions to ontology terms"}]},{"type":"text","value":" to enable enriched ontology-driven search capabilities, improving the interactive "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/diagram","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS Catalog diagram"}]},{"type":"text","value":" and "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/ancestry","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"capturing ancestry more completely"}]},{"type":"text","value":". Accurate characterisation of ancestry is essential to interpret human genomics data. Recently, we have developed a new framework for describing detailed ancestry information and recommendations for reporting ancestry systematically [(Morales et al 2018 Genome Biology 19:2)].("},{"type":"element","tag":"a","props":{"href":"https://europepmc.org/abstract/MED/29448949","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"https://europepmc.org/abstract/MED/29448949"}]},{"type":"text","value":")"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"So what will the future hold for the GWAS Catalog? We have just started hosting "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/downloads/summary-statistics","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"summary statistics for studies in the GWAS Catalog"}]},{"type":"text","value":" with support from the scientific community (the files contain the full set of p values for every SNP on the array used). In the future we plan to integrate these datasets with structured meta-data to make it even easier to access GWAS results. We also aim to expand our scope to include other types of association analyses, including targeted arrays and sequencing-based genotyping, which enable deeper interrogation of diseases of interest and inclusion of less common variants."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"With these developments come many challenges, including how best to represent the increasing complexity of study designs, trait architecture and statistical analyses. But with the help of the authors of publications in the GWAS Catalog, the scientific community using our resource and our colleagues at Ensembl, we are excited about what the next ten years of GWAS will hold!"}]}]},"text":"\nThis year marks a special anniversary for the [GWAS Catalog](https://www.ebi.ac.uk/gwas/), as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers and clinicians without them spending a lot of time undertaking regular systematic reviews of the literature. This is where we can help you!\n\nThe project is a collaboration between [the National Human Genome Research Institute (NHGRI)](https://www.genome.gov/) in Bethesda, Maryland, USA, and the European Molecular Biology Laboratory – [European Bioinformatics Institute (EMBL-EBI)](https://www.ebi.ac.uk/) in Hinxton, UK, where the majority of our curators and all of our software developers are based\n\n\n\n\nAt EMBL-EBI, we share the building with [the Ensembl team](https://www.ensembl.org/index.html) and work very closely with them – which is very handy for both teams!\n\n\nOur curators extract genotype-phenotype associations from all published GWAS studies, along with study information to allow you to interpret the data accurately. We make the data structured, searchable and visualisable, annotate it and integrate it with other resources including Ensembl. Each association with a standard variant identifier (rsID) enters our automated Ensembl mapping pipeline which provides the genomic annotation, i.e. the location on the chromosome and cytogenetic band as well as the mapped genes. We use this pipeline to check for errors as part of our quality control and display the information in our interface.\n\nOur colleagues at Ensembl benefit from the GWAS Catalog as they import variant and phenotype data from it. [More than 74,000 human variants associated with phenotype data from the GWAS catalog were included in the latest Ensembl release (e92)](http://www.ensembl.org/info/genome/variation/species/sources_documentation.html); and the number is increasing continuously!\n\n[video]\n\nOver the last ten years, the GWAS Catalog has grown, from a single published GWAS on age-related macular degeneration, into the primary source of disease-related associations with genetic variants. Watch how the number of associations has increased over time in this video!\n\n\nToday, the GWAS Catalog contains over 3,400 publications and over 62,000 unique SNP-trait associations. More than 10,000 visitors from around the globe access the GWAS Catalog per month. People using the resource have a wide range of goals – they include researchers wanting to narrow down or prioritise candidate loci, scientists investigating disease mechanisms, clinicians aiming to predict disease risk, pharma industry professionals improving the drug discovery process and anyone wanting to get the latest statistics on disease knowledge or summary data from particular populations of individuals.\n\nThe GWAS landscape has evolved over these ten years, with new developments in study design and genotyping technologies. More and more publications on GWAS come out each month. Often a publication contains more than one GWAS – sometimes there are over a thousand in a single paper! The number of individuals studied in a GWAS is also increasing, and so is the number of variants assayed, with bigger arrays and better imputation methods.\n\nThe nature of the traits studied is evolving too. Researchers are still publishing GWAS on major common diseases such as type 2 diabetes and breast cancer. However, these studies are now being published alongside studies on ever more specific traits. For example, [searching the GWAS Catalog for “schizophrenia”](https://www.ebi.ac.uk/gwas/search?query=schizophrenia) brings up more than 50 different traits related to this disorder. These include sub-phenotypes such as specific symptoms in schizophrenia, endophenotypes such as brain imaging measurements, multi-trait analyses such as “schizophrenia and bipolar disorder”, drug response measurements as well as analyses of the interaction between genetic associations and environmental factors.\n\nAll of this means more data to curate. We are adapting our infrastructure constantly to improve curation efficiency so that we can keep up with the rate of data generation. At the same time, we need to maintain the accuracy of the resource and make sure it contains the most relevant and up-to-date research results.\n\n\n\nHighlights over the last ten years include releasing a new [GWAS Catalog website](https://www.ebi.ac.uk/gwas/) in 2015, [mapping curated trait descriptions to ontology terms](https://www.ebi.ac.uk/gwas/docs/ontology) to enable enriched ontology-driven search capabilities, improving the interactive [GWAS Catalog diagram](https://www.ebi.ac.uk/gwas/diagram) and [capturing ancestry more completely](https://www.ebi.ac.uk/gwas/ancestry). Accurate characterisation of ancestry is essential to interpret human genomics data. Recently, we have developed a new framework for describing detailed ancestry information and recommendations for reporting ancestry systematically [(Morales et al 2018 Genome Biology 19:2)].(https://europepmc.org/abstract/MED/29448949)\n\nSo what will the future hold for the GWAS Catalog? We have just started hosting [summary statistics for studies in the GWAS Catalog](https://www.ebi.ac.uk/gwas/downloads/summary-statistics) with support from the scientific community (the files contain the full set of p values for every SNP on the array used). In the future we plan to integrate these datasets with structured meta-data to make it even easier to access GWAS results. We also aim to expand our scope to include other types of association analyses, including targeted arrays and sequencing-based genotyping, which enable deeper interrogation of diseases of interest and inclusion of less common variants.\n\nWith these developments come many challenges, including how best to represent the increasing complexity of study designs, trait architecture and statistical analyses. But with the help of the authors of publications in the GWAS Catalog, the scientific community using our resource and our colleagues at Ensembl, we are excited about what the next ten years of GWAS will 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For each study, we annotate the trait in two ways, a reported trait that reflects the author’s description of the disease or phenotypic characteristic under investigation. This is a free text description and sometimes.","title":"Trait annotation in the GWAS Catalog","author":"Elliot Sollis","date":"August 3, 2021","img":"blog-1.jpg","toc":[{"id":"annotating-different-study-types","depth":2,"text":"Annotating different study types"},{"id":"a-single-trait-studies","depth":3,"text":"A. Single-trait studies"},{"id":"b-multi-trait-studies","depth":3,"text":"B. Multi-trait studies"},{"id":"c-studies-with-a-background-trait","depth":3,"text":"C. Studies with a background trait"},{"id":"room-for-improvement","depth":2,"text":"Room for improvement"},{"id":"changes-to-trait-annotation","depth":2,"text":"Changes to trait annotation"},{"id":"changes-to-the-website","depth":2,"text":"Changes to the website"},{"id":"data-tables","depth":3,"text":"Data tables"},{"id":"study-pages","depth":3,"text":"Study pages"},{"id":"trait-pages","depth":3,"text":"Trait pages"},{"id":"downloads","depth":3,"text":"Downloads"},{"id":"api","depth":3,"text":"API"},{"id":"expected-scope-of-the-changes","depth":2,"text":"Expected scope of the changes"},{"id":"questions-and-feedback","depth":2,"text":"Questions and feedback"}],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Each study in the GWAS Catalog investigates the association between variants in the human genome and a particular trait or phenotypic characteristic."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For each study, we annotate the trait in two ways:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"A reported trait that reflects the author’s description of the disease or phenotypic characteristic under investigation. This is a free text description and sometimes different studies might use a different wording to describe similar traits or to capture more nuanced distinctions. Reported traits can also include multiple component traits, depending on the study design."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"One or more trait terms from the Experimental Factor Ontology (EFO) that represent phenotypic characteristics in a more standardised way. These terms make studies on similar traits easier for users to find and compare. For multifaceted traits, each component is represented by a separate term."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/rep-trait-example.png","alt":"Reported traits mapped to EFO terms"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"annotating-different-study-types"},"children":[{"type":"element","tag":"a","props":{"href":"#annotating-different-study-types","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Annotating different study types"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"a-single-trait-studies"},"children":[{"type":"element","tag":"a","props":{"href":"#a-single-trait-studies","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"A. Single-trait studies"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The vast majority (>80%) of studies in the GWAS Catalog only analyse a single trait. We annotate these studies with a single EFO term."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Some common examples include:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Standard case-control studies comparing individuals with a disease or phenotypic characteristic, versus controls individuals without that trait."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-sg-trait-binary.png","alt":"A case-control study with a single binary trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Quantitative studies looking at a single measurement"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-sg-trait-quant.png","alt":"A quantitative study with a single continuous trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In these examples, any reported variants are clearly and straightforwardly associated with that single trait."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"b-multi-trait-studies"},"children":[{"type":"element","tag":"a","props":{"href":"#b-multi-trait-studies","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"B. Multi-trait studies"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In about 10% of studies, there are multiple traits of interest that are analysed simultaneously. We annotate these studies with multiple EFO terms separated by a comma, indicating that any significant variants reported in the study are associated with both traits in some way."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Some common examples include:"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Studies comparing individuals with two comorbid diseases (or other traits), versus control individuals who have neither disease. In the reported trait we write “Disease 1 "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"and"}]},{"type":"text","value":" Disease 2”. In the trait we list multiple EFO terms separated by a comma."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-multi-trait-and.png","alt":"A case-control study where cases have two co-occuring traits"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Studies comparing individuals with either of two diseases (or other traits), versus control individuals who have neither disease. Often these are two traits that are hypothesised to have some common underlying genetic factors (pleiotropy). In the reported trait we write “Disease 1 "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"or"}]},{"type":"text","value":" Disease 2”. In the trait we list multiple EFO terms separated by a comma."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-multi-trait-or.png","alt":"A case-control study where cases may have either of two distinct traits"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In these examples, any significant variants reported are associated with either or both of the annotated traits. If a user is searching for associations with either trait term, then these results will come up in their search."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"c-studies-with-a-background-trait"},"children":[{"type":"element","tag":"a","props":{"href":"#c-studies-with-a-background-trait","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"C. Studies with a background trait"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Finally, about 6% of studies analyse only one main trait of interest, but in the context of a background trait that is shared by all of the participants in the study."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Some examples include:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Studies comparing cases and controls of one disease, but only within a sample of people who have another disease. In the reported trait we write “Disease 1 "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" Disease 2”. In the past, we listed multiple EFO terms in the trait column, as for scenario B."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-bg-trait-binary.png","alt":"A case-control study for one binary trait within a second background trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Quantitative studies analysing a measurement in the context of a disease. In the reported trait we write “Measurement "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" Disease”. In the past, we listed multiple EFO terms in the trait column, as for scenario B."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-bg-trait-quant.png","alt":"A quantitative study for one continuous trait within a second background trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In these examples, any reported variants are associated with the main trait, but not with the background trait."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"room-for-improvement"},"children":[{"type":"element","tag":"a","props":{"href":"#room-for-improvement","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Room for improvement"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In the past, we annotated background trait studies (scenario C) in the same way as we have for multi-trait studies (scenario B): with multiple EFO terms listed together in the trait field, separated by commas."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This had some benefits:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We were able to indicate that the study has something to do with the background trait - e.g. a particular association with allergic rhinitis may only hold true in asthmatics."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Users could search for associations with a trait, as well as associations that are found in the context of that trait as a background characteristic. Both kinds of associations might be relevant to users working in a particular field."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"However, there were some are also some disadvantages:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"It was not possible to tell that a particular EFO term was a background trait, without looking at the reported trait field. This complicated analysis particularly when accessing the data programmatically."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"When searching for a trait term (e.g. asthma), there was no easy way to distinguish which studies were direct associations with asthma, and which have asthma as the background trait, without reading the reported trait for each one."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The overall study and association numbers provided for some traits could be misleading since they included studies and associations where the trait of interest was a background trait."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"changes-to-trait-annotation"},"children":[{"type":"element","tag":"a","props":{"href":"#changes-to-trait-annotation","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Changes to trait annotation"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"To make our trait annotations more informative, we have added an additional background trait field to the GWAS Catalog database."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We have moved all EFO terms related to background traits to this new field, and removed them from the original trait field:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/annotation-change.png","alt":"We have moved EFO terms for background traits to a separate field"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"changes-to-the-website"},"children":[{"type":"element","tag":"a","props":{"href":"#changes-to-the-website","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Changes to the website"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We have also updated our web interface in order to display the restructured background trait information."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"data-tables"},"children":[{"type":"element","tag":"a","props":{"href":"#data-tables","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Data tables"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"New columns have been added to the Associations and Studies tables to clearly indicate the main and background traits."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Associations:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/new-assoc-table.png","alt":"Separate main and background traits displayed in Associations table"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Studies:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/new-study-table.png","alt":"Separate main and background traits displayed in Studies table"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The improved tables will be displayed on all Publication, Study, Trait, Variant, Gene and Region pages."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"study-pages"},"children":[{"type":"element","tag":"a","props":{"href":"#study-pages","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Study pages"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Each Study page also displays the main and background traits in separate fields in the Study Information panel.\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/study-info-panel.png","alt":"Main and background traits displayed in the Study Information panel"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"trait-pages"},"children":[{"type":"element","tag":"a","props":{"href":"#trait-pages","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Trait pages"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The Trait page has been updated so that background trait studies and associations are no longer shown by default. For example, the asthma page only shows associations with asthma itself, and not associations with others traits in asthma.\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/asthma-no-bg.png","alt":"Asthma Trait page showing associations with asthma only"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"Shows associations with:"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"asthma"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"del","props":{},"children":[{"type":"text","value":"[other traits] "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" asthma"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"However, users can select the box to include background trait data if they wish:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/asthma-with-bg.png","alt":"Asthma Trait page showing associations with asthma AND with other traits in a background of asthma."},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"Shows associations with:"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"asthma"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"[other traits] "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" asthma"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Some trait terms may only be used in the GWAS Catalog to annotate background traits. These terms will continue to have their own Trait page, but no associations or studies will be displayed under the default view. For example, autoimmune pancreatitis type 1 (EFO_1000780) currently appears in the Catalog only as a background trait for one study (“Lachrymal/Salivary gland lesion in type 1 autoimmune pancreatitis”), so no associations with autoimmune pancreatitis type 1 are displayed by default:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/no-records.png","alt":"A Trait page with no main trait associations"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The association plot on each Trait page will now display only main trait associations by default, but background trait data can be added by selecting the box:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/assoc-plot-change.png","alt":"Association plot with with or without background trait data"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"downloads"},"children":[{"type":"element","tag":"a","props":{"href":"#downloads","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Downloads"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In "},{"type":"element","tag":"a","props":{"href":"http://www.ebi.ac.uk/gwas/docs/file-downloads","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the full Catalog downloads"}]},{"type":"text","value":", the MAPPED_TRAIT and MAPPED_TRAIT_URI columns will now only show EFO terms for main traits. Background trait terms have been removed from these columns."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In the newest version of the download (v1.0.3), new columns have been added for the background trait: MAPPED BACKGROUND TRAIT and MAPPED BACKGROUND TRAIT URI. This is not included in earlier versions of the download."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"api"},"children":[{"type":"element","tag":"a","props":{"href":"#api","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"API"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For users of the GWAS Catalog API, searching for “associationByEfoTrait” will now return only main trait associations."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"There is currently no background trait field available in the API, but we plan to add this feature in the future."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"expected-scope-of-the-changes"},"children":[{"type":"element","tag":"a","props":{"href":"#expected-scope-of-the-changes","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Expected scope of the changes"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We reviewed all of the studies in the Catalog and identified just over 1000 studies (about 6% of the total) with background traits that have now been moved to the new field. These studies contain around 10,000 associations (about 4% of all associations in the Catalog) which have also been reannotated."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The changes affect some trait terms more than others. Here are some of the terms that will see the greatest change in the number of annotated Associations:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/most-affected-traits.png","alt":"Graph of selected trait terms affected by this change"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"questions-and-feedback"},"children":[{"type":"element","tag":"a","props":{"href":"#questions-and-feedback","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Questions and feedback"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"If you have any questions or comments about this change, please contact us as "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":"."}]}]},"text":"\nEach study in the GWAS Catalog investigates the association between variants in the human genome and a particular trait or phenotypic characteristic.\n\nFor each study, we annotate the trait in two ways:\n\n1. A reported trait that reflects the author’s description of the disease or phenotypic characteristic under investigation. This is a free text description and sometimes different studies might use a different wording to describe similar traits or to capture more nuanced distinctions. Reported traits can also include multiple component traits, depending on the study design.\n\n2. One or more trait terms from the Experimental Factor Ontology (EFO) that represent phenotypic characteristics in a more standardised way. These terms make studies on similar traits easier for users to find and compare. For multifaceted traits, each component is represented by a separate term.\n\n\n\n## Annotating different study types \n\n### A. Single-trait studies\n\nThe vast majority (>80%) of studies in the GWAS Catalog only analyse a single trait. We annotate these studies with a single EFO term.\n\nSome common examples include:\n\n1. Standard case-control studies comparing individuals with a disease or phenotypic characteristic, versus controls individuals without that trait.\n\n\n2. Quantitative studies looking at a single measurement\n\n\n\nIn these examples, any reported variants are clearly and straightforwardly associated with that single trait.\n\n### B. Multi-trait studies\n\nIn about 10% of studies, there are multiple traits of interest that are analysed simultaneously. We annotate these studies with multiple EFO terms separated by a comma, indicating that any significant variants reported in the study are associated with both traits in some way.\n\n**Some common examples include:**\n\n1. Studies comparing individuals with two comorbid diseases (or other traits), versus control individuals who have neither disease. In the reported trait we write “Disease 1 _and_ Disease 2”. In the trait we list multiple EFO terms separated by a comma.\n\n\n2. Studies comparing individuals with either of two diseases (or other traits), versus control individuals who have neither disease. Often these are two traits that are hypothesised to have some common underlying genetic factors (pleiotropy). In the reported trait we write “Disease 1 _or_ Disease 2”. In the trait we list multiple EFO terms separated by a comma.\n\n\nIn these examples, any significant variants reported are associated with either or both of the annotated traits. If a user is searching for associations with either trait term, then these results will come up in their search.\n\n### C. Studies with a background trait\n\nFinally, about 6% of studies analyse only one main trait of interest, but in the context of a background trait that is shared by all of the participants in the study.\n\nSome examples include:\n\n1. Studies comparing cases and controls of one disease, but only within a sample of people who have another disease. In the reported trait we write “Disease 1 _in_ Disease 2”. In the past, we listed multiple EFO terms in the trait column, as for scenario B.\n\n\n2. Quantitative studies analysing a measurement in the context of a disease. In the reported trait we write “Measurement _in_ Disease”. In the past, we listed multiple EFO terms in the trait column, as for scenario B.\n\n\nIn these examples, any reported variants are associated with the main trait, but not with the background trait.\n\n## Room for improvement\n\nIn the past, we annotated background trait studies (scenario C) in the same way as we have for multi-trait studies (scenario B): with multiple EFO terms listed together in the trait field, separated by commas.\n\nThis had some benefits:\n\n* We were able to indicate that the study has something to do with the background trait - e.g. a particular association with allergic rhinitis may only hold true in asthmatics.\n\n* Users could search for associations with a trait, as well as associations that are found in the context of that trait as a background characteristic. Both kinds of associations might be relevant to users working in a particular field.\n\nHowever, there were some are also some disadvantages:\n\n* It was not possible to tell that a particular EFO term was a background trait, without looking at the reported trait field. This complicated analysis particularly when accessing the data programmatically.\n\n* When searching for a trait term (e.g. asthma), there was no easy way to distinguish which studies were direct associations with asthma, and which have asthma as the background trait, without reading the reported trait for each one.\n\n* The overall study and association numbers provided for some traits could be misleading since they included studies and associations where the trait of interest was a background trait.\n\n## Changes to trait annotation\n\nTo make our trait annotations more informative, we have added an additional background trait field to the GWAS Catalog database.\n\nWe have moved all EFO terms related to background traits to this new field, and removed them from the original trait field:\n\n\n## Changes to the website\n\nWe have also updated our web interface in order to display the restructured background trait information.\n\n### Data tables\n\nNew columns have been added to the Associations and Studies tables to clearly indicate the main and background traits.\n\nAssociations:\n\n\nStudies:\n\n\nThe improved tables will be displayed on all Publication, Study, Trait, Variant, Gene and Region pages.\n\n### Study pages\n\nEach Study page also displays the main and background traits in separate fields in the Study Information panel.\n\n\n### Trait pages\n\nThe Trait page has been updated so that background trait studies and associations are no longer shown by default. For example, the asthma page only shows associations with asthma itself, and not associations with others traits in asthma.\n\n\n_Shows associations with:_\n* asthma\n* ~[other traits] _in_ asthma~\n\nHowever, users can select the box to include background trait data if they wish:\n\n\n_Shows associations with:_\n* asthma\n* [other traits] _in_ asthma\n\nSome trait terms may only be used in the GWAS Catalog to annotate background traits. These terms will continue to have their own Trait page, but no associations or studies will be displayed under the default view. For example, autoimmune pancreatitis type 1 (EFO_1000780) currently appears in the Catalog only as a background trait for one study (“Lachrymal/Salivary gland lesion in type 1 autoimmune pancreatitis”), so no associations with autoimmune pancreatitis type 1 are displayed by default:\n\n\nThe association plot on each Trait page will now display only main trait associations by default, but background trait data can be added by selecting the box:\n\n\n### Downloads\n\nIn [the full Catalog downloads](http://www.ebi.ac.uk/gwas/docs/file-downloads), the MAPPED_TRAIT and MAPPED_TRAIT_URI columns will now only show EFO terms for main traits. Background trait terms have been removed from these columns.\n\nIn the newest version of the download (v1.0.3), new columns have been added for the background trait: MAPPED BACKGROUND TRAIT and MAPPED BACKGROUND TRAIT URI. This is not included in earlier versions of the download.\n\n### API\n\nFor users of the GWAS Catalog API, searching for “associationByEfoTrait” will now return only main trait associations.\n\nThere is currently no background trait field available in the API, but we plan to add this feature in the future.\n\n## Expected scope of the changes\n\nWe reviewed all of the studies in the Catalog and identified just over 1000 studies (about 6% of the total) with background traits that have now been moved to the new field. These studies contain around 10,000 associations (about 4% of all associations in the Catalog) which have also been reannotated.\n\nThe changes affect some trait terms more than others. Here are some of the terms that will see the greatest change in the number of annotated Associations:\n\n\n## Questions and feedback\n\nIf you have any questions or comments about this change, please contact us as gwas-info@ebi.ac.uk.","dir":"/blog","path":"/blog/background-trait-update","extension":".md","createdAt":"2024-03-11T15:03:22.553Z","updatedAt":"2024-03-11T15:03:22.553Z","meta":{"version":0,"revision":0,"created":1710171028997},"$loki":1},{"slug":"gwas-ui-production-release-2023","description":"We are pleased to announce the release of a new version of the GWAS Catalog web search interface, which is now in production. Here is a guide to the main differences you will see.","title":"GWAS Catalog web search - new release!","author":"GWAS Catalog team","date":"12 July 2023","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"h1","props":{"id":"new-gwas-web-search-interface"},"children":[{"type":"element","tag":"a","props":{"href":"#new-gwas-web-search-interface","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"New GWAS web search interface"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We are excited to announce the full production release of the new GWAS Catalog web search interface."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideA.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We’ve also main some small changes to the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideB.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Interested to find studies with full genomewide summary statistics available for download? They now have their own tab, making it easier to see when summary statistics are available:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideC.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Interested to find the study with the biggest sample size for your trait of interest? Just use the sort option on the Discovery sample number columnn, which now sorts by the total number of samples in the Discovery stage, irrespective of ancestry groups:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideD.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Hope you enjoy using the new features! Please report any bugs to "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":" or via "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-ui/issues","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GitHub"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Lastly, watch this space for the next generation of the GWAS Diagram - we're working on it!"}]}]},"text":"\n# New GWAS web search interface\n\nWe are excited to announce the full production release of the new GWAS Catalog web search interface. \n\nThe biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect! \n\n\n\nWe’ve also main some small changes to the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest: \n\n\n\nInterested to find studies with full genomewide summary statistics available for download? They now have their own tab, making it easier to see when summary statistics are available: \n\n\n\nInterested to find the study with the biggest sample size for your trait of interest? Just use the sort option on the Discovery sample number columnn, which now sorts by the total number of samples in the Discovery stage, irrespective of ancestry groups: \n\n\n\nHope you enjoy using the new features! Please report any bugs to gwas-info@ebi.ac.uk or via [GitHub](https://github.com/EBISPOT/gwas-ui/issues)\n\nLastly, watch this space for the next generation of the GWAS Diagram - we're working on it!\n","dir":"/blog","path":"/blog/gwas-ui-production-release-2023","extension":".md","createdAt":"2024-03-11T15:03:22.556Z","updatedAt":"2024-03-11T15:03:22.556Z","meta":{"version":0,"revision":0,"created":1710171029019},"$loki":2},{"slug":"gwas-ssf-release","description":"Next month the GWAS Catalog is switching to a NEW summary statistics format. Find out more about what this means for submitters and consumers of summary statistics","title":"New submissions standard - release date","author":"GWAS Catalog team","date":"22 March 2023","img":"blog-1.jpg","toc":[{"id":"the-new-summary-statistics-format-is-coming---what-you-need-to-know","depth":2,"text":"The new summary statistics format is coming - what you need to know"},{"id":"what-does-this-mean-for-you","depth":2,"text":"What does this mean for you?"},{"id":"frequently-asked-questions","depth":2,"text":"Frequently asked questions"},{"id":"for-submitters","depth":3,"text":"For submitters:"},{"id":"for-consumers","depth":3,"text":"For consumers:"}],"body":{"type":"root","children":[{"type":"element","tag":"h2","props":{"id":"the-new-summary-statistics-format-is-coming---what-you-need-to-know"},"children":[{"type":"element","tag":"a","props":{"href":"#the-new-summary-statistics-format-is-coming---what-you-need-to-know","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"The new summary statistics format is coming - what you need to know"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"From 12th April we are switching to our NEW summary statistics format, GWAS-SSF, an overview of which is described in our "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v2","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"manuscript"}]},{"type":"text","value":" and "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-summary-statistics-standard","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"github repository"}]},{"type":"text","value":". There will be a short period of submission system downtime while we switch over to the new pipelines."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"what-does-this-mean-for-you"},"children":[{"type":"element","tag":"a","props":{"href":"#what-does-this-mean-for-you","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"What does this mean for you?"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"If you are a SUBMITTER of summary statistics, you will need to submit additional mandatory information, including effect size, allele frequency, and genomic location for each variant, and columns must be presented in a specific order. You are also encouraged to provide additional structured data (e.g. reference allele, imputation info score) and metadata (e.g. ancestry method, sex of participants) which will increase the usability of your data to others, and hopefully get you "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.09.27.509657v2","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"more citations"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"If you are a CONSUMER of summary statistics, you will start to see changes to the way we make these available. All newly submitted summary statistics will include the additional fields described "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-summary-statistics-standard","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"here"}]},{"type":"text","value":", with metadata available in a text file (.yaml) stored in the same ftp location as the data file, as well as its existing availability via the GWAS Catalog website and downloads. Over the coming months, we will generate metadata files for all existing summary statistics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"frequently-asked-questions"},"children":[{"type":"element","tag":"a","props":{"href":"#frequently-asked-questions","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Frequently asked questions"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"for-submitters"},"children":[{"type":"element","tag":"a","props":{"href":"#for-submitters","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"For submitters:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I’ve already started a submission and prepared my files in the old format. What should I do?"}]},{"type":"text","value":"\nYou can submit files in the old format until April 11th. If you are unable to complete your submission before this deadline, please contact us via "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-subs@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-subs@ebi.ac.uk"}]},{"type":"text","value":" for assistance."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I submitted summary statistics in the old format a while back and they are under embargo or in pre-published state. Do I need to change anything?"}]},{"type":"text","value":"\nNo. Your data will be made available in the old format (upon journal publication, if embargoed), and the metadata file will be prepared from the information you submitted."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I want to start preparing my submission now, but won’t be ready to submit till after April 12th. How can I find instructions on how to prepare the new format?"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Detailed documentation on the new format is available as follows:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/gwas-ssf/summary-statistics-format","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Summary statistics data file preparation"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics-plus-metadata","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Submission instructions for summary statistics and sample metadata"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Submission instructions for summary statistics only"}]},{"type":"text","value":" (for publications with metadata already available in the GWAS Catalog)."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"An example new-style metadata template is available to download "},{"type":"element","tag":"a","props":{"href":"http://ftp.ebi.ac.uk/pub/databases/gwas/sumstats_and_meta_submission_template_v1_11.xlsx","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"here"}]},{"type":"text","value":"\nA recorded "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/training/events/data-submission-genome-wide-association-studies-gwas-catalog/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"webinar"}]},{"type":"text","value":" is available, describing the submission process and the new format."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Are there tools available to help me prepare data in GWAS-SSF format?"}]},{"type":"text","value":"\nYou can find some useful tools to format your files and validate them prior to upload "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-sumstats-tools","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"here"}]},{"type":"text","value":"\nPLINK 2.0 will shortly include an option to generate output in the accepted format, using the option --gwas-ssf."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I made a submission in the old format but would like to update it to include the new fields. Can I do this?"}]},{"type":"text","value":"\nAt the moment our update process is manual and we don’t have capacity to update large numbers of files unless there is an error. We plan to improve this process in the future."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"for-consumers"},"children":[{"type":"element","tag":"a","props":{"href":"#for-consumers","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"For consumers:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"How can I tell if summary statistics are in the old or new format?"}]},{"type":"text","value":"\nThis is indicated in the metadata file. If a metadata file exists, and the field named “fileType” has the value “GWAS-SSF v1.0”, then the file is in the new GWAS-SSF format. Other values in this field indicate that it is not in the new GWAS-SSF format, and may be lacking mandatory fields. If no metadata file exists, the summary statistics are in the old format, or a non-standard format."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"How can I combine or compare new and old format summary statistics?"}]},{"type":"text","value":"\nData fields included in the old format (chromosome, base_pair_location, effect_allele, other_allele, effect_allele_frequency, odds_ratio, ci_lower, ci_upper, beta, standard_error) are the same in the new format. There are two exceptions: variant_id and p_value. In the old format, variant_id contained rsIDs, whereas in the new format rsID has a separate column rsid, and variant_ID contains a variant identifier in the format chr_bp_ref_alt. In the old format, p_value was a mandatory field and could only contain p-values between 0 and 1. In the new format, p-values may be either presented in the p_value field OR as negative log10 p-values, with the header neg_log_10_p_value, but always in column 7."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"More questions?"}]},{"type":"text","value":" Get in touch: contact "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":" for general queries or "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-subs@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-subs@ebi.ac.uk"}]},{"type":"text","value":" for submission support"}]}]},"text":"\n## The new summary statistics format is coming - what you need to know\n\nFrom 12th April we are switching to our NEW summary statistics format, GWAS-SSF, an overview of which is described in our [manuscript](https://www.biorxiv.org/content/10.1101/2022.07.15.500230v2) and [github repository](https://github.com/EBISPOT/gwas-summary-statistics-standard). There will be a short period of submission system downtime while we switch over to the new pipelines.\n\n## What does this mean for you?\n\nIf you are a SUBMITTER of summary statistics, you will need to submit additional mandatory information, including effect size, allele frequency, and genomic location for each variant, and columns must be presented in a specific order. You are also encouraged to provide additional structured data (e.g. reference allele, imputation info score) and metadata (e.g. ancestry method, sex of participants) which will increase the usability of your data to others, and hopefully get you [more citations](https://www.biorxiv.org/content/10.1101/2022.09.27.509657v2).\n\nIf you are a CONSUMER of summary statistics, you will start to see changes to the way we make these available. All newly submitted summary statistics will include the additional fields described [here](https://github.com/EBISPOT/gwas-summary-statistics-standard), with metadata available in a text file (.yaml) stored in the same ftp location as the data file, as well as its existing availability via the GWAS Catalog website and downloads. Over the coming months, we will generate metadata files for all existing summary statistics.\n\n## Frequently asked questions\n\n### For submitters:\n\n**I’ve already started a submission and prepared my files in the old format. What should I do?**\nYou can submit files in the old format until April 11th. If you are unable to complete your submission before this deadline, please contact us via gwas-subs@ebi.ac.uk for assistance.\n\n**I submitted summary statistics in the old format a while back and they are under embargo or in pre-published state. Do I need to change anything?**\nNo. Your data will be made available in the old format (upon journal publication, if embargoed), and the metadata file will be prepared from the information you submitted.\n\n**I want to start preparing my submission now, but won’t be ready to submit till after April 12th. How can I find instructions on how to prepare the new format?**\n\nDetailed documentation on the new format is available as follows:\n- [Summary statistics data file preparation](https://www.ebi.ac.uk/gwas/docs/gwas-ssf/summary-statistics-format)\n- [Submission instructions for summary statistics and sample metadata](https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics-plus-metadata)\n- [Submission instructions for summary statistics only](https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics) (for publications with metadata already available in the GWAS Catalog).\n\nAn example new-style metadata template is available to download [here](http://ftp.ebi.ac.uk/pub/databases/gwas/sumstats_and_meta_submission_template_v1_11.xlsx)\nA recorded [webinar](https://www.ebi.ac.uk/training/events/data-submission-genome-wide-association-studies-gwas-catalog/) is available, describing the submission process and the new format.\n\n**Are there tools available to help me prepare data in GWAS-SSF format?**\nYou can find some useful tools to format your files and validate them prior to upload [here]( https://github.com/EBISPOT/gwas-sumstats-tools)\nPLINK 2.0 will shortly include an option to generate output in the accepted format, using the option --gwas-ssf.\n\n**I made a submission in the old format but would like to update it to include the new fields. Can I do this?**\nAt the moment our update process is manual and we don’t have capacity to update large numbers of files unless there is an error. We plan to improve this process in the future.\n\n### For consumers:\n\n**How can I tell if summary statistics are in the old or new format?**\nThis is indicated in the metadata file. If a metadata file exists, and the field named “fileType” has the value “GWAS-SSF v1.0”, then the file is in the new GWAS-SSF format. Other values in this field indicate that it is not in the new GWAS-SSF format, and may be lacking mandatory fields. If no metadata file exists, the summary statistics are in the old format, or a non-standard format.\n\n**How can I combine or compare new and old format summary statistics?**\nData fields included in the old format (chromosome, base_pair_location, effect_allele, other_allele, effect_allele_frequency, odds_ratio, ci_lower, ci_upper, beta, standard_error) are the same in the new format. There are two exceptions: variant_id and p_value. In the old format, variant_id contained rsIDs, whereas in the new format rsID has a separate column rsid, and variant_ID contains a variant identifier in the format chr_bp_ref_alt. In the old format, p_value was a mandatory field and could only contain p-values between 0 and 1. In the new format, p-values may be either presented in the p_value field OR as negative log10 p-values, with the header neg_log_10_p_value, but always in column 7.\n\n\n**More questions?** Get in touch: contact [gwas-info@ebi.ac.uk](mailto:gwas-info@ebi.ac.uk) for general queries or [gwas-subs@ebi.ac.uk](mailto:gwas-subs@ebi.ac.uk) for submission support\n","dir":"/blog","path":"/blog/gwas-ssf-release","extension":".md","createdAt":"2024-03-11T15:03:22.554Z","updatedAt":"2024-03-11T15:03:22.555Z","meta":{"version":0,"revision":0,"created":1710171029033},"$loki":3},{"slug":"gwas-ui-labs-release","description":"We are pleased to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing at www.ebi.ac.uk/gwas/labs. Here is a guide to the main differences you will see.","title":"GWAS Catalog web search updates - help us with testing","author":"GWAS Catalog team","date":"5 May 2023","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"h1","props":{"id":"new-gwas-web-search-interface-ready-for-testing"},"children":[{"type":"element","tag":"a","props":{"href":"#new-gwas-web-search-interface-ready-for-testing","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"New GWAS web search interface ready for testing"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We are excited to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing on our "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/labs","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"labs site"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This is a guide to the main differences you will see."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-labs-release/SlideA.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We’ve also changed the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-labs-release/SlideB.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Studies with full summary statistics available for download are now easier to find in their own tab:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-labs-release/SlideC.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Please try out the new site and let us know what you like, what you don’t like, if you find any bugs or if any of your favourite features are missing. You can send feedback via "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":" or report bugs directly via "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-ui/issues","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GitHub"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Lastly, note that links to the GWAS Diagram have been disabled in the labs site. We are working on the next generation of the Diagram and will share this with you soon!"}]}]},"text":"\n# New GWAS web search interface ready for testing\n\nWe are excited to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing on our [labs site](https://www.ebi.ac.uk/gwas/labs).\n\nThis is a guide to the main differences you will see.\n\nThe biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect! \n\n\n\nWe’ve also changed the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest: \n\n\n\nStudies with full summary statistics available for download are now easier to find in their own tab: \n\n\n\nPlease try out the new site and let us know what you like, what you don’t like, if you find any bugs or if any of your favourite features are missing. You can send feedback via gwas-info@ebi.ac.uk or report bugs directly via [GitHub](https://github.com/EBISPOT/gwas-ui/issues)\n\nLastly, note that links to the GWAS Diagram have been disabled in the labs site. We are working on the next generation of the Diagram and will share this with you soon!\n","dir":"/blog","path":"/blog/gwas-ui-labs-release","extension":".md","createdAt":"2024-03-11T15:03:22.555Z","updatedAt":"2024-03-11T15:03:22.555Z","meta":{"version":0,"revision":0,"created":1710171029039},"$loki":4},{"slug":"open-sharing-of-cancer-summary-statistics","description":"The NHGRI-EBI GWAS Catalog is a central resource for the Genome Wide Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations. For more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable)","title":"Sharing is caring, why we need more freely available cancer GWAS summary statistics","author":"Annalisa Buniello","date":"March 11, 2021","img":"blog-1.jpg","toc":[{"id":"the-gwas-catalog-summary-statistics-repository","depth":3,"text":"The GWAS Catalog summary statistics repository"},{"id":"why-are-few-open-access-cancer-gwas-summary-statistics-available","depth":3,"text":"Why are few open access cancer GWAS summary statistics available?"},{"id":"how-can-we-promote-sharing","depth":3,"text":"How can we promote sharing?"}],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"NHGRI-EBI GWAS Catalog"}]},{"type":"text","value":" is a central resource for the Genome Wides Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable), while serving as a starting point for investigations to identify causal variants, calculate disease risk, understand disease mechanisms and establish targets for novel therapies."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Here we aim to illuminate progress that has been made to incorporate GWAS summary statistics into the GWAS Catalog, and reach out to the cancer genetics community in particular to promote submission of summary statistics for GWAS studies in cancer cohorts."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"the-gwas-catalog-summary-statistics-repository"},"children":[{"type":"element","tag":"a","props":{"href":"#the-gwas-catalog-summary-statistics-repository","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"The GWAS Catalog summary statistics repository"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Three years ago, the GWAS Catalog and Open Targets started to work together to expand the Catalog’s scientific scope to include full p-value summary statistics (aggregate p-values and association data for every variant analysed in a genome-wide association study) in addition to the manually curated top associations."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Summary statistics provide more detailed GWAS results than a lead SNP table, which allows other scientists to better use the data for sophisticated downstream analysis, whilst maintaining patient privacy. For example, summary statistics are integrated into the Open Targets Genetics Portal, where they are used to narrow down candidate loci and prioritise new drug targets through PheWAS, colocalisation analysis, fine mapping and mendelian randomisation"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"[GWAS-catalog-statistics-1.png"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS Catalog is now one of the largest, most visited and most frequently updated resources of freely available GWAS summary statistics. The repository includes summary statistics from 3,623 independent analyses (from 447 publications), accounting for a total of more than 22,000 datasets from a wide variety of traits. The Catalog users can easily access and download summary statistics from the GWAS Catalog "},{"type":"element","tag":"a","props":{"href":"ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/"},"children":[{"type":"text","value":"FTP site"}]},{"type":"text","value":" or via a dedicated "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/summary-statistics/docs/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"summary statistics API"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The increasing trend to share these datasets is reflected in the availability of summary statistics in the GWAS Catalog over time. There was also a significant increase in data downloads of summary statistics in 2020 compared to the previous year."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-Fig-1a-V2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-Fig-1b-V2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"why-are-few-open-access-cancer-gwas-summary-statistics-available"},"children":[{"type":"element","tag":"a","props":{"href":"#why-are-few-open-access-cancer-gwas-summary-statistics-available","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Why are few open access cancer GWAS summary statistics available?"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This expansion of the GWAS Catalog summary statistics repository, while promising, required a considerable outreach effort from the Catalog data team and the main stakeholders, including Open Targets. Interestingly, the rate of summary statistics data sharing noticeably differs among different genetics cohorts and research groups, with the lowest submission rate in cancer genetics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-FIg-2a-new-data.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For example 33% of epilepsy and 21% of diabetes summary statistics are publicly available, compared to only 7.5% for cancer. The trend slightly differs amongst different cancer groups, and we have observed a more positive trend for the few papers published in the last two years."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-2b-c-1.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In a recent "},{"type":"element","tag":"a","props":{"href":"https://twitter.com/GWASCatalog/status/1365359253175881742","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Twitter poll"}]},{"type":"text","value":", we asked the community to tell us what they thought was the most important reason for the low rate of cancer summary statistics submission."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The results suggest that the main barriers to the sharing of summary statistics are:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"div","props":{"className":["nuxt-content-highlight"]},"children":[{"type":"element","tag":"pre","props":{"className":["language-text","line-numbers"]},"children":[{"type":"element","tag":"code","props":{},"children":[{"type":"text","value":"1. Results are usually embargoed for use in future research,\n2. Data privacy issues (e.g. patient confidentiality agreement), and\n3. Lack of awareness/knowledge on the appropriate data repository.\n"}]}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We believe it is necessary for the genetics community to have a more comprehensive and transparent discussion about such barriers, starting from the privacy issues. As highlighted in the recently revised "},{"type":"element","tag":"a","props":{"href":"https://osp.od.nih.gov/2018/11/01/provide-access-gsr/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"NIH genomic data sharing (GDS) policy"}]},{"type":"text","value":", summary statistics do not include individual-level information and can empower researchers to determine which genomic variants potentially contribute to a disease"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"how-can-we-promote-sharing"},"children":[{"type":"element","tag":"a","props":{"href":"#how-can-we-promote-sharing","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"How can we promote sharing?"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"As a part of a wider plan to identify and remove barriers to data sharing among different research communities, we have launched a dedicated public engagement campaign to motivate and encourage the genetics community to make their summary statistics publicly available – and make the difference in the field. In particular, following up from a pressing request from our users and stakeholders, we hope to understand the reasons why so few summary statistics are available for cancer studies. Open sharing of cancer GWAS data will lead the way to new, improved therapies and shed some light on the molecular mechanisms involved in such complex disease."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-quote.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We call on researchers, journals, funders and charities to support our cause and spread our message. The Nature Journals Group are already proactively supporting an open access policy for their published cancer GWAS data. We hope to get more journals on board, including the top cancer journals."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In an aim to facilitate data sharing and interoperability, the GWAS Catalog team has recently released a "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/deposition","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"web-based deposition interface"}]},{"type":"text","value":" to support scalable author submission of summary statistics and metadata from published and pre-published (submitted at the time of journal submission upon request from the reviewers) GWAS. The datasets are submitted in a standard format and harmonised against the latest genome build and forward strand. This facilitates downstream analysis and integration into resources like the Open Targets Genetics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Please submit your summary statistics to the GWAS Catalog."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"What do you think are the most important barriers to the open sharing of summary statistics in cancer studies? Please let us know your thoughts by completing "},{"type":"element","tag":"a","props":{"href":"https://docs.google.com/forms/d/e/1FAIpQLSfuAO-cZWdIm8aF0140QXsZOxVlw7gLKj1a__KZ3aF4CUNamQ/viewform?usp=sf_link","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"this short survey"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"You can also reach out to us via "},{"type":"element","tag":"a","props":{"href":"https://twitter.com/GWASCatalog","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Twitter (@GWASCatalog)"}]},{"type":"text","value":" or "},{"type":"element","tag":"a","props":{"href":"gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"email (gwas-info@ebi.ac.uk)"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Together, we can make the difference."}]}]},"text":"\nThe [NHGRI-EBI GWAS Catalog](https://www.ebi.ac.uk/gwas/) is a central resource for the Genome Wides Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations.\n\nFor more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable), while serving as a starting point for investigations to identify causal variants, calculate disease risk, understand disease mechanisms and establish targets for novel therapies.\n\nHere we aim to illuminate progress that has been made to incorporate GWAS summary statistics into the GWAS Catalog, and reach out to the cancer genetics community in particular to promote submission of summary statistics for GWAS studies in cancer cohorts.\n\n### The GWAS Catalog summary statistics repository\n\nThree years ago, the GWAS Catalog and Open Targets started to work together to expand the Catalog’s scientific scope to include full p-value summary statistics (aggregate p-values and association data for every variant analysed in a genome-wide association study) in addition to the manually curated top associations.\n\nSummary statistics provide more detailed GWAS results than a lead SNP table, which allows other scientists to better use the data for sophisticated downstream analysis, whilst maintaining patient privacy. For example, summary statistics are integrated into the Open Targets Genetics Portal, where they are used to narrow down candidate loci and prioritise new drug targets through PheWAS, colocalisation analysis, fine mapping and mendelian randomisation\n\n[GWAS-catalog-statistics-1.png\n\nThe GWAS Catalog is now one of the largest, most visited and most frequently updated resources of freely available GWAS summary statistics. The repository includes summary statistics from 3,623 independent analyses (from 447 publications), accounting for a total of more than 22,000 datasets from a wide variety of traits. The Catalog users can easily access and download summary statistics from the GWAS Catalog [FTP site](ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/) or via a dedicated [summary statistics API](https://www.ebi.ac.uk/gwas/summary-statistics/docs/).\n\nThe increasing trend to share these datasets is reflected in the availability of summary statistics in the GWAS Catalog over time. There was also a significant increase in data downloads of summary statistics in 2020 compared to the previous year.\n\n\n\n\n\n### Why are few open access cancer GWAS summary statistics available?\n\nThis expansion of the GWAS Catalog summary statistics repository, while promising, required a considerable outreach effort from the Catalog data team and the main stakeholders, including Open Targets. Interestingly, the rate of summary statistics data sharing noticeably differs among different genetics cohorts and research groups, with the lowest submission rate in cancer genetics.\n\n\n\nFor example 33% of epilepsy and 21% of diabetes summary statistics are publicly available, compared to only 7.5% for cancer. The trend slightly differs amongst different cancer groups, and we have observed a more positive trend for the few papers published in the last two years.\n\n\n\n\nIn a recent [Twitter poll](https://twitter.com/GWASCatalog/status/1365359253175881742), we asked the community to tell us what they thought was the most important reason for the low rate of cancer summary statistics submission.\n\nThe results suggest that the main barriers to the sharing of summary statistics are:\n\n 1. Results are usually embargoed for use in future research,\n 2. Data privacy issues (e.g. patient confidentiality agreement), and\n 3. Lack of awareness/knowledge on the appropriate data repository.\n\nWe believe it is necessary for the genetics community to have a more comprehensive and transparent discussion about such barriers, starting from the privacy issues. As highlighted in the recently revised [NIH genomic data sharing (GDS) policy](https://osp.od.nih.gov/2018/11/01/provide-access-gsr/), summary statistics do not include individual-level information and can empower researchers to determine which genomic variants potentially contribute to a disease\n\n### How can we promote sharing?\n\nAs a part of a wider plan to identify and remove barriers to data sharing among different research communities, we have launched a dedicated public engagement campaign to motivate and encourage the genetics community to make their summary statistics publicly available – and make the difference in the field. In particular, following up from a pressing request from our users and stakeholders, we hope to understand the reasons why so few summary statistics are available for cancer studies. Open sharing of cancer GWAS data will lead the way to new, improved therapies and shed some light on the molecular mechanisms involved in such complex disease.\n\n\n\nWe call on researchers, journals, funders and charities to support our cause and spread our message. The Nature Journals Group are already proactively supporting an open access policy for their published cancer GWAS data. We hope to get more journals on board, including the top cancer journals.\n\nIn an aim to facilitate data sharing and interoperability, the GWAS Catalog team has recently released a [web-based deposition interface](https://www.ebi.ac.uk/gwas/deposition) to support scalable author submission of summary statistics and metadata from published and pre-published (submitted at the time of journal submission upon request from the reviewers) GWAS. The datasets are submitted in a standard format and harmonised against the latest genome build and forward strand. This facilitates downstream analysis and integration into resources like the Open Targets Genetics.\n\nPlease submit your summary statistics to the GWAS Catalog.\n\nWhat do you think are the most important barriers to the open sharing of summary statistics in cancer studies? Please let us know your thoughts by completing [this short survey](https://docs.google.com/forms/d/e/1FAIpQLSfuAO-cZWdIm8aF0140QXsZOxVlw7gLKj1a__KZ3aF4CUNamQ/viewform?usp=sf_link).\n\nYou can also reach out to us via [Twitter (@GWASCatalog)](https://twitter.com/GWASCatalog) or [email (gwas-info@ebi.ac.uk)](gwas-info@ebi.ac.uk).\n\nTogether, we can make the difference.\n","dir":"/blog","path":"/blog/open-sharing-of-cancer-summary-statistics","extension":".md","createdAt":"2024-03-11T15:03:22.557Z","updatedAt":"2024-03-11T15:03:22.558Z","meta":{"version":0,"revision":0,"created":1710171029071},"$loki":5},{"slug":"everything-you-need-to-know-about-the-new-gwas-catalog","description":"Genetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation.","title":"Everything you need to know about the new GWAS Catalog","author":"Annalisa Buniello","date":"Jun 13, 2018","img":"blog-1.jpg","toc":[{"id":"new-and-expanded-gwas-catalog-to-improve-target-identification","depth":3,"text":"New and expanded GWAS Catalog to improve target identification"},{"id":"selecting-and-curating-non-genome-wide-array-studies","depth":3,"text":"Selecting and curating non-genome-wide array studies"},{"id":"modifying-the-gwas-catalog-user-interface","depth":3,"text":"Modifying the GWAS Catalog user interface"},{"id":"stay-tuned","depth":3,"text":"Stay tuned!"}],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Genetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The NHGRI-EBI GWAS Catalog, an awesome public resource of human genotype-phenotype associations from published GWAS analyses, is one of the data sources for manually curated genetic evidence for the Open Targets Platform. The GWAS Catalog data is widely used to prioritise new candidate loci, predict disease risk and understand disease mechanisms, with a number of users and applications that is always increasing."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/FINAL_USERS_withOT-2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"new-and-expanded-gwas-catalog-to-improve-target-identification"},"children":[{"type":"element","tag":"a","props":{"href":"#new-and-expanded-gwas-catalog-to-improve-target-identification","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"New and expanded GWAS Catalog to improve target identification"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"I am happy to announce an expansion of the GWAS Catalog scientific scope to host non genome-wide studies. During its first 10 years of life, in fact, the GWAS Catalog has only included array studies with genome-wide coverage, therefore curation of exome and targeted array studies were outside its scientific scope. These data are however valuable to Open Targets’ scientific goals of target identification and prioritisation, so much so they decided to invest in a new project aiming to expand the scope of the Catalog to include targeted arrays, such as MetaboChip, ImmunoChip, and Exome array. The new GWAS will now allow variants associated with immunologic, metabolic and oncologic phenotypes to be included in the Open Targets Platform for the expansion of target identification and selection in these traits."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"selecting-and-curating-non-genome-wide-array-studies"},"children":[{"type":"element","tag":"a","props":{"href":"#selecting-and-curating-non-genome-wide-array-studies","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Selecting and curating non-genome-wide array studies"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"As a first step, Open Targets selected almost 60 publications for inclusion in the GWAS Catalog. I then extracted a total of 120 new independent association studies and 823 SNP-trait associations from these publications. These are impressive numbers, and give you an idea of how many studies and SNP-trait associations (and potential targets) we were missing by not including targeted array studies in the GWAS Catalog!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The final step for me to do was to map all these traits, diseases and phenotypes to EFO terms, the ontology used by\n"},{"type":"element","tag":"a","props":{"href":"https://docs.targetvalidation.org/faq/describing-diseases","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Open Targets to describe its diseases"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"modifying-the-gwas-catalog-user-interface"},"children":[{"type":"element","tag":"a","props":{"href":"#modifying-the-gwas-catalog-user-interface","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Modifying the GWAS Catalog user interface"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS Catalog team have changed the way the user interface works to support searching, displaying, filtering and download of targeted and exome array studies. "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/search?query=celiac%20disease","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Try it out using \"celiac disease\""}]},{"type":"text","value":", for example. The interface will now give you:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Drop-down filter by genotyping technology to help you focus on your favourite array"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/filter1-5.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Small \"target\" icon (designed for us by the EMBL-EBI media team. Thanks for that!) in the study accession number column for all targeted array studies prioritised by Open Targets"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/target-1.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"New \"genotyping technology\" shown for study, e.g. genome-wide genotyping array, exome-wide genotyping array or targeted genotyping array with an optional field for additional array information, for example ImmunoChip or MetaboChip"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/gen-tech-1.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"stay-tuned"},"children":[{"type":"element","tag":"a","props":{"href":"#stay-tuned","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Stay tuned!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This is an important milestone for Open Targets and the GWAS Catalog, and a lot more is yet to come. The next step for the collaboration between GWAS and Open Targets will be to develop a comprehensive database of all available GWAS full summary statistics stored in a common format and harmonised across studies to enable searching, easy comparison and downstream analysis. This effort will generate more comprehensive new raw genetic data accessible by the entire research community through the GWAS Catalog, the Open Targets Platform and additional resources the Open Targets team are working on."}]}]},"text":"\nGenetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation.\n\nThe NHGRI-EBI GWAS Catalog, an awesome public resource of human genotype-phenotype associations from published GWAS analyses, is one of the data sources for manually curated genetic evidence for the Open Targets Platform. The GWAS Catalog data is widely used to prioritise new candidate loci, predict disease risk and understand disease mechanisms, with a number of users and applications that is always increasing.\n\n\n\n### New and expanded GWAS Catalog to improve target identification\n\nI am happy to announce an expansion of the GWAS Catalog scientific scope to host non genome-wide studies. During its first 10 years of life, in fact, the GWAS Catalog has only included array studies with genome-wide coverage, therefore curation of exome and targeted array studies were outside its scientific scope. These data are however valuable to Open Targets’ scientific goals of target identification and prioritisation, so much so they decided to invest in a new project aiming to expand the scope of the Catalog to include targeted arrays, such as MetaboChip, ImmunoChip, and Exome array. The new GWAS will now allow variants associated with immunologic, metabolic and oncologic phenotypes to be included in the Open Targets Platform for the expansion of target identification and selection in these traits.\n\n### Selecting and curating non-genome-wide array studies\n\nAs a first step, Open Targets selected almost 60 publications for inclusion in the GWAS Catalog. I then extracted a total of 120 new independent association studies and 823 SNP-trait associations from these publications. These are impressive numbers, and give you an idea of how many studies and SNP-trait associations (and potential targets) we were missing by not including targeted array studies in the GWAS Catalog!\n\nThe final step for me to do was to map all these traits, diseases and phenotypes to EFO terms, the ontology used by \n[Open Targets to describe its diseases](https://docs.targetvalidation.org/faq/describing-diseases)\n\n### Modifying the GWAS Catalog user interface\n\nThe GWAS Catalog team have changed the way the user interface works to support searching, displaying, filtering and download of targeted and exome array studies. [Try it out using \"celiac disease\"](https://www.ebi.ac.uk/gwas/search?query=celiac%20disease), for example. The interface will now give you:\n\n- **Drop-down filter by genotyping technology to help you focus on your favourite array**\n\n\n\n- **Small \"target\" icon (designed for us by the EMBL-EBI media team. Thanks for that!) in the study accession number column for all targeted array studies prioritised by Open Targets**\n\n\n\n- **New \"genotyping technology\" shown for study, e.g. genome-wide genotyping array, exome-wide genotyping array or targeted genotyping array with an optional field for additional array information, for example ImmunoChip or MetaboChip**\n\n\n\n### Stay tuned!\n\nThis is an important milestone for Open Targets and the GWAS Catalog, and a lot more is yet to come. The next step for the collaboration between GWAS and Open Targets will be to develop a comprehensive database of all available GWAS full summary statistics stored in a common format and harmonised across studies to enable searching, easy comparison and downstream analysis. This effort will generate more comprehensive new raw genetic data accessible by the entire research community through the GWAS Catalog, the Open Targets Platform and additional resources the Open Targets team are working on.\n","dir":"/blog","path":"/blog/everything-you-need-to-know-about-the-new-gwas-catalog","extension":".md","createdAt":"2024-03-11T15:03:22.554Z","updatedAt":"2024-03-11T15:03:22.554Z","meta":{"version":0,"revision":0,"created":1710171029084},"$loki":6},{"slug":"cohorts-now-available","description":"We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics.","title":"Cohorts now available in the GWAS Catalog","author":"GWAS Catalog team","date":"Nov 14, 2023","img":"blog-1.img","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"There are two new studies "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/file-downloads","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"downloads"}]},{"type":"text","value":" named as follows:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"gwas-catalog-studies_ontology-annotated_v1.0.2.1.tsv"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"gwas-catalog-download-studies-v1.0.3.1.tsv"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Versions 1.0.2 (gwas-catalog-studies_ontology-annotated.tsv) and 1.0.3 (gwas-catalog-download-studies-v1.0.3.tsv) of the studies downloads will be deprecated and cease to be generated on 1 December 2023."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The COHORT field describes the discovery stage (genome-wide) cohorts used in each study. Since we started piloting curation of cohort information in 2020, over 700 different discovery cohorts have been described in GWAS publications."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Cohort abbreviations from discovery stage GWAS either are extracted from the literature or supplied by sumitters, to match a predefined list which we share with the PGS Catalog. The initial list of common cohorts used in genetics studies that seeded these annotations is from Mills & Rahal, Communications Biology (2019), and has been expanded during our pilot phase. A full list of the abbreviations and corresponding full cohort names in use is available to download separately ("},{"type":"element","tag":"a","props":{"href":"https://ftp.ebi.ac.uk/pub/databases/spot/pgs/metadata/pgs_all_metadata_cohorts.csv","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"https://ftp.ebi.ac.uk/pub/databases/spot/pgs/metadata/pgs_all_metadata_cohorts.csv"}]},{"type":"text","value":"). We share this list with the PGS Catalog, so you may find cohorts listed there that are not currently associated with a GWAS Catalog study. It's important to remember we only extract cohorts when they have been clearly and unambiguously named by authors, so a cohort may have been used in other publications which don't have annotation.\nWhere a sample cohort in the literature was not already in the predefined list at the time of curation, or was ambiguously described, “other” will appear in our studies download file. Where a sample in the literature had no cohort reported, “NR” will appear in our studies download file. Consortium names are extracted if the component cohorts are not provided, or there are too many component cohorts to curate. Empty cohort fields appear for studies curated before the pilot work to extract this information began (~2020).\nWe also accept submissions of unpublished data and these are listed in separate download files. Cohorts appearing in the unpublished download files are yet to undergo in-house curation and therefore may not exactly match against the predefined list."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Cohort data annotations for published studies are also now available via the REST API, accessible via the /studies and /studies/{accessionId} endpoints. The field is called "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"cohort"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"As well as cohort data, the new studies downloads also contain clear annotation of which studies have full genomewide summary statistics available for download in the column FULL SUMMARY STATISTICS with the ftp location available in the adjacent column SUMMARY STATS LOCATION."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We hope you make great use of these data! Questions? Feedback? Contact "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]}]}]},"text":"We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics. \n\nThere are two new studies [downloads](https://www.ebi.ac.uk/gwas/docs/file-downloads) named as follows:\n- gwas-catalog-studies_ontology-annotated_v1.0.2.1.tsv\n- gwas-catalog-download-studies-v1.0.3.1.tsv\n\nVersions 1.0.2 (gwas-catalog-studies_ontology-annotated.tsv) and 1.0.3 (gwas-catalog-download-studies-v1.0.3.tsv) of the studies downloads will be deprecated and cease to be generated on 1 December 2023. \n\nThe COHORT field describes the discovery stage (genome-wide) cohorts used in each study. Since we started piloting curation of cohort information in 2020, over 700 different discovery cohorts have been described in GWAS publications. \n\nCohort abbreviations from discovery stage GWAS either are extracted from the literature or supplied by sumitters, to match a predefined list which we share with the PGS Catalog. The initial list of common cohorts used in genetics studies that seeded these annotations is from Mills & Rahal, Communications Biology (2019), and has been expanded during our pilot phase. A full list of the abbreviations and corresponding full cohort names in use is available to download separately (https://ftp.ebi.ac.uk/pub/databases/spot/pgs/metadata/pgs_all_metadata_cohorts.csv). We share this list with the PGS Catalog, so you may find cohorts listed there that are not currently associated with a GWAS Catalog study. It's important to remember we only extract cohorts when they have been clearly and unambiguously named by authors, so a cohort may have been used in other publications which don't have annotation. \nWhere a sample cohort in the literature was not already in the predefined list at the time of curation, or was ambiguously described, “other” will appear in our studies download file. Where a sample in the literature had no cohort reported, “NR” will appear in our studies download file. Consortium names are extracted if the component cohorts are not provided, or there are too many component cohorts to curate. Empty cohort fields appear for studies curated before the pilot work to extract this information began (~2020).\nWe also accept submissions of unpublished data and these are listed in separate download files. Cohorts appearing in the unpublished download files are yet to undergo in-house curation and therefore may not exactly match against the predefined list.\n\nCohort data annotations for published studies are also now available via the REST API, accessible via the /studies and /studies/{accessionId} endpoints. The field is called *cohort*. \n\nAs well as cohort data, the new studies downloads also contain clear annotation of which studies have full genomewide summary statistics available for download in the column FULL SUMMARY STATISTICS with the ftp location available in the adjacent column SUMMARY STATS LOCATION. \n\nWe hope you make great use of these data! Questions? Feedback? Contact gwas-info@ebi.ac.uk\n","dir":"/blog","path":"/blog/cohorts-now-available","extension":".md","createdAt":"2024-03-11T15:03:22.554Z","updatedAt":"2024-03-11T15:03:22.554Z","meta":{"version":0,"revision":0,"created":1710171029096},"$loki":7},{"slug":"new-standard-for-gwas-summary-statistics","description":"Today we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, GWAS-SSF. The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a recent analysis of 327 summary statistics","title":"A new standard for GWAS summary statistics","author":"Laura Harris","date":"July 20, 2022","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Today we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v1","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS-SSF"}]},{"type":"text","value":". The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a "},{"type":"element","tag":"a","props":{"href":"https://academic.oup.com/bioinformatics/article/37/23/4593/6380562","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"recent analysis"}]},{"type":"text","value":" of 327 summary statistics files finding over 100 unique formats. We were about to launch the GWAS Catalog author submission system and needed to enforce a standard format as QC on submissions, and to ensure that submitted data could be passed through our harmonisation pipeline. So we quickly developed a "},{"type":"element","tag":"a","props":{"href":"https://pubmed.ncbi.nlm.nih.gov/30445434/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"minimal standard format"}]},{"type":"text","value":" based on the most commonly included fields, with variant ID/genomic location and p-value as mandatory fields, and standard headers for other fields such as beta and CI. This was enough for our goal of getting data into the GWAS Catalog. But we heard many consumers of the data complain that datasets with the minimum mandatory fields did not meet their requirements, for example to use the data for Mendelian randomisation or to generate polygenic scores. We needed to encourage our submitters to share their data more fully. A second issue is that while the GWAS Catalog curates metadata from the literature, there is no standard for GWAS metadata reporting, resulting in missing data that limits re-use of the summary statistics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We started on a process of consulting with the community that began with our workshop on summary statistics data and sharing in 2020. This was a wide ranging discussion over 2 days that you can read all about in our "},{"type":"element","tag":"a","props":{"href":"https://www.sciencedirect.com/science/article/pii/S2666979X21000045","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"workshop proceedings"}]},{"type":"text","value":". At the end of that process we had an outline of some mandatory fields for data and metadata, but refining these and ensuring they met all the major use cases would take a more focused discussion. Our working group on summary statistics data content and format, chaired by Ines Barroso, held 3 meetings over the course of 2021. We held a parallel working group to discuss diversity and privacy in data sharing (to be reported elsewhere) and incorporated their recommendations (for example on the sharing of allele frequency data) into our decision making process. Both the original workshop and the working group were designed to represent a wide range of stakeholders - from industry and academia, developers of other resources & tools, data generators and data consumers, research groups primarily performing wet-lab research and high-throughput bioinformatics users. Our discussions focused on the need to enforce key mandatory fields to maximise the reuse of the data, but also to recognise that setting the bar too high could discourage data sharing. Similarly, data need to be made accessible in a way that suits the high-throughput user who might be piping data in bulk into a computational pipeline, but also users without advanced bioinformatics skills who might be downloading a single file to investigate a locus of interest. We heard that preparing data for submission to databases can be a huge drain for under-resourced labs, so needs to be as straightforward and user-friendly as possible, and that interoperability between different databases (such as "},{"type":"element","tag":"a","props":{"href":"https://www.ncbi.nlm.nih.gov/gap/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"dbGaP"}]},{"type":"text","value":", "},{"type":"element","tag":"a","props":{"href":"http://www.ebi.ac.uk/gwas","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the GWAS Catalog"}]},{"type":"text","value":" and "},{"type":"element","tag":"a","props":{"href":"https://gwas.mrcieu.ac.uk/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"OpenGWAS"}]},{"type":"text","value":") is important to reduce the workload for data generators. You can read the outcome of our discussions in our "},{"type":"element","tag":"a","props":{"href":"https://biohackrxiv.org/7zxht","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"new manuscript"}]},{"type":"text","value":". Behind the scenes, we are making the necessary changes to the GWAS Catalog infrastructure to validate new submissions according to the new standard and where possible, make all our existing summary statistics available in the new format. We are working on tools (together with PLINK and others) to make data preparation and formatting as easy as possible. In the meantime, we welcome feedback from the wider community on the standard and will collate this at the end of August prior to release of the new infrastructure."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS-SSF is suitable for array-based GWAS and single variant seqGWAS. As data sharing has become more widely accepted, there is appetite from the genomics community to share full p value datasets from gene-based analyses of sequencing data and CNVs. Whilst we don’t have a standard format for these yet, by gathering data we hope to go through a similar process to work towards a standard format. Please share your data, and let us know your thoughts!"}]}]},"text":"\nToday we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, [GWAS-SSF](https://www.biorxiv.org/content/10.1101/2022.07.15.500230v1). The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a [recent analysis](https://academic.oup.com/bioinformatics/article/37/23/4593/6380562) of 327 summary statistics files finding over 100 unique formats. We were about to launch the GWAS Catalog author submission system and needed to enforce a standard format as QC on submissions, and to ensure that submitted data could be passed through our harmonisation pipeline. So we quickly developed a [minimal standard format](https://pubmed.ncbi.nlm.nih.gov/30445434/) based on the most commonly included fields, with variant ID/genomic location and p-value as mandatory fields, and standard headers for other fields such as beta and CI. This was enough for our goal of getting data into the GWAS Catalog. But we heard many consumers of the data complain that datasets with the minimum mandatory fields did not meet their requirements, for example to use the data for Mendelian randomisation or to generate polygenic scores. We needed to encourage our submitters to share their data more fully. A second issue is that while the GWAS Catalog curates metadata from the literature, there is no standard for GWAS metadata reporting, resulting in missing data that limits re-use of the summary statistics.\n\nWe started on a process of consulting with the community that began with our workshop on summary statistics data and sharing in 2020. This was a wide ranging discussion over 2 days that you can read all about in our [workshop proceedings](https://www.sciencedirect.com/science/article/pii/S2666979X21000045). At the end of that process we had an outline of some mandatory fields for data and metadata, but refining these and ensuring they met all the major use cases would take a more focused discussion. Our working group on summary statistics data content and format, chaired by Ines Barroso, held 3 meetings over the course of 2021. We held a parallel working group to discuss diversity and privacy in data sharing (to be reported elsewhere) and incorporated their recommendations (for example on the sharing of allele frequency data) into our decision making process. Both the original workshop and the working group were designed to represent a wide range of stakeholders - from industry and academia, developers of other resources & tools, data generators and data consumers, research groups primarily performing wet-lab research and high-throughput bioinformatics users. Our discussions focused on the need to enforce key mandatory fields to maximise the reuse of the data, but also to recognise that setting the bar too high could discourage data sharing. Similarly, data need to be made accessible in a way that suits the high-throughput user who might be piping data in bulk into a computational pipeline, but also users without advanced bioinformatics skills who might be downloading a single file to investigate a locus of interest. We heard that preparing data for submission to databases can be a huge drain for under-resourced labs, so needs to be as straightforward and user-friendly as possible, and that interoperability between different databases (such as [dbGaP](https://www.ncbi.nlm.nih.gov/gap/), [the GWAS Catalog](http://www.ebi.ac.uk/gwas) and [OpenGWAS](https://gwas.mrcieu.ac.uk/)) is important to reduce the workload for data generators. You can read the outcome of our discussions in our [new manuscript](https://biohackrxiv.org/7zxht). Behind the scenes, we are making the necessary changes to the GWAS Catalog infrastructure to validate new submissions according to the new standard and where possible, make all our existing summary statistics available in the new format. We are working on tools (together with PLINK and others) to make data preparation and formatting as easy as possible. In the meantime, we welcome feedback from the wider community on the standard and will collate this at the end of August prior to release of the new infrastructure.\n\nThe GWAS-SSF is suitable for array-based GWAS and single variant seqGWAS. As data sharing has become more widely accepted, there is appetite from the genomics community to share full p value datasets from gene-based analyses of sequencing data and CNVs. Whilst we don’t have a standard format for these yet, by gathering data we hope to go through a similar process to work towards a standard format. Please share your data, and let us know your thoughts!\n","dir":"/blog","path":"/blog/new-standard-for-gwas-summary-statistics","extension":".md","createdAt":"2024-03-11T15:03:22.556Z","updatedAt":"2024-03-11T15:03:22.557Z","meta":{"version":0,"revision":0,"created":1710171029115},"$loki":8},{"slug":"streamlining-GWAS-Summary-Statistics-Data-Interpretation-with-Improved-YAML-Metadata","description":"Having clear and accessible metadata is essential for enhancing data interpretation and ensuring its reusability. In the case of Genome-Wide Association Studies (GWAS), having a standardized and easy-to-understand format for documenting study metadata is crucial. In the GWAS Catalog, metadata associated with full genome-wide summary statistics files is accessible via multiple routes - searchable in the main Catalog via the website and REST API,","title":"Streamlining GWAS Summary Statistics Data Interpretation with Improved YAML Metadata","author":"Yue Ji and Laura Harris","date":"Feburary 22, 2024","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Having clear and accessible metadata is essential for enhancing data interpretation and ensuring its reusability. In the case of Genome-Wide Association Studies (GWAS), having a standardized and easy-to-understand format for documenting study metadata is crucial. In the GWAS Catalog, metadata associated with full genome-wide summary statistics files is accessible via multiple routes - searchable in the main Catalog via the website and REST API, and additionally via a text file in YAML format, contained in the same directory as the data file."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We have identified key fields that should be included in the metadata for a comprehensive understanding, interpretation, and subsequent reuse of GWAS studies. Mandatory fields document essential study details and optional fields cater to specific study designs (such as case/control counts in case-control studies), and provide supplementary analysis information. You can find detailed information about these fields in Table 1 and in our recent preprint (Hayhurst et al 2023 "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v4","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v4"}]},{"type":"text","value":")."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In our latest release, we've introduced several updates to the GWAS metadata YAML file format. The YAML format, known it's user-friendliness, provides a combination of structured information and ease of use for humans and computers. The latest changes aim to enhance the human readability of the files with a clearer, more unambiguous, and user-friendly experience without compromising the file's compatibility with automated systems. 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status"}]},{"type":"element","tag":"td","props":{},"children":[]},{"type":"element","tag":"td","props":{},"children":[]},{"type":"element","tag":"td","props":{},"children":[]},{"type":"element","tag":"td","props":{},"children":[]}]},{"type":"element","tag":"tr","props":{},"children":[{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"is_harmonised"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Description of harmonisation codes"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Text string"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Only given in harmonised datasets"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"false"}]}]},{"type":"element","tag":"tr","props":{},"children":[{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"is_sorted"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Flag whether the file is sorted by genomic location"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Boolean"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Yes"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"false"}]}]},{"type":"element","tag":"tr","props":{},"children":[{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"harmonisation_reference"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"The genome reference file used for harmonising the summary statistics file"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"Text string"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"No"}]},{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"ftp://ftp.ensembl.org/pub/release-104/fasta/homo_sapiens/dna/"}]}]},{"type":"element","tag":"tr","props":{},"children":[{"type":"element","tag":"td","props":{},"children":[{"type":"text","value":"## Questions and feedback"}]},{"type":"element","tag":"td","props":{},"children":[]},{"type":"element","tag":"td","props":{},"children":[]},{"type":"element","tag":"td","props":{},"children":[]},{"type":"element","tag":"td","props":{},"children":[]}]}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Questions or comments about this change? Please contact us as "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":"."}]}]},"text":"\nHaving clear and accessible metadata is essential for enhancing data interpretation and ensuring its reusability. In the case of Genome-Wide Association Studies (GWAS), having a standardized and easy-to-understand format for documenting study metadata is crucial. In the GWAS Catalog, metadata associated with full genome-wide summary statistics files is accessible via multiple routes - searchable in the main Catalog via the website and REST API, and additionally via a text file in YAML format, contained in the same directory as the data file. \n\nWe have identified key fields that should be included in the metadata for a comprehensive understanding, interpretation, and subsequent reuse of GWAS studies. Mandatory fields document essential study details and optional fields cater to specific study designs (such as case/control counts in case-control studies), and provide supplementary analysis information. You can find detailed information about these fields in Table 1 and in our recent preprint (Hayhurst et al 2023 https://www.biorxiv.org/content/10.1101/2022.07.15.500230v4).\n\nIn our latest release, we've introduced several updates to the GWAS metadata YAML file format. The YAML format, known it's user-friendliness, provides a combination of structured information and ease of use for humans and computers. The latest changes aim to enhance the human readability of the files with a clearer, more unambiguous, and user-friendly experience without compromising the file's compatibility with automated systems. Here’s an overview of the significant enhancements:\n\n1. Reordering and Grouping of Fields:\n* The fields within the YAML files have been reorganized and grouped by related topics, each marked with a group name starting with '#' (group names are listed in Table 1). This organization helps users to quickly locate specific fields by group name, such as '# Sample Information', making the file more navigable for humans. Importantly, this structural refinement does not affect the way machines parse and interpret the data.\n\n2. Clear Indentation for Hierarchical Relationships:\n* Indentation has been introduced to visually represent the relationships between different data fields. This visual aid improves the readability of the files, making it easier for users to discern the levels of data organization and relationships between various fields. In the meantime, this indentation does not impact the machine processing of the file.\n\n3. Renaming 'data_last_modified' to 'date_metadata_last_modified':\n* To provide clearer context, the 'data_last_modified' field has been renamed to 'date_metadata_last_modified'. This change emphasizes that the timestamp refers to the last modification date of the metadata, not the summary statistics data. It aids users in tracking any changes or updates in the metadata over time.\n \n4. Addition of 'Ancestry Category' Field:\n* We have introduced a new 'ancestry category' field in addition to the existing 'ancestry' field. This new feature provides a broad overview of the ancestry category that best describes the sample, allowing for a more generalized view of the sample's ancestry. This new field also ensures consistency and facilitates comparative analysis across different studies, in line with the rest of the Catalog. \n\nThese updates reflect our commitment to improving the user experience while ensuring the YAML files maintain their structural integrity for computer readability. Our focus continues to be on making GWAS metadata more accessible, interpretable, and useful for researchers.\n\n\nTable 1. Metadata field definitions\n| Field | Description | Data type and values | Mandatory | Example |\n| -------------------------------- | ---------------------------------------------------------------------------------- | ------------------------------------------------------- | ----------------------------------- | -------------------------------------------------------------------------------------------------------------------- |\n| \\# Study meta-data |\n| gwas_id | GWAS Catalog accession ID | Text string | Yes | GCST90244057 |\n| author_notes | Additional information about this study from the author | Text string | No | File contains GWAS summary statistics from a meta-analysis of NMR metabolic traits in up to 33 cohorts. |\n| gwas_catalog_api | GWAS catalog REST API link | Text string | Yes | [https://www.ebi.ac.uk/gwas/rest/api/studies/GCST90244057](https://www.ebi.ac.uk/gwas/rest/api/studies/GCST90244057) |\n| date_metadata_last_modified | The latest date that metadata YAML file was modified | date | Yes | 2023-11-28 |\n| \\# Trait Information |\n| trait_description | Author reported trait description | Text string (multiple possible) | Yes | Body mass index |\n| ontology_mapping | Short form ontology terms describing the trait | Text string (multiple possible) | No | EFO_0004918 |\n| \\# Genotyping Information |\n| genome_assembly | Genome assembly for the summary statistics. | GRCh/NCBI/UCSC value | Yes | GRCh37 |\n| coordinate_system | Coordinate system used for the summary statistics | Text String (1-based or 0-based) | No | 1-based |\n| genotyping_technology | Method(s) used to genotype variants in the discovery stage. | Text string (multiple possible) | Yes | Genome-wide genotyping array |\n| imputation_panel | Panel used for imputation | Text string | No | HRC + UK10K |\n| imputation_software | Software used for imputation | Text string | No | SHAPEIT3 + IMPUTE4 |\n| \\# Sample Information |\n| sample_ancestry_category | Broad ancestry category that best describes the sample. | Text string | Yes | European |\n| sample_ancestry | The most detailed ancestry descriptor(s) for the sample. | Text string (multiple possible) | Yes | \\- Finnish
- British |\n| sample_size | Sample size | Integer | Yes | 27006 |\n| ancestry_method | Method used to determine sample ancestry e.g. self-reported/genetically determined | Text string (multiple possible) | No | self-reported |\n| case_control_study | Flag whether the study is a case-control study | Boolean | No (default is false) | true |\n| case_count | Number of cases for case/control study | Integer | No, unless caseControlStudy is true | 27006 |\n| control_count | Number of controls for case/control study | Integer | No, unless caseControlStudy is true | 27006 |\n| sex | To indicate a sex-stratified analysis | M (for male), F (for female), combined or NR if unknown | No | combined |\n| \\# Summary Statistic information |\n| data_file_name | The name of the summary statistics file | Text string | Yes | GCST90244057_buildGRCh37.tsv |\n| file_type | The format of the summary statistics file | \"GWAS-SSF v1.0\", \"pre-GWAS-SSF\", \"non-GWAS-SSF\" | Yes | GWAS-SSF v1.0 |\n| data_file_md5sum | The md5 checksum of the summary statistics file. | Text string | Yes | 0ec56396f89edcc21a3d5a25a6fa993d |\n| analysis_software | Software and version used for the association analysis | Text string (multiple possible) | Yes if p-values of 0 given | REGENIE |\n| adjusted_covariates | Any covariates the GWAS is adjusted for | Text string (multiple possible) | No | sex |\n| minor_allele_freq_lower_limit | Lowest possible effect allele frequency | Numeric | No | 0.0003 |\n| \\# Harmonization status |\n| is_harmonised | Description of harmonisation codes | Text string | Only given in harmonised datasets | false |\n| is_sorted | Flag whether the file is sorted by genomic location | Boolean | Yes | false |\n| harmonisation_reference | The genome reference file used for harmonising the summary statistics file | Text string | No | ftp://ftp.ensembl.org/pub/release-104/fasta/homo_sapiens/dna/ |\n## Questions and feedback\n\nQuestions or comments about this change? Please contact us as gwas-info@ebi.ac.uk.\n","dir":"/blog","path":"/blog/streamlining-GWAS-Summary-Statistics-Data-Interpretation-with-Improved-YAML-Metadata","extension":".md","createdAt":"2024-03-11T15:03:22.558Z","updatedAt":"2024-03-11T15:03:22.558Z","meta":{"version":0,"revision":0,"created":1710171029165},"$loki":9},{"slug":"ten-years-of-the-gwas-catalog-past-present-and-future","description":"This year marks a special anniversary for the GWAS Catalog, as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers","title":"Ten years of the GWAS Catalog – Past, present and future","author":"Laura Harris (GWAS Catalog) and Astrid Gall (Ensembl Outreach)","date":"Jun 28, 2018","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This year marks a special anniversary for the "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS Catalog"}]},{"type":"text","value":", as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers and clinicians without them spending a lot of time undertaking regular systematic reviews of the literature. This is where we can help you!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The project is a collaboration between "},{"type":"element","tag":"a","props":{"href":"https://www.genome.gov/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the National Human Genome Research Institute (NHGRI)"}]},{"type":"text","value":" in Bethesda, Maryland, USA, and the European Molecular Biology Laboratory – "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"European Bioinformatics Institute (EMBL-EBI)"}]},{"type":"text","value":" in Hinxton, UK, where the majority of our curators and all of our software developers are based"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"ten-years-of-the-gwas-catalog-past-present-and-future/GWAS-catalog_EBI_group-768x576.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"At EMBL-EBI, we share the building with "},{"type":"element","tag":"a","props":{"href":"https://www.ensembl.org/index.html","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the Ensembl team"}]},{"type":"text","value":" and work very closely with them – which is very handy for both teams!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Our curators extract genotype-phenotype associations from all published GWAS studies, along with study information to allow you to interpret the data accurately. We make the data structured, searchable and visualisable, annotate it and integrate it with other resources including Ensembl. Each association with a standard variant identifier (rsID) enters our automated Ensembl mapping pipeline which provides the genomic annotation, i.e. the location on the chromosome and cytogenetic band as well as the mapped genes. We use this pipeline to check for errors as part of our quality control and display the information in our interface."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Our colleagues at Ensembl benefit from the GWAS Catalog as they import variant and phenotype data from it. "},{"type":"element","tag":"a","props":{"href":"http://www.ensembl.org/info/genome/variation/species/sources_documentation.html","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"More than 74,000 human variants associated with phenotype data from the GWAS catalog were included in the latest Ensembl release (e92)"}]},{"type":"text","value":"; and the number is increasing continuously!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"[video]"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Over the last ten years, the GWAS Catalog has grown, from a single published GWAS on age-related macular degeneration, into the primary source of disease-related associations with genetic variants. Watch how the number of associations has increased over time in this video!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Today, the GWAS Catalog contains over 3,400 publications and over 62,000 unique SNP-trait associations. More than 10,000 visitors from around the globe access the GWAS Catalog per month. People using the resource have a wide range of goals – they include researchers wanting to narrow down or prioritise candidate loci, scientists investigating disease mechanisms, clinicians aiming to predict disease risk, pharma industry professionals improving the drug discovery process and anyone wanting to get the latest statistics on disease knowledge or summary data from particular populations of individuals."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS landscape has evolved over these ten years, with new developments in study design and genotyping technologies. More and more publications on GWAS come out each month. Often a publication contains more than one GWAS – sometimes there are over a thousand in a single paper! The number of individuals studied in a GWAS is also increasing, and so is the number of variants assayed, with bigger arrays and better imputation methods."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The nature of the traits studied is evolving too. Researchers are still publishing GWAS on major common diseases such as type 2 diabetes and breast cancer. However, these studies are now being published alongside studies on ever more specific traits. For example, "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/search?query=schizophrenia","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"searching the GWAS Catalog for “schizophrenia”"}]},{"type":"text","value":" brings up more than 50 different traits related to this disorder. These include sub-phenotypes such as specific symptoms in schizophrenia, endophenotypes such as brain imaging measurements, multi-trait analyses such as “schizophrenia and bipolar disorder”, drug response measurements as well as analyses of the interaction between genetic associations and environmental factors."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"All of this means more data to curate. We are adapting our infrastructure constantly to improve curation efficiency so that we can keep up with the rate of data generation. At the same time, we need to maintain the accuracy of the resource and make sure it contains the most relevant and up-to-date research results."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"ten-years-of-the-gwas-catalog-past-present-and-future/fig2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Highlights over the last ten years include releasing a new "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS Catalog website"}]},{"type":"text","value":" in 2015, "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/ontology","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"mapping curated trait descriptions to ontology terms"}]},{"type":"text","value":" to enable enriched ontology-driven search capabilities, improving the interactive "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/diagram","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS Catalog diagram"}]},{"type":"text","value":" and "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/ancestry","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"capturing ancestry more completely"}]},{"type":"text","value":". Accurate characterisation of ancestry is essential to interpret human genomics data. Recently, we have developed a new framework for describing detailed ancestry information and recommendations for reporting ancestry systematically [(Morales et al 2018 Genome Biology 19:2)].("},{"type":"element","tag":"a","props":{"href":"https://europepmc.org/abstract/MED/29448949","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"https://europepmc.org/abstract/MED/29448949"}]},{"type":"text","value":")"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"So what will the future hold for the GWAS Catalog? We have just started hosting "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/downloads/summary-statistics","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"summary statistics for studies in the GWAS Catalog"}]},{"type":"text","value":" with support from the scientific community (the files contain the full set of p values for every SNP on the array used). In the future we plan to integrate these datasets with structured meta-data to make it even easier to access GWAS results. We also aim to expand our scope to include other types of association analyses, including targeted arrays and sequencing-based genotyping, which enable deeper interrogation of diseases of interest and inclusion of less common variants."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"With these developments come many challenges, including how best to represent the increasing complexity of study designs, trait architecture and statistical analyses. But with the help of the authors of publications in the GWAS Catalog, the scientific community using our resource and our colleagues at Ensembl, we are excited about what the next ten years of GWAS will hold!"}]}]},"text":"\nThis year marks a special anniversary for the [GWAS Catalog](https://www.ebi.ac.uk/gwas/), as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers and clinicians without them spending a lot of time undertaking regular systematic reviews of the literature. This is where we can help you!\n\nThe project is a collaboration between [the National Human Genome Research Institute (NHGRI)](https://www.genome.gov/) in Bethesda, Maryland, USA, and the European Molecular Biology Laboratory – [European Bioinformatics Institute (EMBL-EBI)](https://www.ebi.ac.uk/) in Hinxton, UK, where the majority of our curators and all of our software developers are based\n\n\n\n\nAt EMBL-EBI, we share the building with [the Ensembl team](https://www.ensembl.org/index.html) and work very closely with them – which is very handy for both teams!\n\n\nOur curators extract genotype-phenotype associations from all published GWAS studies, along with study information to allow you to interpret the data accurately. We make the data structured, searchable and visualisable, annotate it and integrate it with other resources including Ensembl. Each association with a standard variant identifier (rsID) enters our automated Ensembl mapping pipeline which provides the genomic annotation, i.e. the location on the chromosome and cytogenetic band as well as the mapped genes. We use this pipeline to check for errors as part of our quality control and display the information in our interface.\n\nOur colleagues at Ensembl benefit from the GWAS Catalog as they import variant and phenotype data from it. [More than 74,000 human variants associated with phenotype data from the GWAS catalog were included in the latest Ensembl release (e92)](http://www.ensembl.org/info/genome/variation/species/sources_documentation.html); and the number is increasing continuously!\n\n[video]\n\nOver the last ten years, the GWAS Catalog has grown, from a single published GWAS on age-related macular degeneration, into the primary source of disease-related associations with genetic variants. Watch how the number of associations has increased over time in this video!\n\n\nToday, the GWAS Catalog contains over 3,400 publications and over 62,000 unique SNP-trait associations. More than 10,000 visitors from around the globe access the GWAS Catalog per month. People using the resource have a wide range of goals – they include researchers wanting to narrow down or prioritise candidate loci, scientists investigating disease mechanisms, clinicians aiming to predict disease risk, pharma industry professionals improving the drug discovery process and anyone wanting to get the latest statistics on disease knowledge or summary data from particular populations of individuals.\n\nThe GWAS landscape has evolved over these ten years, with new developments in study design and genotyping technologies. More and more publications on GWAS come out each month. Often a publication contains more than one GWAS – sometimes there are over a thousand in a single paper! The number of individuals studied in a GWAS is also increasing, and so is the number of variants assayed, with bigger arrays and better imputation methods.\n\nThe nature of the traits studied is evolving too. Researchers are still publishing GWAS on major common diseases such as type 2 diabetes and breast cancer. However, these studies are now being published alongside studies on ever more specific traits. For example, [searching the GWAS Catalog for “schizophrenia”](https://www.ebi.ac.uk/gwas/search?query=schizophrenia) brings up more than 50 different traits related to this disorder. These include sub-phenotypes such as specific symptoms in schizophrenia, endophenotypes such as brain imaging measurements, multi-trait analyses such as “schizophrenia and bipolar disorder”, drug response measurements as well as analyses of the interaction between genetic associations and environmental factors.\n\nAll of this means more data to curate. We are adapting our infrastructure constantly to improve curation efficiency so that we can keep up with the rate of data generation. At the same time, we need to maintain the accuracy of the resource and make sure it contains the most relevant and up-to-date research results.\n\n\n\nHighlights over the last ten years include releasing a new [GWAS Catalog website](https://www.ebi.ac.uk/gwas/) in 2015, [mapping curated trait descriptions to ontology terms](https://www.ebi.ac.uk/gwas/docs/ontology) to enable enriched ontology-driven search capabilities, improving the interactive [GWAS Catalog diagram](https://www.ebi.ac.uk/gwas/diagram) and [capturing ancestry more completely](https://www.ebi.ac.uk/gwas/ancestry). Accurate characterisation of ancestry is essential to interpret human genomics data. Recently, we have developed a new framework for describing detailed ancestry information and recommendations for reporting ancestry systematically [(Morales et al 2018 Genome Biology 19:2)].(https://europepmc.org/abstract/MED/29448949)\n\nSo what will the future hold for the GWAS Catalog? We have just started hosting [summary statistics for studies in the GWAS Catalog](https://www.ebi.ac.uk/gwas/downloads/summary-statistics) with support from the scientific community (the files contain the full set of p values for every SNP on the array used). In the future we plan to integrate these datasets with structured meta-data to make it even easier to access GWAS results. We also aim to expand our scope to include other types of association analyses, including targeted arrays and sequencing-based genotyping, which enable deeper interrogation of diseases of interest and inclusion of less common variants.\n\nWith these developments come many challenges, including how best to represent the increasing complexity of study designs, trait architecture and statistical analyses. But with the help of the authors of publications in the GWAS Catalog, the scientific community using our resource and our colleagues at Ensembl, we are excited about what the next ten years of GWAS will 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Here is a guide to the main differences you will see.","title":"GWAS Catalog web search - new release!","author":"GWAS Catalog team","date":"12 July 2023","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"h1","props":{"id":"new-gwas-web-search-interface"},"children":[{"type":"element","tag":"a","props":{"href":"#new-gwas-web-search-interface","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"New GWAS web search interface"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We are excited to announce the full production release of the new GWAS Catalog web search interface."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideA.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We’ve also main some small changes to the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideB.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Interested to find studies with full genomewide summary statistics available for download? They now have their own tab, making it easier to see when summary statistics are available:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideC.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Interested to find the study with the biggest sample size for your trait of interest? Just use the sort option on the Discovery sample number columnn, which now sorts by the total number of samples in the Discovery stage, irrespective of ancestry groups:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-production-release-2023/SlideD.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Hope you enjoy using the new features! Please report any bugs to "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":" or via "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-ui/issues","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GitHub"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Lastly, watch this space for the next generation of the GWAS Diagram - we're working on it!"}]}]},"text":"\n# New GWAS web search interface\n\nWe are excited to announce the full production release of the new GWAS Catalog web search interface. \n\nThe biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect! \n\n\n\nWe’ve also main some small changes to the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest: \n\n\n\nInterested to find studies with full genomewide summary statistics available for download? They now have their own tab, making it easier to see when summary statistics are available: \n\n\n\nInterested to find the study with the biggest sample size for your trait of interest? Just use the sort option on the Discovery sample number columnn, which now sorts by the total number of samples in the Discovery stage, irrespective of ancestry groups: \n\n\n\nHope you enjoy using the new features! Please report any bugs to gwas-info@ebi.ac.uk or via [GitHub](https://github.com/EBISPOT/gwas-ui/issues)\n\nLastly, watch this space for the next generation of the GWAS Diagram - we're working on it!\n","dir":"/blog","path":"/blog/gwas-ui-production-release-2023","extension":".md","createdAt":"2024-03-11T15:03:22.556Z","updatedAt":"2024-03-11T15:03:22.556Z","meta":{"version":0,"revision":0,"created":1710342672438},"$loki":1},{"slug":"background-trait-update","description":"Each study in the GWAS Catalog investigates the association between variants in the human genome and a particular trait or phenotypic characteristic. For each study, we annotate the trait in two ways, a reported trait that reflects the author’s description of the disease or phenotypic characteristic under investigation. This is a free text description and sometimes.","title":"Trait annotation in the GWAS Catalog","author":"Elliot Sollis","date":"August 3, 2021","img":"blog-1.jpg","toc":[{"id":"annotating-different-study-types","depth":2,"text":"Annotating different study types"},{"id":"a-single-trait-studies","depth":3,"text":"A. Single-trait studies"},{"id":"b-multi-trait-studies","depth":3,"text":"B. Multi-trait studies"},{"id":"c-studies-with-a-background-trait","depth":3,"text":"C. Studies with a background trait"},{"id":"room-for-improvement","depth":2,"text":"Room for improvement"},{"id":"changes-to-trait-annotation","depth":2,"text":"Changes to trait annotation"},{"id":"changes-to-the-website","depth":2,"text":"Changes to the website"},{"id":"data-tables","depth":3,"text":"Data tables"},{"id":"study-pages","depth":3,"text":"Study pages"},{"id":"trait-pages","depth":3,"text":"Trait pages"},{"id":"downloads","depth":3,"text":"Downloads"},{"id":"api","depth":3,"text":"API"},{"id":"expected-scope-of-the-changes","depth":2,"text":"Expected scope of the changes"},{"id":"questions-and-feedback","depth":2,"text":"Questions and feedback"}],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Each study in the GWAS Catalog investigates the association between variants in the human genome and a particular trait or phenotypic characteristic."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For each study, we annotate the trait in two ways:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"A reported trait that reflects the author’s description of the disease or phenotypic characteristic under investigation. This is a free text description and sometimes different studies might use a different wording to describe similar traits or to capture more nuanced distinctions. Reported traits can also include multiple component traits, depending on the study design."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"One or more trait terms from the Experimental Factor Ontology (EFO) that represent phenotypic characteristics in a more standardised way. These terms make studies on similar traits easier for users to find and compare. For multifaceted traits, each component is represented by a separate term."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/rep-trait-example.png","alt":"Reported traits mapped to EFO terms"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"annotating-different-study-types"},"children":[{"type":"element","tag":"a","props":{"href":"#annotating-different-study-types","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Annotating different study types"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"a-single-trait-studies"},"children":[{"type":"element","tag":"a","props":{"href":"#a-single-trait-studies","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"A. Single-trait studies"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The vast majority (>80%) of studies in the GWAS Catalog only analyse a single trait. We annotate these studies with a single EFO term."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Some common examples include:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Standard case-control studies comparing individuals with a disease or phenotypic characteristic, versus controls individuals without that trait."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-sg-trait-binary.png","alt":"A case-control study with a single binary trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Quantitative studies looking at a single measurement"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-sg-trait-quant.png","alt":"A quantitative study with a single continuous trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In these examples, any reported variants are clearly and straightforwardly associated with that single trait."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"b-multi-trait-studies"},"children":[{"type":"element","tag":"a","props":{"href":"#b-multi-trait-studies","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"B. Multi-trait studies"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In about 10% of studies, there are multiple traits of interest that are analysed simultaneously. We annotate these studies with multiple EFO terms separated by a comma, indicating that any significant variants reported in the study are associated with both traits in some way."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Some common examples include:"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Studies comparing individuals with two comorbid diseases (or other traits), versus control individuals who have neither disease. In the reported trait we write “Disease 1 "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"and"}]},{"type":"text","value":" Disease 2”. In the trait we list multiple EFO terms separated by a comma."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-multi-trait-and.png","alt":"A case-control study where cases have two co-occuring traits"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Studies comparing individuals with either of two diseases (or other traits), versus control individuals who have neither disease. Often these are two traits that are hypothesised to have some common underlying genetic factors (pleiotropy). In the reported trait we write “Disease 1 "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"or"}]},{"type":"text","value":" Disease 2”. In the trait we list multiple EFO terms separated by a comma."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-multi-trait-or.png","alt":"A case-control study where cases may have either of two distinct traits"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In these examples, any significant variants reported are associated with either or both of the annotated traits. If a user is searching for associations with either trait term, then these results will come up in their search."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"c-studies-with-a-background-trait"},"children":[{"type":"element","tag":"a","props":{"href":"#c-studies-with-a-background-trait","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"C. Studies with a background trait"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Finally, about 6% of studies analyse only one main trait of interest, but in the context of a background trait that is shared by all of the participants in the study."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Some examples include:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Studies comparing cases and controls of one disease, but only within a sample of people who have another disease. In the reported trait we write “Disease 1 "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" Disease 2”. In the past, we listed multiple EFO terms in the trait column, as for scenario B."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-bg-trait-binary.png","alt":"A case-control study for one binary trait within a second background trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Quantitative studies analysing a measurement in the context of a disease. In the reported trait we write “Measurement "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" Disease”. In the past, we listed multiple EFO terms in the trait column, as for scenario B."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/eg-bg-trait-quant.png","alt":"A quantitative study for one continuous trait within a second background trait"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In these examples, any reported variants are associated with the main trait, but not with the background trait."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"room-for-improvement"},"children":[{"type":"element","tag":"a","props":{"href":"#room-for-improvement","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Room for improvement"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In the past, we annotated background trait studies (scenario C) in the same way as we have for multi-trait studies (scenario B): with multiple EFO terms listed together in the trait field, separated by commas."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This had some benefits:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We were able to indicate that the study has something to do with the background trait - e.g. a particular association with allergic rhinitis may only hold true in asthmatics."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Users could search for associations with a trait, as well as associations that are found in the context of that trait as a background characteristic. Both kinds of associations might be relevant to users working in a particular field."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"However, there were some are also some disadvantages:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"It was not possible to tell that a particular EFO term was a background trait, without looking at the reported trait field. This complicated analysis particularly when accessing the data programmatically."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"When searching for a trait term (e.g. asthma), there was no easy way to distinguish which studies were direct associations with asthma, and which have asthma as the background trait, without reading the reported trait for each one."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The overall study and association numbers provided for some traits could be misleading since they included studies and associations where the trait of interest was a background trait."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"changes-to-trait-annotation"},"children":[{"type":"element","tag":"a","props":{"href":"#changes-to-trait-annotation","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Changes to trait annotation"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"To make our trait annotations more informative, we have added an additional background trait field to the GWAS Catalog database."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We have moved all EFO terms related to background traits to this new field, and removed them from the original trait field:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/annotation-change.png","alt":"We have moved EFO terms for background traits to a separate field"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"changes-to-the-website"},"children":[{"type":"element","tag":"a","props":{"href":"#changes-to-the-website","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Changes to the website"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We have also updated our web interface in order to display the restructured background trait information."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"data-tables"},"children":[{"type":"element","tag":"a","props":{"href":"#data-tables","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Data tables"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"New columns have been added to the Associations and Studies tables to clearly indicate the main and background traits."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Associations:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/new-assoc-table.png","alt":"Separate main and background traits displayed in Associations table"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Studies:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/new-study-table.png","alt":"Separate main and background traits displayed in Studies table"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The improved tables will be displayed on all Publication, Study, Trait, Variant, Gene and Region pages."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"study-pages"},"children":[{"type":"element","tag":"a","props":{"href":"#study-pages","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Study pages"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Each Study page also displays the main and background traits in separate fields in the Study Information panel.\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/study-info-panel.png","alt":"Main and background traits displayed in the Study Information panel"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"trait-pages"},"children":[{"type":"element","tag":"a","props":{"href":"#trait-pages","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Trait pages"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The Trait page has been updated so that background trait studies and associations are no longer shown by default. For example, the asthma page only shows associations with asthma itself, and not associations with others traits in asthma.\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/asthma-no-bg.png","alt":"Asthma Trait page showing associations with asthma only"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"Shows associations with:"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"asthma"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"del","props":{},"children":[{"type":"text","value":"[other traits] "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" asthma"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"However, users can select the box to include background trait data if they wish:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/asthma-with-bg.png","alt":"Asthma Trait page showing associations with asthma AND with other traits in a background of asthma."},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"Shows associations with:"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"asthma"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"[other traits] "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"in"}]},{"type":"text","value":" asthma"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Some trait terms may only be used in the GWAS Catalog to annotate background traits. These terms will continue to have their own Trait page, but no associations or studies will be displayed under the default view. For example, autoimmune pancreatitis type 1 (EFO_1000780) currently appears in the Catalog only as a background trait for one study (“Lachrymal/Salivary gland lesion in type 1 autoimmune pancreatitis”), so no associations with autoimmune pancreatitis type 1 are displayed by default:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/no-records.png","alt":"A Trait page with no main trait associations"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The association plot on each Trait page will now display only main trait associations by default, but background trait data can be added by selecting the box:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/assoc-plot-change.png","alt":"Association plot with with or without background trait data"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"downloads"},"children":[{"type":"element","tag":"a","props":{"href":"#downloads","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Downloads"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In "},{"type":"element","tag":"a","props":{"href":"http://www.ebi.ac.uk/gwas/docs/file-downloads","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the full Catalog downloads"}]},{"type":"text","value":", the MAPPED_TRAIT and MAPPED_TRAIT_URI columns will now only show EFO terms for main traits. Background trait terms have been removed from these columns."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In the newest version of the download (v1.0.3), new columns have been added for the background trait: MAPPED BACKGROUND TRAIT and MAPPED BACKGROUND TRAIT URI. This is not included in earlier versions of the download."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"api"},"children":[{"type":"element","tag":"a","props":{"href":"#api","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"API"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For users of the GWAS Catalog API, searching for “associationByEfoTrait” will now return only main trait associations."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"There is currently no background trait field available in the API, but we plan to add this feature in the future."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"expected-scope-of-the-changes"},"children":[{"type":"element","tag":"a","props":{"href":"#expected-scope-of-the-changes","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Expected scope of the changes"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We reviewed all of the studies in the Catalog and identified just over 1000 studies (about 6% of the total) with background traits that have now been moved to the new field. These studies contain around 10,000 associations (about 4% of all associations in the Catalog) which have also been reannotated."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The changes affect some trait terms more than others. Here are some of the terms that will see the greatest change in the number of annotated Associations:\n"},{"type":"element","tag":"article-image","props":{"src":"background-trait-update/most-affected-traits.png","alt":"Graph of selected trait terms affected by this change"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"questions-and-feedback"},"children":[{"type":"element","tag":"a","props":{"href":"#questions-and-feedback","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Questions and feedback"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"If you have any questions or comments about this change, please contact us as "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":"."}]}]},"text":"\nEach study in the GWAS Catalog investigates the association between variants in the human genome and a particular trait or phenotypic characteristic.\n\nFor each study, we annotate the trait in two ways:\n\n1. A reported trait that reflects the author’s description of the disease or phenotypic characteristic under investigation. This is a free text description and sometimes different studies might use a different wording to describe similar traits or to capture more nuanced distinctions. Reported traits can also include multiple component traits, depending on the study design.\n\n2. One or more trait terms from the Experimental Factor Ontology (EFO) that represent phenotypic characteristics in a more standardised way. These terms make studies on similar traits easier for users to find and compare. For multifaceted traits, each component is represented by a separate term.\n\n\n\n## Annotating different study types \n\n### A. Single-trait studies\n\nThe vast majority (>80%) of studies in the GWAS Catalog only analyse a single trait. We annotate these studies with a single EFO term.\n\nSome common examples include:\n\n1. Standard case-control studies comparing individuals with a disease or phenotypic characteristic, versus controls individuals without that trait.\n\n\n2. Quantitative studies looking at a single measurement\n\n\n\nIn these examples, any reported variants are clearly and straightforwardly associated with that single trait.\n\n### B. Multi-trait studies\n\nIn about 10% of studies, there are multiple traits of interest that are analysed simultaneously. We annotate these studies with multiple EFO terms separated by a comma, indicating that any significant variants reported in the study are associated with both traits in some way.\n\n**Some common examples include:**\n\n1. Studies comparing individuals with two comorbid diseases (or other traits), versus control individuals who have neither disease. In the reported trait we write “Disease 1 _and_ Disease 2”. In the trait we list multiple EFO terms separated by a comma.\n\n\n2. Studies comparing individuals with either of two diseases (or other traits), versus control individuals who have neither disease. Often these are two traits that are hypothesised to have some common underlying genetic factors (pleiotropy). In the reported trait we write “Disease 1 _or_ Disease 2”. In the trait we list multiple EFO terms separated by a comma.\n\n\nIn these examples, any significant variants reported are associated with either or both of the annotated traits. If a user is searching for associations with either trait term, then these results will come up in their search.\n\n### C. Studies with a background trait\n\nFinally, about 6% of studies analyse only one main trait of interest, but in the context of a background trait that is shared by all of the participants in the study.\n\nSome examples include:\n\n1. Studies comparing cases and controls of one disease, but only within a sample of people who have another disease. In the reported trait we write “Disease 1 _in_ Disease 2”. In the past, we listed multiple EFO terms in the trait column, as for scenario B.\n\n\n2. Quantitative studies analysing a measurement in the context of a disease. In the reported trait we write “Measurement _in_ Disease”. In the past, we listed multiple EFO terms in the trait column, as for scenario B.\n\n\nIn these examples, any reported variants are associated with the main trait, but not with the background trait.\n\n## Room for improvement\n\nIn the past, we annotated background trait studies (scenario C) in the same way as we have for multi-trait studies (scenario B): with multiple EFO terms listed together in the trait field, separated by commas.\n\nThis had some benefits:\n\n* We were able to indicate that the study has something to do with the background trait - e.g. a particular association with allergic rhinitis may only hold true in asthmatics.\n\n* Users could search for associations with a trait, as well as associations that are found in the context of that trait as a background characteristic. Both kinds of associations might be relevant to users working in a particular field.\n\nHowever, there were some are also some disadvantages:\n\n* It was not possible to tell that a particular EFO term was a background trait, without looking at the reported trait field. This complicated analysis particularly when accessing the data programmatically.\n\n* When searching for a trait term (e.g. asthma), there was no easy way to distinguish which studies were direct associations with asthma, and which have asthma as the background trait, without reading the reported trait for each one.\n\n* The overall study and association numbers provided for some traits could be misleading since they included studies and associations where the trait of interest was a background trait.\n\n## Changes to trait annotation\n\nTo make our trait annotations more informative, we have added an additional background trait field to the GWAS Catalog database.\n\nWe have moved all EFO terms related to background traits to this new field, and removed them from the original trait field:\n\n\n## Changes to the website\n\nWe have also updated our web interface in order to display the restructured background trait information.\n\n### Data tables\n\nNew columns have been added to the Associations and Studies tables to clearly indicate the main and background traits.\n\nAssociations:\n\n\nStudies:\n\n\nThe improved tables will be displayed on all Publication, Study, Trait, Variant, Gene and Region pages.\n\n### Study pages\n\nEach Study page also displays the main and background traits in separate fields in the Study Information panel.\n\n\n### Trait pages\n\nThe Trait page has been updated so that background trait studies and associations are no longer shown by default. For example, the asthma page only shows associations with asthma itself, and not associations with others traits in asthma.\n\n\n_Shows associations with:_\n* asthma\n* ~[other traits] _in_ asthma~\n\nHowever, users can select the box to include background trait data if they wish:\n\n\n_Shows associations with:_\n* asthma\n* [other traits] _in_ asthma\n\nSome trait terms may only be used in the GWAS Catalog to annotate background traits. These terms will continue to have their own Trait page, but no associations or studies will be displayed under the default view. For example, autoimmune pancreatitis type 1 (EFO_1000780) currently appears in the Catalog only as a background trait for one study (“Lachrymal/Salivary gland lesion in type 1 autoimmune pancreatitis”), so no associations with autoimmune pancreatitis type 1 are displayed by default:\n\n\nThe association plot on each Trait page will now display only main trait associations by default, but background trait data can be added by selecting the box:\n\n\n### Downloads\n\nIn [the full Catalog downloads](http://www.ebi.ac.uk/gwas/docs/file-downloads), the MAPPED_TRAIT and MAPPED_TRAIT_URI columns will now only show EFO terms for main traits. Background trait terms have been removed from these columns.\n\nIn the newest version of the download (v1.0.3), new columns have been added for the background trait: MAPPED BACKGROUND TRAIT and MAPPED BACKGROUND TRAIT URI. This is not included in earlier versions of the download.\n\n### API\n\nFor users of the GWAS Catalog API, searching for “associationByEfoTrait” will now return only main trait associations.\n\nThere is currently no background trait field available in the API, but we plan to add this feature in the future.\n\n## Expected scope of the changes\n\nWe reviewed all of the studies in the Catalog and identified just over 1000 studies (about 6% of the total) with background traits that have now been moved to the new field. These studies contain around 10,000 associations (about 4% of all associations in the Catalog) which have also been reannotated.\n\nThe changes affect some trait terms more than others. Here are some of the terms that will see the greatest change in the number of annotated Associations:\n\n\n## Questions and feedback\n\nIf you have any questions or comments about this change, please contact us as gwas-info@ebi.ac.uk.","dir":"/blog","path":"/blog/background-trait-update","extension":".md","createdAt":"2024-03-11T15:03:22.553Z","updatedAt":"2024-03-11T15:03:22.553Z","meta":{"version":0,"revision":0,"created":1710342672504},"$loki":2},{"slug":"everything-you-need-to-know-about-the-new-gwas-catalog","description":"Genetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation.","title":"Everything you need to know about the new GWAS Catalog","author":"Annalisa Buniello","date":"Jun 13, 2018","img":"blog-1.jpg","toc":[{"id":"new-and-expanded-gwas-catalog-to-improve-target-identification","depth":3,"text":"New and expanded GWAS Catalog to improve target identification"},{"id":"selecting-and-curating-non-genome-wide-array-studies","depth":3,"text":"Selecting and curating non-genome-wide array studies"},{"id":"modifying-the-gwas-catalog-user-interface","depth":3,"text":"Modifying the GWAS Catalog user interface"},{"id":"stay-tuned","depth":3,"text":"Stay tuned!"}],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Genetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The NHGRI-EBI GWAS Catalog, an awesome public resource of human genotype-phenotype associations from published GWAS analyses, is one of the data sources for manually curated genetic evidence for the Open Targets Platform. The GWAS Catalog data is widely used to prioritise new candidate loci, predict disease risk and understand disease mechanisms, with a number of users and applications that is always increasing."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/FINAL_USERS_withOT-2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"new-and-expanded-gwas-catalog-to-improve-target-identification"},"children":[{"type":"element","tag":"a","props":{"href":"#new-and-expanded-gwas-catalog-to-improve-target-identification","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"New and expanded GWAS Catalog to improve target identification"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"I am happy to announce an expansion of the GWAS Catalog scientific scope to host non genome-wide studies. During its first 10 years of life, in fact, the GWAS Catalog has only included array studies with genome-wide coverage, therefore curation of exome and targeted array studies were outside its scientific scope. These data are however valuable to Open Targets’ scientific goals of target identification and prioritisation, so much so they decided to invest in a new project aiming to expand the scope of the Catalog to include targeted arrays, such as MetaboChip, ImmunoChip, and Exome array. The new GWAS will now allow variants associated with immunologic, metabolic and oncologic phenotypes to be included in the Open Targets Platform for the expansion of target identification and selection in these traits."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"selecting-and-curating-non-genome-wide-array-studies"},"children":[{"type":"element","tag":"a","props":{"href":"#selecting-and-curating-non-genome-wide-array-studies","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Selecting and curating non-genome-wide array studies"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"As a first step, Open Targets selected almost 60 publications for inclusion in the GWAS Catalog. I then extracted a total of 120 new independent association studies and 823 SNP-trait associations from these publications. These are impressive numbers, and give you an idea of how many studies and SNP-trait associations (and potential targets) we were missing by not including targeted array studies in the GWAS Catalog!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The final step for me to do was to map all these traits, diseases and phenotypes to EFO terms, the ontology used by\n"},{"type":"element","tag":"a","props":{"href":"https://docs.targetvalidation.org/faq/describing-diseases","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Open Targets to describe its diseases"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"modifying-the-gwas-catalog-user-interface"},"children":[{"type":"element","tag":"a","props":{"href":"#modifying-the-gwas-catalog-user-interface","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Modifying the GWAS Catalog user interface"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS Catalog team have changed the way the user interface works to support searching, displaying, filtering and download of targeted and exome array studies. "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/search?query=celiac%20disease","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Try it out using \"celiac disease\""}]},{"type":"text","value":", for example. The interface will now give you:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Drop-down filter by genotyping technology to help you focus on your favourite array"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/filter1-5.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Small \"target\" icon (designed for us by the EMBL-EBI media team. Thanks for that!) in the study accession number column for all targeted array studies prioritised by Open Targets"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/target-1.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"New \"genotyping technology\" shown for study, e.g. genome-wide genotyping array, exome-wide genotyping array or targeted genotyping array with an optional field for additional array information, for example ImmunoChip or MetaboChip"}]}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"everything-you-need-to-know-about-the-new-gwas-catalog/gen-tech-1.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"stay-tuned"},"children":[{"type":"element","tag":"a","props":{"href":"#stay-tuned","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Stay tuned!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This is an important milestone for Open Targets and the GWAS Catalog, and a lot more is yet to come. The next step for the collaboration between GWAS and Open Targets will be to develop a comprehensive database of all available GWAS full summary statistics stored in a common format and harmonised across studies to enable searching, easy comparison and downstream analysis. This effort will generate more comprehensive new raw genetic data accessible by the entire research community through the GWAS Catalog, the Open Targets Platform and additional resources the Open Targets team are working on."}]}]},"text":"\nGenetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation.\n\nThe NHGRI-EBI GWAS Catalog, an awesome public resource of human genotype-phenotype associations from published GWAS analyses, is one of the data sources for manually curated genetic evidence for the Open Targets Platform. The GWAS Catalog data is widely used to prioritise new candidate loci, predict disease risk and understand disease mechanisms, with a number of users and applications that is always increasing.\n\n\n\n### New and expanded GWAS Catalog to improve target identification\n\nI am happy to announce an expansion of the GWAS Catalog scientific scope to host non genome-wide studies. During its first 10 years of life, in fact, the GWAS Catalog has only included array studies with genome-wide coverage, therefore curation of exome and targeted array studies were outside its scientific scope. These data are however valuable to Open Targets’ scientific goals of target identification and prioritisation, so much so they decided to invest in a new project aiming to expand the scope of the Catalog to include targeted arrays, such as MetaboChip, ImmunoChip, and Exome array. The new GWAS will now allow variants associated with immunologic, metabolic and oncologic phenotypes to be included in the Open Targets Platform for the expansion of target identification and selection in these traits.\n\n### Selecting and curating non-genome-wide array studies\n\nAs a first step, Open Targets selected almost 60 publications for inclusion in the GWAS Catalog. I then extracted a total of 120 new independent association studies and 823 SNP-trait associations from these publications. These are impressive numbers, and give you an idea of how many studies and SNP-trait associations (and potential targets) we were missing by not including targeted array studies in the GWAS Catalog!\n\nThe final step for me to do was to map all these traits, diseases and phenotypes to EFO terms, the ontology used by \n[Open Targets to describe its diseases](https://docs.targetvalidation.org/faq/describing-diseases)\n\n### Modifying the GWAS Catalog user interface\n\nThe GWAS Catalog team have changed the way the user interface works to support searching, displaying, filtering and download of targeted and exome array studies. [Try it out using \"celiac disease\"](https://www.ebi.ac.uk/gwas/search?query=celiac%20disease), for example. The interface will now give you:\n\n- **Drop-down filter by genotyping technology to help you focus on your favourite array**\n\n\n\n- **Small \"target\" icon (designed for us by the EMBL-EBI media team. Thanks for that!) in the study accession number column for all targeted array studies prioritised by Open Targets**\n\n\n\n- **New \"genotyping technology\" shown for study, e.g. genome-wide genotyping array, exome-wide genotyping array or targeted genotyping array with an optional field for additional array information, for example ImmunoChip or MetaboChip**\n\n\n\n### Stay tuned!\n\nThis is an important milestone for Open Targets and the GWAS Catalog, and a lot more is yet to come. The next step for the collaboration between GWAS and Open Targets will be to develop a comprehensive database of all available GWAS full summary statistics stored in a common format and harmonised across studies to enable searching, easy comparison and downstream analysis. This effort will generate more comprehensive new raw genetic data accessible by the entire research community through the GWAS Catalog, the Open Targets Platform and additional resources the Open Targets team are working on.\n","dir":"/blog","path":"/blog/everything-you-need-to-know-about-the-new-gwas-catalog","extension":".md","createdAt":"2024-03-11T15:03:22.554Z","updatedAt":"2024-03-11T15:03:22.554Z","meta":{"version":0,"revision":0,"created":1710342672516},"$loki":3},{"slug":"gwas-ui-labs-release","description":"We are pleased to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing at www.ebi.ac.uk/gwas/labs. Here is a guide to the main differences you will see.","title":"GWAS Catalog web search updates - help us with testing","author":"GWAS Catalog team","date":"5 May 2023","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"h1","props":{"id":"new-gwas-web-search-interface-ready-for-testing"},"children":[{"type":"element","tag":"a","props":{"href":"#new-gwas-web-search-interface-ready-for-testing","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"New GWAS web search interface ready for testing"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We are excited to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing on our "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/labs","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"labs site"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This is a guide to the main differences you will see."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-labs-release/SlideA.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We’ve also changed the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-labs-release/SlideB.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Studies with full summary statistics available for download are now easier to find in their own tab:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"gwas-ui-labs-release/SlideC.jpg","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Please try out the new site and let us know what you like, what you don’t like, if you find any bugs or if any of your favourite features are missing. You can send feedback via "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":" or report bugs directly via "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-ui/issues","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GitHub"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Lastly, note that links to the GWAS Diagram have been disabled in the labs site. We are working on the next generation of the Diagram and will share this with you soon!"}]}]},"text":"\n# New GWAS web search interface ready for testing\n\nWe are excited to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing on our [labs site](https://www.ebi.ac.uk/gwas/labs).\n\nThis is a guide to the main differences you will see.\n\nThe biggest change we have made is to increase the loading speed of pages, to account for recent increases in data volume and ensure we are robust for future growth. Check out a high-level query like “cancer” for maximum effect! \n\n\n\nWe’ve also changed the layout of the data pages, with tabs for the different data facets (i.e. associations, studies, visualisations), so you don’t need to scroll to find your data of interest: \n\n\n\nStudies with full summary statistics available for download are now easier to find in their own tab: \n\n\n\nPlease try out the new site and let us know what you like, what you don’t like, if you find any bugs or if any of your favourite features are missing. You can send feedback via gwas-info@ebi.ac.uk or report bugs directly via [GitHub](https://github.com/EBISPOT/gwas-ui/issues)\n\nLastly, note that links to the GWAS Diagram have been disabled in the labs site. We are working on the next generation of the Diagram and will share this with you soon!\n","dir":"/blog","path":"/blog/gwas-ui-labs-release","extension":".md","createdAt":"2024-03-11T15:03:22.555Z","updatedAt":"2024-03-11T15:03:22.555Z","meta":{"version":0,"revision":0,"created":1710342672526},"$loki":4},{"slug":"new-standard-for-gwas-summary-statistics","description":"Today we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, GWAS-SSF. The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a recent analysis of 327 summary statistics","title":"A new standard for GWAS summary statistics","author":"Laura Harris","date":"July 20, 2022","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Today we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v1","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS-SSF"}]},{"type":"text","value":". The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a "},{"type":"element","tag":"a","props":{"href":"https://academic.oup.com/bioinformatics/article/37/23/4593/6380562","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"recent analysis"}]},{"type":"text","value":" of 327 summary statistics files finding over 100 unique formats. We were about to launch the GWAS Catalog author submission system and needed to enforce a standard format as QC on submissions, and to ensure that submitted data could be passed through our harmonisation pipeline. So we quickly developed a "},{"type":"element","tag":"a","props":{"href":"https://pubmed.ncbi.nlm.nih.gov/30445434/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"minimal standard format"}]},{"type":"text","value":" based on the most commonly included fields, with variant ID/genomic location and p-value as mandatory fields, and standard headers for other fields such as beta and CI. This was enough for our goal of getting data into the GWAS Catalog. But we heard many consumers of the data complain that datasets with the minimum mandatory fields did not meet their requirements, for example to use the data for Mendelian randomisation or to generate polygenic scores. We needed to encourage our submitters to share their data more fully. A second issue is that while the GWAS Catalog curates metadata from the literature, there is no standard for GWAS metadata reporting, resulting in missing data that limits re-use of the summary statistics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We started on a process of consulting with the community that began with our workshop on summary statistics data and sharing in 2020. This was a wide ranging discussion over 2 days that you can read all about in our "},{"type":"element","tag":"a","props":{"href":"https://www.sciencedirect.com/science/article/pii/S2666979X21000045","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"workshop proceedings"}]},{"type":"text","value":". At the end of that process we had an outline of some mandatory fields for data and metadata, but refining these and ensuring they met all the major use cases would take a more focused discussion. Our working group on summary statistics data content and format, chaired by Ines Barroso, held 3 meetings over the course of 2021. We held a parallel working group to discuss diversity and privacy in data sharing (to be reported elsewhere) and incorporated their recommendations (for example on the sharing of allele frequency data) into our decision making process. Both the original workshop and the working group were designed to represent a wide range of stakeholders - from industry and academia, developers of other resources & tools, data generators and data consumers, research groups primarily performing wet-lab research and high-throughput bioinformatics users. Our discussions focused on the need to enforce key mandatory fields to maximise the reuse of the data, but also to recognise that setting the bar too high could discourage data sharing. Similarly, data need to be made accessible in a way that suits the high-throughput user who might be piping data in bulk into a computational pipeline, but also users without advanced bioinformatics skills who might be downloading a single file to investigate a locus of interest. We heard that preparing data for submission to databases can be a huge drain for under-resourced labs, so needs to be as straightforward and user-friendly as possible, and that interoperability between different databases (such as "},{"type":"element","tag":"a","props":{"href":"https://www.ncbi.nlm.nih.gov/gap/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"dbGaP"}]},{"type":"text","value":", "},{"type":"element","tag":"a","props":{"href":"http://www.ebi.ac.uk/gwas","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the GWAS Catalog"}]},{"type":"text","value":" and "},{"type":"element","tag":"a","props":{"href":"https://gwas.mrcieu.ac.uk/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"OpenGWAS"}]},{"type":"text","value":") is important to reduce the workload for data generators. You can read the outcome of our discussions in our "},{"type":"element","tag":"a","props":{"href":"https://biohackrxiv.org/7zxht","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"new manuscript"}]},{"type":"text","value":". Behind the scenes, we are making the necessary changes to the GWAS Catalog infrastructure to validate new submissions according to the new standard and where possible, make all our existing summary statistics available in the new format. We are working on tools (together with PLINK and others) to make data preparation and formatting as easy as possible. In the meantime, we welcome feedback from the wider community on the standard and will collate this at the end of August prior to release of the new infrastructure."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS-SSF is suitable for array-based GWAS and single variant seqGWAS. As data sharing has become more widely accepted, there is appetite from the genomics community to share full p value datasets from gene-based analyses of sequencing data and CNVs. Whilst we don’t have a standard format for these yet, by gathering data we hope to go through a similar process to work towards a standard format. Please share your data, and let us know your thoughts!"}]}]},"text":"\nToday we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, [GWAS-SSF](https://www.biorxiv.org/content/10.1101/2022.07.15.500230v1). The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a [recent analysis](https://academic.oup.com/bioinformatics/article/37/23/4593/6380562) of 327 summary statistics files finding over 100 unique formats. We were about to launch the GWAS Catalog author submission system and needed to enforce a standard format as QC on submissions, and to ensure that submitted data could be passed through our harmonisation pipeline. So we quickly developed a [minimal standard format](https://pubmed.ncbi.nlm.nih.gov/30445434/) based on the most commonly included fields, with variant ID/genomic location and p-value as mandatory fields, and standard headers for other fields such as beta and CI. This was enough for our goal of getting data into the GWAS Catalog. But we heard many consumers of the data complain that datasets with the minimum mandatory fields did not meet their requirements, for example to use the data for Mendelian randomisation or to generate polygenic scores. We needed to encourage our submitters to share their data more fully. A second issue is that while the GWAS Catalog curates metadata from the literature, there is no standard for GWAS metadata reporting, resulting in missing data that limits re-use of the summary statistics.\n\nWe started on a process of consulting with the community that began with our workshop on summary statistics data and sharing in 2020. This was a wide ranging discussion over 2 days that you can read all about in our [workshop proceedings](https://www.sciencedirect.com/science/article/pii/S2666979X21000045). At the end of that process we had an outline of some mandatory fields for data and metadata, but refining these and ensuring they met all the major use cases would take a more focused discussion. Our working group on summary statistics data content and format, chaired by Ines Barroso, held 3 meetings over the course of 2021. We held a parallel working group to discuss diversity and privacy in data sharing (to be reported elsewhere) and incorporated their recommendations (for example on the sharing of allele frequency data) into our decision making process. Both the original workshop and the working group were designed to represent a wide range of stakeholders - from industry and academia, developers of other resources & tools, data generators and data consumers, research groups primarily performing wet-lab research and high-throughput bioinformatics users. Our discussions focused on the need to enforce key mandatory fields to maximise the reuse of the data, but also to recognise that setting the bar too high could discourage data sharing. Similarly, data need to be made accessible in a way that suits the high-throughput user who might be piping data in bulk into a computational pipeline, but also users without advanced bioinformatics skills who might be downloading a single file to investigate a locus of interest. We heard that preparing data for submission to databases can be a huge drain for under-resourced labs, so needs to be as straightforward and user-friendly as possible, and that interoperability between different databases (such as [dbGaP](https://www.ncbi.nlm.nih.gov/gap/), [the GWAS Catalog](http://www.ebi.ac.uk/gwas) and [OpenGWAS](https://gwas.mrcieu.ac.uk/)) is important to reduce the workload for data generators. You can read the outcome of our discussions in our [new manuscript](https://biohackrxiv.org/7zxht). Behind the scenes, we are making the necessary changes to the GWAS Catalog infrastructure to validate new submissions according to the new standard and where possible, make all our existing summary statistics available in the new format. We are working on tools (together with PLINK and others) to make data preparation and formatting as easy as possible. In the meantime, we welcome feedback from the wider community on the standard and will collate this at the end of August prior to release of the new infrastructure.\n\nThe GWAS-SSF is suitable for array-based GWAS and single variant seqGWAS. As data sharing has become more widely accepted, there is appetite from the genomics community to share full p value datasets from gene-based analyses of sequencing data and CNVs. Whilst we don’t have a standard format for these yet, by gathering data we hope to go through a similar process to work towards a standard format. Please share your data, and let us know your thoughts!\n","dir":"/blog","path":"/blog/new-standard-for-gwas-summary-statistics","extension":".md","createdAt":"2024-03-11T15:03:22.556Z","updatedAt":"2024-03-11T15:03:22.557Z","meta":{"version":0,"revision":0,"created":1710342672542},"$loki":5},{"slug":"open-sharing-of-cancer-summary-statistics","description":"The NHGRI-EBI GWAS Catalog is a central resource for the Genome Wide Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations. For more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable)","title":"Sharing is caring, why we need more freely available cancer GWAS summary statistics","author":"Annalisa Buniello","date":"March 11, 2021","img":"blog-1.jpg","toc":[{"id":"the-gwas-catalog-summary-statistics-repository","depth":3,"text":"The GWAS Catalog summary statistics repository"},{"id":"why-are-few-open-access-cancer-gwas-summary-statistics-available","depth":3,"text":"Why are few open access cancer GWAS summary statistics available?"},{"id":"how-can-we-promote-sharing","depth":3,"text":"How can we promote sharing?"}],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"NHGRI-EBI GWAS Catalog"}]},{"type":"text","value":" is a central resource for the Genome Wides Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable), while serving as a starting point for investigations to identify causal variants, calculate disease risk, understand disease mechanisms and establish targets for novel therapies."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Here we aim to illuminate progress that has been made to incorporate GWAS summary statistics into the GWAS Catalog, and reach out to the cancer genetics community in particular to promote submission of summary statistics for GWAS studies in cancer cohorts."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"the-gwas-catalog-summary-statistics-repository"},"children":[{"type":"element","tag":"a","props":{"href":"#the-gwas-catalog-summary-statistics-repository","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"The GWAS Catalog summary statistics repository"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Three years ago, the GWAS Catalog and Open Targets started to work together to expand the Catalog’s scientific scope to include full p-value summary statistics (aggregate p-values and association data for every variant analysed in a genome-wide association study) in addition to the manually curated top associations."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Summary statistics provide more detailed GWAS results than a lead SNP table, which allows other scientists to better use the data for sophisticated downstream analysis, whilst maintaining patient privacy. For example, summary statistics are integrated into the Open Targets Genetics Portal, where they are used to narrow down candidate loci and prioritise new drug targets through PheWAS, colocalisation analysis, fine mapping and mendelian randomisation"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"[GWAS-catalog-statistics-1.png"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS Catalog is now one of the largest, most visited and most frequently updated resources of freely available GWAS summary statistics. The repository includes summary statistics from 3,623 independent analyses (from 447 publications), accounting for a total of more than 22,000 datasets from a wide variety of traits. The Catalog users can easily access and download summary statistics from the GWAS Catalog "},{"type":"element","tag":"a","props":{"href":"ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/"},"children":[{"type":"text","value":"FTP site"}]},{"type":"text","value":" or via a dedicated "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/summary-statistics/docs/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"summary statistics API"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The increasing trend to share these datasets is reflected in the availability of summary statistics in the GWAS Catalog over time. There was also a significant increase in data downloads of summary statistics in 2020 compared to the previous year."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-Fig-1a-V2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-Fig-1b-V2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"why-are-few-open-access-cancer-gwas-summary-statistics-available"},"children":[{"type":"element","tag":"a","props":{"href":"#why-are-few-open-access-cancer-gwas-summary-statistics-available","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Why are few open access cancer GWAS summary statistics available?"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This expansion of the GWAS Catalog summary statistics repository, while promising, required a considerable outreach effort from the Catalog data team and the main stakeholders, including Open Targets. Interestingly, the rate of summary statistics data sharing noticeably differs among different genetics cohorts and research groups, with the lowest submission rate in cancer genetics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-FIg-2a-new-data.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"For example 33% of epilepsy and 21% of diabetes summary statistics are publicly available, compared to only 7.5% for cancer. The trend slightly differs amongst different cancer groups, and we have observed a more positive trend for the few papers published in the last two years."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-2b-c-1.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In a recent "},{"type":"element","tag":"a","props":{"href":"https://twitter.com/GWASCatalog/status/1365359253175881742","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Twitter poll"}]},{"type":"text","value":", we asked the community to tell us what they thought was the most important reason for the low rate of cancer summary statistics submission."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The results suggest that the main barriers to the sharing of summary statistics are:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"div","props":{"className":["nuxt-content-highlight"]},"children":[{"type":"element","tag":"pre","props":{"className":["language-text","line-numbers"]},"children":[{"type":"element","tag":"code","props":{},"children":[{"type":"text","value":"1. Results are usually embargoed for use in future research,\n2. Data privacy issues (e.g. patient confidentiality agreement), and\n3. Lack of awareness/knowledge on the appropriate data repository.\n"}]}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We believe it is necessary for the genetics community to have a more comprehensive and transparent discussion about such barriers, starting from the privacy issues. As highlighted in the recently revised "},{"type":"element","tag":"a","props":{"href":"https://osp.od.nih.gov/2018/11/01/provide-access-gsr/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"NIH genomic data sharing (GDS) policy"}]},{"type":"text","value":", summary statistics do not include individual-level information and can empower researchers to determine which genomic variants potentially contribute to a disease"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"how-can-we-promote-sharing"},"children":[{"type":"element","tag":"a","props":{"href":"#how-can-we-promote-sharing","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"How can we promote sharing?"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"As a part of a wider plan to identify and remove barriers to data sharing among different research communities, we have launched a dedicated public engagement campaign to motivate and encourage the genetics community to make their summary statistics publicly available – and make the difference in the field. In particular, following up from a pressing request from our users and stakeholders, we hope to understand the reasons why so few summary statistics are available for cancer studies. Open sharing of cancer GWAS data will lead the way to new, improved therapies and shed some light on the molecular mechanisms involved in such complex disease."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"open-sharing-of-cancer-summary-statistics/GWAS-Catalog-quote.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We call on researchers, journals, funders and charities to support our cause and spread our message. The Nature Journals Group are already proactively supporting an open access policy for their published cancer GWAS data. We hope to get more journals on board, including the top cancer journals."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In an aim to facilitate data sharing and interoperability, the GWAS Catalog team has recently released a "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/deposition","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"web-based deposition interface"}]},{"type":"text","value":" to support scalable author submission of summary statistics and metadata from published and pre-published (submitted at the time of journal submission upon request from the reviewers) GWAS. The datasets are submitted in a standard format and harmonised against the latest genome build and forward strand. This facilitates downstream analysis and integration into resources like the Open Targets Genetics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Please submit your summary statistics to the GWAS Catalog."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"What do you think are the most important barriers to the open sharing of summary statistics in cancer studies? Please let us know your thoughts by completing "},{"type":"element","tag":"a","props":{"href":"https://docs.google.com/forms/d/e/1FAIpQLSfuAO-cZWdIm8aF0140QXsZOxVlw7gLKj1a__KZ3aF4CUNamQ/viewform?usp=sf_link","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"this short survey"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"You can also reach out to us via "},{"type":"element","tag":"a","props":{"href":"https://twitter.com/GWASCatalog","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Twitter (@GWASCatalog)"}]},{"type":"text","value":" or "},{"type":"element","tag":"a","props":{"href":"gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"email (gwas-info@ebi.ac.uk)"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Together, we can make the difference."}]}]},"text":"\nThe [NHGRI-EBI GWAS Catalog](https://www.ebi.ac.uk/gwas/) is a central resource for the Genome Wides Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations.\n\nFor more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable), while serving as a starting point for investigations to identify causal variants, calculate disease risk, understand disease mechanisms and establish targets for novel therapies.\n\nHere we aim to illuminate progress that has been made to incorporate GWAS summary statistics into the GWAS Catalog, and reach out to the cancer genetics community in particular to promote submission of summary statistics for GWAS studies in cancer cohorts.\n\n### The GWAS Catalog summary statistics repository\n\nThree years ago, the GWAS Catalog and Open Targets started to work together to expand the Catalog’s scientific scope to include full p-value summary statistics (aggregate p-values and association data for every variant analysed in a genome-wide association study) in addition to the manually curated top associations.\n\nSummary statistics provide more detailed GWAS results than a lead SNP table, which allows other scientists to better use the data for sophisticated downstream analysis, whilst maintaining patient privacy. For example, summary statistics are integrated into the Open Targets Genetics Portal, where they are used to narrow down candidate loci and prioritise new drug targets through PheWAS, colocalisation analysis, fine mapping and mendelian randomisation\n\n[GWAS-catalog-statistics-1.png\n\nThe GWAS Catalog is now one of the largest, most visited and most frequently updated resources of freely available GWAS summary statistics. The repository includes summary statistics from 3,623 independent analyses (from 447 publications), accounting for a total of more than 22,000 datasets from a wide variety of traits. The Catalog users can easily access and download summary statistics from the GWAS Catalog [FTP site](ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/) or via a dedicated [summary statistics API](https://www.ebi.ac.uk/gwas/summary-statistics/docs/).\n\nThe increasing trend to share these datasets is reflected in the availability of summary statistics in the GWAS Catalog over time. There was also a significant increase in data downloads of summary statistics in 2020 compared to the previous year.\n\n\n\n\n\n### Why are few open access cancer GWAS summary statistics available?\n\nThis expansion of the GWAS Catalog summary statistics repository, while promising, required a considerable outreach effort from the Catalog data team and the main stakeholders, including Open Targets. Interestingly, the rate of summary statistics data sharing noticeably differs among different genetics cohorts and research groups, with the lowest submission rate in cancer genetics.\n\n\n\nFor example 33% of epilepsy and 21% of diabetes summary statistics are publicly available, compared to only 7.5% for cancer. The trend slightly differs amongst different cancer groups, and we have observed a more positive trend for the few papers published in the last two years.\n\n\n\n\nIn a recent [Twitter poll](https://twitter.com/GWASCatalog/status/1365359253175881742), we asked the community to tell us what they thought was the most important reason for the low rate of cancer summary statistics submission.\n\nThe results suggest that the main barriers to the sharing of summary statistics are:\n\n 1. Results are usually embargoed for use in future research,\n 2. Data privacy issues (e.g. patient confidentiality agreement), and\n 3. Lack of awareness/knowledge on the appropriate data repository.\n\nWe believe it is necessary for the genetics community to have a more comprehensive and transparent discussion about such barriers, starting from the privacy issues. As highlighted in the recently revised [NIH genomic data sharing (GDS) policy](https://osp.od.nih.gov/2018/11/01/provide-access-gsr/), summary statistics do not include individual-level information and can empower researchers to determine which genomic variants potentially contribute to a disease\n\n### How can we promote sharing?\n\nAs a part of a wider plan to identify and remove barriers to data sharing among different research communities, we have launched a dedicated public engagement campaign to motivate and encourage the genetics community to make their summary statistics publicly available – and make the difference in the field. In particular, following up from a pressing request from our users and stakeholders, we hope to understand the reasons why so few summary statistics are available for cancer studies. Open sharing of cancer GWAS data will lead the way to new, improved therapies and shed some light on the molecular mechanisms involved in such complex disease.\n\n\n\nWe call on researchers, journals, funders and charities to support our cause and spread our message. The Nature Journals Group are already proactively supporting an open access policy for their published cancer GWAS data. We hope to get more journals on board, including the top cancer journals.\n\nIn an aim to facilitate data sharing and interoperability, the GWAS Catalog team has recently released a [web-based deposition interface](https://www.ebi.ac.uk/gwas/deposition) to support scalable author submission of summary statistics and metadata from published and pre-published (submitted at the time of journal submission upon request from the reviewers) GWAS. The datasets are submitted in a standard format and harmonised against the latest genome build and forward strand. This facilitates downstream analysis and integration into resources like the Open Targets Genetics.\n\nPlease submit your summary statistics to the GWAS Catalog.\n\nWhat do you think are the most important barriers to the open sharing of summary statistics in cancer studies? Please let us know your thoughts by completing [this short survey](https://docs.google.com/forms/d/e/1FAIpQLSfuAO-cZWdIm8aF0140QXsZOxVlw7gLKj1a__KZ3aF4CUNamQ/viewform?usp=sf_link).\n\nYou can also reach out to us via [Twitter (@GWASCatalog)](https://twitter.com/GWASCatalog) or [email (gwas-info@ebi.ac.uk)](gwas-info@ebi.ac.uk).\n\nTogether, we can make the difference.\n","dir":"/blog","path":"/blog/open-sharing-of-cancer-summary-statistics","extension":".md","createdAt":"2024-03-11T15:03:22.557Z","updatedAt":"2024-03-11T15:03:22.558Z","meta":{"version":0,"revision":0,"created":1710342672564},"$loki":6},{"slug":"cohorts-now-available","description":"We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics.","title":"Cohorts now available in the GWAS Catalog","author":"GWAS Catalog team","date":"Nov 14, 2023","img":"blog-1.img","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"There are two new studies "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/file-downloads","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"downloads"}]},{"type":"text","value":" named as follows:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"gwas-catalog-studies_ontology-annotated_v1.0.2.1.tsv"}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"gwas-catalog-download-studies-v1.0.3.1.tsv"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Versions 1.0.2 (gwas-catalog-studies_ontology-annotated.tsv) and 1.0.3 (gwas-catalog-download-studies-v1.0.3.tsv) of the studies downloads will be deprecated and cease to be generated on 1 December 2023."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The COHORT field describes the discovery stage (genome-wide) cohorts used in each study. Since we started piloting curation of cohort information in 2020, over 700 different discovery cohorts have been described in GWAS publications."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Cohort abbreviations from discovery stage GWAS either are extracted from the literature or supplied by sumitters, to match a predefined list which we share with the PGS Catalog. The initial list of common cohorts used in genetics studies that seeded these annotations is from Mills & Rahal, Communications Biology (2019), and has been expanded during our pilot phase. A full list of the abbreviations and corresponding full cohort names in use is available to download separately ("},{"type":"element","tag":"a","props":{"href":"https://ftp.ebi.ac.uk/pub/databases/spot/pgs/metadata/pgs_all_metadata_cohorts.csv","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"https://ftp.ebi.ac.uk/pub/databases/spot/pgs/metadata/pgs_all_metadata_cohorts.csv"}]},{"type":"text","value":"). We share this list with the PGS Catalog, so you may find cohorts listed there that are not currently associated with a GWAS Catalog study. It's important to remember we only extract cohorts when they have been clearly and unambiguously named by authors, so a cohort may have been used in other publications which don't have annotation.\nWhere a sample cohort in the literature was not already in the predefined list at the time of curation, or was ambiguously described, “other” will appear in our studies download file. Where a sample in the literature had no cohort reported, “NR” will appear in our studies download file. Consortium names are extracted if the component cohorts are not provided, or there are too many component cohorts to curate. Empty cohort fields appear for studies curated before the pilot work to extract this information began (~2020).\nWe also accept submissions of unpublished data and these are listed in separate download files. Cohorts appearing in the unpublished download files are yet to undergo in-house curation and therefore may not exactly match against the predefined list."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Cohort data annotations for published studies are also now available via the REST API, accessible via the /studies and /studies/{accessionId} endpoints. The field is called "},{"type":"element","tag":"em","props":{},"children":[{"type":"text","value":"cohort"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"As well as cohort data, the new studies downloads also contain clear annotation of which studies have full genomewide summary statistics available for download in the column FULL SUMMARY STATISTICS with the ftp location available in the adjacent column SUMMARY STATS LOCATION."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We hope you make great use of these data! Questions? Feedback? Contact "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]}]}]},"text":"We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics. \n\nThere are two new studies [downloads](https://www.ebi.ac.uk/gwas/docs/file-downloads) named as follows:\n- gwas-catalog-studies_ontology-annotated_v1.0.2.1.tsv\n- gwas-catalog-download-studies-v1.0.3.1.tsv\n\nVersions 1.0.2 (gwas-catalog-studies_ontology-annotated.tsv) and 1.0.3 (gwas-catalog-download-studies-v1.0.3.tsv) of the studies downloads will be deprecated and cease to be generated on 1 December 2023. \n\nThe COHORT field describes the discovery stage (genome-wide) cohorts used in each study. Since we started piloting curation of cohort information in 2020, over 700 different discovery cohorts have been described in GWAS publications. \n\nCohort abbreviations from discovery stage GWAS either are extracted from the literature or supplied by sumitters, to match a predefined list which we share with the PGS Catalog. The initial list of common cohorts used in genetics studies that seeded these annotations is from Mills & Rahal, Communications Biology (2019), and has been expanded during our pilot phase. A full list of the abbreviations and corresponding full cohort names in use is available to download separately (https://ftp.ebi.ac.uk/pub/databases/spot/pgs/metadata/pgs_all_metadata_cohorts.csv). We share this list with the PGS Catalog, so you may find cohorts listed there that are not currently associated with a GWAS Catalog study. It's important to remember we only extract cohorts when they have been clearly and unambiguously named by authors, so a cohort may have been used in other publications which don't have annotation. \nWhere a sample cohort in the literature was not already in the predefined list at the time of curation, or was ambiguously described, “other” will appear in our studies download file. Where a sample in the literature had no cohort reported, “NR” will appear in our studies download file. Consortium names are extracted if the component cohorts are not provided, or there are too many component cohorts to curate. Empty cohort fields appear for studies curated before the pilot work to extract this information began (~2020).\nWe also accept submissions of unpublished data and these are listed in separate download files. Cohorts appearing in the unpublished download files are yet to undergo in-house curation and therefore may not exactly match against the predefined list.\n\nCohort data annotations for published studies are also now available via the REST API, accessible via the /studies and /studies/{accessionId} endpoints. The field is called *cohort*. \n\nAs well as cohort data, the new studies downloads also contain clear annotation of which studies have full genomewide summary statistics available for download in the column FULL SUMMARY STATISTICS with the ftp location available in the adjacent column SUMMARY STATS LOCATION. \n\nWe hope you make great use of these data! Questions? Feedback? Contact gwas-info@ebi.ac.uk\n","dir":"/blog","path":"/blog/cohorts-now-available","extension":".md","createdAt":"2024-03-11T15:03:22.554Z","updatedAt":"2024-03-11T15:03:22.554Z","meta":{"version":0,"revision":0,"created":1710342672571},"$loki":7},{"slug":"gwas-ssf-release","description":"Next month the GWAS Catalog is switching to a NEW summary statistics format. Find out more about what this means for submitters and consumers of summary statistics","title":"New submissions standard - release date","author":"GWAS Catalog team","date":"22 March 2023","img":"blog-1.jpg","toc":[{"id":"the-new-summary-statistics-format-is-coming---what-you-need-to-know","depth":2,"text":"The new summary statistics format is coming - what you need to know"},{"id":"what-does-this-mean-for-you","depth":2,"text":"What does this mean for you?"},{"id":"frequently-asked-questions","depth":2,"text":"Frequently asked questions"},{"id":"for-submitters","depth":3,"text":"For submitters:"},{"id":"for-consumers","depth":3,"text":"For consumers:"}],"body":{"type":"root","children":[{"type":"element","tag":"h2","props":{"id":"the-new-summary-statistics-format-is-coming---what-you-need-to-know"},"children":[{"type":"element","tag":"a","props":{"href":"#the-new-summary-statistics-format-is-coming---what-you-need-to-know","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"The new summary statistics format is coming - what you need to know"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"From 12th April we are switching to our NEW summary statistics format, GWAS-SSF, an overview of which is described in our "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v2","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"manuscript"}]},{"type":"text","value":" and "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-summary-statistics-standard","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"github repository"}]},{"type":"text","value":". There will be a short period of submission system downtime while we switch over to the new pipelines."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"what-does-this-mean-for-you"},"children":[{"type":"element","tag":"a","props":{"href":"#what-does-this-mean-for-you","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"What does this mean for you?"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"If you are a SUBMITTER of summary statistics, you will need to submit additional mandatory information, including effect size, allele frequency, and genomic location for each variant, and columns must be presented in a specific order. You are also encouraged to provide additional structured data (e.g. reference allele, imputation info score) and metadata (e.g. ancestry method, sex of participants) which will increase the usability of your data to others, and hopefully get you "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.09.27.509657v2","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"more citations"}]},{"type":"text","value":"."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"If you are a CONSUMER of summary statistics, you will start to see changes to the way we make these available. All newly submitted summary statistics will include the additional fields described "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-summary-statistics-standard","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"here"}]},{"type":"text","value":", with metadata available in a text file (.yaml) stored in the same ftp location as the data file, as well as its existing availability via the GWAS Catalog website and downloads. Over the coming months, we will generate metadata files for all existing summary statistics."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"frequently-asked-questions"},"children":[{"type":"element","tag":"a","props":{"href":"#frequently-asked-questions","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Frequently asked questions"}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"for-submitters"},"children":[{"type":"element","tag":"a","props":{"href":"#for-submitters","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"For submitters:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I’ve already started a submission and prepared my files in the old format. What should I do?"}]},{"type":"text","value":"\nYou can submit files in the old format until April 11th. If you are unable to complete your submission before this deadline, please contact us via "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-subs@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-subs@ebi.ac.uk"}]},{"type":"text","value":" for assistance."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I submitted summary statistics in the old format a while back and they are under embargo or in pre-published state. Do I need to change anything?"}]},{"type":"text","value":"\nNo. Your data will be made available in the old format (upon journal publication, if embargoed), and the metadata file will be prepared from the information you submitted."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I want to start preparing my submission now, but won’t be ready to submit till after April 12th. How can I find instructions on how to prepare the new format?"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Detailed documentation on the new format is available as follows:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/gwas-ssf/summary-statistics-format","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Summary statistics data file preparation"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics-plus-metadata","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Submission instructions for summary statistics and sample metadata"}]}]},{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"Submission instructions for summary statistics only"}]},{"type":"text","value":" (for publications with metadata already available in the GWAS Catalog)."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"An example new-style metadata template is available to download "},{"type":"element","tag":"a","props":{"href":"http://ftp.ebi.ac.uk/pub/databases/gwas/sumstats_and_meta_submission_template_v1_11.xlsx","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"here"}]},{"type":"text","value":"\nA recorded "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/training/events/data-submission-genome-wide-association-studies-gwas-catalog/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"webinar"}]},{"type":"text","value":" is available, describing the submission process and the new format."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"Are there tools available to help me prepare data in GWAS-SSF format?"}]},{"type":"text","value":"\nYou can find some useful tools to format your files and validate them prior to upload "},{"type":"element","tag":"a","props":{"href":"https://github.com/EBISPOT/gwas-sumstats-tools","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"here"}]},{"type":"text","value":"\nPLINK 2.0 will shortly include an option to generate output in the accepted format, using the option --gwas-ssf."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"I made a submission in the old format but would like to update it to include the new fields. Can I do this?"}]},{"type":"text","value":"\nAt the moment our update process is manual and we don’t have capacity to update large numbers of files unless there is an error. We plan to improve this process in the future."}]},{"type":"text","value":"\n"},{"type":"element","tag":"h3","props":{"id":"for-consumers"},"children":[{"type":"element","tag":"a","props":{"href":"#for-consumers","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"For consumers:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"How can I tell if summary statistics are in the old or new format?"}]},{"type":"text","value":"\nThis is indicated in the metadata file. If a metadata file exists, and the field named “fileType” has the value “GWAS-SSF v1.0”, then the file is in the new GWAS-SSF format. Other values in this field indicate that it is not in the new GWAS-SSF format, and may be lacking mandatory fields. If no metadata file exists, the summary statistics are in the old format, or a non-standard format."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"How can I combine or compare new and old format summary statistics?"}]},{"type":"text","value":"\nData fields included in the old format (chromosome, base_pair_location, effect_allele, other_allele, effect_allele_frequency, odds_ratio, ci_lower, ci_upper, beta, standard_error) are the same in the new format. There are two exceptions: variant_id and p_value. In the old format, variant_id contained rsIDs, whereas in the new format rsID has a separate column rsid, and variant_ID contains a variant identifier in the format chr_bp_ref_alt. In the old format, p_value was a mandatory field and could only contain p-values between 0 and 1. In the new format, p-values may be either presented in the p_value field OR as negative log10 p-values, with the header neg_log_10_p_value, but always in column 7."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"strong","props":{},"children":[{"type":"text","value":"More questions?"}]},{"type":"text","value":" Get in touch: contact "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":" for general queries or "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-subs@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-subs@ebi.ac.uk"}]},{"type":"text","value":" for submission support"}]}]},"text":"\n## The new summary statistics format is coming - what you need to know\n\nFrom 12th April we are switching to our NEW summary statistics format, GWAS-SSF, an overview of which is described in our [manuscript](https://www.biorxiv.org/content/10.1101/2022.07.15.500230v2) and [github repository](https://github.com/EBISPOT/gwas-summary-statistics-standard). There will be a short period of submission system downtime while we switch over to the new pipelines.\n\n## What does this mean for you?\n\nIf you are a SUBMITTER of summary statistics, you will need to submit additional mandatory information, including effect size, allele frequency, and genomic location for each variant, and columns must be presented in a specific order. You are also encouraged to provide additional structured data (e.g. reference allele, imputation info score) and metadata (e.g. ancestry method, sex of participants) which will increase the usability of your data to others, and hopefully get you [more citations](https://www.biorxiv.org/content/10.1101/2022.09.27.509657v2).\n\nIf you are a CONSUMER of summary statistics, you will start to see changes to the way we make these available. All newly submitted summary statistics will include the additional fields described [here](https://github.com/EBISPOT/gwas-summary-statistics-standard), with metadata available in a text file (.yaml) stored in the same ftp location as the data file, as well as its existing availability via the GWAS Catalog website and downloads. Over the coming months, we will generate metadata files for all existing summary statistics.\n\n## Frequently asked questions\n\n### For submitters:\n\n**I’ve already started a submission and prepared my files in the old format. What should I do?**\nYou can submit files in the old format until April 11th. If you are unable to complete your submission before this deadline, please contact us via gwas-subs@ebi.ac.uk for assistance.\n\n**I submitted summary statistics in the old format a while back and they are under embargo or in pre-published state. Do I need to change anything?**\nNo. Your data will be made available in the old format (upon journal publication, if embargoed), and the metadata file will be prepared from the information you submitted.\n\n**I want to start preparing my submission now, but won’t be ready to submit till after April 12th. How can I find instructions on how to prepare the new format?**\n\nDetailed documentation on the new format is available as follows:\n- [Summary statistics data file preparation](https://www.ebi.ac.uk/gwas/docs/gwas-ssf/summary-statistics-format)\n- [Submission instructions for summary statistics and sample metadata](https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics-plus-metadata)\n- [Submission instructions for summary statistics only](https://www.ebi.ac.uk/gwas/docs/gwas-ssf/submission-summary-statistics) (for publications with metadata already available in the GWAS Catalog).\n\nAn example new-style metadata template is available to download [here](http://ftp.ebi.ac.uk/pub/databases/gwas/sumstats_and_meta_submission_template_v1_11.xlsx)\nA recorded [webinar](https://www.ebi.ac.uk/training/events/data-submission-genome-wide-association-studies-gwas-catalog/) is available, describing the submission process and the new format.\n\n**Are there tools available to help me prepare data in GWAS-SSF format?**\nYou can find some useful tools to format your files and validate them prior to upload [here]( https://github.com/EBISPOT/gwas-sumstats-tools)\nPLINK 2.0 will shortly include an option to generate output in the accepted format, using the option --gwas-ssf.\n\n**I made a submission in the old format but would like to update it to include the new fields. Can I do this?**\nAt the moment our update process is manual and we don’t have capacity to update large numbers of files unless there is an error. We plan to improve this process in the future.\n\n### For consumers:\n\n**How can I tell if summary statistics are in the old or new format?**\nThis is indicated in the metadata file. If a metadata file exists, and the field named “fileType” has the value “GWAS-SSF v1.0”, then the file is in the new GWAS-SSF format. Other values in this field indicate that it is not in the new GWAS-SSF format, and may be lacking mandatory fields. If no metadata file exists, the summary statistics are in the old format, or a non-standard format.\n\n**How can I combine or compare new and old format summary statistics?**\nData fields included in the old format (chromosome, base_pair_location, effect_allele, other_allele, effect_allele_frequency, odds_ratio, ci_lower, ci_upper, beta, standard_error) are the same in the new format. There are two exceptions: variant_id and p_value. In the old format, variant_id contained rsIDs, whereas in the new format rsID has a separate column rsid, and variant_ID contains a variant identifier in the format chr_bp_ref_alt. In the old format, p_value was a mandatory field and could only contain p-values between 0 and 1. In the new format, p-values may be either presented in the p_value field OR as negative log10 p-values, with the header neg_log_10_p_value, but always in column 7.\n\n\n**More questions?** Get in touch: contact [gwas-info@ebi.ac.uk](mailto:gwas-info@ebi.ac.uk) for general queries or [gwas-subs@ebi.ac.uk](mailto:gwas-subs@ebi.ac.uk) for submission support\n","dir":"/blog","path":"/blog/gwas-ssf-release","extension":".md","createdAt":"2024-03-11T15:03:22.554Z","updatedAt":"2024-03-11T15:03:22.555Z","meta":{"version":0,"revision":0,"created":1710342672588},"$loki":8},{"slug":"ten-years-of-the-gwas-catalog-past-present-and-future","description":"This year marks a special anniversary for the GWAS Catalog, as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers","title":"Ten years of the GWAS Catalog – Past, present and future","author":"Laura Harris (GWAS Catalog) and Astrid Gall (Ensembl Outreach)","date":"Jun 28, 2018","img":"blog-1.jpg","toc":[],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This year marks a special anniversary for the "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS Catalog"}]},{"type":"text","value":", as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers and clinicians without them spending a lot of time undertaking regular systematic reviews of the literature. This is where we can help you!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The project is a collaboration between "},{"type":"element","tag":"a","props":{"href":"https://www.genome.gov/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the National Human Genome Research Institute (NHGRI)"}]},{"type":"text","value":" in Bethesda, Maryland, USA, and the European Molecular Biology Laboratory – "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"European Bioinformatics Institute (EMBL-EBI)"}]},{"type":"text","value":" in Hinxton, UK, where the majority of our curators and all of our software developers are based"}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"ten-years-of-the-gwas-catalog-past-present-and-future/GWAS-catalog_EBI_group-768x576.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"At EMBL-EBI, we share the building with "},{"type":"element","tag":"a","props":{"href":"https://www.ensembl.org/index.html","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"the Ensembl team"}]},{"type":"text","value":" and work very closely with them – which is very handy for both teams!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Our curators extract genotype-phenotype associations from all published GWAS studies, along with study information to allow you to interpret the data accurately. We make the data structured, searchable and visualisable, annotate it and integrate it with other resources including Ensembl. Each association with a standard variant identifier (rsID) enters our automated Ensembl mapping pipeline which provides the genomic annotation, i.e. the location on the chromosome and cytogenetic band as well as the mapped genes. We use this pipeline to check for errors as part of our quality control and display the information in our interface."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Our colleagues at Ensembl benefit from the GWAS Catalog as they import variant and phenotype data from it. "},{"type":"element","tag":"a","props":{"href":"http://www.ensembl.org/info/genome/variation/species/sources_documentation.html","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"More than 74,000 human variants associated with phenotype data from the GWAS catalog were included in the latest Ensembl release (e92)"}]},{"type":"text","value":"; and the number is increasing continuously!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"[video]"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Over the last ten years, the GWAS Catalog has grown, from a single published GWAS on age-related macular degeneration, into the primary source of disease-related associations with genetic variants. Watch how the number of associations has increased over time in this video!"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Today, the GWAS Catalog contains over 3,400 publications and over 62,000 unique SNP-trait associations. More than 10,000 visitors from around the globe access the GWAS Catalog per month. People using the resource have a wide range of goals – they include researchers wanting to narrow down or prioritise candidate loci, scientists investigating disease mechanisms, clinicians aiming to predict disease risk, pharma industry professionals improving the drug discovery process and anyone wanting to get the latest statistics on disease knowledge or summary data from particular populations of individuals."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The GWAS landscape has evolved over these ten years, with new developments in study design and genotyping technologies. More and more publications on GWAS come out each month. Often a publication contains more than one GWAS – sometimes there are over a thousand in a single paper! The number of individuals studied in a GWAS is also increasing, and so is the number of variants assayed, with bigger arrays and better imputation methods."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The nature of the traits studied is evolving too. Researchers are still publishing GWAS on major common diseases such as type 2 diabetes and breast cancer. However, these studies are now being published alongside studies on ever more specific traits. For example, "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/search?query=schizophrenia","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"searching the GWAS Catalog for “schizophrenia”"}]},{"type":"text","value":" brings up more than 50 different traits related to this disorder. These include sub-phenotypes such as specific symptoms in schizophrenia, endophenotypes such as brain imaging measurements, multi-trait analyses such as “schizophrenia and bipolar disorder”, drug response measurements as well as analyses of the interaction between genetic associations and environmental factors."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"All of this means more data to curate. We are adapting our infrastructure constantly to improve curation efficiency so that we can keep up with the rate of data generation. At the same time, we need to maintain the accuracy of the resource and make sure it contains the most relevant and up-to-date research results."}]},{"type":"text","value":"\n"},{"type":"element","tag":"article-image","props":{"src":"ten-years-of-the-gwas-catalog-past-present-and-future/fig2.png","alt":"gwas-catalog"},"children":[{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Highlights over the last ten years include releasing a new "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS Catalog website"}]},{"type":"text","value":" in 2015, "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/docs/ontology","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"mapping curated trait descriptions to ontology terms"}]},{"type":"text","value":" to enable enriched ontology-driven search capabilities, improving the interactive "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/diagram","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"GWAS Catalog diagram"}]},{"type":"text","value":" and "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/ancestry","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"capturing ancestry more completely"}]},{"type":"text","value":". Accurate characterisation of ancestry is essential to interpret human genomics data. Recently, we have developed a new framework for describing detailed ancestry information and recommendations for reporting ancestry systematically [(Morales et al 2018 Genome Biology 19:2)].("},{"type":"element","tag":"a","props":{"href":"https://europepmc.org/abstract/MED/29448949","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"https://europepmc.org/abstract/MED/29448949"}]},{"type":"text","value":")"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"So what will the future hold for the GWAS Catalog? We have just started hosting "},{"type":"element","tag":"a","props":{"href":"https://www.ebi.ac.uk/gwas/downloads/summary-statistics","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"summary statistics for studies in the GWAS Catalog"}]},{"type":"text","value":" with support from the scientific community (the files contain the full set of p values for every SNP on the array used). In the future we plan to integrate these datasets with structured meta-data to make it even easier to access GWAS results. We also aim to expand our scope to include other types of association analyses, including targeted arrays and sequencing-based genotyping, which enable deeper interrogation of diseases of interest and inclusion of less common variants."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"With these developments come many challenges, including how best to represent the increasing complexity of study designs, trait architecture and statistical analyses. But with the help of the authors of publications in the GWAS Catalog, the scientific community using our resource and our colleagues at Ensembl, we are excited about what the next ten years of GWAS will hold!"}]}]},"text":"\nThis year marks a special anniversary for the [GWAS Catalog](https://www.ebi.ac.uk/gwas/), as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers and clinicians without them spending a lot of time undertaking regular systematic reviews of the literature. This is where we can help you!\n\nThe project is a collaboration between [the National Human Genome Research Institute (NHGRI)](https://www.genome.gov/) in Bethesda, Maryland, USA, and the European Molecular Biology Laboratory – [European Bioinformatics Institute (EMBL-EBI)](https://www.ebi.ac.uk/) in Hinxton, UK, where the majority of our curators and all of our software developers are based\n\n\n\n\nAt EMBL-EBI, we share the building with [the Ensembl team](https://www.ensembl.org/index.html) and work very closely with them – which is very handy for both teams!\n\n\nOur curators extract genotype-phenotype associations from all published GWAS studies, along with study information to allow you to interpret the data accurately. We make the data structured, searchable and visualisable, annotate it and integrate it with other resources including Ensembl. Each association with a standard variant identifier (rsID) enters our automated Ensembl mapping pipeline which provides the genomic annotation, i.e. the location on the chromosome and cytogenetic band as well as the mapped genes. We use this pipeline to check for errors as part of our quality control and display the information in our interface.\n\nOur colleagues at Ensembl benefit from the GWAS Catalog as they import variant and phenotype data from it. [More than 74,000 human variants associated with phenotype data from the GWAS catalog were included in the latest Ensembl release (e92)](http://www.ensembl.org/info/genome/variation/species/sources_documentation.html); and the number is increasing continuously!\n\n[video]\n\nOver the last ten years, the GWAS Catalog has grown, from a single published GWAS on age-related macular degeneration, into the primary source of disease-related associations with genetic variants. Watch how the number of associations has increased over time in this video!\n\n\nToday, the GWAS Catalog contains over 3,400 publications and over 62,000 unique SNP-trait associations. More than 10,000 visitors from around the globe access the GWAS Catalog per month. People using the resource have a wide range of goals – they include researchers wanting to narrow down or prioritise candidate loci, scientists investigating disease mechanisms, clinicians aiming to predict disease risk, pharma industry professionals improving the drug discovery process and anyone wanting to get the latest statistics on disease knowledge or summary data from particular populations of individuals.\n\nThe GWAS landscape has evolved over these ten years, with new developments in study design and genotyping technologies. More and more publications on GWAS come out each month. Often a publication contains more than one GWAS – sometimes there are over a thousand in a single paper! The number of individuals studied in a GWAS is also increasing, and so is the number of variants assayed, with bigger arrays and better imputation methods.\n\nThe nature of the traits studied is evolving too. Researchers are still publishing GWAS on major common diseases such as type 2 diabetes and breast cancer. However, these studies are now being published alongside studies on ever more specific traits. For example, [searching the GWAS Catalog for “schizophrenia”](https://www.ebi.ac.uk/gwas/search?query=schizophrenia) brings up more than 50 different traits related to this disorder. These include sub-phenotypes such as specific symptoms in schizophrenia, endophenotypes such as brain imaging measurements, multi-trait analyses such as “schizophrenia and bipolar disorder”, drug response measurements as well as analyses of the interaction between genetic associations and environmental factors.\n\nAll of this means more data to curate. We are adapting our infrastructure constantly to improve curation efficiency so that we can keep up with the rate of data generation. At the same time, we need to maintain the accuracy of the resource and make sure it contains the most relevant and up-to-date research results.\n\n\n\nHighlights over the last ten years include releasing a new [GWAS Catalog website](https://www.ebi.ac.uk/gwas/) in 2015, [mapping curated trait descriptions to ontology terms](https://www.ebi.ac.uk/gwas/docs/ontology) to enable enriched ontology-driven search capabilities, improving the interactive [GWAS Catalog diagram](https://www.ebi.ac.uk/gwas/diagram) and [capturing ancestry more completely](https://www.ebi.ac.uk/gwas/ancestry). Accurate characterisation of ancestry is essential to interpret human genomics data. Recently, we have developed a new framework for describing detailed ancestry information and recommendations for reporting ancestry systematically [(Morales et al 2018 Genome Biology 19:2)].(https://europepmc.org/abstract/MED/29448949)\n\nSo what will the future hold for the GWAS Catalog? We have just started hosting [summary statistics for studies in the GWAS Catalog](https://www.ebi.ac.uk/gwas/downloads/summary-statistics) with support from the scientific community (the files contain the full set of p values for every SNP on the array used). In the future we plan to integrate these datasets with structured meta-data to make it even easier to access GWAS results. We also aim to expand our scope to include other types of association analyses, including targeted arrays and sequencing-based genotyping, which enable deeper interrogation of diseases of interest and inclusion of less common variants.\n\nWith these developments come many challenges, including how best to represent the increasing complexity of study designs, trait architecture and statistical analyses. But with the help of the authors of publications in the GWAS Catalog, the scientific community using our resource and our colleagues at Ensembl, we are excited about what the next ten years of GWAS will hold!\n","dir":"/blog","path":"/blog/ten-years-of-the-gwas-catalog-past-present-and-future","extension":".md","createdAt":"2024-03-11T15:03:22.558Z","updatedAt":"2024-03-11T15:03:22.558Z","meta":{"version":0,"revision":0,"created":1710342672610},"$loki":9},{"slug":"streamlining-GWAS-Summary-Statistics-Data-Interpretation-with-Improved-YAML-Metadata","description":"Having clear and accessible metadata is essential for enhancing data interpretation and ensuring its reusability. In the case of Genome-Wide Association Studies (GWAS), having a standardized and easy-to-understand format for documenting study metadata is crucial. In the GWAS Catalog, metadata associated with full genome-wide summary statistics files is accessible via multiple routes - searchable in the main Catalog via the website and REST API,","title":"Streamlining GWAS Summary Statistics Data Interpretation with Improved YAML Metadata","author":"Yue Ji and Laura Harris","date":"Feburary 22, 2024","img":"meta_yaml.png","toc":[{"id":"questions-and-feedback","depth":2,"text":"Questions and feedback"}],"body":{"type":"root","children":[{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Having clear and accessible metadata is essential for enhancing data interpretation and ensuring its reusability. In the case of Genome-Wide Association Studies (GWAS), having a standardized and easy-to-understand format for documenting study metadata is crucial. In the GWAS Catalog, metadata associated with full genome-wide summary statistics files is accessible via multiple routes - searchable in the main Catalog via the website and REST API, and additionally via a text file in YAML format, contained in the same directory as the data file."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We have identified key fields that should be included in the metadata for a comprehensive understanding, interpretation, and subsequent reuse of GWAS studies. Mandatory fields document essential study details and optional fields cater to specific study designs (such as case/control counts in case-control studies), and provide supplementary analysis information. You can find detailed information about these fields in Table 1 and in our recent preprint (Hayhurst et al 2023 "},{"type":"element","tag":"a","props":{"href":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v4","rel":["nofollow","noopener","noreferrer"],"target":"_blank"},"children":[{"type":"text","value":"https://www.biorxiv.org/content/10.1101/2022.07.15.500230v4"}]},{"type":"text","value":")."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"In our latest release, we've introduced several updates to the GWAS metadata YAML file format. The YAML format, known it's user-friendliness, provides a combination of structured information and ease of use for humans and computers. The latest changes aim to enhance the human readability of the files with a clearer, more unambiguous, and user-friendly experience without compromising the file's compatibility with automated systems. Here’s an overview of the significant enhancements:"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Reordering and Grouping of Fields:"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"The fields within the YAML files have been reorganized and grouped by related topics, each marked with a group name starting with '#' (group names are listed in Table 1). This organization helps users to quickly locate specific fields by group name, such as '# Sample Information', making the file more navigable for humans. Importantly, this structural refinement does not affect the way machines parse and interpret the data."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":2},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Clear Indentation for Hierarchical Relationships:"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Indentation has been introduced to visually represent the relationships between different data fields. This visual aid improves the readability of the files, making it easier for users to discern the levels of data organization and relationships between various fields. In the meantime, this indentation does not impact the machine processing of the file."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":3},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Renaming 'data_last_modified' to 'date_metadata_last_modified':"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"To provide clearer context, the 'data_last_modified' field has been renamed to 'date_metadata_last_modified'. This change emphasizes that the timestamp refers to the last modification date of the metadata, not the summary statistics data. It aids users in tracking any changes or updates in the metadata over time."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ol","props":{"start":4},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"Addition of 'Ancestry Category' Field:"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"ul","props":{},"children":[{"type":"text","value":"\n"},{"type":"element","tag":"li","props":{},"children":[{"type":"text","value":"We have introduced a new 'ancestry category' field in addition to the existing 'ancestry' field. This new feature provides a broad overview of the ancestry category that best describes the sample, allowing for a more generalized view of the sample's ancestry. This new field also ensures consistency and facilitates comparative analysis across different studies, in line with the rest of the Catalog."}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"These updates reflect our commitment to improving the user experience while ensuring the YAML files maintain their structural integrity for computer readability. Our focus continues to be on making GWAS metadata more accessible, interpretable, and useful for researchers."}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Table 1. Metadata field definitions\n"},{"type":"element","tag":"article-image","props":{"src":"streamlining-GWAS-Summary-Statistics-Data-Interpretation-with-Improved-YAML-Metadata/meta_yaml.png","alt":"Metadata field definitions","style":"height: 100%; width: 100%"},"children":[]}]},{"type":"text","value":"\n"},{"type":"element","tag":"h2","props":{"id":"questions-and-feedback"},"children":[{"type":"element","tag":"a","props":{"href":"#questions-and-feedback","ariaHidden":"true","tabIndex":-1},"children":[{"type":"element","tag":"span","props":{"className":["icon","icon-link"]},"children":[]}]},{"type":"text","value":"Questions and feedback"}]},{"type":"text","value":"\n"},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Questions or comments about this change? Please contact us as "},{"type":"element","tag":"a","props":{"href":"mailto:gwas-info@ebi.ac.uk"},"children":[{"type":"text","value":"gwas-info@ebi.ac.uk"}]},{"type":"text","value":"."}]}]},"text":"\nHaving clear and accessible metadata is essential for enhancing data interpretation and ensuring its reusability. In the case of Genome-Wide Association Studies (GWAS), having a standardized and easy-to-understand format for documenting study metadata is crucial. In the GWAS Catalog, metadata associated with full genome-wide summary statistics files is accessible via multiple routes - searchable in the main Catalog via the website and REST API, and additionally via a text file in YAML format, contained in the same directory as the data file. \n\nWe have identified key fields that should be included in the metadata for a comprehensive understanding, interpretation, and subsequent reuse of GWAS studies. Mandatory fields document essential study details and optional fields cater to specific study designs (such as case/control counts in case-control studies), and provide supplementary analysis information. You can find detailed information about these fields in Table 1 and in our recent preprint (Hayhurst et al 2023 https://www.biorxiv.org/content/10.1101/2022.07.15.500230v4).\n\nIn our latest release, we've introduced several updates to the GWAS metadata YAML file format. The YAML format, known it's user-friendliness, provides a combination of structured information and ease of use for humans and computers. The latest changes aim to enhance the human readability of the files with a clearer, more unambiguous, and user-friendly experience without compromising the file's compatibility with automated systems. Here’s an overview of the significant enhancements:\n\n1. Reordering and Grouping of Fields:\n* The fields within the YAML files have been reorganized and grouped by related topics, each marked with a group name starting with '#' (group names are listed in Table 1). This organization helps users to quickly locate specific fields by group name, such as '# Sample Information', making the file more navigable for humans. Importantly, this structural refinement does not affect the way machines parse and interpret the data.\n\n2. Clear Indentation for Hierarchical Relationships:\n* Indentation has been introduced to visually represent the relationships between different data fields. This visual aid improves the readability of the files, making it easier for users to discern the levels of data organization and relationships between various fields. In the meantime, this indentation does not impact the machine processing of the file.\n\n3. Renaming 'data_last_modified' to 'date_metadata_last_modified':\n* To provide clearer context, the 'data_last_modified' field has been renamed to 'date_metadata_last_modified'. This change emphasizes that the timestamp refers to the last modification date of the metadata, not the summary statistics data. It aids users in tracking any changes or updates in the metadata over time.\n \n4. Addition of 'Ancestry Category' Field:\n* We have introduced a new 'ancestry category' field in addition to the existing 'ancestry' field. This new feature provides a broad overview of the ancestry category that best describes the sample, allowing for a more generalized view of the sample's ancestry. This new field also ensures consistency and facilitates comparative analysis across different studies, in line with the rest of the Catalog. \n\nThese updates reflect our commitment to improving the user experience while ensuring the YAML files maintain their structural integrity for computer readability. Our focus continues to be on making GWAS metadata more accessible, interpretable, and useful for researchers.\n\n\nTable 1. Metadata field definitions\n\n\n## Questions and feedback\n\nQuestions or comments about this change? Please contact us as 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z*Ysbo@@jaNJMyvj@f>dnW)*b6!y|2=Fq?LPf@$&NNDoF`IL3cRpi!Da@d9um$7ISr zVn}I*Ycv#WWO=bJ^A^-8=OPUhZh=GX-n)~nDJee4jP{caF!TQ7 zuUGoDJ3L`@48&jOlYhJOJ=3u>`OJFSQ5tU zvhAF)>h=WxbA@_iBr+RCyw`S+~NPa(GQoQKoza(`d2GV*X<-n^=7<4aR z1Oo|9JPrH!8AB?OgnjlWL)F?r)A@+%u||#&HX)mwGe>%`f;eqWmU}TC&=dv)EHWv~ zB<@@rpSHKtE*d5M`duy5O&_xA?uPL$FXj0UmdVMDtkzirJdEz4n04UrYZ+4R!%jz1 zUZaOMab`b7D$~ETxxoFf+lyy{n9}+s^2`-#bQb9gxn$IIzOZ9}6NHC@9ZIM!hKFKy zpXe&AZE1Hb&lQweBdE-TyAPZ=q$s^B;*XLe^ed$cPhcjbe#TzGx+F5ssqgfuKgDyk zsiK_OZ6x7U5>6r^ZvR)@Vq@@lmW-5?)Tyi}RtV;yGl)2?1_DE%it1>x6a|&X zzG!0mZ}XcR1U3h~ - gwas-blog + gwas-blog

Blog Home Search Submit Download Documentation About

GWAS Catalog Blog

@@ -131,6 +131,6 @@

Questions and feedback

If you have any questions or comments about this change, please contact us as gwas-info@ebi.ac.uk.

+
diff --git a/cohorts-now-available/index.html b/cohorts-now-available/index.html index 7968fcf..6cecb1c 100644 --- a/cohorts-now-available/index.html +++ b/cohorts-now-available/index.html @@ -1,7 +1,7 @@ - gwas-blog + gwas-blog
Blog Home Search Submit Download Documentation About

GWAS Catalog Blog

@@ -27,6 +27,6 @@

As well as cohort data, the new studies downloads also contain clear annotation of which studies have full genomewide summary statistics available for download in the column FULL SUMMARY STATISTICS with the ftp location available in the adjacent column SUMMARY STATS LOCATION.

We hope you make great use of these data! Questions? Feedback? Contact gwas-info@ebi.ac.uk

+
diff --git a/everything-you-need-to-know-about-the-new-gwas-catalog/index.html b/everything-you-need-to-know-about-the-new-gwas-catalog/index.html index a6c7dcb..30d371e 100644 --- a/everything-you-need-to-know-about-the-new-gwas-catalog/index.html +++ b/everything-you-need-to-know-about-the-new-gwas-catalog/index.html @@ -1,7 +1,7 @@ - gwas-blog + gwas-blog
Blog Home Search Submit Download Documentation About

GWAS Catalog Blog

@@ -38,6 +38,6 @@

Stay tuned!

This is an important milestone for Open Targets and the GWAS Catalog, and a lot more is yet to come. The next step for the collaboration between GWAS and Open Targets will be to develop a comprehensive database of all available GWAS full summary statistics stored in a common format and harmonised across studies to enable searching, easy comparison and downstream analysis. This effort will generate more comprehensive new raw genetic data accessible by the entire research community through the GWAS Catalog, the Open Targets Platform and additional resources the Open Targets team are working on.

+
diff --git a/gwas-ssf-release/index.html b/gwas-ssf-release/index.html index f214f65..e189eec 100644 --- a/gwas-ssf-release/index.html +++ b/gwas-ssf-release/index.html @@ -1,7 +1,7 @@ - gwas-blog + gwas-blog
+
diff --git a/gwas-ui-labs-release/index.html b/gwas-ui-labs-release/index.html index 6fb8686..31b71f0 100644 --- a/gwas-ui-labs-release/index.html +++ b/gwas-ui-labs-release/index.html @@ -1,7 +1,7 @@ - gwas-blog + gwas-blog
Blog Home Search Submit Download Documentation About

GWAS Catalog Blog

@@ -24,6 +24,6 @@

Please try out the new site and let us know what you like, what you don’t like, if you find any bugs or if any of your favourite features are missing. You can send feedback via gwas-info@ebi.ac.uk or report bugs directly via GitHub

Lastly, note that links to the GWAS Diagram have been disabled in the labs site. We are working on the next generation of the Diagram and will share this with you soon!

+
diff --git a/gwas-ui-production-release-2023/index.html b/gwas-ui-production-release-2023/index.html index fdc20b8..37c8b96 100644 --- a/gwas-ui-production-release-2023/index.html +++ b/gwas-ui-production-release-2023/index.html @@ -1,7 +1,7 @@ - gwas-blog + gwas-blog
Blog Home Search Submit Download Documentation About

GWAS Catalog Blog

@@ -25,6 +25,6 @@

Hope you enjoy using the new features! Please report any bugs to gwas-info@ebi.ac.uk or via GitHub

Lastly, watch this space for the next generation of the GWAS Diagram - we're working on it!

+
diff --git a/index.html b/index.html index 2d6c62a..bf462e2 100644 --- a/index.html +++ b/index.html @@ -1,7 +1,7 @@ - gwas-blog + gwas-blog
Blog Home Search Submit Download Documentation About

GWAS Catalog Blog

@@ -11,18 +11,18 @@ Download

Search For:

Search All

Publications

Genetic Variants

Genomic Regions

Disease EFO Traits

Mapped Genes

Blog Categories -

Release announcements

New data and web features

Other news

Training

Uncategorised

Jobs @GWAS

Ten years of the GWAS Catalog – Past, present and future - Jun 28, 2018 By Laura Harris (GWAS Catalog) and Astrid Gall (Ensembl Outreach)

This year marks a special anniversary for the GWAS Catalog, as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers ...

Streamlining GWAS Summary Statistics Data Interpretation with Improved YAML Metadata - Feburary 22, 2024 By Yue Ji and Laura Harris

Having clear and accessible metadata is essential for enhancing data interpretation and ensuring its reusability. In the case of Genome-Wide Association Studies (GWAS), having a standardized and easy-to-understand format for documenting study metadata is crucial. In the GWAS Catalog, metadata associated with full genome-wide summary statistics files is accessible via multiple routes - searchable in the main Catalog via the website and REST API, ...

Sharing is caring, why we need more freely available cancer GWAS summary statistics - March 11, 2021 By Annalisa Buniello

The NHGRI-EBI GWAS Catalog is a central resource for the Genome Wide Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations. For more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable) ...

GWAS Catalog web search - new release! - 12 July 2023 By GWAS Catalog team

We are pleased to announce the release of a new version of the GWAS Catalog web search interface, which is now in production. Here is a guide to the main differences you will see. ...

A new standard for GWAS summary statistics - July 20, 2022 By Laura Harris

Today we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, GWAS-SSF. The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a recent analysis of 327 summary statistics ...

Streamlining GWAS Summary Statistics Data Interpretation with Improved YAML Metadata + Feburary 22, 2024 By Yue Ji and Laura Harris

Having clear and accessible metadata is essential for enhancing data interpretation and ensuring its reusability. In the case of Genome-Wide Association Studies (GWAS), having a standardized and easy-to-understand format for documenting study metadata is crucial. In the GWAS Catalog, metadata associated with full genome-wide summary statistics files is accessible via multiple routes - searchable in the main Catalog via the website and REST API, ...

Ten years of the GWAS Catalog – Past, present and future + Jun 28, 2018 By Laura Harris (GWAS Catalog) and Astrid Gall (Ensembl Outreach)

This year marks a special anniversary for the GWAS Catalog, as we have reached ten years since our launch in 2008. The GWAS Catalog is a widely used publicly available resource of all published human genome wide association studies (GWAS) and association results. Each GWAS study contains a wealth of information which is effectively inaccessible to researchers ...

Sharing is caring, why we need more freely available cancer GWAS summary statistics + March 11, 2021 By Annalisa Buniello

The NHGRI-EBI GWAS Catalog is a central resource for the Genome Wide Association Study (GWAS) community, demonstrating the benefit of expert data curation and integration of full p-value GWAS summary statistics into a central repository for variant-trait associations. For more than 10 years, the Catalog has aimed to make GWAS data FAIR (Findable, Accessible, Interoperable and Re-usable) ...

A new standard for GWAS summary statistics + July 20, 2022 By Laura Harris

Today we are very pleased to share our manuscript describing a new standard format for GWAS summary statistics, GWAS-SSF. The work towards this goal began in 2017 when we began to host summary statistics in the GWAS Catalog. It rapidly became apparent that summary statistics were formatted in a whole host of different ways, with only a few features in common between them, and little has changed since, with a recent analysis of 327 summary statistics ...

GWAS Catalog web search - new release! + 12 July 2023 By GWAS Catalog team

We are pleased to announce the release of a new version of the GWAS Catalog web search interface, which is now in production. Here is a guide to the main differences you will see. ...

GWAS Catalog web search updates - help us with testing 5 May 2023 By GWAS Catalog team

We are pleased to announce the release of a new version of the GWAS Catalog web search interface, which is available for testing at www.ebi.ac.uk/gwas/labs. Here is a guide to the main differences you will see. ...

New submissions standard - release date 22 March 2023 By GWAS Catalog team

Next month the GWAS Catalog is switching to a NEW summary statistics format. Find out more about what this means for submitters and consumers of summary statistics ...

Cohorts now available in the GWAS Catalog Nov 14, 2023 By GWAS Catalog team

We are pleased to announce our latest data release includes a new version of the Catalog studies downloads containing additional fields describing the discovery stage (genome-wide) cohorts used in each study (see below for more details), and clearer information on the availability of full genomewide summary statistics. ...

Everything you need to know about the new GWAS Catalog Jun 13, 2018 By Annalisa Buniello

Genetic evidence could double the rate of success in drug targets identification - so the scientific community predicts. With this in mind, it is crucial for Open Targets to be able to feed as many SNP-trait associations as possible into its pipeline for systematic target identification and validation. ...

Trait annotation in the GWAS Catalog August 3, 2021 By Elliot Sollis

Each study in the GWAS Catalog investigates the association between variants in the human genome and a particular trait or phenotypic characteristic. For each study, we annotate the trait in two ways, a reported trait that reflects the author’s description of the disease or phenotypic characteristic under investigation. This is a free text description and sometimes. ...

+
diff --git a/new-standard-for-gwas-summary-statistics/index.html b/new-standard-for-gwas-summary-statistics/index.html index f5f0baf..5397fc8 100644 --- a/new-standard-for-gwas-summary-statistics/index.html +++ b/new-standard-for-gwas-summary-statistics/index.html @@ -1,7 +1,7 @@ - gwas-blog + gwas-blog
Blog Home Search Submit Download Documentation About

GWAS Catalog Blog

@@ -16,6 +16,6 @@

We started on a process of consulting with the community that began with our workshop on summary statistics data and sharing in 2020. This was a wide ranging discussion over 2 days that you can read all about in our workshop proceedings. At the end of that process we had an outline of some mandatory fields for data and metadata, but refining these and ensuring they met all the major use cases would take a more focused discussion. Our working group on summary statistics data content and format, chaired by Ines Barroso, held 3 meetings over the course of 2021. We held a parallel working group to discuss diversity and privacy in data sharing (to be reported elsewhere) and incorporated their recommendations (for example on the sharing of allele frequency data) into our decision making process. Both the original workshop and the working group were designed to represent a wide range of stakeholders - from industry and academia, developers of other resources & tools, data generators and data consumers, research groups primarily performing wet-lab research and high-throughput bioinformatics users. Our discussions focused on the need to enforce key mandatory fields to maximise the reuse of the data, but also to recognise that setting the bar too high could discourage data sharing. Similarly, data need to be made accessible in a way that suits the high-throughput user who might be piping data in bulk into a computational pipeline, but also users without advanced bioinformatics skills who might be downloading a single file to investigate a locus of interest. We heard that preparing data for submission to databases can be a huge drain for under-resourced labs, so needs to be as straightforward and user-friendly as possible, and that interoperability between different databases (such as dbGaP, the GWAS Catalog and OpenGWAS) is important to reduce the workload for data generators. You can read the outcome of our discussions in our new manuscript. Behind the scenes, we are making the necessary changes to the GWAS Catalog infrastructure to validate new submissions according to the new standard and where possible, make all our existing summary statistics available in the new format. We are working on tools (together with PLINK and others) to make data preparation and formatting as easy as possible. In the meantime, we welcome feedback from the wider community on the standard and will collate this at the end of August prior to release of the new infrastructure.

The GWAS-SSF is suitable for array-based GWAS and single variant seqGWAS. As data sharing has become more widely accepted, there is appetite from the genomics community to share full p value datasets from gene-based analyses of sequencing data and CNVs. Whilst we don’t have a standard format for these yet, by gathering data we hope to go through a similar process to work towards a standard format. Please share your data, and let us know your thoughts!

+
diff --git a/open-sharing-of-cancer-summary-statistics/index.html b/open-sharing-of-cancer-summary-statistics/index.html index 511f138..df65854 100644 --- a/open-sharing-of-cancer-summary-statistics/index.html +++ b/open-sharing-of-cancer-summary-statistics/index.html @@ -1,7 +1,7 @@ - gwas-blog + gwas-blog
+
diff --git a/streamlining-GWAS-Summary-Statistics-Data-Interpretation-with-Improved-YAML-Metadata/index.html b/streamlining-GWAS-Summary-Statistics-Data-Interpretation-with-Improved-YAML-Metadata/index.html index 8e392fe..b76f77f 100644 --- a/streamlining-GWAS-Summary-Statistics-Data-Interpretation-with-Improved-YAML-Metadata/index.html +++ b/streamlining-GWAS-Summary-Statistics-Data-Interpretation-with-Improved-YAML-Metadata/index.html @@ -1,7 +1,7 @@ - gwas-blog + gwas-blog
Blog Home Search Submit Download Documentation About

GWAS Catalog Blog

@@ -40,267 +40,11 @@

  • We have introduced a new 'ancestry category' field in addition to the existing 'ancestry' field. This new feature provides a broad overview of the ancestry category that best describes the sample, allowing for a more generalized view of the sample's ancestry. This new field also ensures consistency and facilitates comparative analysis across different studies, in line with the rest of the Catalog.

    • These updates reflect our commitment to improving the user experience while ensuring the YAML files maintain their structural integrity for computer readability. Our focus continues to be on making GWAS metadata more accessible, interpretable, and useful for researchers.

    -

    Table 1. Metadata field definitions

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    FieldDescriptionData type and valuesMandatoryExample
    # Study meta-data
    gwas_idGWAS Catalog accession IDText stringYesGCST90244057
    author_notesAdditional information about this study from the authorText stringNoFile contains GWAS summary statistics from a meta-analysis of NMR metabolic traits in up to 33 cohorts.
    gwas_catalog_apiGWAS catalog REST API linkText stringYeshttps://www.ebi.ac.uk/gwas/rest/api/studies/GCST90244057
    date_metadata_last_modifiedThe latest date that metadata YAML file was modifieddateYes2023-11-28
    # Trait Information
    trait_descriptionAuthor reported trait descriptionText string (multiple possible)YesBody mass index
    ontology_mappingShort form ontology terms describing the traitText string (multiple possible)NoEFO_0004918
    # Genotyping Information
    genome_assemblyGenome assembly for the summary statistics.GRCh/NCBI/UCSC valueYesGRCh37
    coordinate_systemCoordinate system used for the summary statisticsText String (1-based or 0-based)No1-based
    genotyping_technologyMethod(s) used to genotype variants in the discovery stage.Text string (multiple possible)YesGenome-wide genotyping array
    imputation_panelPanel used for imputationText stringNoHRC + UK10K
    imputation_softwareSoftware used for imputationText stringNoSHAPEIT3 + IMPUTE4
    # Sample Information
    sample_ancestry_categoryBroad ancestry category that best describes the sample.Text stringYesEuropean
    sample_ancestryThe most detailed ancestry descriptor(s) for the sample.Text string (multiple possible)Yes- Finnish
    - British
    sample_sizeSample sizeIntegerYes27006
    ancestry_methodMethod used to determine sample ancestry e.g. self-reported/genetically determinedText string (multiple possible)Noself-reported
    case_control_studyFlag whether the study is a case-control studyBooleanNo (default is false)true
    case_countNumber of cases for case/control studyIntegerNo, unless caseControlStudy is true27006
    control_countNumber of controls for case/control studyIntegerNo, unless caseControlStudy is true27006
    sexTo indicate a sex-stratified analysisM (for male), F (for female), combined or NR if unknownNocombined
    # Summary Statistic information
    data_file_nameThe name of the summary statistics fileText stringYesGCST90244057_buildGRCh37.tsv
    file_typeThe format of the summary statistics file"GWAS-SSF v1.0", "pre-GWAS-SSF", "non-GWAS-SSF"YesGWAS-SSF v1.0
    data_file_md5sumThe md5 checksum of the summary statistics file.Text stringYes0ec56396f89edcc21a3d5a25a6fa993d
    analysis_softwareSoftware and version used for the association analysisText string (multiple possible)Yes if p-values of 0 givenREGENIE
    adjusted_covariatesAny covariates the GWAS is adjusted forText string (multiple possible)Nosex
    minor_allele_freq_lower_limitLowest possible effect allele frequencyNumericNo0.0003
    # Harmonization status
    is_harmonisedDescription of harmonisation codesText stringOnly given in harmonised datasetsfalse
    is_sortedFlag whether the file is sorted by genomic locationBooleanYesfalse
    harmonisation_referenceThe genome reference file used for harmonising the summary statistics fileText stringNoftp://ftp.ensembl.org/pub/release-104/fasta/homo_sapiens/dna/
    ## Questions and feedback
    +

    Table 1. Metadata field definitions +Metadata field definitions

    +

    Questions and feedback

    Questions or comments about this change? Please contact us as gwas-info@ebi.ac.uk.

    +
    diff --git a/ten-years-of-the-gwas-catalog-past-present-and-future/index.html b/ten-years-of-the-gwas-catalog-past-present-and-future/index.html index 11bdd9e..1cd7f81 100644 --- a/ten-years-of-the-gwas-catalog-past-present-and-future/index.html +++ b/ten-years-of-the-gwas-catalog-past-present-and-future/index.html @@ -1,7 +1,7 @@ - gwas-blog + gwas-blog
    Blog Home Search Submit Download Documentation About

    GWAS Catalog Blog

    @@ -29,6 +29,6 @@

    So what will the future hold for the GWAS Catalog? We have just started hosting summary statistics for studies in the GWAS Catalog with support from the scientific community (the files contain the full set of p values for every SNP on the array used). In the future we plan to integrate these datasets with structured meta-data to make it even easier to access GWAS results. We also aim to expand our scope to include other types of association analyses, including targeted arrays and sequencing-based genotyping, which enable deeper interrogation of diseases of interest and inclusion of less common variants.

    With these developments come many challenges, including how best to represent the increasing complexity of study designs, trait architecture and statistical analyses. But with the help of the authors of publications in the GWAS Catalog, the scientific community using our resource and our colleagues at Ensembl, we are excited about what the next ten years of GWAS will hold!

    +

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