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<html>
<body>
<head>
<link rel="stylesheet" href="plink.css" type="text/css">
<META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=utf-8">
<title>PLINK: Whole genome data analysis toolset</title>
</head>
<!--<html>-->
<!--<title>PLINK</title>-->
<!--<body>-->
<font size="6" color="darkgreen"><b>plink...</b></font>
<div style="position:absolute;right:10px;top:10px;font-size:
75%"><em>Last original <tt>PLINK</tt> release is <b>v1.07</b>
(10-Oct-2009); <b>PLINK 1.9</b> is now <a href="plink2.shtml"> available</a> for beta-testing</em></div>
<h1>Whole genome association analysis toolset</h1>
<font size="1" color="darkgreen">
<em>
<a href="index.shtml">Introduction</a> |
<a href="contact.shtml">Basics</a> |
<a href="download.shtml">Download</a> |
<a href="reference.shtml">Reference</a> |
<a href="data.shtml">Formats</a> |
<a href="dataman.shtml">Data management</a> |
<a href="summary.shtml">Summary stats</a> |
<a href="thresh.shtml">Filters</a> |
<a href="strat.shtml">Stratification</a> |
<a href="ibdibs.shtml">IBS/IBD</a> |
<a href="anal.shtml">Association</a> |
<a href="fanal.shtml">Family-based</a> |
<a href="perm.shtml">Permutation</a> |
<a href="ld.shtml">LD calcualtions</a> |
<a href="haplo.shtml">Haplotypes</a> |
<a href="whap.shtml">Conditional tests</a> |
<a href="proxy.shtml">Proxy association</a> |
<a href="pimputation.shtml">Imputation</a> |
<a href="dosage.shtml">Dosage data</a> |
<a href="metaanal.shtml">Meta-analysis</a> |
<a href="annot.shtml">Result annotation</a> |
<a href="clump.shtml">Clumping</a> |
<a href="grep.shtml">Gene Report</a> |
<a href="epi.shtml">Epistasis</a> |
<a href="cnv.shtml">Rare CNVs</a> |
<a href="gvar.shtml">Common CNPs</a> |
<a href="rfunc.shtml">R-plugins</a> |
<a href="psnp.shtml">SNP annotation</a> |
<a href="simulate.shtml">Simulation</a> |
<a href="profile.shtml">Profiles</a> |
<a href="ids.shtml">ID helper</a> |
<a href="res.shtml">Resources</a> |
<a href="flow.shtml">Flow chart</a> |
<a href="misc.shtml">Misc.</a> |
<a href="faq.shtml">FAQ</a> |
<a href="gplink.shtml">gPLINK</a>
</em></font>
</p>
<table border=0>
<tr>
<td bgcolor="lightblue" valign="top" width=20%>
<font size="1">
<a href="index.shtml">1. Introduction</a> </p>
<a href="contact.shtml">2. Basic information</a> </p>
<ul>
<li> <a href="contact.shtml#cite">Citing PLINK</a>
<li> <a href="contact.shtml#probs">Reporting problems</a>
<li> <a href="news.shtml">What's new?</a>
<li> <a href="pdf.shtml">PDF documentation</a>
</ul>
<a href="download.shtml">3. Download and general notes</a> </p>
<ul>
<li> <a href="download.shtml#download">Stable download</a>
<li> <a href="download.shtml#latest">Development code</a>
<li> <a href="download.shtml#general">General notes</a>
<li> <a href="download.shtml#msdos">MS-DOS notes</a>
<li> <a href="download.shtml#nix">Unix/Linux notes</a>
<li> <a href="download.shtml#compilation">Compilation</a>
<li> <a href="download.shtml#input">Using the command line</a>
<li> <a href="download.shtml#output">Viewing output files</a>
<li> <a href="changelog.shtml">Version history</a>
</ul>
<a href="reference.shtml">4. Command reference table</a> </p>
<ul>
<li> <a href="reference.shtml#options">List of options</a>
<li> <a href="reference.shtml#output">List of output files</a>
<li> <a href="newfeat.shtml">Under development</a>
</ul>
<a href="data.shtml">5. Basic usage/data formats</a>
<ul>
<li> <a href="data.shtml#plink">Running PLINK</a>
<li> <a href="data.shtml#ped">PED files</a>
<li> <a href="data.shtml#map">MAP files</a>
<li> <a href="data.shtml#tr">Transposed filesets</a>
<li> <a href="data.shtml#long">Long-format filesets</a>
<li> <a href="data.shtml#bed">Binary PED files</a>
<li> <a href="data.shtml#pheno">Alternate phenotypes</a>
<li> <a href="data.shtml#covar">Covariate files</a>
<li> <a href="data.shtml#clst">Cluster files</a>
<li> <a href="data.shtml#sets">Set files</a>
</ul>
<a href="dataman.shtml">6. Data management</a> </p>
<ul>
<li> <a href="dataman.shtml#recode">Recode</a>
<li> <a href="dataman.shtml#recode">Reorder</a>
<li> <a href="dataman.shtml#snplist">Write SNP list</a>
<li> <a href="dataman.shtml#updatemap">Update SNP map</a>
<li> <a href="dataman.shtml#updateallele">Update allele information</a>
<li> <a href="dataman.shtml#refallele">Force reference allele</a>
<li> <a href="dataman.shtml#updatefam">Update individuals</a>
<li> <a href="dataman.shtml#wrtcov">Write covariate files</a>
<li> <a href="dataman.shtml#wrtclst">Write cluster files</a>
<li> <a href="dataman.shtml#flip">Flip strand</a>
<li> <a href="dataman.shtml#flipscan">Scan for strand problem</a>
<li> <a href="dataman.shtml#merge">Merge two files</a>
<li> <a href="dataman.shtml#mergelist">Merge multiple files</a>
<li> <a href="dataman.shtml#extract">Extract SNPs</a>
<li> <a href="dataman.shtml#exclude">Remove SNPs</a>
<li> <a href="dataman.shtml#zero">Zero out sets of genotypes</a>
<li> <a href="dataman.shtml#keep">Extract Individuals</a>
<li> <a href="dataman.shtml#remove">Remove Individuals</a>
<li> <a href="dataman.shtml#filter">Filter Individuals</a>
<li> <a href="dataman.shtml#attrib">Attribute filters</a>
<li> <a href="dataman.shtml#makeset">Create a set file</a>
<li> <a href="dataman.shtml#tabset">Tabulate SNPs by sets</a>
<li> <a href="dataman.shtml#snp-qual">SNP quality scores</a>
<li> <a href="dataman.shtml#geno-qual">Genotypic quality scores</a>
</ul>
<a href="summary.shtml">7. Summary stats</a>
<ul>
<li> <a href="summary.shtml#missing">Missingness</a>
<li> <a href="summary.shtml#oblig_missing">Obligatory missingness</a>
<li> <a href="summary.shtml#clustermissing">IBM clustering</a>
<li> <a href="summary.shtml#testmiss">Missingness by phenotype</a>
<li> <a href="summary.shtml#mishap">Missingness by genotype</a>
<li> <a href="summary.shtml#hardy">Hardy-Weinberg</a>
<li> <a href="summary.shtml#freq">Allele frequencies</a>
<li> <a href="summary.shtml#prune">LD-based SNP pruning</a>
<li> <a href="summary.shtml#mendel">Mendel errors</a>
<li> <a href="summary.shtml#sexcheck">Sex check</a>
<li> <a href="summary.shtml#pederr">Pedigree errors</a>
</ul>
<a href="thresh.shtml">8. Inclusion thresholds</a>
<ul>
<li> <a href="thresh.shtml#miss2">Missing/person</a>
<li> <a href="thresh.shtml#maf">Allele frequency</a>
<li> <a href="thresh.shtml#miss1">Missing/SNP</a>
<li> <a href="thresh.shtml#hwd">Hardy-Weinberg</a>
<li> <a href="thresh.shtml#mendel">Mendel errors</a>
</ul>
<a href="strat.shtml">9. Population stratification</a>
<ul>
<li> <a href="strat.shtml#cluster">IBS clustering</a>
<li> <a href="strat.shtml#permtest">Permutation test</a>
<li> <a href="strat.shtml#options">Clustering options</a>
<li> <a href="strat.shtml#matrix">IBS matrix</a>
<li> <a href="strat.shtml#mds">Multidimensional scaling</a>
<li> <a href="strat.shtml#outlier">Outlier detection</a>
</ul>
<a href="ibdibs.shtml">10. IBS/IBD estimation</a>
<ul>
<li> <a href="ibdibs.shtml#genome">Pairwise IBD</a>
<li> <a href="ibdibs.shtml#inbreeding">Inbreeding</a>
<li> <a href="ibdibs.shtml#homo">Runs of homozygosity</a>
<li> <a href="ibdibs.shtml#segments">Shared segments</a>
</ul>
<a href="anal.shtml">11. Association</a>
<ul>
<li> <a href="anal.shtml#cc">Case/control</a>
<li> <a href="anal.shtml#fisher">Fisher's exact</a>
<li> <a href="anal.shtml#model">Full model</a>
<li> <a href="anal.shtml#strat">Stratified analysis</a>
<li> <a href="anal.shtml#homog">Tests of heterogeneity</a>
<li> <a href="anal.shtml#hotel">Hotelling's T(2) test</a>
<li> <a href="anal.shtml#qt">Quantitative trait</a>
<li> <a href="anal.shtml#qtmeans">Quantitative trait means</a>
<li> <a href="anal.shtml#qtgxe">Quantitative trait GxE</a>
<li> <a href="anal.shtml#glm">Linear and logistic models</a>
<li> <a href="anal.shtml#set">Set-based tests</a>
<li> <a href="anal.shtml#adjust">Multiple-test correction</a>
</ul>
<a href="fanal.shtml">12. Family-based association</a>
<ul>
<li> <a href="fanal.shtml#tdt">TDT</a>
<li> <a href="fanal.shtml#ptdt">ParenTDT</a>
<li> <a href="fanal.shtml#poo">Parent-of-origin</a>
<li> <a href="fanal.shtml#dfam">DFAM test</a>
<li> <a href="fanal.shtml#qfam">QFAM test</a>
</ul>
<a href="perm.shtml">13. Permutation procedures</a>
<ul>
<li> <a href="perm.shtml#perm">Basic permutation</a>
<li> <a href="perm.shtml#aperm">Adaptive permutation</a>
<li> <a href="perm.shtml#mperm">max(T) permutation</a>
<li> <a href="perm.shtml#rank">Ranked permutation</a>
<li> <a href="perm.shtml#genedropmodel">Gene-dropping</a>
<li> <a href="perm.shtml#cluster">Within-cluster</a>
<li> <a href="perm.shtml#mkphe">Permuted phenotypes files</a>
</ul>
<a href="ld.shtml">14. LD calculations</a>
<ul>
<li> <a href="ld.shtml#ld1">2 SNP pairwise LD</a>
<li> <a href="ld.shtml#ld2">N SNP pairwise LD</a>
<li> <a href="ld.shtml#tags">Tagging options</a>
<li> <a href="ld.shtml#blox">Haplotype blocks</a>
</ul>
<a href="haplo.shtml">15. Multimarker tests</a>
<ul>
<li> <a href="haplo.shtml#hap1">Imputing haplotypes</a>
<li> <a href="haplo.shtml#precomputed">Precomputed lists</a>
<li> <a href="haplo.shtml#hap2">Haplotype frequencies</a>
<li> <a href="haplo.shtml#hap3">Haplotype-based association</a>
<li> <a href="haplo.shtml#hap3c">Haplotype-based GLM tests</a>
<li> <a href="haplo.shtml#hap3b">Haplotype-based TDT</a>
<li> <a href="haplo.shtml#hap4">Haplotype imputation</a>
<li> <a href="haplo.shtml#hap5">Individual phases</a>
</ul>
<a href="whap.shtml">16. Conditional haplotype tests</a>
<ul>
<li> <a href="whap.shtml#whap1">Basic usage</a>
<li> <a href="whap.shtml#whap2">Specifying type of test</a>
<li> <a href="whap.shtml#whap3">General haplogrouping</a>
<li> <a href="whap.shtml#whap4">Covariates and other SNPs</a>
</ul>
<a href="proxy.shtml">17. Proxy association</a>
<ul>
<li> <a href="proxy.shtml#proxy1">Basic usage</a>
<li> <a href="proxy.shtml#proxy2">Refining a signal</a>
<li> <a href="proxy.shtml#proxy2b">Multiple reference SNPs</a>
<li> <a href="proxy.shtml#proxy3">Haplotype-based SNP tests</a>
</ul>
<a href="pimputation.shtml">18. Imputation (beta)</a>
<ul>
<li> <a href="pimputation.shtml#impute1">Making reference set</a>
<li> <a href="pimputation.shtml#impute2">Basic association test</a>
<li> <a href="pimputation.shtml#impute3">Modifying parameters</a>
<li> <a href="pimputation.shtml#impute4">Imputing discrete calls</a>
<li> <a href="pimputation.shtml#impute5">Verbose output options</a>
</ul>
<a href="dosage.shtml">19. Dosage data</a>
<ul>
<li> <a href="dosage.shtml#format">Input file formats</a>
<li> <a href="dosage.shtml#assoc">Association analysis</a>
<li> <a href="dosage.shtml#output">Outputting dosage data</a>
</ul>
<a href="metaanal.shtml">20. Meta-analysis</a>
<ul>
<li> <a href="metaanal.shtml#basic">Basic usage</a>
<li> <a href="metaanal.shtml#opt">Misc. options</a>
</ul>
<a href="annot.shtml">21. Annotation</a>
<ul>
<li> <a href="annot.shtml#basic">Basic usage</a>
<li> <a href="annot.shtml#opt">Misc. options</a>
</ul>
<a href="clump.shtml">22. LD-based results clumping</a>
<ul>
<li> <a href="clump.shtml#clump1">Basic usage</a>
<li> <a href="clump.shtml#clump2">Verbose reporting</a>
<li> <a href="clump.shtml#clump3">Combining multiple studies</a>
<li> <a href="clump.shtml#clump4">Best single proxy</a>
</ul>
<a href="grep.shtml">23. Gene-based report</a>
<ul>
<li> <a href="grep.shtml#grep1">Basic usage</a>
<li> <a href="grep.shtml#grep2">Other options</a>
</ul>
<a href="epi.shtml">24. Epistasis</a>
<ul>
<li> <a href="epi.shtml#snp">SNP x SNP</a>
<li> <a href="epi.shtml#case">Case-only</a>
<li> <a href="epi.shtml#gene">Gene-based</a>
</ul>
<a href="cnv.shtml">25. Rare CNVs</a>
<ul>
<li> <a href="cnv.shtml#format">File format</a>
<li> <a href="cnv.shtml#maps">MAP file construction</a>
<li> <a href="cnv.shtml#loading">Loading CNVs</a>
<li> <a href="cnv.shtml#olap_check">Check for overlap</a>
<li> <a href="cnv.shtml#type_filter">Filter on type </a>
<li> <a href="cnv.shtml#gene_filter">Filter on genes </a>
<li> <a href="cnv.shtml#freq_filter">Filter on frequency </a>
<li> <a href="cnv.shtml#burden">Burden analysis</a>
<li> <a href="cnv.shtml#burden2">Geneset enrichment</a>
<li> <a href="cnv.shtml#assoc">Mapping loci</a>
<li> <a href="cnv.shtml#reg-assoc">Regional tests</a>
<li> <a href="cnv.shtml#qt-assoc">Quantitative traits</a>
<li> <a href="cnv.shtml#write_cnvlist">Write CNV lists</a>
<li> <a href="cnv.shtml#report">Write gene lists</a>
<li> <a href="cnv.shtml#groups">Grouping CNVs </a>
</ul>
<a href="gvar.shtml">26. Common CNPs</a>
<ul>
<li> <a href="gvar.shtml#cnv2"> CNPs/generic variants</a>
<li> <a href="gvar.shtml#cnv2b"> CNP/SNP association</a>
</ul>
<a href="rfunc.shtml">27. R-plugins</a>
<ul>
<li> <a href="rfunc.shtml#rfunc1">Basic usage</a>
<li> <a href="rfunc.shtml#rfunc2">Defining the R function</a>
<li> <a href="rfunc.shtml#rfunc2b">Example of debugging</a>
<li> <a href="rfunc.shtml#rfunc3">Installing Rserve</a>
</ul>
<a href="psnp.shtml">28. Annotation web-lookup</a>
<ul>
<li> <a href="psnp.shtml#psnp1">Basic SNP annotation</a>
<li> <a href="psnp.shtml#psnp2">Gene-based SNP lookup</a>
<li> <a href="psnp.shtml#psnp3">Annotation sources</a>
</ul>
<a href="simulate.shtml">29. Simulation tools</a>
<ul>
<li> <a href="simulate.shtml#sim1">Basic usage</a>
<li> <a href="simulate.shtml#sim2">Resampling a population</a>
<li> <a href="simulate.shtml#sim3">Quantitative traits</a>
</ul>
<a href="profile.shtml">30. Profile scoring</a>
<ul>
<li> <a href="profile.shtml#prof1">Basic usage</a>
<li> <a href="profile.shtml#prof2">SNP subsets</a>
<li> <a href="profile.shtml#dose">Dosage data</a>
<li> <a href="profile.shtml#prof3">Misc options</a>
</ul>
<a href="ids.shtml">31. ID helper</a>
<ul>
<li> <a href="ids.shtml#ex">Overview/example</a>
<li> <a href="ids.shtml#intro">Basic usage</a>
<li> <a href="ids.shtml#check">Consistency checks</a>
<li> <a href="ids.shtml#alias">Aliases</a>
<li> <a href="ids.shtml#joint">Joint IDs</a>
<li> <a href="ids.shtml#lookup">Lookups</a>
<li> <a href="ids.shtml#replace">Replace values</a>
<li> <a href="ids.shtml#match">Match files</a>
<li> <a href="ids.shtml#qmatch">Quick match files</a>
<li> <a href="ids.shtml#misc">Misc.</a>
</ul>
<a href="res.shtml">32. Resources</a>
<ul>
<li> <a href="res.shtml#hapmap">HapMap (PLINK format)</a>
<li> <a href="res.shtml#teach">Teaching materials</a>
<li> <a href="res.shtml#mmtests">Multimarker tests</a>
<li> <a href="res.shtml#sets">Gene-set lists</a>
<li> <a href="res.shtml#glist">Gene range lists</a>
<li> <a href="res.shtml#attrib">SNP attributes</a>
</ul>
<a href="flow.shtml">33. Flow-chart</a>
<ul>
<li> <a href="flow.shtml">Order of commands</a>
</ul>
<a href="misc.shtml">34. Miscellaneous</a>
<ul>
<li> <a href="misc.shtml#opt">Command options/modifiers</a>
<li> <a href="misc.shtml#output">Association output modifiers</a>
<li> <a href="misc.shtml#species">Different species</a>
<li> <a href="misc.shtml#bugs">Known issues</a>
</ul>
<a href="faq.shtml">35. FAQ & Hints</a>
</p>
<a href="gplink.shtml">36. gPLINK</a>
<ul>
<li> <a href="gplink.shtml">gPLINK mainpage</a>
<li> <a href="gplink_tutorial/index.html">Tour of gPLINK</a>
<li> <a href="gplink.shtml#overview">Overview: using gPLINK</a>
<li> <a href="gplink.shtml#locrem">Local versus remote modes</a>
<li> <a href="gplink.shtml#start">Starting a new project</a>
<li> <a href="gplink.shtml#config">Configuring gPLINK</a>
<li> <a href="gplink.shtml#plink">Initiating PLINK jobs</a>
<li> <a href="gplink.shtml#view">Viewing PLINK output</a>
<li> <a href="gplink.shtml#hv">Integration with Haploview</a>
<li> <a href="gplink.shtml#down">Downloading gPLINK</a></p>
</ul>
</font>
</td><td width=5%>
<td valign="top">
</p>
<h1>Population stratification</h1>
<tt>PLINK</tt> offers a simple but potentially powerful approach to population
stratification, that can use whole genome SNP data (the number of
individuals is a greater determinant of how long it will take to run).
We use complete linkage agglomerative clustering, based on pairwise
identity-by-state (IBS) distance, but with some modifications to
the clustering process: restrictions based on a significance test
for whether two individuals belong to the same population (i.e. do
not merge clusters that contain significantly different individuals) ,
a phenotype criterion (i.e. all pairs must contain at least one case
and one control) and cluster size restrictions (i.e. such that, with a
cluster size of 2, for example, the subsequent association test
would implicitly match every case with its nearest control, as long
as the case and control do not show evidence of belonging to
different populations). In addition, external matching criteria
can be specified, to match on age and sex, for example, as well
as genetic information. Any evidence of population substructure
(from this or any other analysis) can be incorporated in subsequent
association tests via the specification of
<a href="data.shtml#clst">clusters</a>.
</p>
<div align="center">
<strong>All these analyses require genome-wide coverage of autosomal SNPs!</strong>
</div>
</p>
</p>
<a name="cluster">
<h2>IBS clustering</h2></a>
</p>
To perform complete linkage clustering of individuals on the basis of autosomal genome-wide SNP
data, the basic command is:
<h5>
plink --file mydata --cluster
</h5></p>
which generates four output files:
<pre>
plink.cluster0
plink.cluster1
plink.cluster2
plink.cluster3
</pre>
that contain similar information but in different formats. The
</p>The <tt>*.cluster0</tt> file contains some information on the clustering process.
This file can be safely ignored by most users.
</p>The <tt>*.cluster1</tt> file contains information on the final solution, listed by cluster:
e.g. for 4 individuals with the following Family and Individual IDs
<pre>
A 1
B 1
C 1
D 1
</pre>
we see 3 clusters, one line of output per cluster:
<pre>
0 A_1
1 B_1 C_1
2 D_1
</pre>
(note how family and individuals IDs are concatenated with the underscore character in the
output)
</p>The <tt>*.cluster2</tt> file contains the same information but listed
one line per individual: the three columns are family ID, individual ID
and assigned cluster:
<pre>
A 1 0
B 1 1
C 1 1
D 1 2
</pre>
</p>The <tt>*.cluster3</tt> file is in the same format as <tt>cluster2</tt> (one line per
individual) but contains all solutions (i.e. every step of the clustering from moving from N
clusters each of 1 individual (leftmost column after family and individual ID) to 1 cluster
(labelled 0) containing all N individuals (the final, rightmost column):
also shown is the dendrogram this represents:
e.g.
<pre>
A 1 0 0 0 0
B 1 1 1 1 0
C 1 2 1 1 0
D 1 3 2 0 0
</pre><img src="tree1.png">
</p><strong>NOTE</strong> If any constraints have been placed upon the clustering, then
solutions represented in the <tt>*.cluster3</tt> file may not go as far as 1 cluster with
all N individuals: in this case, the file <tt>*.cluster2</tt> will contain the final solution
(i.e. as far as the clustering could go before running up against constraints, e.g. based on
maximum cluster size, etc).
</p><strong>HINT!</strong> In large samples, cluster analyses can be
very slow. Often the most time consuming step is calculating the
pairwise IBS metrics: these only need to be calculated once however, even
if you want to run the cluster analysis multiple times (e.g. with
different constraints). This is achieved with the <tt>--read-genome</tt>
option, assuming you have previously run the <tt>--genome</tt> command. It
is a good idea to not impose a threshold of the <tt>--genome</tt> output
in this case. For example:
<h5>
plink --bfile mydata --genome --out mydata
</h5></p>
followed by multiple clustering commands (see below for descriptions of
the cluster constraint parameters used here)
<h5>
plink --bfile mydata --read-genome mydata.genome --cluster --ppc 0.01
</h5></p>
and
<h5>
plink --bfile mydata --read-genome mydata.genome --cluster --mc 2 --ibm 0.01
</h5></p>
etc.
</p><strong>ADVANCED HINT!</strong> In very large samples, cluster analyses can be
very, very slow. When calculating the <tt>plink.genome</tt> file (as described
above), if you have access to a cluster of computers for parallel computing, you can
use the following approach to greatly reduce the time this step takes. In this case,
we will assume you are familiar with and using a Linux operating system, and that the
<tt>bsub</tt> prefix is used to send a job to the cluster -- obviously, change the
script below as appropriate. This uses the <tt>--genome-lists</tt> option to calculate
IBS statistics for only a subset of the sample at a time. If the binary fileset is <tt>data.*</tt>
then create multiple lists of, for example, 100 individuals per list
<pre>
gawk '{print $1,$2}' data.fam | split -d -a 3 -l 100 - tmp.list
</pre>
If this creates, for example, 39 separate files (labelled 0 to 38), then
run these in all unqiue pairwise combinations in parallel with something
like the following script: (i.e. edit the first line as appropriate)
<pre>
let i=0 a=38
let j=0
while [ $i -le $a ]
do
while [ $j -le $a ]
do
bsub -o /dev/null -e /dev/null ./plink --bfile data \
--read-freq plink.frq \
--genome \
--genome-lists tmp.list`printf "%03i\n" $i` \
tmp.list`printf "%03i\n" $j` \
--out data.sub.$i.$j
let j=$j+1
done
let i=$i+1
let j=$i
done
</pre>
</p><strong>NOTE</strong> If you use this approach to calculate the IBD probabilities,
then you should first perform <tt>--freq</tt> on the whole dataset, then add the line
<tt>--read-freq plink.frq</tt> (obviously replacing the filename with your file)
to make sure that everybody has the sample frequencies used in the
<a href="ibdibs.shtml#genome">IBD calculations</a>.
</p>
The finally, concatenate these individual files back into one
(multiple header rows will be ignored, as FID is a reserved ID):
<pre>
cat data.sub*genome > data.genome
rm tmp.list*
rm data.sub.*
</pre>
<strong>HINT</strong> As of v1.07, PLINK can directly read and write
compressed .genome files. This is the preferred mode for large samples. For example
<h5>
plink --bfile mydata --Z-genome --out mydata
</h5></p>
creates a file
<pre>
mydata.genome.gz
</pre>
This can be handled with the gunzip tool, or zcat, zgrep, zless, etc. PLINK can
read such a file with <tt>--read-genome</tt>. Whether or not the file is compressed
will be automatically detected (indicated by a <tt>.gz</tt> extension)
<h5>
plink --bfile mydata --read-genome mydata.genome.gz ...
</h5></p>
Note that several compressed files can be directly concatenated with the Unix cat command, without
being decompressed first:
<h5>
cat mydata-1.genome.gz mydata-2.genome.gz mydata-3.genome.gz > alldata.genome.gz
</h5></p>
<h5>
plink --bfile example --read-genome alldata.genome.gz ...
</h5></p>
<a name="permtest">
<h2>Permutation test for between group IBS differences</h2></a>
</p>
Given that pairwise IBS distances between all individuals have been calculated,
we can asked whether or not there are group differences in this metric, with
respect to a binary phenotype. The command
<h5>
./plink --bfile mydata --ibs-test
</h5></p>
or, if an appropriate <tt>plink.genome</tt> file has already been created,
<h5>
./plink --bfile mydata --read-genome plink.genome --ibs-test
</h5></p>
will permute case/control label, and then recalculate several between-group
metrics based on average IBS within that group. This command uses a fixed 10,000
permutations.
</p>
All results are written to the LOG file. First, the observed means and
standard deviation of each of the 3 groups (case/control, case/case and control/control, in
that order) will be displayed: e.g.
<pre>
Between-group IBS (mean, SD) = 0.782377, 0.00203459
In-group (2) IBS (mean, SD) = 0.782101, 0.00232296
In-group (1) IBS (mean, SD) = 0.78273, 0.00170816
</pre>
Then 12 separate tests are presented, which have self-explanatory names. If the
label does not explicitly mention a comparison pair-type, it implies
that the first pair type is being compared to the other two
pair-types.
<pre>
T1: Case/control less similar p = 0.97674
T2: Case/control more similar p = 0.0232698
T3: Case/case less similar than control/control p = 0.00285997
T4: Case/case more similar than control/control p = 0.99715
T5: Case/case less similar p = 0.00430996
T6: Case/case more similar p = 0.9957
T7: Control/control less similar p = 0.99883
T8: Control/control more similar p = 0.00117999
T9: Case/case less similar than case/control p = 0.00726993
T10: Case/case more similar than case/control p = 0.99274
T11: Control/control less similar than case/control p = 1
T12: Control/control more similar than case/control p = 9.9999e-06
</pre>
For the purpose of stratification effects between cases and conrtols,
the test <tt>T1</tt> is probably most appropriate, as it directly asks
whether or not, on average, an individual is less similar to another
phenotypically-discordant individual than would be expected by chance
(i.e. if we randomized phenotype labels). That is, to the extent that
cases and controls are from two separate populations, you would expect
pairs within a phenotype group to be more similar than pairs across
the two groups, i.e. <tt>T1</tt>. Of course, the opposite could also
be true (tested by <tt>T2</tt>), which would probably represent
certain ascertainment procedures (i.e. taking this to an extreme,
imagine a discordant sibling pair design: case/control pairs would on
average be more similar than case/case and control/control pairs).
</p>
The other tests are provided for completeness and give a more general
description of the variability between and within each group. The
general pattern shown above would suggest that there is relatively
more variability within the case sample than the control
sample. Bear in mind when interpreting the empirical p-values
that the relative sizes of case and control samples will have an
impact on the exact p-value (i.e. these significance tests should not
be taken to directly represent the magnitude of differences between
groups).
</p>
<strong>Note</strong> This test assumes that individuals have a
disease phenotype; obviously, one could swap in other labels
(e.g. site of collection) via the <tt>--pheno</tt> command, as long as
they are dichotomous.
</p>
<a name="options">
<h2>Constraints on clustering</h2></a>
</p>
This section describes the extra constraints that can be placed on the
clustering procedure, specified via other options in addition to the
<tt>--cluster</tt> option. As further described in the
<a href="anal.shtml">association analysis</a> and
<a href="perm.shtml">permutation</a> sections, these
options can be used to set up various types of analyses
that control for potential stratification in the sample.
</p>
<b>1) Based on pairwise population concordance (PPC) test:</b><br>
This is a simple significance test for whether two individuals belong to the
same random-mating population. To only merge clusters that do not
contain individuals differing at a certain p-value:
<h5>
--ppc 0.0001
</h5></p>
</p><strong>NOTE</strong> This command has been changed from <tt>--pmerge</tt>
in older versions of PLINK (pre 0.99n).</p>
This test is based on the observed binomial proportion of IBS 0 loci pairs
to IBS 2 het/het pairs: counts of these two types should be in the
ratio of 1:2 if the two individuals come from the same population. The
significant p-value indicates fewer IBS2 het/het loci than expected
(based on normal approximation to binomial). These tests are also given by
the <tt>--genome</tt> command.</p>
</p><strong>WARNING!</strong> Unlike the basic IBS clustering, which
places no restrictions on the SNPs that can be used in the analysis,
this test assumes that the SNPs used are in linkage equilibrium. By
default, this test will only count an 'informative' SNP pair (i.e. one
that, for a particular pair of individuals, has two of each allele) as
either an IBS 0 or IBS 2 count for this test (the <tt>HOMHOM</tt> and
<tt>HETHET</tt> counts from the <a href="ibdibs.shtml#genome">
<tt>--genome</tt></a> option) if it is more
than 500 kb more the previous informative pair of SNPs, for that
particular pair of individuals. This gap parameter can be changed with
the option
<h5>
--ppc-gap 100
</h5></p>
which would, in this case, reduce that gap to 100kb. (Note: all SNPs will
still be used to calculate the main IBS distance metric, upon which the
clustering is based).
</p>
<strong>HINT</strong> Also, this test is susceptible to non-random missingness
in genotypes, particularly if heterozygotes are more likely to be dropped.
It is therefore good practice to set the <tt>--geno</tt> very low for
this analysis, i.e. so only SNPs with virtually complete genotyping are
included.
</p>
<b>2) Based on phenotype:</b><br>
To ensure that every cluster has at least one case and one control:
<h5>
--cc
</h5></p>
<b>3) Based on maximum cluster size:</b><br>
To set the maximum cluster size to a certain value, e.g. 2:
<h5>
--mc 2
</h5></p>
Alternatively, to specify a maximum number of cases and a maximum number of controls per
cluster, use the option:
<h5>
--mcc 1 3
</h5></p>
which, in this case, specifies that each cluster can have up to 1 case and 3 controls. Note
the different syntax: <tt>-mcc</tt> as opposed to <tt>--mc</tt>. Using this in conjunction
with the <tt>--cc</tt> constraint (that ensures at least 1 case and 1 control per cluster)
this is an easy way to achieve a certain matching scheme, say 1 case to 3 controls, or 2
cases to 2 controls, etc.
</p>
<b>4) Based on fixed number of clusters:</b><br>
To request that the clustering process stops at a certain fixed number of
clusters, for example, a 2 cluster solution, use:
<h5>
--K 2
</h5></p>
<strong>Note</strong> If other clustering constraints are in place, it is
possible that clustering may stop <em>before</em> reaching the specified
number of clusters with the <tt>--K</tt> option; if other constraints are
specified, you can think of this as stating the <em>minimum</em> number
of clusters possible.
</p>
<b>5) Based on pattern of missing genotype data:</b><br>
To only cluster individuals with sufficiently similar profiles of missing
genotype data (genome-wide) use the option:
<h5>
--ibm 0.02
</h5></p>
which would only match people if they are discordantly missing (i.e.
one person is missing a particular SNP but the other person is not)
for 2 percent of the genome or less. Another way to incorporate missingness
would be by defining overall call rate per individual as an external quantitative
matching criteria (see below); this approach is preferrable however (as it does not
match just on average rate, but also on whether it tends to be the
same SNPs that are missing).
</p>
<b>6) Based on user-specified external matching criteria:</b><br>
To use external matching criteria: for categorical matching
criteria, use the option:
<h5>
--match mydata.match
</h5></p>
where the file <tt>mydata.match</tt> contains the following columns:
family and individual ID and the one or more matching variables, one row
per person:
<pre>
Family ID
Individual ID
Matching criteria 1
Matching criteria 2
...
Matching criteria N
</pre>
The default behavior is that only individuals with the same matching
criteria across all the measures will be paired to make clusters. For example,
if the file were:
<pre>
F1 I1 1 1 1
F2 I2 1 2 1
F3 I3 2 2 2
F4 I4 1 2 1
F5 I5 1 1 1
...
</pre>
then only F1/I1 and F5/I5 could be paired; also F2/I2 and F4/I4 could be
paired. No other combinations of pairings would be possible. Therefore, no
cluster would ever be formed that contained both individuals F1/I1 and
F2/I2, for example.
</p>
One application of this option would be to ensure that individuals are
sex-matched, or matched on some relevant environmental exposure, in
addition to the genetic IBS matching.
</p>
It is possible to adjust the default behaviour to consider two individuals as
potentially 'pairable' is they <em>differ</em> on a particular categorical criteria.
This is achieved with the optional command:
<h5>
--match-type mydata.bt
</h5></p>
where <tt>mydata.bt</tt> is the name of a file that contains a series of 0s and 1s (or "-"
and "+" characters), whitespace delimited, that indicate whether a criteria should be a
"postive match" (i.e. two individuals are potentially pairable only if they have the
<em>same</em> values for this variable) or a "negative match" (i.e. two individuals are
potentially pairable only if they have <em>different values</em> for this variable). In the
above example, if the file <tt>mydata.bt</tt> were
<pre>
+ - +
</pre>
then the following pairs are potentially pairable:
<pre>
F1/I1 and F2/I2
F1/I1 and F4/I4
F5/I5 and F2/I2
F5/I5 and F4/I4
</pre>
i.e. <tt>F1/I1</tt> can no longer be paired with <tt>F5/I5</tt> because they have the
same value for the second matching variable, which is now a negative match criteria.
</p><strong>Note</strong> In this example, the matching variables only took two values:
in practice, one can have any number of categories per matching variable.
</p><strong>Note</strong> Missing variables can be specified for matching variables -- this
means that the criteria will be ignored. As all pairs start out as potentially pairable, this
means that missing matching criteria data will never be used to make a pair unpairable.
</P>
A second form a matching is based on quantitative traits -- in this case,
a maximum difference threshold is specified for each measure, such that
individuals will not be matched if they differ beyond the threshold on the
quantitative traits. This is achieved by the following options:
<h5>
--qmatch mydata.match --qt mydata.qt
</h5></p>
Note that a second <tt>--qt</tt> option is necessary as well as the
<tt>--qmatch</tt> option. The <tt>--qt</tt> specifies a file that contains
the thresholds, e.g. for 3 external quantitative criteria, this should
contain 3 values:
<pre>
5
0.333
120
</pre>
The <tt>--qmatch</tt> should then contain the same number of quantitative
matching criteria per person (again, one row per person):
<pre>
F1 I1 27 -0.23 1003
F2 I2 34 2.22 1038
F3 I3 45 1.99 987
F4 I4 19 -9 2374
F5 I5 18 -0.45 996
...
</pre>
In this case, for example, for the first measure only F4/I4 and F5/I5 are
pairable, as |19-18| is not more than 5. This measure might represent age,
for example. This pair is not matchable on the basis on the third metric,
however, as |2374-996| > 120. As such, no pairs could be formed between
any of these five individuals, in this particular case. Note that
individual is actually missing (default <tt>--missing-phenotype</tt> value
is -9) for the second criterion: see below for a description of how
missing data are handled in this context.
</P>
The <tt>.match</tt> and <tt>.qmatch</tt> files do not need to contain all
individuals and do not need to be in the same order as the original PED
files. Any individuals in these files who are not in the original files
will be ignored. </p>
Missing phenotypes are simply ignored (i.e. two individuals would not be
called non-matching if either one or both had missing matching criteria).
That is, the default for two individuals is that they are pairable -- only
non-missing, non-matching external criteria (as well as the p-value test
based on genetic data, described above) will make a pair unpairable.</p>
<a name="matrix">
<h2>IBS similarity matrix</h2></a>
</p>
For the <em>N</em> individuals in a sample, to create a <em>N x N</em>
matrix of genome-wide average IBS pairwise identities:
<h5>
plink --file mydata --cluster --matrix
</h5></p>
creates the file
<pre>
plink.mibs
</pre>
which contains a square, symmetric matrix of the IBS distances for all
pairs of individuals. These values range, in theory, from 0 to 1. In
practice, one would never expect to observe values near 0 -- even
completely unrelated individuals would be expected to share a very large
proportion of the genome identical by state by chance alone (i.e. as
opposed to identity by descent). A value of 1 would indicate a MZ
twin pair, or a sample duplication. More details on pairwise relatedness can
be obtained by using the <a href="ibdibs.shtml#genome">
<tt>--genome</tt></a> command.
</p>
The default behavior of <tt>--matrix</tt> to to output similarities (proportions of alleles IBS).
To generate a distance matrix (1-IBS) then use the command
<h5>
plink --file mydata --cluster --distance-matrix
</h5></p>
instead. This will generate a file