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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>Multimarker haplotype tests</h1>
All tests described above are based on single SNP tests. It is also
possible to impute haplotypes based on multimarker predictors using
the standard E-M algorithm and to perform simple tests based on the
distribution of probabilistically-inferred set of haplotypes for each
individual.
</p>
As well as the autosomes, X and haploid chromosomes should be
appropriately handled. Phasing can either be based on a sample of
unrelated individuals, or certain kinds of family data. First, all
founders are phased using the E-M algorithm; then all descendents of
these founders are phased given the set of possible parental phases
and assuming random-mating. Currently it is not possible to phase
sibships without parents. The current implementation of the phasing
and haplotype testing algorithm is designed focus on relatively small
regions of the genome, rather than to phase whole chromosomes at once.
</p>
<strong>HINT!</strong> Another approach to haplotype-testing can be
found under the page describing <a href="proxy.shtml">proxy
association</a>. This set of methods essentially just provide a
different interface to the exact same E-M phasing and
haplotype-testing algorithms, one that is centered around a specific
reference SNP.
</p>
<a name="hap1">
<h2>Specification of haplotypes to be estimated</h2>
</a></p>
Haplotype testing in PLINK requires that the user supplies a file
listing the haplotypes to be tested (Some precomputed lists are
given <a href="#precomputed">below</a> which might be useful in some
circumstances.) The formats of these files are described below. An
alternative is to specify a simple, sliding window of fixed haplotype
size (also described below).
</p>
The command
<h5>
plink --file mydata --hap myfile.hlist
</h5></p>
will read the file <tt>myfile.hlist</tt>, each row of which
is expected to have one of the three following formats:
</p>
<b><em>1) Particular allele specified</em></b></p>
The first format specifies a particular haplotype at a given locus. Two
example rows of this format are:
<pre>
rs1001 5 0 201 1 2 TC snp1 snp2
rs1002 5 0 202 A C TTA snp1 snp3 snp4
...
</pre>
The columns represent:
<pre>
Col 1 : Imputed SNP name
Col 2 : Imputed SNP chromosome
Col 3 : Imputed SNP genetic distance (default: Morgan coding)
Col 4 : Imputed SNP physical position (bp units)
Col 5 : Imputed SNP allele 1 name
Col 6 : Imputed SNP allele 2 name
Col 7 : Tag SNP allele/haplotype that equals imputed SNP allele 1
Col 8+ : Tag SNP(s) [in same order as haplotype in Col 7]
</pre>
Here we have explicitly specified the <tt>TC</tt> and <tt>TTA</tt>
haplotypes. For example, in the first case, SNPs <tt>snp1</tt> and
<tt>snp2</tt> may have all four common haplotypes seen in the sample,
<tt>TT</tt>, <tt>CT</tt> and <tt>CC</tt> as well as <tt>TC</tt>; this
command would select only the <tt>TC</tt> haplotype to be imputed, or as
the focus of haplotype analysis. The imputed SNP, <tt>rs1001</tt>
therefore has the following alleles:
<pre>
TC/TC 1/1
TC/* 1/2
*/* 2/2
</pre>
and will be positioned on chromosome 5, and base-positon 201. Haplotypes
other than <tt>TC</tt> will be coded <tt>2</tt>.
</p>
The imputed SNP details (alleles, etc) will only be used if the
<tt>--hap-impute</tt> option has been requested. For <tt>--hap-assoc</tt>
and <tt>--hap-tdt</tt> options (which consider all possible phases rather
than just imputing the most likely) these are not considered (but they are
still required in this input file).
</p>
<b><em>2) 'Wildcard' specification</em></b></p>
Alternatively, all haplotypes at a given locus above the <tt>--maf</tt>
threshold can be automatically estimated by entering a line in
<tt>myfile.hlist</tt> as
follows:
<pre>
* snp1 snp2 snp3
* snp1 snp2
</pre>
i.e. where the first character is an asterisk <tt>*</tt>, which would,
taking just the first line for example, create all 3-SNP haplotypes for
the SNPs labelled in the MAP file as <tt>snp1</tt>, <tt>snp2</tt> and
<tt>snp3</tt>, above
the minor allele frequency threshold. If the haplotypes were, for example,
<tt>AAC</tt>, <tt>AGG</tt> and <tt>TGG</tt>, then the following names
would be automatically assigned:
<pre>
H1_AAC_
H1_AGG_
H1_TGG_
</pre>
Haplotypes based on subsequent lines in the file would be labelled
<tt>H2_*_</tt>, <tt>H3_*_</tt>, etc. In this case, all two-SNP haplotypes
for <tt>snp1</tt> and <tt>snp2</tt> would start <tt>H2_</tt>. The
chromosome and position flags for the new haplotypes are set to equal the
first SNP of the set.
</p>
<b><em>3) 'Named wildcard' specification</em></b></p>
Finally, this format is identical to the previous wildcard specification,
except a name can be given to the haplotype. This uses <tt>**</tt> instead of
<tt>*</tt> to start a row; the second entry is then interpreted as the name
of the haplotype locus rather than the first SNP. For example:
<pre>
** BLOCK1 snp1 snp2 snp3
** BLOCK2 snp6 snp7
</pre>
The only difference is that <tt>BLOCK1</tt> and <tt>BLOCK2</tt> names will be used
in the output instead of <tt>H1</tt> and <tt>H2</tt> being assigned automatically.
</p>
<b><em>4) Sliding window specification</em></b></p>
Finally, instead of specifying a haplotype file with the <tt>--hap</tt> option,
you can use the <tt>--hap-window</tt> option to specifty all haplotypes in
sliding windows of a fixed number of SNPs (shifting 1 SNP at a time).
<h5>
plink --bfile mydata --hap-window 3 --hap-assoc
</h5></p>
to form all 3-SNP haplotypes across the entire dataset
(respecting chromosome boundaries, however). In this case
the windows will be automatically named <tt>WIN1</tt>, <tt>WIN2</tt>, etc. This command can take a
comma-delimited list of values, e.g.
<pre>
--hap-window 1,2,3
</pre>
to perform all single SNP tests (1-SNP haplotypes) as well as sliding
windows of all 2-SNP and 3-SNP haplotypes.
</p>
<a name="precomputed">
<h2>Precomputed lists of multimarker tests</h2></a>
</p>
Below are links to some PLINK-formatted lists of multimarker tests
selected for Affymetrix 500K and Illumina whole genome products, based
on consideration of the CEU Phase 2 HapMap (at r-squared=0.8
threshold). One should download the appropriate file and run with
the <tt>--hap</tt> option (after ensuring that any strand issues have
been resolved). These files were generated by Itsik Pe'er and others,
as described in this manuscript:
<pre>
Pe'er I, de Bakker PI, Maller J, Yelensky R, Altshuler D
& Daly MJ (2006) Evaluating and improving power in whole-genome
association studies using fixed marker sets. Nat Genet, 38(6): 605-6.
</pre>
<ul>
<li>
<a href="../dist/mmtests/Affymetrix.GeneChip.500k.both.CEU.0.8.tests.zip">
Affymetrix.GeneChip.500k.both.CEU.0.8.tests.zip
</a>
<li>
<a href="../dist/mmtests/Illumina.HumanHap.300k.CEU.0.8.tests.zip">
Illumina.HumanHap.300k.CEU.0.8.tests.zip
</a>
<li>
<a href="../dist/mmtests/Illumina.HumanHap.550k.CEU.0.8.tests.zip">
Illumina.HumanHap.550k.CEU.0.8.tests.zip
</a>
<li>
<a href="../dist/mmtests/Illumina.HumanHap.650k.CEU.0.8.tests.zip">
Illumina.HumanHap.650k.CEU.0.8.tests.zip
</a>
</ul>
These tables list all tags for every common HapMap SNP, at the given
r-squared threshold. The same haplotype may therefore appear multiple
times (i.e. if it tags more than 1 SNP). The haplotypes are specified
in terms of the + (positive) strand relative to the HapMap. You might
need to reformat your data prior to using these files (using the
<tt>--flip</tt> command, for instance) before you can use them.
</p>
<strong>Note</strong> These tables obviously assume that all tags on
present in the final, post-quality-control dataset: i.e. if certain
SNPs have been removed, it will be better to reselect the
predictors -- that is, these lists should really only be used as a
first pass, for convenience.
</p>
<a name="hap2">
<h2>Estimating haplotype frequencies</h2></a>
</p>
To obtain the haplotype frequencies for all haplotypes in each window,
use the option:
<h5>
plink --file mydata --hap myfile.hlist --hap-freq
</h5></p>
which will generate the file
<pre>
plink.freq.hap
</pre>
which contains the fields (no header)
<pre>
LOCUS Haplotype locus / window name
HAPLOTYPE Haplotype identifer
F Frequency in sample (founders)
</pre>
<a name="hap3">
<h2>Testing for haplotype-based case/control and quantitative trait association</h2></a>
</p>
In a population-based sample of unrelated individuals, case/control and quantitative
traits can be analysed for haplotype associations, using the option, for example,
<h5>
plink --file mydata --hap myfile.hlist --hap-assoc
</h5></p>
which will generate haplotype-specific tests (1df) for both disease and
quantitative traits; for disease traits only, an omnibus association statistic
will also be computed. This option generates the file
<pre>
plink.assoc.hap
</pre>
which contains the following fields:
<pre>
LOCUS Haplotype locus / window name
HAPLOTYPE Haplotype identifer / "OMNIBUS"
F_A Frequency in cases
F_U Frequency in controls
CHISQ Test for association
DF Degrees of freedom
P Asymptotic p-value
SNPS SNPs forming the haplotype
</pre>
or
<pre>
plink.qassoc.hap
</pre>
which contains the following fields:
<pre>
LOCUS Haplotype locus / window name
HAPLOTYPE Haplotype identifer
NANAL Number of individuals in analysis
BETA Regression coefficient
RSQ Proportion variance explained
STAT Test statistic (T)
P Asymptotic p-value
SNPS SNPs forming the haplotype
</pre>
In all cases, the tests are based on the expected number of haplotypes
each individual has (which might be fractional). The case/control
omnibus test is a H-1 degree of freedom test, if there are H
haplotypes.
<a name="hap3c">
<h2>Haplotype-based association tests with GLMs</h2></a>
</p>
The following options use linear and logistic regression to perform
haplotye-based association analysis. The two main
commands, <tt>--hap-linear</tt> and <tt>--hap-logistic</tt> are
analogous to <tt>--linear</tt> and <tt>--logistic</tt>,
described <a href="anal.shtml#glm">here</a>.
</p>
The main advantages of these commands over
the <a href="#hap3">above</a> approaches, are that they can include
one or more covariates and allow for permutation. The disadvantage is
that they will run a little more slowly.
</p>
The basic command is
<h5>
plink --file mydata --hap myfile.hlist --hap-logistic
</h5></p>
(alternatively, for a quantitative outcome, use <tt>--hap-linear</tt>;
aside from minor differences in the output, the discussion below
applies equally to both forms of these commands).
</p>
<strong>NOTE</strong> Here the haplotypes to be tested are specified in a file with
the <tt>--hap</tt> command, but one could alternatively use a sliding
window analysis, e.g. to cover all 2, 3 and 4-SNP windows, e.g. <tt>--hap-window 2,3,4</tt>
</p>
The output is in the file
<pre>
plink.assoc.hap.logistic
</pre>
(or <tt>plink.assoc.hap.linear</tt>) which has the fields:
<pre>
NSNP Number of SNPs in this haplotype
NHAP Number of common haplotypes (threshold determined by <tt>--mhf</tt>, 0.01 default)
CHR Chromosome code
BP1 Physical position of left-most (5') SNP (base-pair)
BP2 Physical position of right-most (3') SNP (base-pair)
SNP1 SNP ID of left-most (5') SNP
SNP2 SNP ID of left-most (3') SNP
HAPLOTYPE Haplotype
F Frequency in sample
OR Estimated odds ratio
STAT Test statistic (T from Wald test)
P Asymptotic p-value
</pre>
for example: (<em>spaces between rows added for clarity</em>)
<pre>
NSNP NHAP CHR BP1 BP2 SNP1 SNP2 HAPLOTYPE F OR STAT P
2 2 22 15462210 15462259 rs11089263 rs11089264 AA 0.345 1.31 0.693 0.405
2 2 22 15462210 15462259 rs11089263 rs11089264 CG 0.655 0.762 0.693 0.405
3 3 22 15688352 15690057 rs165650 rs165757 GTG 0.117 0.544 1.46 0.227
3 3 22 15688352 15690057 rs165650 rs165757 CTG 0.0167 0.406 0.525 0.469
3 3 22 15688352 15690057 rs165650 rs165757 CGA 0.867 1.7 1.56 0.212
5 5 22 15691787 15699058 rs175152 rs165914 ACACT 0.129 0.515 2.13 0.144
5 5 22 15691787 15699058 rs175152 rs165914 CCACT 0.236 0.917 0.0566 0.812
5 5 22 15691787 15699058 rs175152 rs165914 CCACG 0.0169 1.74 0.198 0.656
5 5 22 15691787 15699058 rs175152 rs165914 CTGTG 0.085 0.565 1.11 0.292
5 5 22 15691787 15699058 rs175152 rs165914 CTATG 0.533 1.88 3.36 0.0666
5 4 22 15902049 15939567 rs2845389 rs4819958 GTAAA 0.0857 0.719 0.388 0.533
5 4 22 15902049 15939567 rs2845389 rs4819958 GTGAA 0.32 1.04 0.0185 0.892
5 4 22 15902049 15939567 rs2845389 rs4819958 CCGGG 0.303 0.548 2.97 0.0847
5 4 22 15902049 15939567 rs2845389 rs4819958 GCGGG 0.292 1.82 3.28 0.0701
</pre>
which illustrates results for the first four haplotype window positions (e.g. the second window position
contains 3 SNPs, and there are 3 common haplotypes, <tt>GTG</tt>, <tt>CTG</tt> and <tt>CGA</tt>).
</p>
The additional command
<pre>
--hap-omnibus
</pre>
instructs PLINK to perform instead of H-1 haplotype-specific tests for
H haplotypes (of each versus all others), a single H-1 df omnibus test
(jointly estimating a testing all haplotype effects at that
position). This will result in a single row per window, with the
following slightly different format. Now the first four window
positions have only a single line of output, and a single p-value (the
degree of freedom will be <tt>NHAP</tt>-1). Also, there is no haplotype-specific
output (e.g. haplotype names, frequencies or odds ratios):
<pre>
NSNP NHAP CHR BP1 BP2 SNP1 SNP2 STAT P
2 2 22 15462210 15462259 rs11089263 rs11089264 0.693 0.405
3 3 22 15688352 15690057 rs165650 rs165757 1.57 0.457
5 5 22 15691787 15699058 rs175152 rs165914 5.08 0.279
5 4 22 15902049 15939567 rs2845389 rs4819958 4.4 0.222
</pre>
As mentioned above, covariates can be incorporated with the
<pre>
--covar myfile.txt
</pre>
command. Note that the coefficients and p-values for the covariates
are not listed in these output files (unlike the default for
the <tt>--logistic</tt> command).
</p>
Permutation procedures can be used, with the command
<pre>
--mperm 10000
</pre>
to specify, for example, ten thousand permutations. The empirical p-values from
this analysis are listed in the file
<pre>
plink.assoc.hap.logistic.mperm
</pre>
Note that there will be no SNP name listed in the permutation output file: rather, it
will be in the form:
<pre>
TEST EMP1 EMP2
T0 0.4158 1
T1 0.1782 1
T2 0.2475 1
T3 0.1683 1
...
</pre>
The number of rows, and the order of the output, will be the same as for the
asymptotic results file, so they can be easily aligned. e.g. here <tt>T0</tt>
would correspond to either the first omnibus test, or the first haplotype-specific
test, <tt>T1</tt> the second, etc.
</p>
<a name="hap3b">
<h2>Haplotype-based TDT association test</h2></a>
</p>
If the case/control data are being analysed, use the option
<h5>
plink --file mydata --hap myfile.hlist --hap-tdt
</h5></p>
to test for TDT haplotype-specific association. This
option generates the file
<pre>
plink.tdt.hap
</pre>
which contains the following fields:
<pre>
LOCUS Haplotype locus / window name
HAPLOTYPE Haplotype identifer / "OMNIBUS"
T Number of transmitted haplotypes
U Number of untransmitted haplotypes
CHISQ Test for association
P Asymptotic p-value
</pre>
<a name="hap4">
<h2>Imputing multimarker haplotypes</h2></a>
</p>
If the <tt>--hap-impute</tt> option is also given, this will create two
new files:
<h5>
plink --file mydata --hap myfile.hlist --hap-impute
</h5></p>
will generate the file:
<pre>
plink.impute.ped
plink.impute.map
</pre>
based on the most likely E-M phase reconstructed haplotypes. One could then
simply treat the most likely haplotype assignments as SNPs and use all the
standard analytic options of <tt>PLINK</tt>, e.g. <tt>--assoc</tt>.</p>
<strong>Warning</strong> <b><em>This represents a quick and dirty
approach to haplotype testing</em></b>. Depending on how accurately
the haplotypes have been imputed (i.e. the range of maximum posterior
probabilities per individual) some bias will be introduced into
subsequent tests based on these 'SNPs'. Typically, as long as cases
and controls are phased together, as they are here, this bias is
likely to be quite small and so should not substantively impact
results (unpublished simulation results, SMP). Furthermore, exact
methods can be used to refine the association for the putative hits
discovered by this approach.
</p>
<strong>NOTE</strong> Future versions will allow for a binary PED
file to be created from the <tt>--hap-impute</tt> command. You do
<b>not</b> need to specify <tt>--recode</tt> when using
<tt>--hap-impute</tt>.
<a name="hap5">
<h2>Tabulating individuals' haplotype phases</h2></a>
</p>
To obtain a summary of all possible haplotype phases and the corresponding
posterior probabilities (i.e. given genotype data), use the command:
<h5>
plink --file mydata --hap myfile.hlist --hap-phase
</h5></p>
which will generate the file
<pre>
plink.phase-*
</pre>
where <tt>*</tt> is the name of the 'window' (i.e. the row of the
haplotype list file). That is, if the haplotype list contains
multiple rows, then multiple phase files will be generated.
These files contain the fields, where each row is one possible
haplotype phase for one individual:
<pre>
FID Family ID
IID Individual ID
PH Phase number for that individual (0-based)
HAP1 First haplotype, H1
HAP2 Second haplotype, H2
POSTPROB P(H1,H2 | G )
BEST 1 if most likely phase for that individual
</pre>
</td>
<td width=5%> </td>
</tr>
</table>
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