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<html>
<title>PLINK</title>
<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>Summary statistics</h1>
<tt>PLINK</tt> will generate a number of standard summary statistics
that are useful for quality control (e.g. missing genotype rate, minor
allele frequency, Hardy-Weinberg equilibrium failures and
non-Mendelian transmission rates). These can also be used as thresholds
for subsequent analyses (described in the <a href="thresh.shtml">next section</a>).
</p>
All the per-SNP summary statistics described below are conducted after
removing individuals with high missing genotype rates, as defined by
the <a href="thresh.shtml#miss2"><tt>--mind</tt></a> option. The
default value of which is 0 however, i.e. do not exclude any
individuals.
</p>
<strong>NOTE</strong> Regarding the calculation of genotype rates for
sex chromosomes: for the Y, females are ignored completely. For the
males, heterozygous X and heterozygous Y genotypes are treated as
missing. Having the correct designation of gender is therefore
important to obtain accurate genotype rate estimates, or avoid
incorrectly removing samples, etc.
</p>
<a name="missing">
<h2>Missing genotypes</h2></a>
</p>
To generate a list genotyping/missingness rate statistics:
<h5>
plink --file data --missing
</h5></p>
This option creates two files:
<pre>
plink.imiss
plink.lmiss
</pre>
which detail missingness by individual and by SNP (<b>l</b>ocus),
respectively. For individuals, the format is:
<pre>
FID Family ID
IID Individual ID
MISS_PHENO Missing phenotype? (Y/N)
N_MISS Number of missing SNPs
N_GENO Number of non-obligatory missing genotypes
F_MISS Proportion of missing SNPs
</pre>
For each SNP, the format is:
<pre>
SNP SNP identifier
CHR Chromosome number
N_MISS Number of individuals missing this SNP
N_GENO Number of non-obligatory missing genotypes
F_MISS Proportion of sample missing for this SNP
</pre>
</p><strong>HINT</strong>
To test for case/control differences in missingness,
see the <a href="anal.shtml#testmiss"><tt>--test-missing</tt></a> option.
</p><strong>HINT</strong>
To produce summary of missingness that is stratified by a categorical
cluster variable, use the <tt>--within</tt> <em>filename</em> option as
well as <tt>--missing</tt>. In this way, the missing rates
will be given separately for each level of the categorical variable. For
example, the categorical variable could be which plate that sample was on
in the genotyping. Details on the format of a cluster file can be found
<a href="data.shtml#clst">here</a>.
<a name="oblig_missing">
<h2>Obligatory missing genotypes</h2></a>
</p>
Often genotypes might be missing obligatorarily rather than because of
genotyping failure. For example, some proportion of the sample might
only have been genotyped on a subset of the SNPs. In these cases, one
might not want to filter out SNPs and individuals based on this type
of missing data. Alternatively, genotypes for specific plates (sets of
SNPs/individuals) might have been blanked out with
the <tt>--zero-cluster</tt> option, but you still might want to be
able to sensibly set missing data thresholds.
</p><strong>HINT</strong> See the section on
<a href="dataman.shtml#zero">data management </a> to see how to make
missing certain sets of genotypes.
</p>
Two functions allow these 'obligatory missing' values to be identified
and subsequently handled specially during the filtering steps:
<h5>
plink --bfile mydata --oblig-missing myfile.zero --oblig-clusters myfile.clst --assoc
</h5></p>
This command applies the default genotyping thresholds (90% per
individual and per SNP) but accounting for the fact that certain SNPs
are obligatory missing (with the 90% only refers to those SNPs
actually attempted, for example).
The file specified by <tt>--oblig-clusters</tt> has the same format as
a <a href="data.shtml#clst">cluster file</a> (except only a single cluster
field is allowed here, i.e. only 3 columns). For example,
<pre>
1 1 0 0 1 1 A A C C A A
2 1 0 0 1 1 C C A A C C
3 1 0 0 1 1 A C A A A C
4 1 0 0 1 1 A A C C A A
5 1 0 0 1 1 C C A A C C
6 1 0 0 1 1 A C A A A C
1b 1 0 0 1 1 A A 0 0 0 0
2b 1 0 0 1 1 C C 0 0 0 0
3b 1 0 0 1 1 A C 0 0 0 0
4b 1 0 0 1 1 A A 0 0 0 0
5b 1 0 0 1 1 C C 0 0 0 0
6b 1 0 0 1 1 A C 0 0 0 0
</pre>
and MAP file <tt>test.map</tt>
<pre>
1 snp1 0 1000
1 snp2 0 2000
1 snp3 0 3000
</pre>
If the obligatory missing file, <tt>test.oblig</tt> is
<pre>
snp2 C1
snp3 C1
</pre>
it implies that SNPs <tt>snp2</tt> and <tt>snp3</tt> are obligatory
missing for all individuals belonging to cluster <tt>C1</tt>. The corresponding
cluster file is <tt>test.clst</tt>
<pre>
1b 1 C1
2b 1 C1
3b 1 C1
4b 1 C1
5b 1 C1
6b 1 C1
</pre>
indicating that the last six individuals belong to
cluster <tt>C1</tt>. (Not all individuals need be specified in this
file.)
</p>
<strong>NOTE</strong> You can have more than one cluster category
specified in these files (i.e. implying different patterns of
obligatory missing data for different sets of individuals).
</p>
Running a <tt>--missing</tt> command on the basic fileset, ignoring the
obligatory missing nature of some of the data, results in the following:
<h5>
plink --file test --missing
</h5></p>
which shows in the LOG file that 6 individuals were removed because of missing data
<pre>
...
6 of 12 individuals removed for low genotyping ( MIND > 0.1 )
...
</pre>
and the corresponding output files (<tt>plink.imiss</tt>
and <tt>plink.lmiss</tt>) indicate no missing data (purely because the
six individuals with 2 of 3 genotypes missing were already filtered
out and everybody else left happens to have complete genotyping).
<pre>
FID IID MISS_PHENO N_MISS F_MISS
1 1 N 0 0
2 1 N 0 0
3 1 N 0 0
4 1 N 0 0
5 1 N 0 0
6 1 N 0 0
</pre>
and
<pre>
CHR SNP N_MISS F_MISS
1 snp1 0 0
1 snp2 0 0
1 snp3 0 0
</pre>
In contrast, if the obligatory missing data are specified as follows:
<h5>
plink --file test --missing --oblig-missing test.oblig --oblig-clusters test.clst
</h5></p>
we now see
<pre>
...
0 of 12 individuals removed for low genotyping ( MIND > 0.1 )
...
</pre>
and the corresponding output files now include an extra field, <tt>N_GENO</tt>,
which indicates the number of non-obligatory missing genotypes, which is the denominator
for the genotyping rate calculations
<pre>
FID IID MISS_PHENO N_MISS N_GENO F_MISS
1 1 N 0 3 0
2 1 N 0 3 0
3 1 N 0 3 0
4 1 N 0 3 0
5 1 N 0 3 0
6 1 N 0 3 0
1b 1 N 0 1 0
2b 1 N 0 1 0
3b 1 N 0 1 0
4b 1 N 0 1 0
5b 1 N 0 1 0
6b 1 N 0 1 0
</pre>
and
<pre>
CHR SNP N_MISS N_GENO F_MISS
1 snp1 0 12 0
1 snp2 0 6 0
1 snp3 0 6 0
</pre>
Seen another way, if one specified <tt>--mind 1</tt> to include all
individuals (i.e. not apply the default 90% genotyping rate threshold
for each individual before this step), then the results would not
change with the obligatory missing specification in place, as
expected; in contrast, without the specification of obligatory missing
data, we would see
<pre>
FID IID MISS_PHENO N_MISS F_MISS
1 1 N 0 0
2 1 N 0 0
3 1 N 0 0
4 1 N 0 0
5 1 N 0 0
6 1 N 0 0
1b 1 N 2 0.666667
2b 1 N 2 0.666667
3b 1 N 2 0.666667
4b 1 N 2 0.666667
5b 1 N 2 0.666667
6b 1 N 2 0.666667
</pre>
and
<pre>
CHR SNP N_MISS F_MISS
1 snp1 0 0
1 snp2 6 0.5
1 snp3 6 0.5
</pre>
In this not particularly exciting example, there are no missing
genotypes that are non-obligatory missing (i.e. that not specified by
the two files) -- if there were, it would counted appropriately in the
above files, and used to filter appropriately also.
</p><strong>NOTE</strong> All subsequent analyses do not distingush
whether genotypes were missing due to failure or were obligatory
missing -- that is, this option only effects the behavior of
the <tt>--mind</tt> and <tt>--geno</tt> filters.
</p><strong>NOTE</strong> If a genotype is set to be obligatory missing
but actually in the genotype file it is not missing, then it will be
set to missing and treated as if missing.
<a name="clustermissing">
<h2>Cluster individuals based on missing genotypes</h2></a>
</p>
Systematic batch effects that induce missingness in parts of the sample will induce
correlation between the patterns of missing data that different individuals display.
One approach to detecting correlation in these patterns, that might possibly idenity
such biases, is to cluster individuals based on their <em>identity-by-missingness</em> (IBM).
This approach use exactly the same procedure as the IBS clustering for population
stratification, except the distance between two individuals is based not on which (non-missing)
allele they have at each site, but rather the proportion of sites for which two individuals are
both missing the same genotype. </p>
To use this option:
<h5>
plink --file data --cluster-missing
</h5></p>
which creates the files:
<pre>
plink.matrix.missing
plink.cluster3.missing
</pre>
which have similar formats to the corresponding IBS clustering files. Specifically, the
<tt>plink.mdist.missing</tt> file can be subjected to a visualisation technique such as
multidimensinoal scaling to reveal any strong systematic patterns of missingness.
</p>
<strong>Note</strong> The values in the <tt>.mdist</tt> file are distances rather than
similarities, unlike for standard IBS clustering. That is, a value of 0 means that two
individuals have the same profile of missing genotypes. The exact value represents the
proportion of all SNPs that are discordantly missing (i.e. where one member of the pair
is missing that SNP but the other individual is not).
</P>
The other constraints (significance test, phenotype, cluster size and external matching
criteria) are not used during IBM clustering. Also, by default, all individuals and all SNPs
are included in an IBM clustering analysis, unlike IBS clustering, i.e. even individuals or
SNPs with very low genotyping, or monomorphic alleles. By explicitly specifying
<tt>--mind</tt> or <tt>--geno</tt> or <tt>--maf</tt> certain individuals or SNPs can be
excluded (although the default is probably what is usually required for quality control
procedures).
<a name="testmiss">
<h2>Test of missingness by case/control status</h2></a>
</p>
To obtain a <em>missing chi-sq</em> test (i.e. does, for each SNP,
missingness differ between cases and controls?), use the option:
<h5>
plink --file mydata --test-missing
</h5></p>
which generates a file
<pre>
plink.missing
</pre>
which contains the fields
<pre>
CHR Chromosome number
SNP SNP identifier
F_MISS_A Missing rate in cases
F_MISS_U Missing rate in controls
P Asymptotic p-value (Fisher's exact test)
</pre>
The actual counts of missing genotypes are available in the
<tt>plink.lmiss</tt> file, which is generated by the <tt>--missing</tt>
option.
</p><strong>Note</strong> This test is only applicable to case/control
data.
<a name="mishap">
<h2>Haplotype-based test for non-random missing genotype data</h2></a>
</p>
The previous test asks whether genotypes are missing at random
or not with respect to phenotype. This test asks whether or not
genotypes are missing at random with respect to the true
(unobserved) genotype, based on the observed genotypes of nearby SNPs.
</P>
<strong>Note</strong> This test assumes dense SNP genotyping such that
flanking SNPs are typically in LD with each other. Also bear in mind that
a negative result on this test may simply reflect the fact that there is
little LD in the region.
</p>
This test works by taking a SNP at a time (the 'reference' SNP) and asking
whether haplotype formed by the two flanking SNPs can predict whether or
not the individual is missing at the reference SNP. The test is a simple
haplotypic case/control test, where the phenotype is missing status at the
reference SNP. If missingness at the reference is not random with respect
to the true (unobserved) genotype, we may often expect to see an
association between missingness and flanking haplotypes.
</p>
<strong>Note</strong> Again, just because we might not see such an association does
not necessarily mean that genotypes are missing at random -- this test has
higher specificity than sensitivity. That is, this test will miss a lot; but, when used
as a QC screening tool, one should pay attention to SNPs that show highly significant
patterns of non-random missingness.
</p>
This option is run with the command:
<h5>
plink --file data --test-mishap
</h5></P>
which generates an output file called
<pre>
plink.missing.hap
</pre>
which has the fields
<pre>
LOCUS Reference SNP
HAPLOTYPE Flanking haplotype, or heterozygosity
F_0 Frequency of HAPLOTYPE if missing reference SNP
F_1 Frequency of HAPLOTYPE if not missing reference SNP
M_H1 N missing/not missing for HAPLOTYPE
M_H2 N missing/not missing for not-HAPLOTYPE
CHISQ Chisquare test for non-random missingness
P Asymptotic p-value
SNPS Identifier for flanking SNPs
</pre>
The <tt>HAPLOTYPE</tt> typically represents each two-SNP flanking haplotype
(i.e. not including the reference SNP itself); each reference SNP will also
have a row labelled <tt>HETERO</tt> in this column, which means
we are testing whether or not being heterozygous for the flanking haplotypes (which would,
under many sets of haplotype frequencies, increase the chance of being heterozygous
for the reference SNP). SNPs with no or very little missing genotype data are skipped. Only haplotypes above the
<tt>--maf</tt> threshold are used in analysis.
</p>
Here is an example from real data (rows split into two sets for clarity):
<pre>
LOCUS HAPLOTYPE F_0 F_1 M_H1 M_H2
rs17012390 CT 0.5238 0.01949 55/104 50/5233
rs17012390 TC 0.4762 0.9805 50/5233 55/104
rs17012390 HETERO 1 0.04252 56/114 0/2567
LOCUS HAPLOTYPE CHISQ P SNPS
rs17012390 CT 923.4 0 rs17012387|rs17012393
rs17012390 TC 923.4 0 rs17012387|rs17012393
rs17012390 HETERO 863.3 0 rs17012387|rs17012393
</pre>
This clearly shows a huge chi-square (the sample is large, N of over 2500 individuals).
We see that of 56 missing genotypes for this reference SNP, all occur when the flanking
haplotypic background is heterozygous (i.e. <tt>M_H1</tt> shows <tt>56/114</tt>, indicating that there
are 114 other instances of a heterozygous haplotypic background when the reference SNP is not missing)
whereas we see not a single missing call when the flanking SNP background is homozygous, of which we see
2567 observations. This is clearly indicative of non-random association between the unobserved genotype and
missing status.
</p>
Looking at the same data a different way, <tt>F_1</tt> indicates that the majority of the sample (people not
missing at the reference SNP) have haplotype frequencies of <tt>CT</tt> and <tt>TC</tt> haplotypes at approximately
0.02 and 0.98 respectively). In contrast, because all people missing this SNP are on heterozygous backgrounds,
these frequencies become approximately 50:50 in this group (shown in <tt>F_0</tt>).
</p>
In the particular dataset this example comes from, this SNP would have passed a
standard quality control test. The <tt>--hardy</tt> command shows that this SNP does
not failure the HWE test; also, it does not show excessive amounts
of missing data (the <tt>--missing</tt> command indicates a missing rate of 0.021). The genotype
counts (obtained by the <tt>--hardy</tt> option) are, for the whole sample, <tt>0/104/2584</tt>.
</p>
In contrast, here are the same results for a different SNP that does not show any evidence
of non-random missingness.
<pre>
LOCUS HAPLOTYPE F_0 F_1 M_H1 M_H2
rs3912752 CC 0.07692 0.06507 2/354 24/5086
rs3912752 TT 0.1154 0.205 3/1115 23/4325
rs3912752 CT 0.8077 0.73 21/3971 5/1469
rs3912752 HETERO 0.2308 0.4279 3/1164 10/1556
LOCUS HAPLOTYPE CHISQ P SNPS
rs3912752 CC 0.05967 0.807 rs3912751|rs351596
rs3912752 TT 1.276 0.2586 rs3912751|rs351596
rs3912752 CT 0.7938 0.3729 rs3912751|rs351596
rs3912752 HETERO 2.056 0.1516 rs3912751|rs351596
</pre>
Here we do not see any deviation between the flanking haplotype frequencies between people
missing versus genotyped for the reference SNP. Of course, there is less missingness for this SNP
(26 missing genotypes) so we might expect power is lower, even if there were non-random missingness.
This only highlights the point made above that, in general, significant results are more interpretable
than non-signficant results for this test. But more importantly, if there are only a handful of missing genotypes,
we do not particular care whether or not they are missing at random, as they would not bias the association with disease
in any case. Of course, whether there is non-random genotyping <em>error</em> is another question...
</p>
By default, we currently just select exactly two flanking SNPs. This can be changed with the option <tt>--mishap-window</tt>. For
example,
<h5>
plink --bfile mydata --test-mishap --mishap-window 4
</h5></p>
Future releases will feature a more
intelligent selection
of flanking markers.
</p>
<strong>Note</strong> This routine currently skips the SNPs on the X and Y chromosomes.
<a name="hardy">
<h2>Hardy-Weinberg Equilibrium</h2></a>
</p>
To generate a list of genotype counts and Hardy-Weinberg test
statistics for each SNP, use the option:
<h5>
plink --file data --hardy
</h5></p>
which creates a file:
<pre>
plink.hwe
</pre>
This file has the following format
<pre>
SNP SNP identifier
TEST Code indicating sample
A1 Minor allele code
A2 Major allele code
GENO Genotype counts: 11/12/22
O(HET) Observed heterozygosity
E(HET) Expected heterozygosity
P H-W p-value
</pre>
For case/control samples, each SNP will have three entries (rows) in this
file, with <tt>TEST</tt> being either <tt>ALL</tt>, <tt>AFF</tt> (cases
only) or <tt>UNAFF</tt> (controls only). For quantitative traits, only a
single row will appear for each SNP, labelled <tt>ALL(QT)</tt>.
</p>
Only founders are considered for the Hardy-Weinberg calculations -- ie.
for family data, any offspring are ignored.
</p><strong>WARNING</strong> By default, this procedure only considers founders, so
no HW results would be given for sibling-only datasets (i.e. if no parents exist).
To perform a rough, somewhat biased test, use the <tt>--nonfounders</tt> option
which means that all individuals will be included. Alternatively, manually extract
one person per family for this calculation and recode these individuals as founders
(see the <tt>--keep</tt> option to facilitate this).
</p>
The default test is an exact one, described and implemented by Wigginton <em>et al</em>
(see reference below), which is more accurate for rare genotypes. You can still perform
the standard asymptotic test with the <tt>--hardy2</tt> option. </p>
<pre>
<font size="-1">A Note on Exact Tests of Hardy-Weinberg Equilibrium.
Wigginton JE, Cutler DJ and Abecasis GR
Am J Hum Genet (2005) 76: 887-93
</font></pre>
</p>
<a name="freq">
<h2>Allele frequency</h2></a>
</p>
To generate a list of minor allele frequencies (MAF) for each SNP,
based on all founders in the sample:
<h5>
plink --file data --freq
</h5></p>
will create a file:
<pre>
plink.frq
</pre>
with five columns:
<pre>
CHR Chromosome
SNP SNP identifier
A1 Allele 1 code (minor allele)
A2 Allele 2 code (major allele)
MAF Minor allele frequency
NCHROBS Non-missing allele count
</pre>
</p><strong>HINT</strong>
To produce summary of allele frequencies that is stratified by
a categorical cluster variable, use the <tt>--within</tt> <em>filename</em>
option as well as <tt>--missing</tt>. In this way, the frequencies
will be given separately for each level of the categorical variable. Details
on the format of a cluster file can be found
<a href="data.shtml#clst">here</a>.