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plink_help.txt
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plink_help.txt
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PLINK v1.90b6.21 64-bit (19 Oct 2020) www.cog-genomics.org/plink/1.9/
(C) 2005-2020 Shaun Purcell, Christopher Chang GNU General Public License v3
In the command line flag definitions that follow,
* <angle brackets> denote a required parameter, where the text between the
angle brackets describes its nature.
* ['square brackets + single-quotes'] denotes an optional modifier. Use the
EXACT text in the quotes.
* [{bar|separated|braced|bracketed|values}] denotes a collection of mutually
exclusive optional modifiers (again, the exact text must be used). When
there are no outer square brackets, one of the choices must be selected.
* ['quoted_text='<description of value>] denotes an optional modifier that
must begin with the quoted text, and be followed by a value with no
whitespace in between. '|' may also be used here to indicate mutually
exclusive options.
* [square brackets without quotes or braces] denote an optional parameter,
where the text between the brackets describes its nature.
* An ellipsis (...) indicates that you may enter multiple parameters of the
specified type.
plink <input flag(s)...> [command flag(s)...] [other flag(s)...]
plink --help [flag name(s)...]
Most PLINK runs require exactly one main input fileset. The following flags
are available for defining its form and location:
--bfile [prefix] : Specify .bed + .bim + .fam prefix (default 'plink').
--bed <filename> : Specify full name of .bed file.
--bim <filename> : Specify full name of .bim file.
--fam <filename> : Specify full name of .fam file.
--keep-autoconv : With --file/--tfile/--lfile/--vcf/--bcf/--data/--23file,
don't delete autogenerated binary fileset at end of run.
--file [prefix] : Specify .ped + .map filename prefix (default 'plink').
--ped <filename> : Specify full name of .ped file.
--map <filename> : Specify full name of .map file.
--no-fid : .fam/.ped file does not contain column 1 (family ID).
--no-parents : .fam/.ped file does not contain columns 3-4 (parents).
--no-sex : .fam/.ped file does not contain column 5 (sex).
--no-pheno : .fam/.ped file does not contain column 6 (phenotype).
--tfile [prefix] : Specify .tped + .tfam filename prefix (default 'plink').
--tped <fname> : Specify full name of .tped file.
--tfam <fname> : Specify full name of .tfam file.
--lfile [prefix] : Specify .lgen + .map + .fam (long-format fileset) prefix.
--lgen <fname> : Specify full name of .lgen file.
--reference <fn> : Specify default allele file accompanying .lgen input.
--allele-count : When used with --lfile/--lgen + --reference, specifies
that the .lgen file contains reference allele counts.
--vcf <filename> : Specify full name of .vcf or .vcf.gz file.
--bcf <filename> : Specify full name of BCF2 file.
--data [prefix] : Specify Oxford .gen + .sample prefix (default 'plink').
--gen <filename> : Specify full name of .gen or .gen.gz file.
--bgen <f> ['snpid-chr'] : Specify full name of .bgen file.
--sample <fname> : Specify full name of .sample file.
--23file <fname> [FID] [IID] [sex] [pheno] [pat. ID] [mat. ID] :
Specify 23andMe input file.
--grm-gz [prfx] : Specify .grm.gz + .grm.id (GCTA rel. matrix) prefix.
--grm-bin [prfx] : Specify .grm.bin + .grm.N.bin + .grm.id (GCTA triangular
binary relationship matrix) filename prefix.
--dummy <sample ct> <SNP ct> [missing geno freq] [missing pheno freq]
[{acgt | 1234 | 12}] ['scalar-pheno']
This generates a fake input dataset with the specified number of samples
and SNPs. By default, the missing genotype and phenotype frequencies are
zero, and genotypes are As and Bs (change the latter with
'acgt'/'1234'/'12'). The 'scalar-pheno' modifier causes a normally
distributed scalar phenotype to be generated instead of a binary one.
--simulate <simulation parameter file> [{tags | haps}] [{acgt | 1234 | 12}]
--simulate-qt <sim. parameter file> [{tags | haps}] [{acgt | 1234 | 12}]
--simulate generates a fake input dataset with disease-associated SNPs,
while --simulate-qt generates a dataset with quantitative trait loci.
Output files have names of the form 'plink.<extension>' by default. You can
change the 'plink' prefix with
--out <prefix> : Specify prefix for output files.
Most runs also require at least one of the following commands:
--make-bed
Create a new binary fileset. Unlike the automatic text-to-binary
converters (which only heed chromosome filters), this supports all of
PLINK's filtering flags.
--make-just-bim
--make-just-fam
Variants of --make-bed which only write a new .bim or .fam file. Can be
used with only .bim/.fam input.
USE THESE CAUTIOUSLY. It is very easy to desynchronize your binary
genotype data and your .bim/.fam indexes if you use these commands
improperly. If you have any doubt, stick with --make-bed.
--recode <output format> [{01 | 12}] [{tab | tabx | spacex | bgz | gen-gz}]
['include-alt'] ['omit-nonmale-y']
Create a new text fileset with all filters applied. The following output
formats are supported:
* '23': 23andMe 4-column format. This can only be used on a single
sample's data (--keep may be handy), and does not support multicharacter
allele codes.
* 'A': Sample-major additive (0/1/2) coding, suitable for loading from R.
If you need uncounted alleles to be named in the header line, add the
'include-alt' modifier.
* 'AD': Sample-major additive (0/1/2) + dominant (het=1/hom=0) coding.
Also supports 'include-alt'.
* 'A-transpose': Variant-major 0/1/2.
* 'beagle': Unphased per-autosome .dat and .map files, readable by early
BEAGLE versions.
* 'beagle-nomap': Single .beagle.dat file.
* 'bimbam': Regular BIMBAM format.
* 'bimbam-1chr': BIMBAM format, with a two-column .pos.txt file. Does not
support multiple chromosomes.
* 'fastphase': Per-chromosome fastPHASE files, with
.chr-<chr #>.recode.phase.inp filename extensions.
* 'fastphase-1chr': Single .recode.phase.inp file. Does not support
multiple chromosomes.
* 'HV': Per-chromosome Haploview files, with .chr-<chr #>{.ped,.info}
filename extensions.
* 'HV-1chr': Single Haploview .ped + .info file pair. Does not support
multiple chromosomes.
* 'lgen': PLINK 1 long-format (.lgen + .fam + .map), loadable with --lfile.
* 'lgen-ref': .lgen + .fam + .map + .ref, loadable with --lfile +
--reference.
* 'list': Single genotype-based list, up to 4 lines per variant. To omit
nonmale genotypes on the Y chromosome, add the 'omit-nonmale-y' modifier.
* 'rlist': .rlist + .fam + .map fileset, where the .rlist file is a
genotype-based list which omits the most common genotype for each
variant. Also supports 'omit-nonmale-y'.
* 'oxford': Oxford-format .gen + .sample. With the 'gen-gz' modifier, the
.gen file is gzipped.
* 'ped': PLINK 1 sample-major (.ped + .map), loadable with --file.
* 'compound-genotypes': Same as 'ped', except that the space between each
pair of same-variant allele codes is removed.
* 'structure': Structure-format.
* 'transpose': PLINK 1 variant-major (.tped + .tfam), loadable with
--tfile.
* 'vcf', 'vcf-fid', 'vcf-iid': VCFv4.2. 'vcf-fid' and 'vcf-iid' cause
family IDs or within-family IDs respectively to be used for the sample
IDs in the last header row, while 'vcf' merges both IDs and puts an
underscore between them. If the 'bgz' modifier is added, the VCF file is
block-gzipped.
The A2 allele is saved as the reference and normally flagged as not based
on a real reference genome (INFO:PR). When it is important for reference
alleles to be correct, you'll also want to include --a2-allele and
--real-ref-alleles in your command.
In addition,
* The '12' modifier causes A1 (usually minor) alleles to be coded as '1'
and A2 alleles to be coded as '2', while '01' maps A1 -> 0 and A2 -> 1.
* The 'tab' modifier makes the output mostly tab-delimited instead of
mostly space-delimited. 'tabx' and 'spacex' force all tabs and all
spaces, respectively.
--flip-scan ['verbose']
(alias: --flipscan)
LD-based scan for case/control strand inconsistency.
--write-covar
If a --covar file is loaded, --make-bed/--make-just-fam and --recode
automatically generate an updated version (with all filters applied).
However, if you do not wish to simultaneously generate a new genotype file,
you can use --write-covar to just produce a pruned covariate file.
--write-cluster ['omit-unassigned']
If clusters are specified with --within/--family, this generates a new
cluster file (with all filters applied). The 'omit-unassigned' modifier
causes unclustered samples to be omitted from the file; otherwise their
cluster is 'NA'.
--write-set
--set-table
If sets have been defined, --write-set dumps 'END'-terminated set
membership lists to <output prefix>.set, while --set-table writes a
variant-by-set membership table to <output prefix>.set.table.
--merge <.ped filename> <.map filename>
--merge <text fileset prefix>
--bmerge <.bed filename> <.bim filename> <.fam filename>
--bmerge <binary fileset prefix>
Merge the given fileset with the initially loaded fileset, writing the
result to <output prefix>.bed + .bim + .fam. (It is no longer necessary to
simultaneously specify --make-bed.)
--merge-list <filename>
Merge all filesets named in the text file with the reference fileset, if
one was specified. (However, this can also be used *without* a reference;
in that case, the newly created fileset is then treated as the reference by
most other PLINK operations.) The text file is interpreted as follows:
* If a line contains only one name, it is assumed to be the prefix for a
binary fileset.
* If a line contains exactly two names, they are assumed to be the full
filenames for a text fileset (.ped first, then .map).
* If a line contains exactly three names, they are assumed to be the full
filenames for a binary fileset (.bed, then .bim, then .fam).
--write-snplist
--list-23-indels
--write-snplist writes a .snplist file listing the names of all variants
which pass the filters and inclusion thresholds you've specified, while
--list-23-indels writes the subset with 23andMe-style indel calls (D/I
allele codes).
--list-duplicate-vars ['require-same-ref'] ['ids-only'] ['suppress-first']
--list-duplicate-vars writes a .dupvar file describing all groups of
variants with matching positions and allele codes.
* By default, A1/A2 allele assignments are ignored; use 'require-same-ref'
to override this.
* Normally, the report contains position and allele codes. To remove them
(and produce a file directly usable with e.g. --extract/--exclude), use
'ids-only'. Note that this command will fail in 'ids-only' mode if any
of the reported IDs are not unique.
* 'suppress-first' causes the first variant ID in each group to be omitted
from the report.
--freq [{counts | case-control}] ['gz']
--freqx ['gz']
--freq generates a basic allele frequency (or count, if the 'counts'
modifier is present) report. This can be combined with --within/--family
to produce a cluster-stratified allele frequency/count report instead, or
the 'case-control' modifier to report case and control allele frequencies
separately.
--freqx generates a more detailed genotype count report, designed for use
with --read-freq.
--missing ['gz']
Generate sample- and variant-based missing data reports. If clusters are
defined, the variant-based report is cluster-stratified. 'gz' causes the
output files to be gzipped.
Unlike most other commands, this doesn't treat het. haploids as missing.
--test-mishap
Check for association between missing calls and flanking haplotypes.
--hardy ['midp'] ['gz']
Generate a Hardy-Weinberg exact test p-value report. (This does NOT
simultaneously filter on the p-value any more; use --hwe for that.) With
the 'midp' modifier, the test applies the mid-p adjustment described in
Graffelman J, Moreno V (2013) The mid p-value in exact tests for
Hardy-Weinberg Equilibrium.
--mendel ['summaries-only']
Generate a Mendel error report. The 'summaries-only' modifier causes the
.mendel file (listing every single error) to be skipped.
--het ['small-sample'] ['gz']
--ibc
Estimate inbreeding coefficients. --het reports method-of-moments
estimates, while --ibc calculates all three values described in Yang J, Lee
SH, Goddard ME and Visscher PM (2011) GCTA: A Tool for Genome-wide Complex
Trait Analysis. (That paper also describes the relationship matrix
computation we reimplement.)
* These functions require decent MAF estimates. If there are very few
samples in your immediate fileset, --read-freq is practically mandatory
since imputed MAFs are wildly inaccurate in that case.
* They also assume the marker set is in approximate linkage equilibrium.
* By default, --het omits the n/(n-1) multiplier in Nei's expected
homozygosity formula. The 'small-sample' modifier causes it to be
included, while forcing --het to use MAFs imputed from founders in the
immediate dataset.
--check-sex [female max F] [male min F]
--check-sex ycount [female max F] [male min F] [female max Y obs]
[male min Y obs]
--check-sex y-only [female max Y obs] [male min Y obs]
--impute-sex [female max F] [male min F]
--impute-sex ycount [female max F] [male min F] [female max Y obs]
[male min Y obs]
--impute-sex y-only [female max Y obs] [male min Y obs]
--check-sex normally compares sex assignments in the input dataset with
those imputed from X chromosome inbreeding coefficients.
* Make sure that the X chromosome pseudo-autosomal region has been split
off (with e.g. --split-x) before using this.
* You also need decent MAF estimates (so, with very few samples in your
immediate fileset, use --read-freq), and your marker set should be in
approximate linkage equilibrium.
* By default, F estimates smaller than 0.2 yield female calls, and values
larger than 0.8 yield male calls. If you pass numeric parameter(s) to
--check-sex, the first two control these thresholds.
There are now two modes which consider Y chromosome data.
* In 'ycount' mode, gender is still imputed from the X chromosome, but
female calls are downgraded to ambiguous whenever more than 0 nonmissing
Y genotypes are present, and male calls are downgraded when fewer than 0
are present. (Note that these are counts, not rates.) These thresholds
are controllable with --check-sex ycount's optional 3rd and 4th numeric
parameters.
* In 'y-only' mode, gender is imputed from nonmissing Y genotype counts.
The male minimum threshold defaults to 1 instead of zero in this case.
--impute-sex changes sex assignments to the imputed values, and is
otherwise identical to --check-sex. It must be used with
--make-bed/--recode/--write-covar.
--fst ['case-control']
(alias: --Fst)
Estimate Wright's Fst for each autosomal diploid variant using the method
introduced in Weir BS, Cockerham CC (1984) Estimating F-statistics for the
analysis of population structure, given a set of subpopulations defined via
--within. Raw and weighted global means are also reported.
* If you're interested in the global means, it is usually best to perform
this calculation on a marker set in approximate linkage equilibrium.
* If you have only two subpopulations, you can represent them with
case/control status and use the 'case-control' modifier.
--indep <window size>['kb'] <step size (variant ct)> <VIF threshold>
--indep-pairwise <window size>['kb'] <step size (variant ct)> <r^2 threshold>
--indep-pairphase <window size>['kb'] <step size (variant ct)> <r^2 thresh>
Generate a list of markers in approximate linkage equilibrium. With the
'kb' modifier, the window size is in kilobase instead of variant count
units. (Pre-'kb' space is optional, i.e. "--indep-pairwise 500 kb 5 0.5"
and "--indep-pairwise 500kb 5 0.5" have the same effect.)
Note that you need to rerun PLINK using --extract or --exclude on the
.prune.in/.prune.out file to apply the list to another computation.
--r [{square | square0 | triangle | inter-chr}] [{gz | bin | bin4}]
['spaces'] ['in-phase'] [{d | dprime | dprime-signed}] ['with-freqs']
['yes-really']
--r2 [{square | square0 | triangle | inter-chr}] [{gz | bin | bin4}]
['spaces'] ['in-phase'] [{d | dprime | dprime-signed}] ['with-freqs']
['yes-really']
LD statistic reports. --r yields raw inter-variant correlations, while
--r2 reports their squares. You can request results for all pairs in
matrix format (if you specify 'bin' or one of the shape modifiers), all
pairs in table format ('inter-chr'), or a limited window in table format
(default).
* The 'gz' modifier causes the output text file to be gzipped.
* 'bin' causes the output matrix to be written in double-precision binary
format, while 'bin4' specifics single-precision binary. The matrix is
square if no shape is explicitly specified.
* By default, text matrices are tab-delimited; 'spaces' switches this.
* 'in-phase' adds a column with in-phase allele pairs to table-formatted
reports. (This cannot be used with very long allele codes.)
* 'dprime' adds the absolute value of Lewontin's D-prime statistic to
table-formatted reports, and forces both r/r^2 and D-prime to be based on
the maximum likelihood solution to the cubic equation discussed in Gaunt
T, Rodriguez S, Day I (2007) Cubic exact solutions for the estimation of
pairwise haplotype frequencies.
'dprime-signed' keeps the sign, while 'd' skips division by D_{max}.
* 'with-freqs' adds MAF columns to table-formatted reports.
* Since the resulting file can easily be huge, you're required to add the
'yes-really' modifier when requesting an unfiltered, non-distributed all
pairs computation on more than 400k variants.
* These computations can be subdivided with --parallel (even when the
'square' modifier is active).
--ld <variant ID> <variant ID> ['hwe-midp']
This displays haplotype frequencies, r^2, and D' for a single pair of
variants. When there are multiple biologically possible solutions to the
haplotype frequency cubic equation, all are displayed (instead of just the
maximum likelihood solution identified by --r/--r2), along with HWE exact
test statistics.
--show-tags <filename>
--show-tags all
* If a file is specified, list all variants which tag at least one variant
named in the file. (This will normally be a superset of the original
list, since a variant is considered to tag itself here.)
* If 'all' mode is specified, for each variant, each *other* variant which
tags it is reported.
--blocks ['no-pheno-req'] ['no-small-max-span']
Estimate haplotype blocks, via Haploview's interpretation of the block
definition suggested by Gabriel S et al. (2002) The Structure of Haplotype
Blocks in the Human Genome.
* Normally, samples with missing phenotypes are not considered by this
computation; the 'no-pheno-req' modifier lifts this restriction.
* Normally, size-2 blocks may not span more than 20kb, and size-3 blocks
are limited to 30kb. The 'no-small-max-span' modifier removes these
limits.
The .blocks file is valid input for PLINK 1.07's --hap command. However,
the --hap... family of flags has not been reimplemented in PLINK 1.9 due to
poor phasing accuracy relative to other software; for now, we recommend
using BEAGLE instead of PLINK for case/control haplotype association
analysis. (You can use "--recode beagle" to export data to BEAGLE 3.3.)
We apologize for the inconvenience, and plan to develop variants of the
--hap... flags which handle pre-phased data effectively.
--distance [{square | square0 | triangle}] [{gz | bin | bin4}] ['ibs']
['1-ibs'] ['allele-ct'] ['flat-missing']
Write a lower-triangular tab-delimited table of (weighted) genomic
distances in allele count units to <output prefix>.dist, and a list of the
corresponding sample IDs to <output prefix>.dist.id. The first row of the
.dist file contains a single <genome 1-genome 2> distance, the second row
has the <genome 1-genome 3> and <genome 2-genome 3> distances in that
order, etc.
* It is usually best to perform this calculation on a marker set in
approximate linkage equilibrium.
* If the 'square' or 'square0' modifier is present, a square matrix is
written instead; 'square0' fills the upper right triangle with zeroes.
* If the 'gz' modifier is present, a compressed .dist.gz file is written
instead of a plain text file.
* If the 'bin' modifier is present, a binary (square) matrix of
double-precision floating point values, suitable for loading from R, is
instead written to <output prefix>.dist.bin. ('bin4' specifies
single-precision numbers instead.) This can be combined with 'square0'
if you still want the upper right zeroed out, or 'triangle' if you don't
want to pad the upper right at all.
* If the 'ibs' modifier is present, an identity-by-state matrix is written
to <output prefix>.mibs. '1-ibs' causes distances expressed as genomic
proportions (i.e. 1 - IBS) to be written to <output prefix>.mdist.
Combine with 'allele-ct' if you want to generate the usual .dist file as
well.
* By default, distance rescaling in the presence of missing genotype calls
is sensitive to allele count distributions: if variant A contributes, on
average, twice as much to other pairwise distances as variant B, a
missing call at variant A will result in twice as large of a missingness
correction. To turn this off (because e.g. your missing calls are highly
nonrandom), use the 'flat-missing' modifier.
* The computation can be subdivided with --parallel.
--distance-matrix
--ibs-matrix
These deprecated commands are equivalent to "--distance 1-ibs flat-missing
square" and "--distance ibs flat-missing square", respectively, except that
they generate space- instead of tab-delimited text matrices.
--make-rel [{square | square0 | triangle}] [{gz | bin | bin4}]
[{cov | ibc2 | ibc3}]
Write a lower-triangular variance-standardized realized relationship matrix
to <output prefix>.rel, and corresponding IDs to <output prefix>.rel.id.
* It is usually best to perform this calculation on a marker set in
approximate linkage equilibrium.
* 'square', 'square0', 'triangle', 'gz', 'bin', and 'bin4' act as they do
on --distance.
* The 'cov' modifier removes the variance standardization step, causing a
covariance matrix to be calculated instead.
* By default, the diagonal elements in the relationship matrix are based on
--ibc's Fhat1; use the 'ibc2' or 'ibc3' modifiers to base them on Fhat2
or Fhat3 instead.
* The computation can be subdivided with --parallel.
--make-grm-gz ['no-gz'] [{cov | ibc2 | ibc3}]
--make-grm-bin [{cov | ibc2 | ibc3}]
--make-grm-gz writes the relationships in GCTA's original gzipped list
format, which describes one pair per line, while --make-grm-bin writes them
in GCTA 1.1+'s single-precision triangular binary format. Note that these
formats explicitly report the number of valid observations (where neither
sample has a missing call) for each pair, which is useful input for some
scripts.
These computations can be subdivided with --parallel.
--rel-cutoff [val]
(alias: --grm-cutoff)
Exclude one member of each pair of samples with relatedness greater than
the given cutoff value (default 0.025). If no later operation will cause
the list of remaining samples to be written to disk, this will save it to
<output prefix>.rel.id.
Note that maximizing the remaining sample size is equivalent to the NP-hard
maximum independent set problem, so we use a greedy algorithm instead of
guaranteeing optimality. (Use the --make-rel and --keep/--remove flags if
you want to try to do better.)
--ibs-test [permutation count]
--groupdist [iters] [d]
Given case/control phenotype data, these commands consider three subsets of
the distance matrix: pairs of affected samples, affected-unaffected pairs,
and pairs of unaffected samples. Each of these subsets has a distribution
of pairwise genomic distances; --ibs-test uses permutation to estimate
p-values re: which types of pairs are most similar, while --groupdist
focuses on the differences between the centers of these distributions and
estimates standard errors via delete-d jackknife.
--regress-distance [iters] [d]
Linear regression of pairwise genomic distances on pairwise average
phenotypes and vice versa, using delete-d jackknife for standard errors. A
scalar phenotype is required.
* With less than two parameters, d is set to <number of people>^0.6 rounded
down. With no parameters, 100k iterations are run.
--regress-rel [iters] [d]
Linear regression of pairwise genomic relationships on pairwise average
phenotypes, and vice versa. Defaults for iters and d are the same as for
--regress-distance.
--genome ['gz'] ['rel-check'] ['full'] ['unbounded'] ['nudge']
Generate an identity-by-descent report.
* It is usually best to perform this calculation on a marker set in
approximate linkage equilibrium.
* The 'rel-check' modifier excludes pairs of samples with different FIDs
from the final report.
* 'full' adds raw pairwise comparison data to the report.
* The P(IBD=0/1/2) estimator employed by this command sometimes yields
numbers outside the range [0,1]; by default, these are clipped. The
'unbounded' modifier turns off this clipping.
* Then, when PI_HAT^2 < P(IBD=2), 'nudge' adjusts the final P(IBD=0/1/2)
estimates to a theoretically possible configuration.
* The computation can be subdivided with --parallel.
--homozyg [{group | group-verbose}] ['consensus-match'] ['extend']
['subtract-1-from-lengths']
--homozyg-snp <min var count>
--homozyg-kb <min length>
--homozyg-density <max inverse density (kb/var)>
--homozyg-gap <max internal gap kb length>
--homozyg-het <max hets>
--homozyg-window-snp <scanning window size>
--homozyg-window-het <max hets in scanning window hit>
--homozyg-window-missing <max missing calls in scanning window hit>
--homozyg-window-threshold <min scanning window hit rate>
These commands request a set of run-of-homozygosity reports, and allow you
to customize how they are generated.
* If you're satisfied with all the default settings described below, just
use --homozyg with no modifiers. Otherwise, --homozyg lets you change a
few binary settings:
* 'group[-verbose]' adds a report on pools of overlapping runs of
homozygosity. (Automatically set when --homozyg-match is present.)
* With 'group[-verbose]', 'consensus-match' causes pairwise segmental
matches to be called based on the variants in the pool's consensus
segment, rather than the variants in the pairwise intersection.
* Due to how the scanning window algorithm works, it is possible for a
reported ROH to be adjacent to a few homozygous variants. The 'extend'
modifier causes them to be included in the reported ROH if that
wouldn't cause a violation of the --homozyg-density bound.
* By default, segment bp lengths are calculated as <end bp position> -
<start bp position> + 1. Therefore, reports normally differ slightly
from PLINK 1.07, which does not add 1 at the end. For testing
purposes, you can use the 'subtract-1-from-lengths' modifier to apply
the old formula.
* By default, only runs of homozygosity containing at least 100 variants,
and of total length >= 1000 kilobases, are noted. You can change these
minimums with --homozyg-snp and --homozyg-kb, respectively.
* By default, a ROH must have at least one variant per 50 kb on average;
change this bound with --homozyg-density.
* By default, if two consecutive variants are more than 1000 kb apart, they
cannot be in the same ROH; change this bound with --homozyg-gap.
* By default, a ROH can contain an unlimited number of heterozygous calls;
you can impose a limit with --homozyg-het.
* By default, the scanning window contains 50 variants; change this with
--homozyg-window-snp.
* By default, a scanning window hit can contain at most 1 heterozygous
call and 5 missing calls; change these limits with --homozyg-window-het
and --homozyg-window-missing, respectively.
* By default, for a variant to be eligible for inclusion in a ROH, the hit
rate of all scanning windows containing the variant must be at least
0.05; change this threshold with --homozyg-window-threshold.
--cluster ['cc'] [{group-avg | old-tiebreaks}] ['missing'] ['only2']
Cluster samples using a pairwise similarity statistic (normally IBS).
* The 'cc' modifier forces every cluster to have at least one case and one
control.
* The 'group-avg' modifier causes clusters to be joined based on average
instead of minimum pairwise similarity.
* The 'missing' modifier causes clustering to be based on
identity-by-missingness instead of identity-by-state, and writes a
space-delimited identity-by-missingness matrix to disk.
* The 'only2' modifier causes only a .cluster2 file (which is valid input
for --within) to be written; otherwise 2 other files will be produced.
* By default, IBS ties are not broken in the same manner as PLINK 1.07, so
final cluster solutions tend to differ. This is generally harmless.
However, to simplify testing, you can use the 'old-tiebreaks' modifier to
force emulation of the old algorithm.
--pca [count] ['header'] ['tabs'] ['var-wts']
Calculates a variance-standardized relationship matrix (use
--make-rel/--make-grm-gz/--make-grm-bin to dump it), and extracts the top
20 principal components.
* It is usually best to perform this calculation on a marker set in
approximate linkage equilibrium.
* You can change the number of PCs by passing a numeric parameter.
* The 'header' modifier adds a header line to the .eigenvec output file.
(For compatibility with the GCTA flag of the same name, the default is no
header line.)
* The 'tabs' modifier causes the .eigenvec file(s) to be tab-delimited.
* The 'var-wts' modifier requests an additional .eigenvec.var file with PCs
expressed as variant weights instead of sample weights.
--neighbour <n1> <n2>
(alias: --neighbor)
Report IBS distances from each sample to their n1th- to n2th-nearest
neighbors, associated Z-scores, and the identities of those neighbors.
Useful for outlier detection.
--assoc ['perm' | 'mperm='<value>] ['perm-count'] [{fisher | fisher-midp}]
['counts'] ['set-test']
--assoc ['perm' | 'mperm='<value>] ['perm-count'] ['qt-means'] ['lin']
['set-test']
--model ['perm' | 'mperm='<value>] ['perm-count']
[{fisher | fisher-midp | trend-only}] ['set-test']
[{dom | rec | gen | trend}]
Basic association analysis report.
Given a case/control phenotype, --assoc performs a 1df chi-square allelic
test, while --model performs 4 other tests as well (1df dominant gene
action, 1df recessive gene action, 2df genotypic, Cochran-Armitage trend).
* With 'fisher'/'fisher-midp', Fisher's exact test is used to generate
p-values. 'fisher-midp' also applies Lancaster's mid-p adjustment.
* 'perm' causes an adaptive permutation test to be performed.
* 'mperm='<value> causes a max(T) permutation test with the specified
number of replications to be performed.
* 'perm-count' causes the permutation test report to include counts instead
of frequencies.
* 'counts' causes --assoc to report allele counts instead of frequencies.
* 'set-test' tests the significance of variant sets. Requires permutation;
can be customized with --set-p/--set-r2/--set-max.
* 'dom', 'rec', 'gen', and 'trend' force the corresponding test to be used
as the basis for --model permutation. (By default, the most significant
result among the allelic, dominant, and recessive tests is used.)
* 'trend-only' causes only the trend test to be performed.
Given a quantitative phenotype, --assoc normally performs a Wald test.
* In this case, the 'qt-means' modifier causes trait means and standard
deviations stratified by genotype to be reported as well.
* 'lin' causes the Lin statistic to be computed, and makes it the basis for
multiple-testing corrections and permutation tests.
Several other flags (most notably, --aperm) can be used to customize the
permutation test.
--mh ['perm' | 'mperm='<value>] ['perm-count'] ['set-test']
(alias: --cmh)
--bd ['perm' | 'perm-bd' | 'mperm='<value>] ['perm-count'] ['set-test']
--mh2
--homog
Given a case/control phenotype and a set of clusters, --mh computes 2x2xK
Cochran-Mantel-Haenszel statistics for each variant, while --bd also
performs the Breslow-Day test for odds ratio homogeneity. Permutation and
variant set testing based on the CMH (default) or Breslow-Day (when
'perm-bd' is present) statistic are supported.
The following similar analyses are also available:
* --mh2 swaps the roles of case/control status and cluster membership,
performing a phenotype-stratified IxJxK Cochran-Mantel-Haenszel test on
association between cluster assignments and genotypes.
* --homog executes an alternative to the Breslow-Day test, based on
partitioning of the chi-square statistic.
--gxe [covariate index]
Given both a quantitative phenotype and a case/control covariate loaded
with --covar defining two groups, --gxe compares the regression coefficient
derived from considering only members of one group to the regression
coefficient derived from considering only members of the other. By
default, the first covariate in the --covar file defines the groups; use
e.g. "--gxe 3" to base them on the third covariate instead.
--linear ['perm' | 'mperm='<value>] ['perm-count'] ['set-test']
[{genotypic | hethom | dominant | recessive | no-snp}]
['hide-covar'] [{sex | no-x-sex}] ['interaction'] ['beta']
['standard-beta'] ['intercept']
--logistic ['perm' | 'mperm='<value>] ['perm-count'] ['set-test']
[{genotypic | hethom | dominant | recessive | no-snp}]
['hide-covar'] [{sex | no-x-sex}] ['interaction'] ['beta']
['intercept']
Multi-covariate association analysis on a quantitative (--linear) or
case/control (--logistic) phenotype. Normally used with --covar.
* 'perm' normally causes an adaptive permutation test to be performed on
the main effect, while 'mperm='<value> starts a max(T) permutation test.
* 'perm-count' causes the permutation test report to include counts instead
of frequencies.
* 'set-test' tests the significance of variant sets. Requires permutation;
can be customized with --set-p/--set-r2/--set-max.
* The 'genotypic' modifier adds an additive effect/dominance deviation 2df
joint test (0/1/2 and 0/1/0 coding), while 'hethom' uses 0/0/1 and 0/1/0
coding instead. If permutation is also requested, these modifiers cause
permutation to be based on the joint test.
* 'dominant' and 'recessive' specify a model assuming full dominance or
recessiveness, respectively, for the A1 allele.
* 'no-snp' causes regression to be performed only on the phenotype and the
covariates, without reference to genomic data. If permutation is also
requested, results are reported for all covariates.
* 'hide-covar' removes covariate-specific lines from the report.
* By default, sex (male = 1, female = 0) is automatically added as a
covariate on X chromosome variants, and nowhere else. The 'sex' modifier
causes it to be added everywhere, while 'no-x-sex' excludes it.
* 'interaction' adds genotype x covariate interactions to the model. This
cannot be used with the usual permutation tests; use --tests to define
the permutation test statistic instead.
* 'intercept' causes intercepts to be included in the main report.
* For logistic regressions, the 'beta' modifier causes regression
coefficients instead of odds ratios to be reported.
* With --linear, the 'standard-beta' modifier standardizes the phenotype
and all predictors to zero mean and unit variance before regression.
--dosage <allele dosage file> ['noheader'] ['skip0='<i>] ['skip1='<j>]
['skip2='<k>] ['dose1'] ['format='<m>] ['Zout']
[{occur | standard-beta}] ['sex'] ['case-control-freqs']
--dosage <list file> list [{sepheader | noheader}] ['skip0='<i>]
['skip1='<j>] ['skip2='<k>] ['dose1'] ['format='<m>] ['Zout']
[{occur | standard-beta}] ['sex'] ['case-control-freqs']
--write-dosage
Process (possibly gzipped) text files with variant-major allelic dosage
data. This cannot be used with a regular input fileset; instead, you must
*only* specify a .fam and possibly a .map file, and you can't specify any
other commands.
* PLINK 2.0 will have first-class support for genotype probabilities. An
equivalent data import flag will be provided then, and --dosage will be
retired.
* By default, --dosage assumes that only one allelic dosage file should be
loaded. To specify multiple files,
1. create a master list with one entry per line. There are normally two
supported formats for this list: just a filename per line, or variant
batch numbers in the first column and filenames in the second.
2. Provide the name of that list as the first --dosage parameter.
3. Add the 'list' modifier.
* By default, --dosage assumes the allelic dosage file(s) contain a header
line, which has 'SNP' in column i+1, 'A1' in column i+j+2, 'A2' in column
i+j+3, and sample FID/IIDs starting from column i+j+k+4. (i/j/k are
normally zero, but can be changed with 'skip0', 'skip1', and 'skip2'
respectively.) If such a header line is not present,
* when all samples appear in the same order as they do in the .fam file,
you can use the 'noheader' modifier.
* Otherwise, use the 'sepheader' modifier, and append sample ID filenames
to your 'list' file entries.
* The 'format=' modifier lets you specify the number of values used to
represent each dosage. 'format=1' normally indicates a single 0..2 A1
expected count; 'dose1' modifies this to a 0..1 frequency. 'format=2'
(the default) indicates a 0..1 homozygous A1 likelihood followed by a
0..1 het likelihood, while 'format=3' indicates 0..1 hom A1, 0..1 het,
0..1 hom A2.
* 'Zout' causes the output file to be gzipped.
* Normally, an association analysis is performed. 'standard-beta' and
'sex' behave as they are supposed to with --linear/--logistic.
'case-control-freqs' causes case and control allele frequencies to be
reported separately.
* There are three alternate modes which cause the association analysis to
be skipped.
* 'occur' requests a simple variant occurrence report.
* --write-dosage causes a simple merged file matching the 'format'
specification (not including 'dose1') to be generated.
* --score applies a linear scoring system to the dosages.
--lasso <h2 estimate> [min lambda] ['report-zeroes']
Estimate variant effect sizes via LASSO regression. You must provide an
additive heritability estimate to calibrate the regression.
Note that this method may require a very large sample size (e.g. hundreds
of thousands) to be effective on complex polygenic traits.
--test-missing ['perm' | 'mperm='<value>] ['perm-count'] ['midp']
Check for association between missingness and case/control status, using
Fisher's exact test. (Het. haploids are treated as missing.)
The 'midp' modifier causes Lancaster's mid-p adjustment to be applied.
--make-perm-pheno <ct>
Generate phenotype permutations and write them to disk, without invoking an
association test.
--tdt [{exact | exact-midp | poo}] ['perm' | 'mperm='<value>] ['perm-count']
[{parentdt1 | parentdt2 | pat | mat}] ['set-test']
Report transmission disequilibrium test statistics, given case/control
phenotypes and pedigree information.
* A Mendel error check is performed before the main tests; offending
genotypes are treated as missing by this analysis.
* By default, the basic TDT p-value is based on a chi-square test unless
you request the exact binomial test with 'exact' or 'exact-midp'.
* 'perm'/'mperm=' requests a family-based adaptive or max(T) permutation
test. By default, the permutation test statistic is the basic TDT
p-value; 'parentdt1'/'parentdt2' cause parenTDT or combined test
p-values, respectively, to be considered instead.
* 'set-test' tests the significance of variant sets. This cannot be used
with exact tests for now.
The 'poo' modifier causes a parent-of-origin analysis to be performed
instead, with transmissions from heterozygous fathers and heterozygous
mothers considered separately.
* The parent-of-origin analysis does not currently support exact tests.
* By default, the permutation test statistic is the absolute
parent-of-origin test Z score; 'pat'/'mat' cause paternal or maternal TDT
chi-square statistics, respectively, to be considered instead.
--qfam ['perm' | 'mperm='<value>] ['perm-count'] ['emp-se']
--qfam-parents ['perm' | 'mperm='<value>] ['perm-count'] ['emp-se']
--qfam-between ['perm' | 'mperm='<value>] ['perm-count'] ['emp-se']
--qfam-total ['perm' | 'mperm='<value>] ['perm-count'] ['emp-se']
QFAM family-based association test for quantitative traits.
* A Mendel error check is performed before the main tests; offending
genotypes are treated as missing by this analysis.
* This procedure requires permutation. 'perm' and 'perm-count' have the
usual meanings. However, 'mperm='<value> just specifies a fixed number
of permutations; the method does not support a proper max(T) test.
* The 'emp-se' modifier adds BETA and EMP_SE (empirical standard error for
beta) fields to the .perm output file.
--annotate <PLINK report> ['attrib='<file>] ['ranges='<file>]
['filter='<file>] ['snps='<file>] [{NA | prune}] ['block']
['subset='<file>] ['minimal'] ['distance']
Add annotations to a variant-based PLINK report. This requires an
annotation source:
* 'attrib='<file> specifies a (possibly gzipped) attribute file.
* 'ranges='<file> specifies a gene/range list file.
(Both source types can be specified simultaneously.) The following options
are also supported:
* 'filter='<file> causes only variants within one of the ranges in the file
to be included in the new report.
* 'snps='<file> causes only variants named in the file to be included in
the new report.
* The 'NA' modifier causes unannotated variants to have 'NA' instead of '.'
in the new report's ANNOT column, while the 'prune' modifier excludes
them entirely.
* The 'block' modifier replaces the single ANNOT column with a 0/1-coded
column for each possible annotation.
* With 'ranges',
* 'subset='<file> causes only intervals named in the subset file to be
loaded from the ranges file.
* interval annotations normally come with a parenthesized signed distance
to the interval boundary (0 if the variant is located inside the
interval; this is always true without --border). They can be excluded
with the 'minimal' modifier.
* the 'distance' modifier adds 'DIST' and 'SGN' columns describing signed
distance to the nearest interval.
* When --pfilter is present, high p-values are filtered out.
--clump <PLINK report filename(s)...>
Process association analysis report(s) with 'SNP' and p-value columns,
organizing results by LD-based clumps. Multiple filenames can be separated
by spaces or commas.
--gene-report <PLINK report> <gene range file>
Generate a gene-based report from a variant-based report.
* When --pfilter is present, high p-values are filtered out.
* When --extract (without 'range') is present, only variants named in the
--extract file are considered.
--meta-analysis <PLINK report filenames...>
--meta-analysis <PLINK report filenames...> + [{logscale | qt}]
[{no-map | no-allele}] ['study'] ['report-all']
['weighted-z']
Perform a meta-analysis on several variant-based reports with 'SNP' and
'SE' fields.
* Normally, an 'OR' odds ratio field must also be present in each input
file. With 'logscale', 'BETA' log-odds values/regression coefficients
are expected instead, but the generated report will still contain odds
ratio estimates. With 'qt', both input and output values are regression
betas.
* 'CHR', 'BP', and 'A1' fields are also normally required. 'no-map' causes
them to all be ignored, while 'no-allele' causes just 'A1' to be ignored.
* If 'A2' fields are present, and neither 'no-map' nor 'no-allele' was
specified, A1/A2 allele flips are handled properly. Otherwise, A1
mismatches are thrown out.
* 'study' causes study-specific effect estimates to be collated in the
meta-analysis report.
* 'report-all' causes variants present in only a single input file to be
included in the meta-analysis report.
* 'weighted-z' requests weighted Z-score-based p-values (as computed by the
Abecasis Lab's METAL software) in addition to the usual inverse
variance-based analysis. This requires P and effective sample size
fields.
* When --extract (without 'range') is present, only variants named in the
--extract file are considered.
* Unless 'no-map' is specified, chromosome filters are also respected.
--fast-epistasis [{boost | joint-effects | no-ueki}] ['case-only']
[{set-by-set | set-by-all}] ['nop']
--epistasis [{set-by-set | set-by-all}]
Scan for epistatic interactions. --fast-epistasis inspects 3x3 joint
genotype count tables and only applies to case/control phenotypes, while
--epistasis performs linear or logistic regression.
* By default, --fast-epistasis uses the PLINK 1.07 allele-based test. Two
newer tests are now supported: 'boost' invokes the likelihood ratio test
introduced by Wan X et al. (2010) BOOST: A Fast Approach to Detecting
Gene-Gene Interactions in Genome-wide Case-Control Studies, while
'joint-effects' applies the joint effects test introduced in Ueki M,
Cordell HJ (2012) Improved statistics for genome-wide interaction
analysis.
* The original --fast-epistasis test normally applies the variance and
empty cell corrections suggested by Ueki and Cordell's paper. To disable
them, use the 'no-ueki' modifier.
* 'case-only' requests a case-only instead of a case/control test.
* By default, all pairs of variants across the entire genome are tested.
To just test pairs of variants within a single set, add the 'set-by-set'
modifier and load exactly one set with --set/--make-set; with exactly two
sets loaded, all variants in one set are tested against all variants in
the other. 'set-by-all' tests all variants in one set against the entire
genome instead.
* 'nop' strips p-values from the main report.
* These computations can be subdivided with --parallel; however...
--epistasis-summary-merge <common file prefix> <ct>
When a --[fast-]epistasis job is subdivided with --parallel, the main
report can be assembled at the end by applying Unix 'cat' in the usual
manner, but the .summary.1, .summary.2, ... files may require a specialized
merge. --epistasis-summary-merge takes care of the latter.
--twolocus <variant ID> <variant ID>
Two-locus joint genotype count report.
--score <filename> [i] [j] [k] ['header'] [{sum | no-sum}]
[{no-mean-imputation | center}] ['include-cnt'] ['double-dosage']
Apply a linear scoring system to each sample.
The input file should have one line per scored variant. Variant IDs are
read from column #i, allele codes are read from column #j, and scores are
read from column #k, where i defaults to 1, j defaults to i+1, and k
defaults to j+1.
* The 'header' modifier causes the first nonempty line of the input file to
be ignored; otherwise, --score assumes there is no header line.
* By default, final scores are averages of the valid per-variant scores.
The 'sum' modifier causes sums to be reported instead. (This cannot be
used with 'no-mean-imputation'. And for backward compatibility, 'sum' is
automatically on with dosage data unless 'no-sum' is specified.)
* By default, copies of the unnamed allele contribute zero to score, while
missing genotypes contribute an amount proportional to the loaded (via
--read-freq) or imputed allele frequency. To throw out missing
observations instead (decreasing the denominator in the final average
when this happens), use the 'no-mean-imputation' modifier.
* Alternatively, you can use the 'center' modifier to shift all scores to
mean zero.
* This command can be used with dosage data. By default, the 'CNT' column
is omitted from the output file in this case; use 'include-cnt' to keep
it. Also, note that scores are multiplied by 0..1 dosages, not 0..2
diploid allele counts, unless the 'double-dosage' modifier is present.
--R <R script file> ['debug']
Connect to a Rserve (preferably version 1.7 or later) background process,
and execute the Rplink function defined in the input file. (Unless the
'debug' modifier is present; in that case, the R commands that PLINK would
have tried to execute are logged to a file.)
--write-var-ranges <block ct>
Divide the set of variants into equal-size blocks. (Can be used with
--snps to split a job across multiple machines.)
The following other flags are supported. (Order of operations is described at
https://www.cog-genomics.org/plink/1.9/order .)
--script <fname> : Include command-line options from file.
--rerun [log] : Rerun commands in log (default 'plink.log').
--version : Display only version number before exiting.
--silent : Suppress output to console.
--gplink : Reserved for interoperation with gPLINK.
--missing-genotype <char> : Set missing genotype code (normally '0').
--double-id : Set both FIDs and IIDs to the VCF/BCF sample ID.
--const-fid [ID] : Set all FIDs to the given constant (default '0').
--id-delim [d] : Parse sample IDs as <FID><d><IID> (default delim '_').
--vcf-idspace-to <c> : Convert spaces in sample IDs to the given character.
--biallelic-only ['strict'] ['list'] : Skip VCF variants with 2+ ALT alleles.
--vcf-min-qual <val> : Skip VCF variants with low/missing QUAL.
--vcf-filter [exception(s)...] : Skip variants which have FILTER failures.
--vcf-require-gt : Skip variants with no GT field.
--vcf-min-gq <val> : No-call a genotype when GQ is below the
given threshold.
--vcf-min-gp <val> : No-call a genotype when 0-1 scaled GP is
below the given threshold.
--vcf-half-call <m> : Specify how '0/.' and similar VCF GT values should be
handled. The following four modes are supported:
* 'error'/'e' (default) errors out and reports line #.
* 'haploid'/'h' treats them as haploid calls.
* 'missing'/'m' treats them as missing.
* 'reference'/'r' treats the missing value as 0.
--oxford-single-chr <chr nm> : Specify single-chromosome .gen file with
ignorable first column.
--oxford-pheno-name <col nm> : Import named phenotype from the .sample file.
--hard-call-threshold <val> : When an Oxford-format fileset is loaded, calls
--hard-call-threshold random with uncertainty level greater than 0.1 are
normally treated as missing. You can adjust
this threshold by providing a numeric
parameter, or randomize all calls with
'random'.
--missing-code [string list] : Comma-delimited list of missing phenotype
(alias: --missing_code) values for Oxford-format filesets (def. 'NA').
--simulate-ncases <num> : Set --simulate case count (default 1000).
--simulate-ncontrols <n> : Set --simulate control count (default 1000).
--simulate-prevalence <p> : Set --simulate disease prevalence (default 0.01).
--simulate-n <num> : Set --simulate-qt sample count (default 1000).
--simulate-label <prefix> : Set --simulate[-qt] FID/IID name prefix.
--simulate-missing <freq> : Set --simulate[-qt] missing genotype frequency.
--allow-extra-chr ['0'] : Permit unrecognized chromosome codes. The '0'
(alias: --aec) modifier causes them to be treated as if they had
been set to zero.
--chr-set <autosome ct> ['no-x'] ['no-y'] ['no-xy'] ['no-mt'] :
Specify a nonhuman chromosome set. The first parameter sets the number of
diploid autosome pairs if positive, or haploid chromosomes if negative.
Given diploid autosomes, the remaining modifiers indicate the absence of
the named non-autosomal chromosomes.
--cow/--dog/--horse/--mouse/--rice/--sheep : Shortcuts for those species.
--autosome-num <value> : Alias for "--chr-set <value> no-y no-xy no-mt".
--cm-map <fname pattern> [chr] : Use SHAPEIT-format recombination maps to set
centimorgan positions. To process more than
one chromosome, include a '@' in the first
parameter where the chrom. number belongs,
e.g. 'genetic_map_chr@_combined_b37.txt'.
--zero-cms : Zero out centimorgan positions.
--allow-no-samples : Allow the input fileset to contain no samples.
--allow-no-vars : Allow the input fileset to contain no variants.
--pheno <fname> : Load phenotype data from the specified file, instead of
using the values in the main input fileset.
--all-pheno : For basic association tests, loop through all phenotypes
in --pheno file.
--mpheno <n> : Load phenotype from column (n+2) in --pheno file.
--pheno-name <c> : If --pheno file has a header row, use column with the
given name.
--pheno-merge : When the main input fileset contains an phenotype value
for a sample, but the --pheno file does not, use the
original value instead of treating the phenotype as
missing.
--missing-phenotype <v> : Set missing phenotype value (normally -9).
--1 : Expect case/control phenotypes to be coded as
0 = control, 1 = case, instead of the usual
0 = missing, 1 = control, 2 = case. This also
forces phenotypes to be interpreted as case/ctrl.
--make-pheno <fn> <val> : Define a new case/control phenotype. If the val
parameter is '*', all samples listed in the given
file are cases, and everyone else is a control.
(Note that, in some shells, it is necessary to
surround the * with quotes.)
Otherwise, all samples with third column entry
equal to the val parameter are cases, and all other
samples mentioned in the file are controls.
--tail-pheno <Lt> [Hbt] : Downcode a scalar phenotype to a case/control
phenotype. All samples with phenotype values
greater than Hbt are cases, and all with values
less than or equal to Lt are controls. If Hbt is
unspecified, it is equal to Lt; otherwise,
in-between phenotype values are set to missing.
--covar <filename> ['keep-pheno-on-missing-cov'] : Specify covariate file.
--covar-name <...> : Specify covariate(s) in --covar file by name.
Separate multiple names with spaces or commas, and
use dashes to designate ranges.
--covar-number <...> : Specify covariate(s) in --covar file by index.
--no-const-covar : Exclude constant covariates.
--allow-no-covars : Allow no covariates to be loaded from --covar