Introduction, downloads

D: 12 Nov 2019

Recent version history

What's new?

Coming next

General usage

Getting started

Column set descriptors

Citation instructions

Standard data input

PLINK 1 binary (.bed)

PLINK 2 binary (.pgen)

Autoconversion behavior

VCF (.vcf[.gz])

Oxford genotype (.bgen)

Oxford haplotype (.haps)

PLINK 1 dosage

Dosage import settings

Generate random

Unusual chromosome IDs

Allele frequencies



'Cluster' import

Reference genome (.fa)

Input filtering

Sample ID file

Variant ID file

Interval-BED file




SNPs only

Simple variant window

Multiple variant ranges

Deduplicate variants

Sample/variant thinning

Pheno./covar. condition


Category subset

--keep-fcol (was --filter)

Missing genotypes

Number of distinct alleles

Allele frequencies/counts


Imputation quality


Founder status

Main functions

Data management


















Basic statistics






Linkage disequilibrium


Sample comparison

Sample-distance matrices







1000 Genomes phase 3

Output file list

Order of operations


File formats

Linkage disequilibrium

All of the following calculations only consider founders. If your dataset has a shortage of them, PLINK 1.9 --make-founders may come in handy.

Since two-variant r2 only makes sense for biallelic variants, these collapse multiallelic variants down to most common allele vs. the rest.

Variant pruning

--indep-pairwise <window size>['kb'] [step size (variant ct)]
                 <unphased-hardcall-r^2 threshold>
--indep-pairphase <window size>['kb'] [step size (variant ct)]
                  <phased-r^2 threshold>
--indep <window size>['kb'] [step size (variant ct)] <VIF threshold>

These commands produce a pruned subset of markers that are in approximate linkage equilibrium with each other, writing the IDs to (and the IDs of all excluded variants to plink2.prune.out). These files are valid input for --extract/--exclude in a future PLINK run.

--indep-pairwise is the simplest approach, which only considers correlations between unphased-hardcall allele counts. It takes three parameters: a required window size in variant count or kilobase (if the 'kb' modifier is present) units, an optional variant count to shift the window at the end of each step (default 1, and now required to be 1 when a kilobase window is used), and a required r2 threshold. At each step, pairs of variants in the current window with squared correlation greater than the threshold are noted, and variants are greedily pruned from the window until no such pairs remain.

LD statistic reports

--ld <variant ID> <variant ID> ['dosage'] ['hwe-midp']

To inspect the relation between a single pair of variants in more detail, you can use the --ld flag, which displays observed and expected (based on MAFs) frequencies of each haplotype, as well as haplotype-based r2 and D'.

  • By default, only hardcalls are considered in this computation; add the 'dosage' modifier to change this.
  • When unphased calls are present, and 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.

Pairwise sample comparison >>