BioSkills bio-population-genetics-plink-basics

PLINK file formats, format conversion, and quality control filtering for population genetics. Convert between VCF, BED/BIM/FAM, and PED/MAP formats, apply MAF, genotyping rate, and HWE filters using PLINK 1.9 and 2.0. Use when working with PLINK format files or running QC.

install

source · Clone the upstream repo

git clone https://github.com/GPTomics/bioSkills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/GPTomics/bioSkills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/population-genetics/plink-basics" ~/.claude/skills/gptomics-bioskills-bio-population-genetics-plink-basics && rm -rf "$T"

manifest: population-genetics/plink-basics/SKILL.md

source content

Version Compatibility

Reference examples tested with: pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

Python:
```
pip show <package>
```
then
```
help(module.function)
```
to check signatures
CLI:
```
<tool> --version
```
then
```
<tool> --help
```
to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

PLINK Basics

"Convert my VCF to PLINK format and run QC" → Handle PLINK file format conversions (VCF, BED/BIM/FAM, PED/MAP) and apply standard genotype QC filters for MAF, genotyping rate, and HWE.

CLI:
```
plink2 --vcf input.vcf --make-bed
```
for format conversion

CLI:

plink2 --maf 0.01 --geno 0.05 --hwe 1e-6

for QC filtering

File formats, conversion, and quality control filtering with PLINK 1.9 and 2.0.

File Formats

Binary Format (Recommended)

File	Contents
`.bed`	Binary genotype data
`.bim`	Variant information (chr, ID, cM, pos, A1, A2)
`.fam`	Sample information (FID, IID, father, mother, sex, pheno)

PLINK 2.0 Format

File	Contents
`.pgen`	Binary genotype data (compressed)
`.pvar`	Variant information
`.psam`	Sample information

Text Format (Legacy)

File	Contents
`.ped`	Genotypes (FID, IID, father, mother, sex, pheno, genotypes)
`.map`	Variant positions (chr, ID, cM, pos)

Format Conversion

VCF to PLINK Binary

# PLINK 1.9
plink --vcf input.vcf.gz --make-bed --out output

# PLINK 2.0
plink2 --vcf input.vcf.gz --make-bed --out output

# With sample ID handling
plink2 --vcf input.vcf.gz --double-id --make-bed --out output

PLINK Binary to VCF

# PLINK 1.9
plink --bfile input --recode vcf --out output

# PLINK 2.0
plink2 --bfile input --export vcf --out output

# Compressed VCF
plink2 --bfile input --export vcf bgz --out output

PED/MAP to Binary (PLINK 1.9 Only)

# PLINK 1.9 (PLINK 2.0 doesn't support .ped/.map directly)
plink --file input --make-bed --out output

Binary to PED/MAP

# PLINK 1.9
plink --bfile input --recode --out output

# PLINK 2.0
plink2 --bfile input --export ped --out output

PLINK 1.9 to 2.0 Format

# Convert to PGEN format
plink2 --bfile input --make-pgen --out output

# Convert back to BED
plink2 --pfile input --make-bed --out output

Quality Control Filtering

MAF Filter (Minor Allele Frequency)

# Remove variants with MAF < 0.01
plink --bfile input --maf 0.01 --make-bed --out output

# PLINK 2.0
plink2 --bfile input --maf 0.01 --make-bed --out output

# Remove rare variants (MAF < 0.05)
plink2 --bfile input --maf 0.05 --make-bed --out output

Genotyping Rate Filters

# Per-variant missing rate (remove if >5% missing)
plink2 --bfile input --geno 0.05 --make-bed --out output

# Per-sample missing rate (remove if >5% missing)
plink2 --bfile input --mind 0.05 --make-bed --out output

Hardy-Weinberg Equilibrium Filter

# Remove variants with HWE p-value < 1e-6
plink2 --bfile input --hwe 1e-6 --make-bed --out output

# Different threshold for cases vs controls
plink2 --bfile input --hwe 1e-6 --hwe-all --make-bed --out output

Combined QC Pipeline

# Standard QC filtering
plink2 --bfile input \
    --maf 0.01 \
    --geno 0.05 \
    --mind 0.05 \
    --hwe 1e-6 \
    --make-bed --out qc_filtered

Sample and Variant Selection

Keep/Remove Samples

# Keep specific samples (samples.txt: FID IID per line)
plink2 --bfile input --keep samples.txt --make-bed --out output

# Remove specific samples
plink2 --bfile input --remove samples.txt --make-bed --out output

# Keep single sample
plink2 --bfile input --keep-fam sample_id --make-bed --out output

Extract/Exclude Variants

# Extract specific variants (variants.txt: variant IDs)
plink2 --bfile input --extract variants.txt --make-bed --out output

# Exclude specific variants
plink2 --bfile input --exclude variants.txt --make-bed --out output

# Extract by range
plink2 --bfile input --extract range chr1:1000000-2000000 --make-bed --out output

Chromosome Selection

# Single chromosome
plink2 --bfile input --chr 22 --make-bed --out chr22

# Multiple chromosomes
plink2 --bfile input --chr 1-22 --make-bed --out autosomes

# Exclude chromosome
plink2 --bfile input --not-chr 23,24,25,26 --make-bed --out autosomes

Allele Frequency

# PLINK 1.9 (MAF-based)
plink --bfile input --freq --out output

# PLINK 2.0 (ALT allele frequency - not MAF!)
plink2 --bfile input --freq --out output

# PLINK 2.0 with MAF
plink2 --bfile input --freq cols=+mac,+mafreq --out output

Missing Data Statistics

# Per-sample and per-variant missing rates
plink2 --bfile input --missing --out output

# Output files:
# output.smiss - sample missing rates
# output.vmiss - variant missing rates

Sex Check

Verify reported sex matches X chromosome heterozygosity.

# PLINK 1.9
plink --bfile input --check-sex --out sex_check

# PLINK 2.0
plink2 --bfile input --split-par hg38 --check-sex --out sex_check

Interpret Results

import pandas as pd

sex = pd.read_csv('sex_check.sexcheck', sep='\s+')

problems = sex[sex['STATUS'] == 'PROBLEM']
print(f'Sex mismatches: {len(problems)}')

# F statistic: <0.2 = female, >0.8 = male, between = ambiguous
# PEDSEX: reported sex (1=male, 2=female, 0=unknown)
# SNPSEX: inferred sex (1=male, 2=female, 0=undetermined)

Update or Remove

# Update sex from check results
plink2 --bfile input --update-sex sex_check.sexcheck col-num=4 --make-bed --out updated

# Remove sex mismatches
awk '$5 == "PROBLEM" {print $1, $2}' sex_check.sexcheck > sex_problems.txt
plink2 --bfile input --remove sex_problems.txt --make-bed --out output

Sample Information

Update Phenotypes

# phenotypes.txt: FID IID pheno (1=control, 2=case, -9=missing)
plink2 --bfile input --pheno phenotypes.txt --make-bed --out output

# Quantitative phenotype
plink2 --bfile input --pheno phenotypes.txt --make-bed --out output

Update Sex

# sex.txt: FID IID sex (1=male, 2=female, 0=unknown)
plink2 --bfile input --update-sex sex.txt --make-bed --out output

Update Sample IDs

# ids.txt: old_FID old_IID new_FID new_IID
plink2 --bfile input --update-ids ids.txt --make-bed --out output

Merging Datasets

# Merge two datasets (PLINK 1.9)
plink --bfile data1 --bmerge data2.bed data2.bim data2.fam --make-bed --out merged

# Merge list of datasets
plink --bfile data1 --merge-list merge_list.txt --make-bed --out merged
# merge_list.txt contains: data2.bed data2.bim data2.fam (one set per line)

# Handle strand flips
plink --bfile data1 --bmerge data2 --make-bed --out merged
# If error: plink --bfile data2 --flip missnps.txt --make-bed --out data2_flipped

Variant Information

Set Variant IDs

# Set ID based on position
plink2 --bfile input --set-all-var-ids @:#:\$r:\$a --make-bed --out output
# Format: chr:pos:ref:alt

Update Variant Names

# update.txt: old_id new_id
plink2 --bfile input --update-name update.txt --make-bed --out output

PLINK 2.0 vs 1.9 Summary

Feature	PLINK 2.0	PLINK 1.9
Status	Current	Legacy
Command	`plink2`	`plink`
Format	`.pgen/.pvar/.psam`	`.bed/.bim/.fam`
Speed	Faster	Baseline
Memory	More efficient	Higher for large data
Export VCF	`--export vcf`	`--recode vcf`
Frequency output	ALT frequency	MAF
Missing output	`.smiss/.vmiss`	`.imiss/.lmiss`
PED/MAP support	No (convert via 1.9)	Yes ( `--file` )

Related Skills

association-testing - GWAS with filtered data
population-structure - PCA after QC
variant-calling/vcf-basics - VCF format before conversion