Bio Population Genetics Plink Basics

PLINK Basics

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

File Formats

Binary Format (Recommended)

PLINK 2.0 Format

Text Format (Legacy)

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

Related Skills

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

PLINK Basics - Usage Guide

Overview

PLINK is the standard tool for population genetic analysis, handling format conversion, quality control, and basic statistics. PLINK 2.0 is faster and more memory-efficient but doesn't support all legacy formats.

Prerequisites

# PLINK 1.9
conda install -c bioconda plink

# PLINK 2.0
conda install -c bioconda plink2

Quick Start

Tell your AI agent what you want to do:

• "Convert my VCF file to PLINK format"
• "Run quality control on my GWAS data"
• "Filter out low-quality variants and samples"
• "Merge these two PLINK datasets"
• "Extract samples from a specific population"

Example Prompts

Format Conversion

"Convert data.vcf.gz to PLINK binary format"

"Convert my PED/MAP files to binary BED format"

"Export my PLINK data back to VCF"

Quality Control

"Run standard QC filtering on my GWAS data with MAF 0.01, genotype missingness 5%, and sample missingness 5%"

"Apply strict QC filters for population structure analysis"

"Check for samples with high missingness rates"

Data Management

"Merge these three PLINK datasets into one"

"Extract only the European samples from my data"

"Remove all variants not in my keep list"

"Calculate allele frequencies for each population"

What the Agent Will Do

1. Assess input data format (VCF, PED/MAP, or binary) 2. Determine appropriate PLINK version (1.9 for legacy formats, 2.0 for analysis) 3. Run quality control or conversion commands 4. Report statistics before and after filtering 5. Verify output file integrity

Tips

• Use --double-id when converting VCF to handle sample ID parsing
• PLINK 2.0's --pfile is faster than --bfile for large datasets
• Always check variant/sample counts before and after QC
• Use --snps-only just-acgt to remove indels and non-standard alleles
• For merging datasets, ensure consistent chromosome naming and strand orientation

Standard QC Workflow

1. Convert VCF to PLINK

plink2 --vcf data.vcf.gz --double-id --make-bed --out data

2. Check Initial Statistics

# Sample and variant counts
wc -l data.fam data.bim

# Missing rates
plink2 --bfile data --missing --out data_missing

3. Apply QC Filters

plink2 --bfile data \
    --maf 0.01 \
    --geno 0.05 \
    --mind 0.05 \
    --hwe 1e-6 \
    --make-bed --out data_qc

4. Report Filtering

echo "Before QC:"
wc -l data.fam data.bim

echo "After QC:"
wc -l data_qc.fam data_qc.bim

Choosing Thresholds

Common Issues

ID Mismatch

VCF sample IDs may need parsing:

# Double ID (same FID and IID)
plink2 --vcf input.vcf.gz --double-id --make-bed --out output

Allele Code Issues

# Handle non-ACGT alleles
plink2 --bfile input --snps-only just-acgt --make-bed --out output

Duplicate IDs

# Check for duplicates
awk '{print $2}' data.bim | sort | uniq -d

# Remove duplicates
plink2 --bfile input --rm-dup force-first --make-bed --out output

Resources

• PLINK 1.9 Documentation
• PLINK 2.0 Documentation