plink2-gwas-analysis

PLINK2 — GWAS and Population Genetics

Overview

PLINK2 is the high-performance successor to PLINK 1.9, designed for genome-wide association studies (GWAS) and population genetics analysis on large cohorts. It processes genotype data in PLINK binary format (.bed/.bim/.fam), VCF, and BGEN formats — performing sample and variant quality control (QC), kinship estimation, principal component analysis (PCA), and linear/logistic regression association testing. PLINK2 is 10–100× faster than PLINK 1.9 on most tasks due to multithreading and optimized I/O. Output files are compatible with downstream visualization (Manhattan/QQ plots) and meta-analysis tools.

When to Use

• Running GWAS on a case-control or quantitative trait cohort after genotyping array QC
• Performing sample QC: missingness, heterozygosity outliers, sex check, cryptic relatedness
• Computing genome-wide LD pruning for PCA or relatedness estimation
• Running PCA on genotype data to identify population stratification
• Converting between PLINK binary, VCF, and BGEN formats
• Filtering variants by MAF, HWE, missingness, or INFO score in VCF/imputed data
• Use regenie or SAIGE instead for biobank-scale GWAS (>100k samples) requiring mixed model association to control for population structure
• Use VCFtools as an alternative for VCF-specific population genetics statistics

Prerequisites

• Software: PLINK2 (pre-compiled binary; no pip/conda package)
• Input: PLINK binary files (.bed/.bim/.fam) or VCF/BGEN from array genotyping or imputation

Check before installing: The tool may already be available (e.g., inside a pixi / conda env). Always run command -v plink2 first and skip the install block if it returns a path. When executing tools inside a pixi project, prefer pixi run <tool> over plain <tool>.

# Skip install if already present
if command -v plink2 >/dev/null 2>&1; then
    echo "plink2 already installed: $(plink2 --version)"
else
    # Download PLINK2 pre-compiled binary (Linux)
    wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_linux_avx2_20241112.zip
    unzip plink2_linux_avx2_20241112.zip
    chmod +x plink2
    export PATH="$PWD:$PATH"

    # macOS
    # wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_mac_20241112.zip
    # unzip plink2_mac_20241112.zip

    plink2 --version
    # PLINK v2.00a6LM
fi

# Python for downstream analysis
pip install pandas numpy matplotlib scipy

Quick Start

# Run GWAS: linear regression for quantitative trait
plink2 \
    --bfile cohort_qc \
    --pheno phenotypes.txt \
    --covar covariates.txt \
    --linear hide-covar \
    --out results/gwas_result \
    --threads 8

# View top hits
head results/gwas_result.*.glm.linear | sort -k12,12g | head -20

Workflow

Step 1: Convert VCF/Imputed Data to PLINK Binary Format

Convert input genotype data to PLINK binary format for fast processing.

# Convert VCF to PLINK binary
plink2 \
    --vcf cohort_genotyped.vcf.gz \
    --make-bed \
    --out cohort_plink \
    --threads 8

# Convert BGEN (imputed data from Michigan/TopMed imputation server)
plink2 \
    --bgen cohort_imputed.bgen ref-first \
    --sample cohort_imputed.sample \
    --make-bed \
    --out cohort_imputed_plink \
    --threads 8

echo "Files created:"
ls -lh cohort_plink.{bed,bim,fam}
echo "Samples: $(wc -l < cohort_plink.fam)"
echo "Variants: $(wc -l < cohort_plink.bim)"

Step 2: Sample QC — Missingness and Heterozygosity

Remove samples with high missingness or heterozygosity outliers.

# Compute per-sample and per-variant missingness
plink2 \
    --bfile cohort_plink \
    --missing \
    --out qc/sample_missingness \
    --threads 8

# Remove samples with > 2% missingness and variants with > 5% missing
plink2 \
    --bfile cohort_plink \
    --mind 0.02 \
    --geno 0.05 \
    --make-bed \
    --out cohort_sample_qc \
    --threads 8

echo "After sample QC:"
echo "Samples: $(wc -l < cohort_sample_qc.fam)"
echo "Variants: $(wc -l < cohort_sample_qc.bim)"

Step 3: Variant QC — MAF, HWE, and INFO Score Filtering

Filter variants by minor allele frequency, Hardy-Weinberg equilibrium, and imputation quality.

# Variant QC: MAF, HWE, missingness
plink2 \
    --bfile cohort_sample_qc \
    --maf 0.01 \
    --hwe 1e-6 \
    --geno 0.05 \
    --make-bed \
    --out cohort_variantqc \
    --threads 8

echo "After variant QC:"
echo "Variants remaining: $(wc -l < cohort_variantqc.bim)"

# For imputed data: also filter by INFO score (using VCF INFO field)
# plink2 --bfile cohort_variantqc --var-min-qual 0.8 --make-bed --out cohort_info_qc

Step 4: LD Pruning and PCA for Population Stratification

Compute principal components from LD-pruned variants.

# LD pruning: remove one variant from each pair with r² > 0.2
plink2 \
    --bfile cohort_variantqc \
    --indep-pairwise 50 5 0.2 \
    --out qc/ld_pruned \
    --threads 8

# Compute PCA on LD-pruned variants (top 20 PCs)
plink2 \
    --bfile cohort_variantqc \
    --extract qc/ld_pruned.prune.in \
    --pca 20 \
    --out pca/cohort_pca \
    --threads 8

echo "PCA files: pca/cohort_pca.eigenvec (PCs) and pca/cohort_pca.eigenval (variance)"
head pca/cohort_pca.eigenvec

Step 5: Run GWAS Association Analysis

Perform logistic regression (case-control) or linear regression (quantitative trait).

# Case-control GWAS: logistic regression with covariates
plink2 \
    --bfile cohort_variantqc \
    --pheno phenotypes.txt \
    --1 \
    --covar covariates.txt \
    --covar-variance-standardize \
    --logistic hide-covar \
    --ci 0.95 \
    --out results/gwas_cc \
    --threads 8

# Quantitative trait GWAS: linear regression
plink2 \
    --bfile cohort_variantqc \
    --pheno phenotypes.txt \
    --covar covariates.txt \
    --covar-variance-standardize \
    --linear hide-covar \
    --ci 0.95 \
    --out results/gwas_qt \
    --threads 8

echo "GWAS complete. Files: results/gwas_*.glm.*"
wc -l results/gwas_qt.*.glm.linear

Step 6: Plot Manhattan and QQ Plots

Visualize GWAS results with Python.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import chi2

# Load GWAS results (PLINK2 linear output)
df = pd.read_csv("results/gwas_qt.PHENO1.glm.linear", sep="\t",
                 usecols=["#CHROM", "POS", "ID", "P"])
df.columns = ["CHR", "POS", "SNP", "P"]
df = df.dropna(subset=["P"])
df["P"] = pd.to_numeric(df["P"], errors="coerce")
df = df[df["P"] > 0].copy()
df["-log10P"] = -np.log10(df["P"])

print(f"Variants: {len(df)}")
print(f"Genome-wide significant (p<5e-8): {(df['P'] < 5e-8).sum()}")

# Manhattan plot
fig, ax = plt.subplots(figsize=(14, 4))
colors = plt.cm.Set1.colors
chrom_pos = 0
ticks = []
for chrom, group in df.groupby("CHR", sort=False):
    col = colors[int(chrom) % 2] if str(chrom).isdigit() else "gray"
    ax.scatter(group["POS"] + chrom_pos, group["-log10P"],
               c=[col], s=2, alpha=0.7)
    ticks.append((chrom_pos + group["POS"].mean(), str(chrom)))
    chrom_pos += group["POS"].max() + 1e7

ax.axhline(-np.log10(5e-8), color="red", linestyle="--", lw=1, label="p=5×10⁻⁸")
ax.set_xticks([t[0] for t in ticks[::2]])
ax.set_xticklabels([t[1] for t in ticks[::2]], fontsize=6)
ax.set_xlabel("Chromosome")
ax.set_ylabel("-log₁₀(p)")
ax.set_title("GWAS Manhattan Plot")
plt.tight_layout()
plt.savefig("manhattan.png", dpi=150)
print("Saved: manhattan.png")

Key Parameters

| Parameter | Default | Range/Options | Effect | |-----------|---------|---------------|--------| | --maf | — | 0–0.5 | Minor allele frequency threshold; 0.01 removes singletons | | --hwe | — | 0–1 | Hardy-Weinberg equilibrium p-value threshold; 1e-6 typical | | --geno | — | 0–1 | Maximum variant missingness; 0.05 = 5% | | --mind | — | 0–1 | Maximum sample missingness; 0.02 = 2% | | --linear / --logistic | — | flag | Regression mode: linear for quantitative, l