Population Structure - Usage Guide

Population Structure - Usage Guide

Overview

Population structure analysis identifies genetic ancestry and stratification using PCA (continuous clustering) and ADMIXTURE (discrete ancestry proportions). Essential for GWAS stratification control and ancestry inference.

Prerequisites

# PLINK 2.0
conda install -c bioconda plink2

# ADMIXTURE
conda install -c bioconda admixture

# FlashPCA2 (for large datasets)
conda install -c bioconda flashpca

# Visualization
pip install pandas matplotlib

Quick Start

Tell your AI agent what you want to do:

• "Run PCA on my genetic data"
• "Run fast PCA on biobank-scale data with FlashPCA2"
• "Estimate ancestry proportions with ADMIXTURE"
• "Check for population stratification before GWAS"
• "Plot PC1 vs PC2 colored by population"

Example Prompts

PCA Analysis

"Calculate the first 10 principal components from my PLINK data"

"Run PCA and identify any outlier samples"

"Generate a PCA plot colored by self-reported ancestry"

"Run FlashPCA2 on my large dataset (100k+ samples)"

Admixture Analysis

"Run ADMIXTURE for K=2 through K=6 and find the best K"

"Estimate ancestry proportions assuming 3 ancestral populations"

"Create a stacked bar plot of admixture proportions"

Combined Analysis

"Perform full population structure analysis with LD pruning, PCA, and ADMIXTURE"

"Check my data for population stratification and generate covariates for GWAS"

"Compare PCA clustering with ADMIXTURE assignments"

What the Agent Will Do

1. LD prune the data for unbiased estimation
2. Run PCA to calculate principal components
3. Run ADMIXTURE across multiple K values if requested
4. Identify optimal K using cross-validation error
5. Generate visualizations (PCA scatter, ADMIXTURE bar plots)
6. Flag outlier samples if detected

Tips

• Always LD prune before PCA/ADMIXTURE (r2 < 0.1)
• PC1-2 usually capture the largest population splits
• Choose K with lowest cross-validation error
• Outliers may indicate sample swaps, contamination, or unique ancestry
• Remove related individuals (IBD > 0.125) before analysis
• Use FlashPCA2 for biobank-scale data (100k+ samples) for better performance

Quick Reference

PCA Only

# Simple PCA
plink2 --bfile data --pca 10 --out pca

Full Pipeline

# 1. LD prune
plink2 --bfile data --indep-pairwise 50 10 0.1 --out prune
plink2 --bfile data --extract prune.prune.in --make-bed --out pruned

# 2. PCA
plink2 --bfile pruned --pca 10 --out pca

# 3. Admixture
for K in 2 3 4 5; do
    admixture --cv pruned.bed $K
done

Interpreting Results

PCA

• PC1: Usually largest population split
• Clusters: Groups with similar ancestry
• Outliers: Sample swaps, contamination, or unique ancestry

Admixture

• K: Number of ancestral populations
• Q values: Proportion of each ancestry
• CV error: Lower is better for choosing K

Common Issues

PCA Shows No Structure

• May be homogeneous population
• Try more PCs
• Check for batch effects

Admixture Won't Converge

• LD prune more aggressively
• Remove closely related individuals
• Increase iterations

Resources

• ADMIXTURE Manual
• PLINK PCA Documentation

Related Skills

• plink-basics - Data preparation and QC
• linkage-disequilibrium - LD pruning details
• association-testing - Use PCs as covariates
• ecological-genomics/landscape-genomics - Population structure correction for GEA