Agent skill

bio-population-genetics-population-structure

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npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-population-genetics-population-structure

SKILL.md

name: bio-population-genetics-population-structure description: Analyze population structure using PCA and admixture analysis with PLINK and ADMIXTURE. Identify population clusters, assess ancestry proportions, visualize genetic structure, and choose optimal K for admixture models. Use when analyzing population stratification with PCA or admixture. tool_type: cli primary_tool: plink2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

Population Structure

Analyze genetic ancestry and population stratification using PCA and ADMIXTURE.

Principal Component Analysis (PCA)

PLINK 2.0 PCA

bash
# Basic PCA (10 PCs)
plink2 --bfile data --pca 10 --out pca_results

# More PCs
plink2 --bfile data --pca 20 --out pca_results

# Approximate PCA (faster for large datasets)
plink2 --bfile data --pca 10 approx --out pca_results

# Output variant loadings
plink2 --bfile data --pca 10 var-wts --out pca_results

Output Files

File Contents
.eigenvec PC scores per sample (FID, IID, PC1, PC2, ...)
.eigenval Eigenvalues (variance explained)
.eigenvec.var Variant loadings (if var-wts)

Variance Explained

python
import numpy as np

eigenvalues = np.loadtxt('pca_results.eigenval')
variance_explained = eigenvalues / eigenvalues.sum() * 100
cumulative = np.cumsum(variance_explained)

for i, (ve, cum) in enumerate(zip(variance_explained, cumulative), 1):
    print(f'PC{i}: {ve:.2f}% (cumulative: {cum:.2f}%)')

PCA Visualization

python
import pandas as pd
import matplotlib.pyplot as plt

eigenvec = pd.read_csv('pca_results.eigenvec', sep='\s+', header=None)
eigenvec.columns = ['FID', 'IID'] + [f'PC{i}' for i in range(1, len(eigenvec.columns) - 1)]

pop_info = pd.read_csv('population_labels.txt', sep='\t')  # FID, IID, Population
eigenvec = eigenvec.merge(pop_info, on=['FID', 'IID'])

plt.figure(figsize=(10, 8))
for pop in eigenvec['Population'].unique():
    subset = eigenvec[eigenvec['Population'] == pop]
    plt.scatter(subset['PC1'], subset['PC2'], label=pop, s=20, alpha=0.7)

plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
plt.savefig('pca_plot.png', dpi=150)

LD Pruning (Before Admixture)

ADMIXTURE requires LD-pruned SNPs:

bash
# Calculate LD and identify pruned set
plink2 --bfile data --indep-pairwise 50 10 0.1 --out prune

# Extract pruned variants
plink2 --bfile data --extract prune.prune.in --make-bed --out data_pruned

Pruning Parameters

Parameter Description
Window (50) SNPs in each window
Step (10) SNPs to shift per step
r² threshold (0.1) Max LD allowed

ADMIXTURE Analysis

Basic Usage

bash
# Run ADMIXTURE for K=3 clusters
admixture data_pruned.bed 3

# With cross-validation
admixture --cv data_pruned.bed 3

# Multithreaded
admixture -j4 data_pruned.bed 3

Output Files

File Contents
.Q Ancestry proportions (samples × K)
.P Allele frequencies per cluster

Testing Multiple K Values

bash
# Run for K=2 through K=10
for K in $(seq 2 10); do
    admixture --cv -j4 data_pruned.bed $K 2>&1 | tee log${K}.out
done

# Extract CV errors
grep -h "CV" log*.out | awk '{print NR+1, $4}' > cv_errors.txt

Choose Optimal K

python
import matplotlib.pyplot as plt

cv_errors = []
with open('cv_errors.txt') as f:
    for line in f:
        k, cv = line.strip().split()
        cv_errors.append((int(k), float(cv)))

ks, cvs = zip(*cv_errors)
plt.figure(figsize=(8, 5))
plt.plot(ks, cvs, 'o-')
plt.xlabel('K')
plt.ylabel('Cross-validation error')
plt.title('Admixture CV Error')
plt.savefig('admixture_cv.png', dpi=150)

optimal_k = ks[cvs.index(min(cvs))]
print(f'Optimal K: {optimal_k}')

Visualize Admixture

python
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

K = 3
Q = pd.read_csv(f'data_pruned.{K}.Q', sep='\s+', header=None)
fam = pd.read_csv('data_pruned.fam', sep='\s+', header=None)
Q.columns = [f'Cluster{i}' for i in range(1, K + 1)]
Q['IID'] = fam[1].values

pop_info = pd.read_csv('population_labels.txt', sep='\t')
Q = Q.merge(pop_info, on='IID')
Q = Q.sort_values('Population')

colors = plt.cm.Set1(range(K))
fig, ax = plt.subplots(figsize=(14, 4))

bottom = np.zeros(len(Q))
for i in range(K):
    ax.bar(range(len(Q)), Q[f'Cluster{i+1}'], bottom=bottom, color=colors[i], width=1)
    bottom += Q[f'Cluster{i+1}'].values

ax.set_xlim(0, len(Q))
ax.set_ylim(0, 1)
ax.set_ylabel('Ancestry proportion')
plt.savefig('admixture_barplot.png', dpi=150, bbox_inches='tight')

FlashPCA2 (Fast PCA for Large Datasets)

FlashPCA2 is optimized for very large datasets (100,000+ samples). Uses randomized algorithms for speed.

Installation

bash
# From conda
conda install -c bioconda flashpca

# Or download binaries from GitHub
# https://github.com/gabraham/flashpca

Basic Usage

bash
# Standard PCA
flashpca2 --bfile data --ndim 10 --outpc pcs.txt --outvec loadings.txt --outval eigenvalues.txt

# --ndim 10: Number of PCs to compute
# --outpc: Principal components output
# --outvec: Eigenvectors (variant loadings)
# --outval: Eigenvalues

FlashPCA2 Options

Option Description
--bfile PLINK binary prefix
--ndim Number of PCs (default 10)
--outpc PC scores output file
--outvec Eigenvectors output
--outval Eigenvalues output
--numthreads CPU threads to use
--mem Memory limit (GB)
--seed Random seed for reproducibility

Large Dataset Settings

bash
# For biobank-scale data (>100k samples)
# numthreads=16: Adjust to available cores.
# mem=64: Memory in GB. Increase for larger datasets.
flashpca2 \
    --bfile large_data \
    --ndim 20 \
    --numthreads 16 \
    --mem 64 \
    --outpc pcs.txt \
    --outval eigenvalues.txt \
    --seed 42

FlashPCA2 vs PLINK2

Feature FlashPCA2 PLINK2
Speed (100k samples) Faster Good
Memory efficiency Better Good
Randomized algorithm Yes Optional (approx)
Part of standard toolkit No Yes

Use FlashPCA2 for biobank-scale data; PLINK2 sufficient for most studies.

Parse FlashPCA2 Output

python
import pandas as pd

# Load PCs
pcs = pd.read_csv('pcs.txt', sep='\t', header=None)
pcs.columns = ['FID', 'IID'] + [f'PC{i}' for i in range(1, len(pcs.columns) - 1)]

# Load eigenvalues
eigenvals = pd.read_csv('eigenvalues.txt', header=None)[0].values
var_explained = eigenvals / eigenvals.sum() * 100

print('Variance explained:')
for i, ve in enumerate(var_explained[:10], 1):
    print(f'  PC{i}: {ve:.2f}%')

MDS (Alternative to PCA)

bash
# PLINK 1.9 MDS
plink --bfile data --cluster --mds-plot 10 --out mds_results

# Output: mds_results.mds (sample coordinates)

Kinship/Relatedness

PLINK 2.0 KING-robust

bash
# Calculate kinship matrix
plink2 --bfile data --make-king-table --out kinship

# Output: kinship.kin0 (pairs with kinship > 0.0442)

Identify Related Individuals

python
import pandas as pd

kin = pd.read_csv('kinship.kin0', sep='\t')
related = kin[kin['KINSHIP'] > 0.0884]  # First-degree relatives
print(f'Related pairs (1st degree): {len(related)}')

related = kin[kin['KINSHIP'] > 0.0442]  # Second-degree
print(f'Related pairs (2nd degree): {len(related)}')

Remove Related Individuals

bash
# Create list to remove (keep one per pair)
plink2 --bfile data --king-cutoff 0.0884 --out unrelated

# Filter to unrelated
plink2 --bfile data --keep unrelated.king.cutoff.in.id --make-bed --out unrelated

Complete Workflow

bash
# 1. QC filtering
plink2 --bfile raw --maf 0.01 --geno 0.05 --hwe 1e-6 --make-bed --out qc

# 2. LD pruning
plink2 --bfile qc --indep-pairwise 50 10 0.1 --out prune
plink2 --bfile qc --extract prune.prune.in --make-bed --out pruned

# 3. PCA
plink2 --bfile pruned --pca 20 --out pca

# 4. Admixture (multiple K)
for K in 2 3 4 5 6; do
    admixture --cv -j4 pruned.bed $K 2>&1 | tee log${K}.out
done

Related Skills

  • plink-basics - Data preparation and QC
  • linkage-disequilibrium - LD pruning details
  • association-testing - Use PCs as covariates

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