Bio population genetics association testing skills bio population genetics association testing
全球最大的 Claude Code 技能聚合库 · 收录 3900+ 来自 12+ 来源的技能，提供在线搜索与趋势分析看板 / The world's largest Claude Code skill aggregation hub — 3900+ skills from 12+ sources with online search and trend dashboard
npx -y skills add bg-szy/TOP-SKILLS --skill bio-population-genetics-association-testing__skills-bio-population-genetics-association-testingAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Genome-wide association studies (GWAS) with PLINK. Perform case-control and quantitative trait association testing using logistic/linear regression with covariates, generate Manhattan and QQ plots for result visualization. Use when running GWAS or association tests.
SKILL.md
7.1 KB, as published. Nobody here has run it
<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <[email protected]>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA
-->
Association Testing
GWAS analysis using PLINK 2.0's unified --glm command for case-control and quantitative traits.
PLINK 2.0 Association Testing
Basic Case-Control (Binary Phenotype)
# Basic logistic regression
plink2 --bfile data --glm --out results
# With phenotype file
plink2 --bfile data --pheno pheno.txt --glm --out results
Quantitative Trait (Continuous Phenotype)
# Linear regression for quantitative traits
plink2 --bfile data --pheno pheno.txt --glm --out results
With Covariates
# Include covariates (sex, age, PCs)
plink2 --bfile data \
--pheno pheno.txt \
--covar covariates.txt \
--glm --out results
# Specify which covariates to use
plink2 --bfile data \
--pheno pheno.txt \
--covar covariates.txt \
--covar-name PC1,PC2,PC3,age,sex \
--glm --out results
Covariate Files
Phenotype File Format
# pheno.txt: FID IID pheno
# For binary: 1=control, 2=case, -9=missing
# For quantitative: continuous values
FAM001 IND001 2
FAM002 IND002 1
FAM003 IND003 1.5
Covariate File Format
# covariates.txt: FID IID cov1 cov2 ...
FAM001 IND001 0.15 35 1
FAM002 IND002 -0.22 42 2
FAM003 IND003 0.08 28 1
GLM Options
Phenotype Handling
# Multiple phenotypes (test all)
plink2 --bfile data --pheno pheno_multi.txt --glm --out results
# Specific phenotype column
plink2 --bfile data --pheno pheno_multi.txt --pheno-name trait1 --glm --out results
# Missing phenotype handling
plink2 --bfile data --glm allow-no-covars --out results
Model Options
# Additive model (default)
plink2 --bfile data --glm --out results
# Dominant model
plink2 --bfile data --glm dominant --out results
# Recessive model
plink2 --bfile data --glm recessive --out results
# Genotypic (2df test)
plink2 --bfile data --glm genotypic --out results
# Hide covariates from output (cleaner output)
plink2 --bfile data --covar cov.txt --glm hide-covar --out results
Firth Regression (Rare Variants)
# Enable Firth fallback for case-control (default in PLINK 2.0)
plink2 --bfile data --glm firth-fallback --out results
# Force Firth regression
plink2 --bfile data --glm firth --out results
# Disable Firth
plink2 --bfile data --glm no-firth --out results
Output Format
Output Columns
# Default output: results.PHENO1.glm.logistic or results.PHENO1.glm.linear
# Columns: CHROM, POS, ID, REF, ALT, A1, FIRTH?, TEST, OBS_CT, OR/BETA, SE, Tstat, P
Custom Output Columns
# Add specific columns
plink2 --bfile data --glm cols=+a1freq,+machr2 --out results
# Available columns:
# +a1freq: A1 allele frequency
# +machr2: MaCH R-squared
# +ax: Reference allele dosage
# +err: Standard errors
Population Stratification Control
Include Principal Components
# 1. Run PCA
plink2 --bfile data --pca 10 --out pca_results
# 2. Use PCs as covariates
plink2 --bfile data \
--pheno pheno.txt \
--covar pca_results.eigenvec \
--covar-name PC1,PC2,PC3,PC4,PC5 \
--glm --out results
Combined Workflow
# QC, PCA, and GWAS in sequence
plink2 --bfile raw --maf 0.01 --geno 0.05 --hwe 1e-6 --make-bed --out qc
plink2 --bfile qc --pca 10 --out pca
plink2 --bfile qc \
--pheno pheno.txt \
--covar pca.eigenvec \
--covar-name PC1-PC5 \
--glm hide-covar --out gwas
Result Filtering
Command Line Filtering
# Filter significant results
awk 'NR==1 || $13 < 5e-8' results.PHENO1.glm.logistic > significant.txt
# Extract top hits
sort -k13 -g results.PHENO1.glm.logistic | head -100 > top_hits.txt
Python Analysis
import pandas as pd
results = pd.read_csv('results.PHENO1.glm.logistic', sep='\t')
significant = results[results['P'] < 5e-8]
print(f'Genome-wide significant hits: {len(significant)}')
suggestive = results[results['P'] < 1e-5]
print(f'Suggestive hits: {len(suggestive)}')
Visualization
Manhattan Plot (Python)
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
results = pd.read_csv('results.PHENO1.glm.logistic', sep='\t')
results = results[results['TEST'] == 'ADD']
results['-log10P'] = -np.log10(results['P'])
chrom_colors = ['#1f77b4', '#ff7f0e']
results['color'] = results['#CHROM'].apply(lambda x: chrom_colors[x % 2])
cumulative_pos = []
offset = 0
for chrom in sorted(results['#CHROM'].unique()):
chrom_data = results[results['#CHROM'] == chrom]
cumulative_pos.extend(chrom_data['POS'] + offset)
offset += chrom_data['POS'].max()
results['cumulative_pos'] = cumulative_pos
plt.figure(figsize=(14, 6))
plt.scatter(results['cumulative_pos'], results['-log10P'], c=results['color'], s=1)
plt.axhline(y=-np.log10(5e-8), color='red', linestyle='--', label='Genome-wide (5e-8)')
plt.axhline(y=-np.log10(1e-5), color='blue', linestyle='--', label='Suggestive (1e-5)')
plt.xlabel('Chromosome')
plt.ylabel('-log10(P)')
plt.legend()
plt.savefig('manhattan.png', dpi=150)
QQ Plot (Python)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
results = pd.read_csv('results.PHENO1.glm.logistic', sep='\t')
observed_p = results[results['TEST'] == 'ADD']['P'].dropna().sort_values()
n = len(observed_p)
expected_p = np.arange(1, n + 1) / (n + 1)
plt.figure(figsize=(6, 6))
plt.scatter(-np.log10(expected_p), -np.log10(observed_p), s=1)
plt.plot([0, 8], [0, 8], 'r--')
plt.xlabel('Expected -log10(P)')
plt.ylabel('Observed -log10(P)')
lambda_gc = np.median(stats.chi2.ppf(1 - observed_p, 1)) / stats.chi2.ppf(0.5, 1)
plt.title(f'QQ Plot (λ = {lambda_gc:.3f})')
plt.savefig('qqplot.png', dpi=150)
Genomic Inflation
from scipy import stats
import numpy as np
results = pd.read_csv('results.PHENO1.glm.logistic', sep='\t')
pvalues = results[results['TEST'] == 'ADD']['P'].dropna()
chisq = stats.chi2.ppf(1 - pvalues, 1)
lambda_gc = np.median(chisq) / stats.chi2.ppf(0.5, 1)
print(f'Genomic inflation factor: {lambda_gc:.3f}')
# Good: 1.0-1.05, Acceptable: 1.05-1.1, Concerning: >1.1
Related Skills
- plink-basics - Data preparation and QC
- population-structure - PCA for stratification control
- linkage-disequilibrium - LD pruning before analysis