--- name: bio-workflows-gwas-pipeline description: End-to-end GWAS workflow from VCF to association results. Covers PLINK QC, population structure correction, and association testing for case-control or quantitative traits. Use when running genome-wide association studies. tool_type: mixed primary_tool: PLINK2 workflow: true depends_on: - population-genetics/plink-basics - population-genetics/population-structure - population-genetics/association-testing - population-genetics/linkage-disequilibrium qc_checkpoints: - after_qc: "Sample/variant call rates >95%, HWE p>1e-6" - after_structure: "No population stratification bias" - after_association: "Lambda ~1.0, expected QQ plot" measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- # GWAS Pipeline Complete workflow for genome-wide association studies from genotype data to significant associations. ## Workflow Overview ``` VCF/PLINK files | v [1. QC Filtering] ------> Sample and variant QC | v [2. LD Pruning] --------> Independent variants for PCA | v [3. Population Structure] --> PCA for covariates | v [4. Association Testing] --> Logistic/linear regression | v [5. Results] -----------> Manhattan plot, QQ plot | v Significant associations ``` ## Step 1: Data Import and QC ### Convert VCF to PLINK ```bash # VCF to PLINK binary format plink2 --vcf input.vcf.gz \ --make-bed \ --out study # Or with phenotype/covariate files plink2 --vcf input.vcf.gz \ --pheno phenotypes.txt \ --make-bed \ --out study ``` ### Sample QC ```bash # Calculate sample statistics plink2 --bfile study \ --missing \ --out study_stats # Remove samples with high missing rate (>5%) plink2 --bfile study \ --mind 0.05 \ --make-bed \ --out study_sample_qc # Check for sex discrepancies (if sex chromosome data available) plink2 --bfile study_sample_qc \ --check-sex \ --out study_sex_check # Remove related individuals (optional, requires IBD) plink2 --bfile study_sample_qc \ --king-cutoff 0.0884 \ --make-bed \ --out study_unrelated ``` ### Variant QC ```bash # Apply standard variant filters plink2 --bfile study_sample_qc \ --geno 0.05 \ --maf 0.01 \ --hwe 1e-6 \ --make-bed \ --out study_qc # Summary plink2 --bfile study_qc --freq --out study_qc ``` **QC Checkpoint:** - Sample call rate >95% - Variant call rate >95% - MAF >1% - HWE p-value >1e-6 (controls only for case-control) ## Step 2: LD Pruning for PCA ```bash # Identify independent variants plink2 --bfile study_qc \ --indep-pairwise 50 5 0.2 \ --out pruned # Extract pruned variants plink2 --bfile study_qc \ --extract pruned.prune.in \ --make-bed \ --out study_pruned ``` ## Step 3: Population Structure (PCA) ```bash # Calculate principal components plink2 --bfile study_pruned \ --pca 10 \ --out study_pca # The eigenvec file contains PCs for use as covariates ``` ### Visualize PCA ```r library(ggplot2) # Load PCA results pca <- read.table('study_pca.eigenvec', header = FALSE) colnames(pca) <- c('FID', 'IID', paste0('PC', 1:10)) # Load phenotype for coloring pheno <- read.table('phenotypes.txt', header = TRUE) pca <- merge(pca, pheno, by = c('FID', 'IID')) # Plot ggplot(pca, aes(x = PC1, y = PC2, color = as.factor(PHENO))) + geom_point(alpha = 0.5) + labs(title = 'PCA of Study Samples', color = 'Phenotype') + theme_minimal() ggsave('pca_plot.pdf', width = 8, height = 6) ``` ## Step 4: Association Testing ### Case-Control (Binary Trait) ```bash # Logistic regression with PCA covariates plink2 --bfile study_qc \ --pheno phenotypes.txt \ --covar study_pca.eigenvec \ --covar-col-nums 3-12 \ --glm hide-covar \ --out gwas_results # Results in gwas_results.PHENO.glm.logistic ``` ### Quantitative Trait ```bash # Linear regression plink2 --bfile study_qc \ --pheno phenotypes.txt \ --pheno-name BMI \ --covar study_pca.eigenvec \ --covar-col-nums 3-12 \ --glm hide-covar \ --out gwas_bmi # Results in gwas_bmi.BMI.glm.linear ``` ### With Additional Covariates ```bash # Include age, sex, and PCs plink2 --bfile study_qc \ --pheno phenotypes.txt \ --covar covariates.txt \ --covar-name AGE,SEX,PC1-PC10 \ --glm hide-covar \ --out gwas_adjusted ``` ## Step 5: Results Visualization ### Manhattan Plot ```r library(qqman) # Load results results <- read.table('gwas_results.PHENO.glm.logistic', header = TRUE) results <- results[!is.na(results$P),] # Manhattan plot png('manhattan.png', width = 1200, height = 600) manhattan(results, chr = 'X.CHROM', bp = 'POS', snp = 'ID', p = 'P', suggestiveline = -log10(1e-5), genomewideline = -log10(5e-8)) dev.off() # QQ plot png('qq_plot.png', width = 600, height = 600) qq(results$P) dev.off() ``` ### Calculate Genomic Inflation ```r # Lambda (genomic inflation factor) chisq <- qchisq(1 - results$P, 1) lambda <- median(chisq) / qchisq(0.5, 1) cat('Lambda:', round(lambda, 3), '\n') # Lambda should be close to 1.0 (1.0-1.1 acceptable) ``` ### Extract Significant Hits ```bash # Genome-wide significant (p < 5e-8) awk '$12 < 5e-8' gwas_results.PHENO.glm.logistic > significant_hits.txt # Suggestive (p < 1e-5) awk '$12 < 1e-5' gwas_results.PHENO.glm.logistic > suggestive_hits.txt ``` ## Parameter Recommendations | Step | Parameter | Value | |------|-----------|-------| | Sample QC | --mind | 0.05 | | Variant QC | --geno | 0.05 | | Variant QC | --maf | 0.01 | | Variant QC | --hwe | 1e-6 | | LD pruning | --indep-pairwise | 50 5 0.2 | | PCA | --pca | 10 | | Significance | p-value | 5e-8 | ## Troubleshooting | Issue | Likely Cause | Solution | |-------|--------------|----------| | High lambda (>1.1) | Population stratification | Add more PCs, check ancestry | | No significant hits | Low power | Increase sample size, meta-analysis | | Deflated lambda (<1) | Over-correction | Reduce PC covariates | | QQ deviation at low end | Batch effects | Check for technical artifacts | ## Complete Pipeline Script ```bash #!/bin/bash set -e INPUT_VCF="genotypes.vcf.gz" PHENO="phenotypes.txt" OUTDIR="gwas_results" mkdir -p ${OUTDIR} # Step 1: Convert and QC plink2 --vcf ${INPUT_VCF} --make-bed --out ${OUTDIR}/raw plink2 --bfile ${OUTDIR}/raw --mind 0.05 --geno 0.05 --maf 0.01 --hwe 1e-6 \ --make-bed --out ${OUTDIR}/qc # Step 2: LD pruning plink2 --bfile ${OUTDIR}/qc --indep-pairwise 50 5 0.2 --out ${OUTDIR}/pruned plink2 --bfile ${OUTDIR}/qc --extract ${OUTDIR}/pruned.prune.in \ --make-bed --out ${OUTDIR}/pruned # Step 3: PCA plink2 --bfile ${OUTDIR}/pruned --pca 10 --out ${OUTDIR}/pca # Step 4: Association plink2 --bfile ${OUTDIR}/qc --pheno ${PHENO} \ --covar ${OUTDIR}/pca.eigenvec --covar-col-nums 3-12 \ --glm hide-covar --out ${OUTDIR}/gwas echo "=== GWAS Complete ===" echo "Results: ${OUTDIR}/gwas.*.glm.*" ``` ## Related Skills - population-genetics/plink-basics - PLINK file formats and commands - population-genetics/population-structure - PCA and admixture - population-genetics/association-testing - Statistical models - population-genetics/linkage-disequilibrium - LD concepts --- Source attribution: GPTomics Original project: https://github.com/GPTomics/bioSkills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 FridrichMethod Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --- NOTE ON BUNDLED CONTENT This repository aggregates skills from multiple upstream projects (see the README for the source list). Each `skills//` directory may carry its own license inherited from its upstream source. The MIT license above applies to this repository's curation and the scripts/workflows in `/`, `/scripts/`, and `/.github/` — not to the upstream skill content itself. Consult the upstream repository linked in the README and any LICENSE file inside a skill directory before reusing skill content commercially. # Mega AI Hub resource metadata Creator / publisher: GPTomics Original project: https://github.com/GPTomics/bioSkills Attribution basis: Exact package in biomedical source index and publisher source table Fields of work: Data & Analytics Roles: Business Intelligence Analyst, Data Scientist, Market Research Analyst Specialties: Frontend, Playwright, Testing Tools: Plink2 Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: d22a851032fa40762a62ba66105793b3c7bd31a74e38f9d33a827c5c70d08e39 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.