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bio-workflows-gwas-pipeline

Run a genome-wide association study pipeline from genotype data to results.

Updated Aug 23, 2026
Search Tags:#gwas#pca#genotype#phenotype#qc#plink
One-click install
npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-workflows-gwas-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-workflows-gwas-pipeline
Source: https://github.com/stellaromics/fast-bioinfo/tree/main/.claude/agents/spatial-analysis/skills/bio-workflows-gwas-pipeline
Command: npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-workflows-gwas-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Genome-wide association studies can be complex and error-prone; this workflow provides an end-to-end pipeline from raw genotype data to association results, including QC, population structure adjustment, and association testing for case-control or quantitative traits.

Core Features & Use Cases

  • End-to-end GWAS: QC, LD pruning, PCA, and regression-based association tests.
  • Flexible trait support: Works for both binary (case-control) and quantitative traits with covariate handling.
  • Outputs: Manhattan and QQ plots, significant hits, and summary statistics for downstream interpretation.

Quick Start

Run an end-to-end GWAS from VCF to association results using PLINK2, including QC, LD pruning, PCA, and association testing.

Frequently Asked Questions about bio-workflows-gwas-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run an end-to-end GWAS pipeline from VCF genotype data?▼

Run an end-to-end GWAS pipeline from VCF files by applying QC checkpoints, LD pruning, PCA, and association testing to generate Manhattan and QQ plots with summary statistics.

Can I use PLINK2 for both case-control and quantitative trait association testing?▼

Yes, PLINK2 supports regression-based association testing for both binary case-control and quantitative traits, including covariate handling for population structure adjustment.

What is the best way to adjust for population structure in a genome-wide association study?▼

Adjust for population structure in a GWAS by performing LD pruning and PCA to generate principal components, then include them as covariates in the regression-based association testing.

Do I need to perform QC and LD pruning before running PCA on genotype data?▼

Yes, performing QC checkpoints and LD pruning before PCA is necessary to remove low-quality variants and correlated markers, ensuring accurate population structure adjustment for association testing.

What outputs do I get from a GWAS pipeline using PLINK2?▼

Outputs from a PLINK2 GWAS pipeline include Manhattan and QQ plots for result interpretation, significant association hits, and summary statistics for downstream analysis.

Why does my GWAS association test require covariate handling for population structure?▼

Covariate handling is required because unadjusted population structure causes false positives in association testing; PCA generates covariates that correct this confounding during regression.

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