plink2-gwas-analysis
GWAS and population genetics tool. Processes PLINK (.bed/.bim/.fam), VCF, and BGEN; runs QC (MAF, HWE, missingness), IBD estimation, PCA, and linear/logistic regression GWAS. Outputs Manhattan-ready summary stats. Use regenie or SAIGE for biobanks (>100k samples) needing mixed models.
npx skills add BioTender-max/awesome-bio-agent-skills --skill plink2-gwas-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PLINK2 — GWAS and Population Genetics ## Overview PLINK2 is the high-performance successor to PLINK 1.9, designed for genome-wide association studies (GWAS) and population genetics analysis on large cohorts. It processes genotype data in PLINK binary format (.bed/.bim/.fam), VCF, and BGEN formats — performing sample and variant quality control (QC), kinship estimation, principal component analysis (PCA), and linear/logistic regression association testing. PLINK2 is 10–100× faster than PLINK 1.9 on most tasks due to multithreading and optimized I/O. Output files are compatible with downstream visualization (Manhattan/QQ plots) and meta-analysis tools. ## When to Use - Running GWAS on a case-control or quantitative trait cohort after genotyping array QC - Performing sample QC: missingness, heterozygosity outliers, sex check, cryptic relatedness - Computing genome-wide LD pruning for PCA or relatedness estimation - Running PCA on genotype data to identify population stratification - Converting between PLINK binary, VCF, and BGEN formats - Filtering variants by MAF, HWE, missingness, or INFO score in VCF/imputed data - Use **regenie** or **SAIGE** instead for biobank-scale GWAS (>100
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Step 1: Convert VCF/Imputed Data to PLINK Binary Format
- Step 2: Sample QC — Missingness and Heterozygosity
- Step 3: Variant QC — MAF, HWE, and INFO Score Filtering
- Step 4: LD Pruning and PCA for Population Stratification
- Step 5: Run GWAS Association Analysis
- Step 6: Plot Manhattan and QQ Plots
- Key Parameters
- Common Recipes
- Recipe 1: Relatedness QC and Remove One from Each Related Pair
Skip install if already present if command -v plink2 >/dev/null 2>&1; then echo "plink2 already installed: $(plink2 --version)" else wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_linux_avx2_20241112.zip unzip plink2_linux_avx2_20241112.zip chmod +x plink2 export PATH="$PWD:$PATH" plink2 --version fi
What does the plink2-gwas-analysis skill do?
GWAS and population genetics tool. Processes PLINK (.bed/.bim/.fam), VCF, and BGEN; runs QC (MAF, HWE, missingness), IBD estimation, PCA, and linear/logistic regression GWAS. Outputs Manhattan-ready summary stats. Use regenie or SAIGE for biobanks (>100k samples) needing mixed models.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill plink2-gwas-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 144 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
