Tooluniverse Gwas Finemapping

This handles the statistical heavy lifting of GWAS fine-mapping: taking a lead SNP and figuring out which variant in the LD block is actually causal, not just the best-tagged proxy on the genotyping array. It wraps Open Targets Genetics and GWAS Catalog APIs to pull credible sets from SuSiE and FINEMAP, then connects variants to genes using locus-to-gene scores that factor in eQTL colocalization and chromatin data. The reasoning framework is the real value here: it walks you through LD structure interpretation, posterior probability thresholds, and why the nearest gene is usually wrong. If you're doing target identification from GWAS hits and tired of people assuming the lead SNP is causal, this codifies the right questions to ask.

skills.sh installs: 364
GitHub stars: 1.8K
last updated: Aug 21, 2026

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Install

$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-gwas-finemapping --agent claude-code

Installs into .claude/skills of the current project.

Files

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

GWAS Fine-Mapping & Causal Variant Prioritization

Identify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions.

Overview

Genome-wide association studies (GWAS) identify genomic regions associated with traits, but linkage disequilibrium (LD) makes it difficult to pinpoint the causal variant. Fine-mapping uses Bayesian statistical methods to compute the posterior probability that each variant is causal, given the GWAS summary statistics.

SKILL.md on GitHub