tooluniverse-gwas-finemapping
This skill applies Bayesian fine-mapping methods to pinpoint causal variants within GWAS-associated regions, moving beyond the lead SNP to compute posterior probabilities for each variant in a credible set. It integrates locus-to-gene predictions and functional annotations to link variants to their target genes, helping researchers distinguish true causal effects from linkage disequilibrium artifacts.
tooluniverse-gwas-finemapping identifies causal variants at GWAS loci by computing posterior probabilities and linking variants to genes via statistical fine-mapping.
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Install
mims-harvard/ToolUniverse/tooluniverse-gwas-finemapping · repository language: Python
git clone https://github.com/mims-harvard/ToolUniverse
cp -r ToolUniverse/skills/tooluniverse-gwas-finemapping ~/.claude/skills/tooluniverse-gwas-finemappingFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
Which variant is causal at this GWAS locus?
tooluniverse-gwas-finemapping uses Bayesian fine-mapping to compute posterior probabilities for each variant at a GWAS locus, identifying which SNPs are most likely causal rather than just in linkage disequilibrium with the lead variant. The skill generates credible sets—ranked lists of variants with cumulative posterior probability—so you can see the most probable causal candidates and their confidence levels.
How does tooluniverse-gwas-finemapping link variants to genes?
tooluniverse-gwas-finemapping integrates locus-to-gene (L2G) predictions and eQTL evidence to connect GWAS variants to their target genes. This moves beyond physical proximity, using functional data to show which genes are likely affected by causal variants at a locus, helping distinguish true causal effects from linkage artifacts.
What fine-mapping methods does this skill support?
tooluniverse-gwas-finemapping supports comparison of multiple Bayesian fine-mapping approaches, including methods like SuSiE and FINEMAP. The skill helps you interpret credible sets from different methods, understand their assumptions, and choose the most appropriate approach for your GWAS data and LD structure.
How does tooluniverse-gwas-finemapping handle LD structure?
tooluniverse-gwas-finemapping accounts for linkage disequilibrium (LD) structure to resolve ambiguous causal signals at GWAS loci. By modeling LD patterns, it distinguishes between variants that are truly causal and those merely correlated with the lead SNP, improving the accuracy of credible set construction.
Can tooluniverse-gwas-finemapping prioritize variants by function?
Yes, tooluniverse-gwas-finemapping annotates variants with functional consequences—such as coding impact, regulatory effects, and conservation scores—to help prioritize causal candidates. Combining posterior probabilities with functional annotations strengthens evidence for which variants are most likely driving disease associations.
What is a credible set in fine-mapping analysis?
A credible set is a ranked list of variants at a GWAS locus ordered by posterior probability of causality. tooluniverse-gwas-finemapping constructs credible sets (typically at 95% confidence) containing the minimum number of variants needed to capture that cumulative probability, helping you focus on the most likely causal SNPs.
SKILL.md
rendered from the published skill — quoted content, verbatim
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
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skills/tooluniverse-gwas-finemapping/.env.template
skills/tooluniverse-gwas-finemapping/SKILL.md
skills/tooluniverse-gwas-finemapping/python_implementation.py
skills/tooluniverse-gwas-finemapping/test_skill_comprehensive.py