Tooluniverse Gwas Trait To Gene

Maps GWAS hits to candidate genes using Open Targets locus-to-gene scoring instead of the unreliable nearest-gene heuristic. Pulls associations from GWAS Catalog and Open Targets Genetics, filters by genome-wide significance (p < 5e-8), and ranks genes by L2G scores that integrate eQTL, chromatin interaction, and distance evidence. L2G > 0.5 is considered strong prediction. Useful when you need to go from "what SNPs are associated with type 2 diabetes" to "what genes are actually implicated" with some statistical rigor. The skill emphasizes looking up real data rather than guessing, which is the right approach since GWAS catalogs update constantly and LD patterns differ across ancestries.

skills.sh installs: 365
GitHub stars: 1.8K
last updated: Aug 21, 2026

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Install

$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-gwas-trait-to-gene --agent claude-code

Installs into .claude/skills of the current project.

Files

GWAS Trait-to-Gene Discovery

Nearest gene is often wrong. Use L2G (locus-to-gene) scores from Open Targets which integrate eQTL, chromatin interaction, and distance data. L2G > 0.5 is a strong prediction; positional mapping alone should not be used to claim a causal gene. A single GWAS study with p < 5e-8 is suggestive — replication across independent cohorts is required for high confidence. GWAS hits are associations in the studied population; effect sizes and even the implicated gene can differ across ancestries due to differing LD patterns. Treat gene lists from GWAS as ranked candidates for validation, not confirmed causal genes.

LOOK UP DON'T GUESS: never assume trait-to-gene mappings or L2G scores — always call gwas_search_associations and OpenTargets_get_study_credible_sets to retrieve current data; associations are updated as new GWAS are published.

Discover genes associated with diseases and traits using genome-wide association studies (GWAS)