Tooluniverse Gwas Snp Interpretation

Pulls together GWAS Catalog and Open Targets data to answer "what does this SNP actually do" without you having to check five databases manually. You throw in an rsID like rs7903146, and it returns trait associations, fine-mapping credible sets, L2G gene predictions, and a clinical summary. The real value is that it understands a GWAS hit is a region, not a causal variant, so it checks LD structure and uses SuSiE/FINEMAP evidence before claiming mechanism. Takes 10 to 30 seconds for a full run. Most useful when you need to triage variants from a GWAS or explain why a lead SNP might regulate a gene 500kb away through an enhancer instead of the nearest gene.

skills.sh installs: 376
GitHub stars: 1.8K
last updated: Aug 21, 2026

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Install

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

Installs into .claude/skills of the current project.

Files

GWAS SNP Interpretation Skill

SNP interpretation: a GWAS hit is a REGION, not a single causal variant. The lead SNP may not be causal — it may be in LD with the causal variant. Always check LD structure and functional annotation before concluding a specific SNP is mechanistically responsible. Use LDlink_get_proxies(variant="rs...", population="EUR") to retrieve the high-R² LD proxies (needs a free LDLINK_TOKEN) — a proxy in a coding/regulatory region is a better mechanistic candidate than the lead SNP itself. Fine-mapping (SuSiE, FINEMAP credible sets) narrows the causal set but rarely identifies a single variant with certainty. L2G scores integrate eQTL, chromatin interaction, and distance data to predict the causal gene — a lead SNP mapping to gene A may actually regulate gene B 500 kb away via a distal enhancer.

LOOK UP DON'T GUESS: never assume a SNP's functional consequence, mapped gene, or population frequency — always call gwas_get_snp_by_id and OpenTargets_get_variant_info to retrieve current annotations.