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tooluniverse-gwas-drug-discovery

Transform GWAS associations into prioritized drug targets and repurposing candidates.

1.7k|254|Updated Mar 3, 2025
Search Tags:#genomics#drug discovery#chembl#gwas#drug repurposing#target prioritization#Open Targets
One-click install
npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-gwas-drug-discovery
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-gwas-drug-discovery
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/plugins/tooluniverse/skills/tooluniverse-gwas-drug-discovery
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-gwas-drug-discovery

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tooluniverse.

What problem does it solve?

Genome-wide association studies produce thousands of disease-linked loci, but translating those statistical signals into actionable drug targets requires connecting variants to causal genes, assessing druggability, and finding existing compounds. This Skill automates that GWAS-to-drug pipeline so genetic evidence directly informs target discovery and repurposing decisions.

Core Features & Use Cases

  • GWAS Gene Discovery: Query GWAS Catalog and Open Targets for disease associations, map variants to genes via fine-mapping, eQTL, and L2G scores.
  • Druggability & Prioritization: Score targets using a composite formula (GWAS evidence 40%, druggability 30%, clinical evidence 20%, novelty 10%) with tractability data from Open Targets.
  • Drug Repurposing: Match approved drugs from ChEMBL and DGIdb to new disease indications, with safety profiles from FDA adverse event data.
  • Use Case: Given Alzheimer's disease, retrieve GWAS hits (APOE, TREM2, CLU), rank them by genetic evidence and druggability, then surface repurposing candidates like Anakinra with mechanistic rationale and clinical phase.

Quick Start

Ask the AI to discover and rank druggable gene targets for type 2 diabetes using GWAS associations, then list any approved drugs that could be repurposed for it.

Frequently Asked Questions about tooluniverse-gwas-drug-discovery

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find drug targets from GWAS data?▼

Query GWAS associations for a disease trait, map significant variants (p < 5e-8) to genes using fine-mapping and eQTL evidence, then assess druggability via Open Targets tractability scores. Rank candidates with a composite score weighting genetic evidence, druggability, and clinical data.

How to identify drug repurposing opportunities from genetic data?▼

Match genes implicated in your disease's GWAS to targets of approved drugs in ChEMBL or DGIdb. A drug whose target shares genetic architecture with a new indication is a repurposing candidate, especially when its safety profile and dosing are already established.

What makes a gene a good drug target from GWAS?▼

Strong candidates have genome-wide significant, replicated associations with high L2G scores and eQTL colocalization, belong to druggable families like GPCRs or kinases, and show a clear direction of effect. Targets with genetic support have roughly double the clinical approval rate.

Why does gwas_get_associations_for_trait return no results?▼

The disease name may not match GWAS Catalog terminology or lack significant associations. Try synonyms or broader trait names, and note the parameter is disease_trait, not trait, with no p_value_threshold parameter available.

What are the limitations of GWAS-based drug target discovery?▼

GWAS associations are correlational, not causal, and common variants explain only part of heritability. Small effect sizes may not translate to clinically meaningful drug effects, so findings require functional validation, fine-mapping, and experimental confirmation before development.

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