GWAS Drug Discovery (ToolUniverse Claude Skill)

A ToolUniverse agent skill that connects genome-wide association signals to causal genes, ranks the druggable candidates, and matches them to existing drugs for repurposing.

   
Type Claude Skill (one of ToolUniverse’s pre-built agent skills)
Supplier Zitnik Lab, Harvard Medical School
Availability GA — part of the ToolUniverse skills collection (skills/tooluniverse-gwas-drug-discovery/)
Pricing Free / OSS (Apache-2.0); wraps public APIs (GWAS Catalog, Open Targets, DGIdb, ChEMBL, openFDA, PubMed, ClinicalTrials.gov)
Capabilities Read-only — drives ToolUniverse tool calls; no data writes
Verified works · 2026-07-20
Security cleared · 2026-07-20 — provenance matches Zitnik Lab, Apache-2.0, skill dir confirmed, no OSV advisories

How to install

This skill calls ToolUniverse tools, so the ToolUniverse MCP server must be installed first (see the ToolUniverse page). Simplest registration:

claude mcp add --transport stdio tooluniverse -- uvx tooluniverse

Then add the skills:

  • Claude Code — install the whole skill collection (the skill resolves as tooluniverse-gwas-drug-discovery): 
    npx skills add mims-harvard/ToolUniverse
  • Manual / other agents — copy just this skill directory into your skills folder: 
    git clone https://github.com/mims-harvard/ToolUniverse
cp -r ToolUniverse/skills/tooluniverse-gwas-drug-discovery ~/.claude/skills/

    (replace ~/.claude/skills/ with your agent’s skills directory if you are not using Claude Code/Desktop.)

The skill sets disable-model-invocation: true upstream, so invoke it explicitly (e.g. ask Claude to “use the gwas-drug-discovery skill”) rather than relying on automatic dispatch.

What it does

Runs a six-phase workflow from GWAS signal to actionable drug hypothesis:

  1. GWAS gene discovery — identify disease-associated variants and map them to causal genes (gwas_get_associations_for_trait, gwas_search_associations, OpenTargets_get_variant_credible_sets for fine-mapping/eQTL).
  2. Druggability assessment — evaluate target tractability and safety (OpenTargets_get_target_tractability_by_ensemblID, _get_target_classes_by_ensemblID, _get_target_safety_profile_by_ensemblID).
  3. Target prioritisation — composite score (GWAS 40%, druggability 30%, clinical evidence 20%, novelty 10%).
  4. Existing drug search — approved compounds and clinical candidates (OpenTargets_get_associated_drugs_by_disease_efoId, ChEMBL_get_target_activities, DGIdb_get_drug_gene_interactions).
  5. Clinical evidence and safety — adverse-reaction and warning data (FDA_get_adverse_reactions_by_drug_name, OpenTargets_get_drug_warnings_by_chemblId).
  6. Repurposing opportunities — match existing drugs to new disease indications, supported by literature and trial evidence (PubMed_search_articles, ClinicalTrials_search_studies).

Primary use cases: post-GWAS target triage, genetics-anchored repurposing, prioritising loci for follow-up given druggability and clinical precedent.

Notes

It is a reasoning layer over ToolUniverse; without the MCP server registered, the tool calls fail. Phases 1–5 lean heavily on Open Targets OpenTargets_* tools — if the ToolUniverse Open Targets surface is degraded (see the Open Targets flag), tractability and drug-association steps may be incomplete. Complements the Drug Repurposing and Drug Target Validation skills. ToolUniverse ships ~68 such skills; other workflows are catalogued separately.

Sources

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