Tooluniverse Gwas Trait To Gene

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by mims-harvard · vlatest · Repository

This skill translates GWAS traits into potential genes, accelerating genetic discovery and understanding disease mechanisms.

Install on your platform

We auto-selected Claude Code based on this skill’s supported platforms.

1

Run in terminal (recommended)

terminal
claude mcp add tooluniverse-gwas-trait-to-gene npx -- -y @trustedskills/tooluniverse-gwas-trait-to-gene
2

Or manually add to ~/.claude/settings.json

~/.claude/settings.json
{
  "mcpServers": {
    "tooluniverse-gwas-trait-to-gene": {
      "command": "npx",
      "args": [
        "-y",
        "@trustedskills/tooluniverse-gwas-trait-to-gene"
      ]
    }
  }
}

Requires Claude Code (claude CLI). Run claude --version to verify your install.

About This Skill

What it does

This skill helps discover genes associated with diseases and traits by analyzing data from genome-wide association studies (GWAS). It leverages two primary resources: the GWAS Catalog and Open Targets Genetics, which provides locus-to-gene (L2G) predictions integrating eQTL, chromatin interaction, and distance data. The tool prioritizes gene candidates for validation rather than presenting them as confirmed causal genes.

When to use it

  • Clinical Research: Identifying genes associated with conditions like type 2 diabetes, coronary artery disease, or Alzheimer's disease susceptibility.
  • Drug Target Discovery: Prioritizing potential drug targets based on genetic evidence and L2G scores.
  • Functional Genomics: Mapping disease-associated variants to candidate genes and analyzing the genetic architecture of complex traits.

Key capabilities

  • GWAS Catalog Search: Searches the GWAS Catalog for associations related to a given trait or disease.
  • SNP Aggregation: Collects genome-wide significant SNPs (p < 5e-8).
  • Gene Mapping: Extracts mapped genes from GWAS associations.
  • Locus-to-Gene (L2G) Scoring: Uses L2G scores from Open Targets Genetics to rank potential gene candidates. A score above 0.5 is considered a strong prediction.
  • Evidence Ranking: Ranks candidate genes based on p-value, replication across studies, and fine-mapping data.

Example prompts

  • "What genes are associated with type 2 diabetes?"
  • "Find genetic risk factors for coronary artery disease."
  • "Which genes contribute to Alzheimer's disease susceptibility?"

Tips & gotchas

  • Always retrieve current data: Use gwas_search_associations and OpenTargets_get_study_credible_sets to obtain the latest information, as associations are updated with new GWAS publications. Do not assume trait-to-gene mappings or L2G scores.
  • Replication is key: A single GWAS study with p < 5e-8 is suggestive; replication across independent cohorts is required for high confidence.
  • Ancestry matters: Effect sizes and implicated genes can vary across ancestries due to differing LD patterns.

Tags

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Details

Version
vlatest
License
Author
mims-harvard
Installs
84

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Passed automated security scans.