Agent skill · Data & Analytics

tooluniverse-gwas-trait-to-gene

Discover genes associated with diseases and traits using GWAS data from the GWAS Catalog (500,000+ associations) and Open Targets Genetics (L2G predictions). Identifies genetic risk factors, prioritizes causal genes via locus-to-gene scoring, and assesses druggability. Use when asked to find genes associated with a disease or trait, discover genetic risk factors, translate GWAS signals to gene targets, or answer questions like "What genes are associated with type 2 diabetes?

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-gwas-trait-to-gene --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 7
SKILL.md size: 8 KB
Bundled scripts: yes
Path: skills/openclaw/tooluniverse-gwas-trait-to-gene/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# GWAS Trait-to-Gene Discovery **Discover genes associated with diseases and traits using genome-wide association studies (GWAS)** ## Overview This skill enables systematic discovery of genes linked to diseases/traits by analyzing GWAS data from two major resources: - **GWAS Catalog** (EBI/NHGRI): Curated catalog of published GWAS with >500,000 associations - **Open Targets Genetics**: Fine-mapped GWAS signals with locus-to-gene (L2G) predictions ## Use Cases **Clinical Research** - "What genes are associated with type 2 diabetes?" - "Find genetic risk factors for coronary artery disease" - "Which genes contribute to Alzheimer's disease susceptibility?" **Drug Target Discovery** - Identify genes with strong genetic evidence for disease causation - Prioritize targets based on L2G scores and replication across studies - Find genes with genome-wide significant associations (p < 5e-8) **Functional Genomics** - Map disease-associated variants to candidate genes - Analyze genetic architecture of complex traits - Understand polygenic disease mechanisms ## Workflow ``` 1. Trait Search → Search GWAS Catalog by disease/trait name ↓ 2. SNP Aggregation → Collect genome-wide significant SNPs (p

What's inside
Steps it walks through
  1. Overview
  2. Use Cases
  3. Workflow
  4. Key Concepts
  5. Required ToolUniverse Tools
  6. GWAS Catalog (11 tools)
  7. Open Targets Genetics (6 tools)
  8. Parameters
  9. Output Schema
  10. Example Results
  11. Best Practices
  12. Limitations
  13. Related Skills
  14. Data Sources
Ships with 6 files
  • .env.template
  • QUICK_START.md
  • README.md
  • python_implementation.py
  • test_gwas_tools.py
  • test_skill_comprehensive.py
More from awesome-bio-agent-skills
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About this skill
What does the tooluniverse-gwas-trait-to-gene skill do?

Discover genes associated with diseases and traits using GWAS data from the GWAS Catalog (500,000+ associations) and Open Targets Genetics (L2G predictions). Identifies genetic risk factors, prioritizes causal genes via locus-to-gene scoring, and assesses druggability. Use when asked to find genes associated with a disease or trait, discover genetic risk factors, translate GWAS signals to gene targets, or answer questions like "What genes are associated with type 2 diabetes?

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-gwas-trait-to-gene --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going