Agent skill

tooluniverse-gwas-snp-interpretation

Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple databases (GWAS Catalog, Open Targets Genetics, ClinVar). Retrieves variant annotations, GWAS trait associations, fine-mapping evidence, locus-to-gene predictions, and clinical significance. Use when asked to interpret a SNP by rsID, find disease associations for a variant, assess clinical significance, or answer questions like "What diseases is rs429358 associated with?" or "Interpret rs7903146".

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

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

Facts
Files in the skill folder: 9
SKILL.md size: 7 KB
Bundled scripts: yes
Path: skills/openclaw/tooluniverse-gwas-snp-interpretation/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 SNP Interpretation Skill ## Overview Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple sources to provide comprehensive clinical and biological context. **Use Cases:** - "Interpret rs7903146" (TCF7L2 diabetes variant) - "What diseases is rs429358 associated with?" (APOE Alzheimer's variant) - "Clinical significance of rs1801133" (MTHFR variant) - "Is rs12913832 in any fine-mapped loci?" (Eye color variant) ## What It Does The skill provides a comprehensive interpretation of SNPs by: 1. **SNP Annotation**: Retrieves basic variant information including genomic coordinates, alleles, functional consequence, and mapped genes 2. **Association Discovery**: Finds all GWAS trait/disease associations with statistical significance 3. **Fine-Mapping Evidence**: Identifies credible sets the variant belongs to (fine-mapped causal loci) 4. **Gene Mapping**: Uses Locus-to-Gene (L2G) predictions to identify likely causal genes 5. **Clinical Summary**: Aggregates evidence into actionable clinical significance ## Workflow ``` User Input: rs7903146 ↓ [1] SNP Lookup → Get location, consequence, MAF → gwas_get_snp_by_id ↓ [2] Association Search → Find all t

What's inside
Steps it walks through
  1. Overview
  2. What It Does
  3. Workflow
  4. Data Sources
  5. GWAS Catalog (EMBL-EBI)
  6. Open Targets Genetics
  7. Input Parameters
  8. Required
  9. Optional
  10. Output Format
  11. 1. SNP Basic Info
  12. 2. Trait Associations
  13. 3. Credible Sets (Fine-Mapping)
  14. 4. Clinical Significance
Ships with 8 files
  • .env.template
  • FINAL_VERIFICATION.txt
  • QUICK_START.md
  • README.md
  • python_implementation.py
  • test_gwas_snp_tools.py
  • test_gwas_snp_tools_simple.py
  • test_skill_comprehensive.py
More from awesome-bio-agent-skills
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About this skill
What does the tooluniverse-gwas-snp-interpretation skill do?

Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple databases (GWAS Catalog, Open Targets Genetics, ClinVar). Retrieves variant annotations, GWAS trait associations, fine-mapping evidence, locus-to-gene predictions, and clinical significance. Use when asked to interpret a SNP by rsID, find disease associations for a variant, assess clinical significance, or answer questions like "What diseases is rs429358 associated with?" or "Interpret rs7903146".

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-gwas-snp-interpretation --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