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tooluniverse-pathway-disease-genetics
Connect GWAS variants to biological pathways and druggable targets. Maps GWAS hits to causal genes (via fine-mapping/eQTL), then to pathways (Reactome, KEGG, WikiPathways), then to existing drugs hitting those pathways. Use for pathway-level disease mechanisms, druggable-pathway prioritization from GWAS, SNP-to-pathway-to-target tracing, and tissue-specific eQTL evidence for drug target hypotheses.
plugins/tooluniverse/skills/tooluniverse-pathway-disease-genetics/SKILL.md
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Pathway-Disease Genetics: GWAS to Drug Targets via Pathways
Connect genome-wide association study (GWAS) variants to biological pathways for mechanistic understanding and drug target discovery.
## When to Use
- "What pathways are disrupted in [disease] based on GWAS?"
- "Which GWAS genes for [trait] are in druggable pathways?"
- "Map [SNP/variant] to its causal gene and pathway"
- "Find drug targets from GWAS data for [disease]"
- "What is the eQTL evidence for [gene] in [tissue]?"