| name | tooluniverse-pathway-disease-genetics |
| description | 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. |
| disable-model-invocation | true |
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]?"
Core Reasoning Principles
Gene-to-Pathway Reasoning
A gene found in GWAS doesn't tell you which pathway is dysregulated. To connect gene -> pathway -> disease mechanism, ask: what biological process does this gene participate in? Use Reactome/KEGG to find pathways, then ask: which of these pathways is relevant to the disease phenotype?
For example, TCF7L2 is the strongest T2D GWAS gene. It participates in the Wnt signaling pathway. The question is then: how does disrupted Wnt signaling impair beta-cell function or insulin secretion? That reasoning step — from pathway membership to disease mechanism — requires combining pathway data with tissue expression (GTEx) and disease biology.
