| 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. |
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.
Non-coding GWAS variants (the majority) rarely affect the nearest gene. They act through regulatory elements that alter expression of genes sometimes hundreds of kilobases away. Always check eQTL evidence before assuming the nearest gene is causal.
