What problem does it solve?
GWAS studies produce lists of disease-associated variants, but turning those statistical hits into mechanistic insight and actionable drug targets requires chaining together variant annotation, eQTL evidence, pathway enrichment, and druggability analysis across many databases. This Skill orchestrates that entire workflow so researchers can trace SNPs to causal genes, genes to pathways, and pathways to existing or novel drug targets.
Core Features & Use Cases
- GWAS-to-Gene Mapping: Collect genome-wide significant variants from the GWAS Catalog, annotate them with Ensembl VEP, and prioritize causal genes using GTEx eQTL evidence in disease-relevant tissues.
- Cross-Database Pathway Enrichment: Run enrichment across Reactome, KEGG, STRING, and PANTHER, then prioritize pathways that converge across multiple databases for stronger mechanistic evidence.
- Druggability and Drug Landscape Analysis: Assess pathway members with DGIdb and Open Targets to classify candidates as repurposing opportunities, clinical-stage validations, or novel targets.
- Use Case: Given type 2 diabetes GWAS hits, identify that multiple genes converge on Wnt signaling, confirm eQTL evidence in pancreatic tissue, and surface approved drugs hitting pathway members as repurposing candidates.
Quick Start
Use the pathway-disease-genetics skill to map GWAS hits for type 2 diabetes to causal genes, enriched pathways, and druggable targets.
