tooluniverse-gwas-trait-to-gene
GWAS Trait-to-Gene Discovery
Nearest gene is often wrong. Use L2G (locus-to-gene) scores from Open Targets which integrate eQTL, chromatin interaction, and distance data. L2G > 0.5 is a strong prediction; positional mapping alone should not be used to claim a causal gene. A single GWAS study with p < 5e-8 is suggestive — replication across independent cohorts is required for high confidence. GWAS hits are associations in the studied population; effect sizes and even the implicated gene can differ across ancestries due to differing LD patterns. Treat gene lists from GWAS as ranked candidates for validation, not confirmed causal genes.
LOOK UP DON'T GUESS: never assume trait-to-gene mappings or L2G scores — always call gwas_search_associations and OpenTargets_get_study_credible_sets to retrieve current data; associations are updated as new GWAS are published.
Discover genes associated with diseases and traits using genome-wide association studies (GWAS)
Overview
This skill enables systematic discovery of genes linked to diseases/traits by analyzing GWAS data from two major resources:
- GWAS Catalog (EBI/NHGRI): Curated catalog of published GWAS with >500,000 associations
- Open Targets Genetics: Fine-mapped GWAS signals with locus-to-gene (L2G) predictions
Use Cases
Clinical Research
- "What genes are associated with type 2 diabetes?"
- "Find genetic risk factors for coronary artery disease"
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