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tooluniverse-gwas-trait-to-gene

Discover causal genes for diseases/traits from GWAS data using Open Targets L2G (locus-to-gene) scoring — integrates eQTL, chromatin interaction, and distance evidence. Use for trait-to-gene mapping, drug-target hypothesis generation from GWAS, and replacing the 'nearest gene' heuristic with multi-evidence L2G scores.

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# 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