GWAS Trait-to-Gene Discovery

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"

"Which genes contribute to Alzheimer's disease susceptibility?"

Drug Target Discovery

Identify genes with strong genetic evidence for disease causation

Prioritize targets based on L2G scores and replication across studies

Find genes with genome-wide significant associations (p < 5e-8)

Functional Genomics

Map disease-associated variants to candidate genes

Analyze genetic architecture of complex traits

Understand polygenic disease mechanisms

Workflow

1. Trait Search → Search GWAS Catalog by disease/trait name

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2. SNP Aggregation → Collect genome-wide significant SNPs (p < 5e-8)

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3. Gene Mapping → Extract mapped genes from associations

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4. Evidence Ranking → Score by p-value, replication, fine-mapping

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5. Annotation (Optional) → Add L2G predictions from Open Targets

Key Concepts

Genome-wide Significance

Standard threshold: p < 5×10⁻⁸

Accounts for multiple testing burden across ~1M common variants

Higher confidence: p < 5×10⁻¹⁰ or replicated across studies

Gene Mapping Methods

Positional

Nearest gene to lead SNP

Fine-mapping

Statistical refinement to credible variants

Locus-to-Gene (L2G)

Integrative score combining multiple evidence types

Evidence Confidence Levels

High

L2G score > 0.5 OR multiple studies with p < 5e-10

Medium

2+ studies with p < 5e-8

Low

Single study or marginal significance Required ToolUniverse Tools GWAS Catalog (11 tools) gwas_get_associations_for_trait - Get all associations for a trait (sorted by p-value) gwas_search_snps - Search SNPs by gene mapping gwas_get_snp_by_id - Get SNP details (MAF, consequence, location) gwas_get_study_by_id - Get study metadata gwas_search_associations - Search associations with filters gwas_search_studies - Search studies by trait/cohort gwas_get_associations_for_snp - Get all associations for a SNP gwas_get_variants_for_trait - Get variants for a trait gwas_get_studies_for_trait - Get studies for a trait gwas_get_snps_for_gene - Get SNPs mapped to a gene gwas_get_associations_for_study - Get associations from a study Open Targets Genetics (6 tools) OpenTargets_search_gwas_studies_by_disease - Search studies by disease ontology OpenTargets_get_study_credible_sets - Get fine-mapped loci for a study OpenTargets_get_variant_credible_sets - Get credible sets for a variant OpenTargets_get_variant_info - Get variant annotation (frequencies, consequences) OpenTargets_get_gwas_study - Get study metadata OpenTargets_get_credible_set_detail - Get detailed credible set information Parameters Required trait - Disease/trait name (e.g., "type 2 diabetes", "coronary artery disease") Optional p_value_threshold - Significance threshold (default: 5e-8) min_evidence_count - Minimum number of studies (default: 1) max_results - Maximum genes to return (default: 100) use_fine_mapping - Include L2G predictions (default: true) disease_ontology_id - Disease ontology ID for Open Targets (e.g., "MONDO_0005148") Output Schema { "genes" : [ { "symbol" : str ,

Gene symbol (e.g., "TCF7L2")

"min_p_value" : float ,

Most significant p-value

"evidence_count" : int ,

Number of independent studies

"snps" : [ str ] ,

Associated SNP rs IDs

"studies" : [ str ] ,

GWAS study accessions

"l2g_score" : float | null ,

Locus-to-gene score (0-1)

"credible_sets" : int ,

Number of credible sets

"confidence_level" : str

"High", "Medium", or "Low"

} ] , "summary" : { "trait" : str , "total_associations" : int , "significant_genes" : int , "data_sources" : [ "GWAS Catalog" , "Open Targets" ] } } Example Results Type 2 Diabetes TCF7L2: p=1.2e-98, 15 studies, L2G=0.82 → High confidence KCNJ11: p=3.4e-67, 12 studies, L2G=0.76 → High confidence PPARG: p=2.1e-45, 8 studies, L2G=0.71 → High confidence FTO: p=5.6e-42, 10 studies, L2G=0.68 → High confidence IRS1: p=8.9e-38, 6 studies, L2G=0.54 → High confidence Alzheimer's Disease APOE: p=1.0e-450, 25 studies, L2G=0.95 → High confidence BIN1: p=2.3e-89, 18 studies, L2G=0.88 → High confidence CLU: p=4.5e-67, 16 studies, L2G=0.82 → High confidence ABCA7: p=6.7e-54, 14 studies, L2G=0.79 → High confidence CR1: p=8.9e-52, 13 studies, L2G=0.75 → High confidence Best Practices 1. Use Disease Ontology IDs for Precision

Instead of:

discover_gwas_genes("diabetes") # Ambiguous

Use:

discover_gwas_genes( "type 2 diabetes", disease_ontology_id="MONDO_0005148" # Specific ) 2. Filter by Evidence Strength

For drug targets, require strong evidence:

discover_gwas_genes(

"coronary artery disease",

p_value_threshold=5e-10, # Stricter than GWAS threshold

min_evidence_count=3, # Multiple independent studies

use_fine_mapping=True # Include L2G predictions

)

3. Interpret Results Carefully

Association ≠ Causation

GWAS identifies correlated variants, not necessarily causal genes

Linkage Disequilibrium

Lead SNP may tag the true causal variant in a nearby gene

Fine-mapping

L2G scores provide better causal gene evidence than positional mapping

Functional Evidence

Validate with orthogonal data (eQTLs, knockout models, etc.) Limitations Gene Mapping Uncertainty Positional mapping assigns SNPs to nearest gene (may be incorrect) Fine-mapping available for only a subset of studies Intergenic variants difficult to map Population Bias Most GWAS in European populations Effect sizes may differ across ancestries Rare variants often under-represented Sample Size Dependence Larger studies detect more associations Older small studies may have false negatives p-values alone don't indicate effect size Validation Bug Some ToolUniverse tools have oneOf validation issues Use validate=False parameter if needed This is automatically handled in the Python implementation