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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 associationsOpen 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
↓
2. SNP Aggregation → Collect genome-wide significant SNPs (p < 5e-8)
↓
3. Gene Mapping → Extract mapped genes from associations
↓
4. Evidence Ranking → Score by p-value, replication, fine-mapping
↓
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 SNPFine-mapping : Statistical refinement to credible variantsLocus-to-Gene (L2G) : Integrative score combining multiple evidence types Evidence Confidence Levels
High : L2G score > 0.5 OR multiple studies with p < 5e-10Medium : 2+ studies with p < 5e-8Low : 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 mappinggwas_get_snp_by_id - Get SNP details (MAF, consequence, location)gwas_get_study_by_id - Get study metadatagwas_search_associations - Search associations with filtersgwas_search_studies - Search studies by trait/cohortgwas_get_associations_for_snp - Get all associations for a SNPgwas_get_variants_for_trait - Get variants for a traitgwas_get_studies_for_trait - Get studies for a traitgwas_get_snps_for_gene - Get SNPs mapped to a genegwas_get_associations_for_study - Get associations from a study
Open Targets Genetics (6 tools)
OpenTargets_search_gwas_studies_by_disease - Search studies by disease ontologyOpenTargets_get_study_credible_sets - Get fine-mapped loci for a studyOpenTargets_get_variant_credible_sets - Get credible sets for a variantOpenTargets_get_variant_info - Get variant annotation (frequencies, consequences)OpenTargets_get_gwas_study - Get study metadataOpenTargets_get_credible_set_detail - Get detailed credible set information
Parameters
trait - Disease/trait name (e.g., "type 2 diabetes", "coronary artery disease")
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 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
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 genesLinkage Disequilibrium : Lead SNP may tag the true causal variant in a nearby geneFine-mapping : L2G scores provide better causal gene evidence than positional mappingFunctional 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
Related Skills
Variant-to-Disease Association : Look up specific SNPs (e.g., rs7903146 → T2D)Gene-to-Disease Links : Find diseases associated with known genesDrug Target Prioritization : Rank targets by genetic evidencePopulation Genetics Analysis : Compare allele frequencies across populations
Data Sources
Curator: EBI and NHGRI
URL: https://www.ebi.ac.uk/gwas/
Coverage: 100,000+ publications, 500,000+ associations
Update Frequency: Weekly
Citation If you use this skill in research, please cite:
Buniello A, et al. (2019) The NHGRI-EBI GWAS Catalog of published genome-wide
association studies. Nucleic Acids Research, 47(D1):D1005-D1012.
Mountjoy E, et al. (2021) An open approach to systematically prioritize causal
variants and genes at all published human GWAS trait-associated loci.
Nature Genetics, 53:1527-1533.
Support
Skill functionality : Open issue at tooluniverse/skillsGWAS data : Contact GWAS Catalog or Open Targets supportTool errors : Check ToolUniverse tool status 