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
       ↓
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 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

Related Skills

Variant-to-Disease Association: Look up specific SNPs (e.g., rs7903146 → T2D)
Gene-to-Disease Links: Find diseases associated with known genes
Drug Target Prioritization: Rank targets by genetic evidence
Population Genetics Analysis: Compare allele frequencies across populations

Data Sources

GWAS Catalog

Curator: EBI and NHGRI
URL: https://www.ebi.ac.uk/gwas/
Coverage: 100,000+ publications, 500,000+ associations
Update Frequency: Weekly

Open Targets Genetics

Curator: Open Targets consortium
URL: https://genetics.opentargets.org/
Coverage: Fine-mapped GWAS, L2G predictions, QTL colocalization
Update Frequency: Quarterly

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

For issues with:

Skill functionality: Open issue at tooluniverse/skills
GWAS data: Contact GWAS Catalog or Open Targets support
Tool errors: Check ToolUniverse tool status

tooluniverse-gwas-trait-to-gene