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

1. 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

2. Population Bias

   • Most GWAS in European populations
   • Effect sizes may differ across ancestries
   • Rare variants often under-represented

3. Sample Size Dependence

   • Larger studies detect more associations
   • Older small studies may have false negatives
   • p-values alone don't indicate effect size

4. 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