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| name | gwas-trait-to-gene |
| description | ToolUniverse workflow — Gwas Trait To Gene |
| source | https://github.com/mims-harvard/ToolUniverse/tree/main/skills/tooluniverse-gwas-trait-to-gene |
| metadata | null |
Discover genes associated with diseases and traits using genome-wide association studies (GWAS)
This skill enables systematic discovery of genes linked to diseases/traits by analyzing GWAS data from two major resources:
Clinical Research
Drug Target Discovery
Functional Genomics
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
Genome-wide Significance
Gene Mapping Methods
Evidence Confidence Levels
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 studyOpenTargets_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 informationRequired
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"){
"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"]
}
}
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
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
Gene Mapping Uncertainty
Population Bias
Sample Size Dependence
Validation Bug
validate=False parameter if neededGWAS Catalog
Open Targets Genetics
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.
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