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SKILL.md
npx skills add mims-harvard/tooluniverse --skill tooluniverse-gwas-trait-to-gene
Discover causal genes for diseases/traits from GWAS data using Open Targets L2G (locus-to-gene) scoring — integrates eQTL, chromatin interaction, and distance evidence. Use for trait-to-gene mapping, drug-target hypothesis generation from GWAS, and replacing the 'nearest gene' heuristic with multi-evidence L2G scores.
npx skills add mims-harvard/tooluniverse --skill tooluniverse-gwas-trait-to-gene
Nearest gene is often wrong. Use L2G (locus-to-gene) scores from Open Targets which integrate eQTL, chromatin interaction, and distance data. L2G > 0.5 is a strong prediction; positional mapping alone should not be used to claim a causal gene. A single GWAS study with p < 5e-8 is suggestive — replication across independent cohorts is required for high confidence. GWAS hits are associations in the studied population; effect sizes and even the implicated gene can differ across ancestries due to differing LD patterns. Treat gene lists from GWAS as ranked candidates for validation, not confirmed causal genes.
LOOK UP DON'T GUESS: never assume trait-to-gene mappings or L2G scores — always call gwas_search_associations and OpenTargets_get_study_credible_sets to retrieve current data; associations are updated as new GWAS are published.
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
Genome-wide Significance
Gene Mapping Methods
Evidence Confidence Levels
gwas_get_associations_for_trait - Get all associations for a trait (sorted by p-value). NOTE: This tool is BROKEN -- use gwas_search_associations(query=trait) as a working alternativegwas_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 filters (RECOMMENDED for trait lookups)gwas_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 trait. No p-value threshold parameter (one passed in is silently ignored) -- filter client-side on pvalue_mantissa/pvalue_exponent (see notes below)gwas_get_studies_for_trait - Get studies for a traitgwas_get_snps_for_gene - Get SNPs mapped to a gene. Parameter is gene_symbol (NOT mapped_gene)gwas_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")Type 2 Diabetes
Alzheimer's Disease
1. Use Disease Ontology IDs for Precision
2. Filter by Evidence Strength
3. Interpret Results Carefully
gwas_get_variants_for_trait -- p-value FilteringThis tool has no p-value threshold parameter. A p_value_threshold=... argument is
silently ignored (verified live: output identical with and without it), so filter the
returned associations yourself. Use sort="p_value", direction="asc" to get the strongest
associations first, and page/size to page (metadata.pagination.totalElements is the total).
Filter on pvalue_mantissa and pvalue_exponent, not p_value: the p_value float underflows
to 0.0 for very small p-values (e.g. 3 x 10^-1315), so it cannot be compared to a threshold.
gwas_get_associations_for_trait -- BROKENThis tool returns errors for most queries. Use gwas_search_associations(query=<trait>)
as a reliable alternative. The response format is {data: [{...}], metadata: {...}}.
gwas_get_snps_for_gene -- Parameter RenameThe parameter was renamed from mapped_gene to gene_symbol for clarity. Use:
When ToolUniverse tools return limited results or you need the full GWAS Catalog:
See tooluniverse-data-wrangling skill for pagination, bulk download, and format parsing patterns.
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:
For issues with:
{
"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"]
}
}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 confidenceAPOE: 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# Instead of:
discover_gwas_genes("diabetes") # Ambiguous
# Use:
discover_gwas_genes(
"type 2 diabetes",
disease_ontology_id="MONDO_0005148" # Specific
)# 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
)result = tu.tools.gwas_get_variants_for_trait(
trait="type 2 diabetes", size=200, sort="p_value", direction="asc"
)
def genome_wide_significant(a, exp=-8, mant=5): # p < 5e-8
return (a['pvalue_exponent'], a['pvalue_mantissa']) < (exp, mant)
hits = [a for a in result['data'] if genome_wide_significant(a)]result = tu.tools.gwas_get_snps_for_gene(gene_symbol="TCF7L2")import requests, pandas as pd
# Download full GWAS Catalog (all associations, ~37MB TSV)
url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative"
df = pd.read_csv(url, sep="\t")
# Filter locally by trait or gene
hits = df[df["DISEASE/TRAIT"].str.contains("type 2 diabetes", case=False, na=False)]
gene_hits = df[df["MAPPED_GENE"].str.contains("TCF7L2", na=False)]
# Per-study associations via REST
study_id = "GCST001234"
assocs = requests.get(f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations").json()
# Summary statistics (when available)
# Check study page for fullPvalueSet=true, then download from linked FTPBuniello 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.