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-e

GWAS Trait to Gene Discovery 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) 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 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). NOTE: This tool is BROKEN use gwas search associations(query=trait) as a working alternative 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 (RECOMMENDED for trait lookups) 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. Supports p value threshold parameter for server side filtering (see notes below) gwas get studies for trait Get studies for a trait gwas 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 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 Example Results Type 2 Diabetes Alzheimer's Disease Best Practices 1. Use Disease Ontology IDs for Precision 2. Filter by Evidence Strength 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.) Tool Specific Notes (Updated) gwas get variants for trait p value Filtering This tool now accepts an optional p value threshold parameter for server side p value filtering. When provided, the GWAS Catalog API filters variants to only return those below the specified threshold. Client side fallback : When the API returns unfiltered results (some trait queries ignore the threshold parameter), the tool also applies client side p value filtering. This means you may see fewer results than expected if the API returned pre filtered data and the client filter applies again. Always check the actual p values in the returned data. gwas get associations for trait BROKEN This 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 Rename The parameter was renamed from mapped gene to gene symbol for clarity. Use: Programmatic Access (Beyond Tools) 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. 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: 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