tooluniverse-gwas-trait-to-gene
Identify genes associated with diseases and traits by querying GWAS Catalog and Open Targets Genetics data. This skill ranks candidate genes using locus-to-gene scores that combine eQTL, chromatin interaction, and distance evidence—moving beyond simple nearest-gene approaches. Results include p-values, replication counts, and confidence levels to guide target prioritization for drug discovery and functional validation.
tooluniverse-gwas-trait-to-gene discovers disease-linked genes by analyzing GWAS data with evidence-based locus-to-gene scoring.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-27
tooluniverse-gwas-trait-to-gene discovers disease-linked genes by analyzing GWAS data with evidence-based locus-to-gene scoring. Identify genes associated with diseases and traits by querying GWAS Catalog and Open Targets Genetics data. This skill ranks candidate genes using locus-to-gene scores that combine eQTL, chromatin interaction, and distance evidence—moving beyond simple nearest-gene approaches. Results include p-values, replication counts, and confidence levels to guide target prioritization for drug discovery and functional validation.
Use it when
- tooluniverse-gwas-trait-to-gene performs GWAS trait to gene mapping by connecting genome-wide significant loci to their causal genes.
- Yes.
Verify before relying
Read SKILL.md below before installing (5 files). Open directory: indexed for reading, not audited.
Install
mims-harvard/ToolUniverse/tooluniverse-gwas-trait-to-gene · repository language: Python
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Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How does tooluniverse-gwas-trait-to-gene find genes associated with type 2 diabetes?
tooluniverse-gwas-trait-to-gene queries GWAS Catalog and Open Targets Genetics data to identify genetic variants associated with type 2 diabetes. It then ranks candidate genes using locus-to-gene (L2G) scores that combine eQTL, chromatin interaction, and distance evidence—going beyond simple nearest-gene assignment. Results include p-values and replication counts to help prioritize the strongest associations.
What is GWAS trait to gene mapping and how does this skill perform it?
tooluniverse-gwas-trait-to-gene performs GWAS trait to gene mapping by connecting genome-wide significant loci to their causal genes. Rather than assigning variants to the nearest gene, it uses L2G scores that integrate eQTL data, chromatin interactions, and genomic distance. This multi-evidence approach improves accuracy in identifying true disease-causing genes from GWAS signals.
Can tooluniverse-gwas-trait-to-gene help discover drug targets from genetic evidence?
Yes. tooluniverse-gwas-trait-to-gene prioritizes drug targets by ranking genes based on genetic evidence strength. It combines L2G scores, replication counts across studies, and confidence levels to identify the most robust trait-gene associations. This genetic evidence helps guide target selection for drug discovery and reduces the risk of pursuing weak or non-replicated associations.
How does this skill map GWAS variants to genes using fine-mapping?
tooluniverse-gwas-trait-to-gene maps GWAS variants to genes by integrating fine-mapping credible sets with locus-to-gene scoring. L2G scores combine eQTL evidence (which genes are regulated by variants), chromatin interactions (which regulatory elements contact genes), and distance metrics. This multi-layered approach narrows candidate genes from broad loci to likely causal targets.
What data sources does tooluniverse-gwas-trait-to-gene use for gene discovery?
tooluniverse-gwas-trait-to-gene integrates GWAS Catalog data with Open Targets Genetics resources. It leverages eQTL databases, chromatin interaction maps, and genome-wide association study results to build L2G scores. These combined sources enable comprehensive trait-to-gene mapping and validation of genetic associations across multiple independent studies.
How can I validate trait-to-gene associations using this skill?
tooluniverse-gwas-trait-to-gene validates associations by reporting replication counts and confidence levels from multiple studies. It returns p-values and evidence strength metrics that indicate whether associations are genome-wide significant and consistently observed across independent cohorts, helping distinguish robust findings from spurious signals.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
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
(truncated - see the full file via the links below)
File tree — 5 files
skills/tooluniverse-gwas-trait-to-gene/.env.template
skills/tooluniverse-gwas-trait-to-gene/SKILL.md
skills/tooluniverse-gwas-trait-to-gene/python_implementation.py
skills/tooluniverse-gwas-trait-to-gene/test_gwas_tools.py
skills/tooluniverse-gwas-trait-to-gene/test_skill_comprehensive.py
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