| name | tooluniverse-gwas-study-explorer |
| description | Compare GWAS studies, perform meta-analyses across cohorts, and assess signal replication. Uses GWAS Catalog metadata, study-level statistics, and cross-cohort comparison. Use for evaluating GWAS reproducibility for a trait, meta-analysis sample size and effect-size aggregation, and detecting study heterogeneity (population, design, ancestry). |
| disable-model-invocation | true |
GWAS Study Deep Dive & Meta-Analysis
Compare GWAS studies, perform meta-analyses, and assess replication across cohorts
Overview
The GWAS Study Deep Dive & Meta-Analysis skill enables comprehensive comparison of genome-wide association studies (GWAS) for the same trait, meta-analysis of genetic loci across studies, and systematic assessment of replication and study quality. It integrates data from the NHGRI-EBI GWAS Catalog and Open Targets Genetics to provide a complete picture of the genetic architecture of complex traits.
Key Capabilities
- Study Comparison: Compare all GWAS studies for a trait, assessing sample sizes, ancestries, and platforms
- Meta-Analysis: Aggregate effect sizes across studies and calculate heterogeneity statistics
- Replication Assessment: Identify replicated vs novel findings across discovery and replication cohorts
- Quality Evaluation: Assess statistical power, ancestry diversity, and data availability
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
