Tooluniverse Gwas Study Explorer
This pulls GWAS study data from NHGRI-EBI GWAS Catalog and Open Targets Genetics to compare genome-wide association studies for the same trait. You can run meta-analyses across cohorts, calculate heterogeneity statistics like I² to see if effect sizes are consistent, and check which loci actually replicate across populations. The workflow is compute-first: retrieve associations with the ToolUniverse tools, then run Python code to aggregate effect sizes, generate forest plots, and assess study quality by sample size and ancestry. Useful when you need to know if a genetic signal holds up across studies or if you're seeing population-specific effects versus methodological noise. The documentation is thorough on when heterogeneity means you shouldn't trust a combined estimate.
- skills.sh installs
- 359
- GitHub stars
- 1.8K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-gwas-study-explorer --agent claude-codeInstalls into .claude/skills of the current project.
Files
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
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- Repository
- github.com/mims-harvard/tooluniverse
- Categories
- Data Science & ML, Design & UI/UX
- First seen
- Jun 3, 2026
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