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SKILL.md
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
Use Cases
1. Comprehensive Trait Analysis
Scenario: "I want to understand all available GWAS data for type 2 diabetes"
Workflow:
Search for all T2D studies in GWAS Catalog
Filter by sample size and ancestry
Extract top associations from each study
Identify consistently replicated loci
Assess ancestry-specific effects
Outcome: Complete landscape of T2D genetics with replicated findings and population-specific signals
2. Locus-Specific Meta-Analysis
Scenario: "Is the TCF7L2 association with T2D consistent across all studies?"
Workflow:
Retrieve all TCF7L2 (rs7903146) associations for T2D
Calculate combined effect size and p-value
Assess heterogeneity (I² statistic)
Generate forest plot data
Interpret heterogeneity level
Outcome: Quantitative assessment of effect size consistency with heterogeneity interpretation
3. Replication Analysis
Scenario: "Which findings from the discovery cohort replicated in the independent sample?"
Workflow:
Get top hits from discovery study
Check for presence and significance in replication study
Assess direction consistency
Calculate replication rate
Identify novel vs failed replication
Outcome: Systematic replication report with success rates and failed findings
4. Multi-Ancestry Comparison
Scenario: "Are T2D loci consistent across European and East Asian populations?"
Workflow:
Filter studies by ancestry
Compare top associations between populations
Identify shared vs population-specific loci
Assess allele frequency differences
Evaluate transferability of genetic risk scores
Outcome: Ancestry-specific genetic architecture with transferability assessment
Statistical Methods
Meta-Analysis Approach
This skill implements standard GWAS meta-analysis methods:
Fixed-Effects Model:
Used when heterogeneity is low (I² < 25%)
Weights studies by inverse variance
Assumes true effect size is the same across studies
Random-Effects Model (recommended when I² > 50%):
Accounts for between-study variation
More conservative than fixed-effects
Better for diverse ancestries or methodologies
Heterogeneity Assessment:
The I² statistic measures the percentage of variance due to between-study heterogeneity:
