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

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 1. Study Comparison : Compare all GWAS studies for a trait, assessing sample sizes, ancestries, and platforms 2. Meta Analysis : Aggregate effect sizes across studies and calculate heterogeneity statistics 3. Replication Assessment : Identify replicated vs novel findings across discovery and replication cohorts 4. 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. Domain Reasoning: Comparing Studies for the Same Trait When comparing GWAS studies for the same trait, ask: do they replicate? The same lead SNPs appearing in independent studies is strong evidence of a true association. Different lead SNPs at the same locus may reflect LD differences between populations — they may tag the same causal variant. Different loci entirely may reflect different study designs, phenotype definitions, or population ancestry. Before concluding that a finding failed to replicate, check whether the SNP was even genotyped or imputed in the replication cohort. LOOK UP DON'T GUESS: effect sizes, p values, allele frequencies, and LD structure for specific loci. Do not assume a SNP present in one study is present in another — use gwas get associations for snp to retrieve cross study data. Do not infer LD blocks from genomic proximity; use credible sets from Open Targets for fine mapping results. 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 Honesty rule (important) : A real inverse variance meta analysis needs each study's beta + 95% CI . python implementation.py parses these from the GWAS Catalog beta / or value + range fields and only then pools effect sizes and computes Cochran's Q I². When the matched associations don't report usable effect sizes (common), it returns method="descriptive" , combined beta=None , heterogeneity i2=None , and combined p value = the smallest reported p (not a pooled p) — do NOT present a descriptive result as a formal meta analysis or invent an I². 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: Interpretation Guidelines : I² < 25% : Low heterogeneity → fixed effects appropriate I² = 25 50% : Moderate heterogeneity → investigate sources I² = 50 75% : Substantial heterogeneity → random effects preferred I² 75% : Considerable heterogeneity → meta analysis may not be appropriate Sources of Heterogeneity Common reasons for high I²: 1. Ancestry differences : Different allele frequencies and LD structure 2. Phenotype heterogeneity : Trait definition varies across studies 3. Platform differences : Imputation quality and coverage 4. Winner's curse : Discovery studies overestimate effect sizes 5. Cohort characteristics : Age, sex, environmental factors Recommendations : Perform subgroup analysis by ancestry Use meta regression to investigate sources Consider excluding outlier studies Apply genomic control correction Study Quality Assessment Quality Metrics The skill evaluates studies based on: 1. Sample Size : Power to detect associations (80% power requires n 10,000 for OR=1.2) Precision of effect size estimates Ability to detect modest effects 2. Ancestry Diversity : Single ancestry vs multi ancestry Population stratification control Transferability of findings 3. Data Availability : Summary statistics available for meta analysis Individual level data vs summary level Imputation quality scores 4. Genotyping Quality : Platform density and coverage Imputation reference panel Quality control measures 5. Statistical Rigor : Genome wide significance threshold (p < 5×10⁻⁸) Multiple testing correction Replication in independent cohort Quality Tiers Tier 1 (High Quality) : n ≥ 50,000 Summary statistics available Multi ancestry or large single ancestry Imputed to high quality reference Independent replication Tier 2 (Moderate Quality) : n ≥ 10,000 Standard GWAS platform Adequate power for common variants Some data availability Tier 3 (Limited) : n < 10,000 Limited power May miss modest effects Use with caution Best Practices Before Meta Analysis 1. Check phenotype consistency : Ensure studies measure the same trait 2. Verify ancestry overlap : High heterogeneity expected if ancestries differ 3. Harmonize alleles : Align effect alleles across studies 4. Quality control : Exclude low quality studies or associations Interpreting Results 1. Genome wide significance : p < 5×10⁻⁸ (Bonferroni for ~1M independent tests) 2. Replication threshold : p < 0.05 in independent cohort 3. Direction consistency : Effect should be same direction across studies 4. Heterogeneity : I² 50% suggests caution in interpretation Common Pitfalls ❌ Don't : Meta analyze without checking heterogeneity Ignore ancestry differences Over interpret nominal p values Assume replication failure means false positive ✅ Do : Always report I² statistic Perform sensitivity analyses Consider ancestry stratified analysis Account for winner's curse in discovery studies Limitations & Caveats Data Limitations 1. Incomplete Overlap : Studies may analyze different SNPs 2. Cohort Overlap : Some cohorts participate in multiple studies (inflates significance) 3. Publication Bias : Significant findings more likely to be published 4. Winner's Curse : Discovery studies overestimate effect sizes 5. Imputation Quality : Varies across studies and populations Statistical Limitations 1. Heterogeneity : High I² may preclude meaningful meta analysis 2. Sample Size Differences : Large studies dominate fixed effects models 3. Allele Frequency Differences : Same variant has different effects across ancestries 4. Linkage Disequilibrium : Fine mapping needed to identify causal variants 5. Gene Environment Interactions : Not captured in standard meta analysis Interpretation Guidelines When I² 75% : Meta analysis results should be interpreted with extreme caution Investigate sources of heterogeneity systematically Consider ancestry specific or subgroup analyses Descriptive comparison may be more appropriate than meta analysis When Studies Conflict : Check for methodological differences Verify phenotype definitions match Investigate population stratification Consider conditional analysis Tools Used GWAS Catalog API gwas search studies : Find studies by trait gwas get study by id : Get detailed study metadata gwas get associations for study : Retrieve study associations gwas get associations for snp : Get SNP associations across studies gwas search associations : Search associations by trait Open Targets Genetics GraphQL API OpenTargets search gwas studies by disease : Disease based study search OpenTargets get gwas study : Detailed study information with LD populations OpenTargets get variant credible sets : Fine mapped loci for variant OpenTargets get study credible sets : All credible sets for study OpenTargets get variant info : Variant annotation and allele frequencies Glossary Credible Set : Set of variants likely to contain the causal variant (from fine mapping) L2G (Locus to Gene) : Score predicting which gene is affected by a GWAS locus License : Open source (MIT)

tooluniverse-gwas-study-explorer