From mims-harvard-tooluniverse
Compares GWAS studies for traits, performs meta-analyses of loci, assesses replication and heterogeneity using NHGRI-EBI Catalog and Open Targets Genetics.
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**Compare GWAS studies, perform meta-analyses, and assess replication across cohorts**
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Compare GWAS studies, perform meta-analyses, and assess replication across cohorts
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.
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.
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.
Scenario: "I want to understand all available GWAS data for type 2 diabetes"
Workflow:
Outcome: Complete landscape of T2D genetics with replicated findings and population-specific signals
Scenario: "Is the TCF7L2 association with T2D consistent across all studies?"
Workflow:
Outcome: Quantitative assessment of effect size consistency with heterogeneity interpretation
Scenario: "Which findings from the discovery cohort replicated in the independent sample?"
Workflow:
Outcome: Systematic replication report with success rates and failed findings
Scenario: "Are T2D loci consistent across European and East Asian populations?"
Workflow:
Outcome: Ancestry-specific genetic architecture with transferability assessment
This skill implements standard GWAS meta-analysis methods:
Fixed-Effects Model:
Random-Effects Model (recommended when I² > 50%):
Heterogeneity Assessment:
The I² statistic measures the percentage of variance due to between-study heterogeneity:
I² = [(Q - df) / Q] × 100%
where Q = Cochran's Q statistic
df = degrees of freedom (n_studies - 1)
Interpretation Guidelines:
Common reasons for high I²:
Recommendations:
The skill evaluates studies based on:
1. Sample Size:
2. Ancestry Diversity:
3. Data Availability:
4. Genotyping Quality:
5. Statistical Rigor:
Tier 1 (High Quality):
Tier 2 (Moderate Quality):
Tier 3 (Limited):
❌ Don't:
✅ Do:
When I² > 75%:
When Studies Conflict:
gwas_search_studies: Find studies by traitgwas_get_study_by_id: Get detailed study metadatagwas_get_associations_for_study: Retrieve study associationsgwas_get_associations_for_snp: Get SNP associations across studiesgwas_search_associations: Search associations by traitOpenTargets_search_gwas_studies_by_disease: Disease-based study searchOpenTargets_get_gwas_study: Detailed study information with LD populationsOpenTargets_get_variant_credible_sets: Fine-mapped loci for variantOpenTargets_get_study_credible_sets: All credible sets for studyOpenTargets_get_variant_info: Variant annotation and allele frequenciesCredible 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)