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home/skills/gptomics/bioskills/genomic-sem
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genomic-sem

bygptomics· 59 skills

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Forks

187

Category

Data Science & Analytics

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TL;DR

Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

How to install genomic-sem?

gptomics/bioskills/genomic-sem
$npx -y skills add gptomics/bioskills --skill genomic-sem

Installs into the current project.

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Use this skill

Run `npx skills use "https://github.com/gptomics/bioskills" --skill "gptomics/bioskills/genomic-sem"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.

Use the whole pack

Use the skills in "https://github.com/gptomics/bioskills" that are relevant to the current task. Run `npx skills add "https://github.com/gptomics/bioskills"` and select the relevant skills, then follow their instructions.

Preview

gptomics/bioskillsgptomics/bioskills

$ npx -y skills add gptomics/bioskills --skill genomic-sem

▸ installing to .claude/skills…

✓ genomic-sem ready

Repogptomics/bioskills
TypeSkills
CategoryData Science & Analytics
ForResearcherAnalyst
UpdatedJul 2026
License—
First seenJul 27, 2026

Tags

Skill

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