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Genomic Structural Equation Modeling (Genomic SEM)

Definition
AI-generated

Genomic structural equation modeling (Genomic SEM) fits structural equation models to GWAS summary statistics—using the genetic covariance matrix between traits—to infer latent genetic factors, causal directions among traits (under stated assumptions), and how variants load on those factors.

Topics
Post-GWAS
Related
Genetic Correlation Latent Genetic Factors in Pleiotropy Matrix Factorization for Pleiotropy

Why it matters in GWAS

It provides a principled way to summarize high-dimensional pleiotropy, test hierarchical models of related traits, and connect genetic architecture to multivariate trait structure beyond pairwise genetic correlations.

Example usage

"We applied Genomic SEM to define a general metabolic factor that explained a large fraction of cross-trait SNP heritability."

Genetic Correlation Latent Genetic Factors in Pleiotropy Matrix Factorization for Pleiotropy Multi-Trait GWAS Pleiotropy Dissection
Related-term network

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Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Grotzinger AD, et al. (2019). Genomic structural equation modelling provides insights into multivariate genetic architecture of complex traits. Nat Hum Behav.
  • Jee J, et al. (2026). The pleiotropic landscape of the human genome. Nat Rev Genet. https://doi.org/10.1038/s41576-025-00908-0
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