to incorporate such architectures into multivariate discovery. Importantly, shared genetic liabilities across phenotypes can be explicitly modeled as factors that may be treated as broad genetic risk factors with equally broad downstream consequences. Multivariate genetic methods have existed for decades in the twin literature, with Martin and Eaves (1977)35 providing a framework for fitting structural equation models of genetic and environmental variance components to multivariate twin data. Using GWAS summary data from unrelated individuals, Genomic SEM can be used to estimate multivariate genetic models similar to those from the existing twin literature. Moreover, Genomic SEM offers new promise as a method that allows for modeling genetic covariance even among phenotypes for which phenotypic covariance cannot be estimated.
