Applications of genome-wide methods to data from large scale population-based samples have uncovered clear evidence of pervasive statistical pleiotropy. Genomic SEM is a method for modeling the multivariate genetic architecture of constellations of genetically correlated traits and incorporating genetic covariance structure into multivariate GWAS discovery. In contrast to methods9 that model phenotypic, rather than genetic covariance structure, and rely on raw data, Genomic SEM employs summary GWAS data to model genetic covariance structure. Genomic SEM is computationally efficient, accounts for potentially unknown degrees of sample overlap, and allows for flexible specification of covariance structure, such that several broad classes of structured covariance models can be applied. The Genomic SEM approach shares benefits of some existing approaches11 for boosting power by combining information across genetically correlated phenotypes. However, Genomic SEM uniquely allows one to compare different hypothesized genetic covariance architectures and 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
