Existing multivariate methods use summary statistics of genetically correlated phenotypes to boost power for discovery and prediction for a particular trait.11,26,27 Boosting power is only one application of Genomic SEM. That said, a Genomic SEM common factor GWAS approach has already been shown by an independent research group to perform comparably to existing multivariate approaches for out-of-sample prediction.28 Moreover, as a flexible modeling framework, Genomic SEM may encompass other multivariate approaches. For example, we show mathematically that Genomic SEM can be specified to satisfy the same moment conditions as multi-trait analysis of GWAS (MTAG11; see Supplementary Methods). Simulation results also revealed near perfect correspondence from a linear regression in which Z statistics from MTAG were used to predict those from a Genomic SEM specified to satisfy the MTAG moment conditions (Supplementary Figure 18; unstandardized slope = .999, intercept = 2.65E-4).
