Genomic SEM is not the first method for multivariate GWAS. Other methods, such as MTAG,11 SHom/SHet,36 metaUSTAT,37 min-P,38 and TATES27 allow researchers to perform multivariate meta-analyses based solely on summary data. The methods can generally be divided into 2 distinct classes: methods that aggregate test statistics or effect sizes based on a model (Genomic SEM, SHom and MTAG) and those that select from the univariate p-values while taking care not to inflate Type-I error (min-P, TATES, and SHet). As we show with respect to MTAG, models on which existing methods are based may can be fit within the Genomic SEM framework. We also anticipate that the approaches for selecting the p-values from a set of analyses while maintaining proper Type-I error control could be integrated into the Genomic SEM framework. For instance, whereas TATES is currently applied to select p-values from a series of univariate analyses of correlated traits, the same analysis could be used to select p-values from a series of Genomic SEM models. The multivariate methods available need not be mutually exclusive. With respect to other multivariate analyses
