Cross-trait LDSC applies the same underlying principles to pairs of summary statistics to estimate genetic co-heritability (i.e., genetic covariance; Bulik-Sullivan et al., 2015b). Genomic Structural Equation Modeling (Genomic SEM; Grotzinger et al., 2019) builds on bivariate LDSC to formally model genetic covariance matrices estimated across multiple pairs of summary statistics. Numerous forms of models can be estimated in Genomic SEM. This includes the ability to model latent factors that reflect shared variance across psychiatric disorders or symptoms within a disorder, multiple regression models that examine shared and unique associations across disorders and relevant external correlates (e.g., socioeconomic outcomes), or some combination of the two. Notably, the summary statistics used to construct these genetic covariance matrices can be estimated using participant samples with varying levels of overlap, including entirely independent sets of participants. Relationships within and across disorders should not be taken as causal as the sets of traits included in the model are unlikely to be longitudinal, much less measured in the same sample. However, it is specifically because the summary statistics can come from independent samples that presents such
