One powerful feature of Genomic SEM is the capability to model shared genetic architecture across phenotypes with factors representing broad genetic liabilities, and compare the fit of different factor structures to the empirical data. When an appropriate model has been identified at the level of the genome-wide covariance structure, the researcher may incorporate individual SNPs into the model in order to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more valid and predictive polygenic scores. Genomic SEM can also evaluate whether the multivariate genetic architecture implied by a specific model is applicable at the level of individual variants using developed estimates of heterogeneity. When certain SNPs only influence a subset of genetically correlated traits, a key assumption of other multivariate approaches is violated.11 SNPs with high heterogeneity estimates can be flagged as likely to confer disproportionate liability toward individual traits, be removed when constructing polygenic risk scores, or be studied specifically to understand the nature of heterogeneity.
