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Using Genome-Wide Data to Investigate the Joint Genetic Architecture of Complex Traits
Methods for using genome-wide data to estimate genetic overlap between pairwise combinations of traits have produced “atlases” of genetic architecture. These atlases have revealed pervasive genetic sharing across different social, behavioral, and mental health outcomes, and individual risk variants are often found to be associated with multiple such outcomes. My group has recently a developed formal multivariate framework for analyzing the joint genetic architectures of constellations of traits using summary statistics from existing Genome-Wide Association Studies. This method, Genomic Structural Equation Modeling (Genomic SEM), can be used to identify variants with effects on both general and specific dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Genomic SEM can further be used to construct and test hypothesized cascade models of trait development and disease progression. In this talk, I describe the Genomic SEM framework and present results of a series of substantive applications of the method in the areas of psychology and mental health.