Multivariate Genomic Architecture of Cortical Thickness and Surface Area at Multiple Levels of Analysis

Abstract

Recent work in imaging genetics suggests high levels of genetic overlap within cortical regions for cortical thickness (CT) and surface area (SA). We model this multivariate system of genetic relationships by applying Genomic Structural Equation Modeling (Genomic SEM) to parsimoniously define five genomic brain factors for both CT and SA. We reify these factors by demonstrating the generalizability of the model in a semi-independent sample and show that the factors align with biologically and functionally relevant parcellations of the cortex. We apply Stratified Genomic SEM to identify specific categories of genes (e.g., neuronal cell types) that are disproportionately associated with pleiotropy across specific subclusters of brain regions, as indexed by the genomic factors. Finally, we examine genetic associations with psychiatric and cognitive correlates, finding that SA is associated with both broad aspects of cognitive function and specific risk pathways for psychiatric disorders. These analyses provide key insights into the multivariate genomic architecture of two critical features of the cerebral cortex.