The high prevalence of comorbidity between specific learning disabilities (SLDs), with other conditions, and their associations with genes in common with all of them, strongly suggest substantial pleiotropic effects that transcend diagnostic boundaries. While several of these genes with associations across SLDs are known, the nature, scope, and mechanisms of these pleiotropic effects remain unclear. The goal of this project is to examine genetic convergence and divergence that transcend clinical diagnostic categories for SLDs that could account for the high prevalence of comorbidity. Based on our track record of genetic research in comorbid SLDs, we hypothesize that elucidating the biological significance of cross-disorder genetic influences will reveal fundamental properties of pleiotropic loci that differentiate them from disorder-specific loci and help to clarify the biological substrates shared across the diagnostic landscape of SLDs. To test this hypothesis, we have assembled data for genome-wide association study (GWAS) and summary statistics for SLD-related traits in 17 samples that we utilize to examine shared genetic effects. In Aim 1 we propose to apply genomic structural equation modeling (Genomic SEM) to model the multivariate, genome-wide architecture of SLD-related traits by bringing together the largest available GWAS summary statistics within the learning disorder space. This will describe how different SLD components and disorders cluster together at the genomic level of analysis and identify clinically relevant external traits, for example anxiety and attention, that are associated with these clusters. Next, in Aim 2 we propose to apply Stratified Genomic SEM to identify mechanistic correlates of shared genetic variance for SLD-traits that frequently co- occur, implicating transdiagnostic genetic risk pathways. These mechanistic correlates are with functional data developed in our lab, in collaboration with other labs, and from open-source consortiums (e.g., GETex, ENCODE). These include gene-expression datasets from brain tissue, developing neurons, and organoids (transcriptome), methylation datasets (epigenome) and regulatory pathways (regulome), and fMRI imaging data (brain connectome). Mechanistic correlates will identify pleiotropic effects that transcend diagnostic categories and offer new ways to understand SLDs and their neurodevelopmental underpinnings. In Aim 3 we propose to perform a multivariate GWAS of SLDs to identify pleiotropic genetic variants. This will categorize SLD-associated genetic variants as either pleiotropic or disorder-specific. SLD-associated genetic variants that are pleiotropic could partially account for comorbidity. We will also analyze longitudinal data to show whether the genetic components that underlie achievement at a single time point are distinct from those important for growth over time, both of which are vital aspects of academic success. Completion of this project will identify mechanis...