This paper characterizes the genetic pathways for autism spectrum disorder (ASD) that are unique from the psychiatric disorder it has the highest genetic correlation with, ADHD. Results revealed clinical correlates (e.g., cognitive functions), functional annotations, and patterns of gene expression associated with this unique genetic signal in ASD.
Transcriptome-wide structural equation modeling (T-SEM) is applied to identify genes whose expression is associated with clusters of psychiatric disorders (e.g., internalizing disorders). Existing drugs that target these gene products are then identified for possible repurposing. As these pharmacological interventions target the genetic signal shared across multiple psychiatric disorders they could aid in reducing growing levels of polypharmacy, where several drugs are given to a single individual with comorbid presentations.
Here we apply Genomic SEM to model the genetic architecture across 11 psychiatric disorders to find four genomic factors (Compulsive, Thought, Neurodevelopmental, Internalizing) defined by subclusters of disorders. We also introduce and validate Stratified Genomic SEM which can be used to model enrichment in a multivariate space.
This article provides an overview of recent findings in cross-disorder psychiatric genomics. This includes considering results at the genome-wide, functional, and genetic variant level of analysis along with possible future directions for the field.
In this publication we introduce and validate Genomic Structural Equation Modeling (Genomic SEM). Genomic SEM is a flexible, open-source, multivariate framework for modeling genetic overlap as estimated from GWAS summary statistics.
