Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing

• Published in: JAMA psychiatry (Chicago, Ill.) Vol. 80; no. 8; p. 811
• Main Authors: Grotzinger, Andrew D, Singh, Kritika, Miller-Fleming, Tyne W, Lam, Max, Mallard, Travis T, Chen, Yu, Liu, Zhaowen, Ge, Tian, Smoller, Jordan W
• Format: Journal Article
• Language: English
• Published: United States 01.08.2023
• Subjects: Bipolar Disorder - drug therapy; Bipolar Disorder - genetics; Drug Repositioning; Genetic Predisposition to Disease - genetics; Genome-Wide Association Study; Humans; Latent Class Analysis; Mental Disorders - drug therapy; Mental Disorders - genetics; Transcriptome - genetics
• ISSN: 2168-6238
• DOI: 10.1001/jamapsychiatry.2023.1808

Psychiatric disorders display high levels of comorbidity and genetic overlap, necessitating multivariate approaches for parsing convergent and divergent psychiatric risk pathways. Identifying gene expression patterns underlying cross-disorder risk also stands to propel drug discovery and repurposing in the face of rising levels of polypharmacy. To identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes. This genomic study applied a multivariate transcriptomic method, transcriptome-wide structural equation modeling (T-SEM), to investigate gene expression patterns associated with 5 genomic factors indexing shared risk across 13 major psychiatric disorders. Follow-up tests, including overlap with gene sets for other outcomes and phenome-wide association studies, were conducted to better characterize T-SEM results. The Broad Institute Connectivity Map Drug Repurposing Database and Drug-Gene Interaction Database public databases of drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk. Data were collected from database inception up to February 20, 2023. Gene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes. In total, T-SEM identified 466 genes whose expression was significantly associated (z ≥ 5.02) with genomic factors and 36 genes with disorder-specific effects. Most associated genes were found for a thought disorders factor, defined by bipolar disorder and schizophrenia. Several existing pharmacological interventions were identified that could be repurposed to target genes whose expression was associated with the thought disorders factor or a transdiagnostic p factor defined by all 13 disorders. The findings from this study shed light on patterns of gene expression associated with genetic overlap and uniqueness across psychiatric disorders. Future versions of the multivariate drug repurposing framework outlined here have the potential to identify novel pharmacological interventions for increasingly common, comorbid psychiatric presentations.