Institute for Behavioral Genetics, University of Colorado Boulder, Boulder.
Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder.
JAMA Psychiatry. 2023 Aug 1;80(8):811-821. 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.
DESIGN, SETTING, AND PARTICIPANTS: 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.
精神障碍表现出高度的共病和遗传重叠,因此需要采用多变量方法来解析趋同和发散的精神疾病风险途径。识别跨疾病风险的基因表达模式也有望在多药治疗水平不断上升的情况下推动药物发现和再利用。
确定跨精神障碍遗传趋同和趋异的基因表达模式,以及针对这些基因的现有药物干预措施。
设计、设置和参与者:本基因组研究应用了一种多变量转录组方法,即全转录组结构方程建模(T-SEM),来研究与 5 个基因组因素相关的基因表达模式,这些因素标记了 13 种主要精神障碍的共同风险。进行了后续测试,包括与其他结果的基因集重叠和全基因组关联研究,以更好地描述 T-SEM 结果。利用 Broad Institute Connectivity Map 药物再利用数据库和 Drug-Gene Interaction Database 公共药物-基因对数据库,确定可用于针对与跨疾病风险相关基因的再利用药物。数据收集自数据库建立到 2023 年 2 月 20 日。
与基因组因素或疾病特异性风险相关的基因表达模式以及针对这些基因的现有药物。
T-SEM 总共确定了 466 个基因,其表达与基因组因素显著相关(z≥5.02),以及 36 个与疾病特异性效应相关的基因。与思维障碍因素相关的大多数关联基因是由双相情感障碍和精神分裂症定义的。确定了几种现有的药物干预措施,可以重新用于针对思维障碍因素或由所有 13 种疾病定义的跨诊断 p 因素相关基因的表达。
本研究的结果揭示了与精神障碍遗传重叠和独特性相关的基因表达模式。这里概述的多变量药物再利用框架的未来版本有可能为越来越常见的、共病的精神疾病表现确定新的药物干预措施。