Characterizing and targeting subphenotypes of schizophrenia and bipolar disorder via individually imputed tissue and cell-type specific transcriptomes

项目总结 精神分裂症(SCZ)和双相情感障碍(BD)是高度遗传性、严重和复杂的脑部疾病 具有显著的临床和生物异质性的特点。尽管如此，病例对照研究往往忽略了 这种异质性通过他们对普通患者的关注，这可能是缺乏健壮的核心原因 指示个人治疗反应和结果的生物标记物。尽管它们被归类为 独立的诊断实体、SCZ和BD具有高度的遗传相关性，在 BD和SCZ患者的亲属，症状和治疗部分重叠。在这个项目中 我们将在我们的退伍军人管理局使用组织和细胞类型的特定转录本用于患有SCZ或BD的个体 发现队列包括百万退伍军人计划(MVP)和合作研究计划572(CSP #572，《精神分裂症和双相情感障碍的遗传学》)，作为中间分子 表型，以识别、表征和靶向这些疾病的亚型。退伍军人管理局的发现 队列将在心理队列和生物群队列中进行验证。 首先，我们将推测所有精神分裂症患者(SCZ)的组织和细胞类型特异性转录本。 或双相情感障碍(BD)在退伍军人事务部发现队列中。为了实现这一点，我们将训练组织(大脑和外周 组织)和细胞类型(谷氨酸能和GABA能神经元、星形胶质细胞、少突胶质细胞和小胶质细胞 DLPFC)特异性EpiXcan在基因和异构体水平上的转录分配模型。其次，我们将使用 推测的转录本作为鉴定基因调控基因的中间分子表型 基于表达(Grex)的亚群及其内部使用深度神经网络的关键分子驱动因素 (DNNS)。最后，我们将确定关键的非遗传生物标记物和有效的治疗方法 亚型。非遗传生物标记物将基于电子健康中预先挖掘的特征 记录(EHR)和通过自然语言处理(NLP)从EHR中提取的特征。亚表型 将在平民队列中得到验证，心理和生物群落。 这个项目将在伊坎医学院进行，这是领先的数据科学中心之一， 基因组学和精准医学。指导委员会由计算机领域的专家组成。 以及功能基因组学、综合分析、机器学习(包括DNN和NLP)和EHR挖掘。 Voloudakis博士将开发必要的技能，以开始在遗传学领域的独立学术生涯 电子病历-精准精神病学。

PROJECT SUMMARY Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable, severe and complex brain disorders characterized by substantial clinical and biological heterogeneity. Despite this, case-control studies often ignore such heterogeneity through their focus on the average patient, which may be the core reason for a lack of robust biomarkers indicative of an individual’s treatment response and outcome. Although they are classified as independent diagnostic entities, SCZ and BD are highly genetically correlated, exhibit high relative risks among relatives of both BD & SCZ patients, and have partially overlapping symptomatology and treatment. In this project we will use tissue and cell-type specific imputed transcriptomes for individuals with SCZ or BD in our VA discovery cohort comprising the Million Veteran Program (MVP) and Cooperative Studies Program 572 (CSP #572, “The Genetics of Functional Disability in Schizophrenia and Bipolar Illness”), as an intermediate molecular phenotype, to identify, characterize and target subphenotypes of these disorders. Findings from the VA discovery cohort will be validated in the PsycheMERGE and BioMe cohorts. First, we will impute tissue and cell-type specific transcriptomes for all individuals with schizophrenia (SCZ) or bipolar disorder (BD) in the VA discovery cohort. To achieve this, we will train tissue (brain and peripheral tissues) and cell-type (glutamatergic & GABAergic neurons, astrocytes, oligodendrocytes, and microglia from DLPFC) specific EpiXcan transcriptomic imputation models at the gene and isoform level. Secondly, we will use the imputed transcriptomes as an intermediate molecular phenotype to identify genetically-regulated gene expression (GReX) based subpopulations and within them the key molecular drivers using deep neural networks (DNNs). Lastly, we will identify key non-genetic biomarkers and effective treatments for each validated subphenotype. Non-genetic biomarkers will be based on pre-mined features available from the electronic health records (EHR) and features extracted from the EHR via natural language processing (NLP). The subphenotypes will be validated in the civilian cohorts PsycheMERGE and BioMe. This project will take place at the Icahn School of Medicine, one of the leading centers of data science, genomics and precision medicine. The mentoring committee comprises experts in the fields of computational and functional genomics, integrative analysis, machine learning (including DNNs and NLP), and EHR mining. Dr. Voloudakis will develop the skills necessary to launch an independent academic career in genetically based EHR-informed precision psychiatry.