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

项目摘要 精神分裂症（SCZ）和双相情感障碍（BD）是高度遗传的、严重的和复杂的脑部疾病 其特征在于显著的临床和生物学异质性。尽管如此，病例对照研究往往忽视了 这种异质性通过他们对普通患者的关注，这可能是缺乏强大的核心原因。 生物标志物指示个体的治疗反应和结果。虽然它们被归类为 独立的诊断实体，SCZ和BD是高度遗传相关的， BD和SCZ患者的亲属，并且具有部分重叠的病理学和治疗。在这个项目中 我们将使用组织和细胞类型特异性插补转录组对我们VA中的SCZ或BD患者进行检测 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功能障碍的遗传学”），作为中间分子 表型，以识别，表征和靶向这些疾病的亚表型。VA发现的结果 将在PsycheMERGE和BioMe队列中验证队列。 首先，我们将对所有精神分裂症（SCZ）患者的组织和细胞类型特异性转录组进行估算。 或双相情感障碍（BD）。为了实现这一目标，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转录组插补模型。其次，我们将使用 插补转录组作为鉴定遗传调节基因的中间分子表型 基于GReX表达的亚群，以及其中使用深度神经网络的关键分子驱动程序 （DNN）。最后，我们将确定关键的非遗传生物标志物和有效的治疗方法， 亚表型非遗传生物标志物将基于电子健康信息系统提供的预先挖掘的特征。 记录（EHR）和通过自然语言处理（NLP）从EHR提取的特征。亚表型 将在平民群体PsycheMERGE和BioMe中进行验证。 该项目将在伊坎医学院进行，这是数据科学的领先中心之一， 基因组学和精准医疗。指导委员会由计算机领域的专家组成， 功能基因组学、综合分析、机器学习（包括DNN和NLP）和EHR挖掘。 博士Voloudakis将发展必要的技能，以启动一个独立的学术生涯，在遗传学为基础的 电子健康记录的精确精神病学

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.