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

通过单独估算的组织和细胞类型特异性转录组来表征和靶向精神分裂症和双相情感障碍的亚表型

• 批准号: 10166951
• 负责人: Georgios Voloudakis
• 金额: $ 19.22万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10166951
• 关键词: Astrocytes; Biological; Biological Markers; Biology; Bipolar Disorder; Brain; Brain Diseases; Case-Control Studies; Classification; Complex; Data Science; Development; Diagnostic; Disease; Electronic Health Record; Epigenetic Process; Exhibits; Exposure to; Functional disorder; Future; Gene Expression; Genes; Genetic; Genomic medicine; Genomics; Genotype; Glutamates; Goals; Heritability; Heterogeneity; Individual; Intervention; Machine Learning; Mentors; Methods; Microglia; Mining; Modeling; Molecular; Natural Language Processing; Neurons; Neurosciences; Oligodendroglia; Outcome; Patients; Peripheral; Pharmaceutical Preparations; Pharmacology; Phenotype; Population Genetics; Positioning Attribute; Precision therapeutics; Prefrontal Cortex; Productivity; Protein Isoforms; Psychiatry; Relative Risks; Research; Risk; Sample Size; Schizophrenia; Selection for Treatments; Severity of illness; Symptoms; Tissues; Training; Treatment outcome; Variant; Veterans; base; biological heterogeneity; career; cell type; clinical heterogeneity; cohort; comorbidity; computational basis; cooperative study; deep learning; deep neural network; drug repurposing; effective therapy; experience; functional disability; functional genomics; improved; medical schools; molecular phenotype; neuropsychiatric disorder; next generation; non-genetic; novel; novel therapeutic intervention; patient subsets; polygenic risk score; precision medicine; programs; psychopharmacologic; skills; symptomatology; trait; transcriptome; transcriptomics; treatment response

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.

项目概要 精神分裂症 (SCZ) 和双相情感障碍 (BD) 是高度遗传、严重且复杂的脑部疾病 其特点是显着的临床和生物学异质性。尽管如此，病例对照研究常常忽视 这种异质性是由于他们对普通患者的关注，这可能是缺乏稳健的核心原因 指示个体治疗反应和结果的生物标志物。尽管它们被分类为 独立的诊断实体，SCZ 和 BD 具有高度的遗传相关性，表现出较高的相对风险 BD 和 SCZ 患者的亲属，并且症状和治疗部分重叠。在这个项目中 我们将为 VA 中患有 SCZ 或 BD 的个体使用组织和细胞类型特异性的推算转录组 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，“精神分裂症和躁郁症功能障碍的遗传学”），作为中间分子 表型，以识别、表征和瞄准这些疾病的亚表型。 VA 发现的结果 队列将在 PsycheMERGE 和 BioMe 队列中得到验证。 首先，我们将估算所有精神分裂症 (SCZ) 患者的组织和细胞类型特异性转录组 或 VA 发现队列中的双相情感障碍 (BD)。为了实现这一目标，我们将训练组织（大脑和外周组织） 组织）和细胞类型（谷氨酸和 GABA 能神经元、星形胶质细胞、少突胶质细胞和小胶质细胞） DLPFC）在基因和亚型水平上特定的 EpiXcan 转录组插补模型。其次，我们将使用 将估算的转录组作为中间分子表型来识别遗传调控基因 基于表达 (GReX) 的亚群以及其中使用深度神经网络的关键分子驱动因素 （DNN）。最后，我们将为每个经过验证的关键非遗传生物标志物和有效治疗方法 亚表型。非遗传生物标志物将基于电子健康中预先挖掘的特征 记录 (EHR) 以及通过自然语言处理 (NLP) 从 EHR 中提取的特征。亚表型 将在平民群体 PsycheMERGE 和 BioMe 中得到验证。 该项目将在伊坎医学院进行，该学院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。 Voloudakis 博士将培养在遗传学领域开展独立学术生涯所需的技能 基于 EHR 的精准精神病学。