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: 10055546
• 负责人: Georgios Voloudakis
• 金额: $ 19.19万
• 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/10055546
• 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; 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.

项目摘要 精神分裂症（SCZ）和躁郁症（BD）是高度可遗传的，严重且复杂的脑部疾病 以实质性的临床和生物异质性为特征。尽管如此，病例对照研究通常忽略 通过关注普通患者的这种异质性，这可能是缺乏强大的核心原因 生物标志物表明个人的治疗反应和结果。尽管它们被归类为 独立的诊断实体，SCZ和BD在遗传上高度相关，暴露的高相对风险 BD和SCZ患者的亲属，并且具有部分重叠的症状和治疗。在这个项目中 我们将为我们的VA中的SCZ或BD的个体使用组织和细胞类型的特异性转录组 Discovery Cohort完成了百万退伍军人计划（MVP）和合作研究计划572（CSP） ＃572，“精神分裂症和双极疾病中功能障碍的遗传学”），作为中间分子 表型，识别，特征和靶向这些疾病的亚表征。 VA发现的发现 队列将在Psychemerge和Biome队列中得到验证。 首先，我们将为所有精神分裂症患者（SCZ）估算组织和细胞类型的特异性转录组 VA发现队列中的躁郁症（BD）。为了实现这一目标，我们将训练组织（大脑和周围 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）在基因和同工型水平上的特定Epixcan转录组插补模型。其次，我们将使用 估算的转录组作为中间分子表型，以识别一般调节的基因 基于表达（GREX）的亚群，并使用深层神经网络的关键分子驱动器 （DNNS）。最后，我们将确定关键的非遗传生物标志物和每个经过验证的有效治疗方法 亚表征。非遗传生物标志物将基于电子健康可用的预定功能 记录（EHR）和通过自然语言处理（NLP）从EHR提取的功能。子表型 将在平民队列的Psychemerge和Biome中得到验证。 该项目将在伊坎医学院（Data Cocience）的主要中心之一， 基因组学和精确医学。心理委员会包括计算领域的专家 以及功能性基因组学，综合分析，机器学习（包括DNN和NLP）和EHR挖掘。 Voloudakis博士将发展以一般为基础的独立学术职业所必需的技能 EHR信息精神病学。