Discovering Biology for Neuropsychiatric Diseases Through Omics Studies on Comorbidities

摘要： 我们建议在这个应用程序中使用真正独特的资源提供给范德比尔特大学的研究 社区识别和表征神经精神疾病的遗传风险因素。我们的总体 一种假设是，跨越神经精神障碍的共病表型可用于 确定更同质的遗传风险因素，这些因素也将与神经精神疾病交叉。 疾病为了解决这个假设，我们将利用神经科学的长期优势， 范德比尔特在进行体内和体外实验验证方面拥有丰富的专业知识 研究，强大的研究人员团队与长期的研究计划，在关键的共病 表型和神经精神疾病，以及我们在开发和应用新的 基因组研究的综合方法。范德比尔特的临床数据仓库被称为 合成衍生物（SD），并包含持续更新的电子健康记录（EHR）， 250万人。在SD中有超过217，000人的DNA样本 通过范德比尔特大学的生物库BioVU。有更多纵向数据的个人，有些可以追溯到 长达20-30年的基因组研究和基因组询问（GWAS）已被优先考虑 或全基因组测序）将在2018年对这些受试者中的> 120，000人提供。SD提供 前所未有的能力来表征神经精神疾病的交叉共病， 大量的BioVU样本与基因组询问结合新的分析方法， 设计优化BioVU的基因组研究，为发现研究创建一个动态引擎。我们 具体目标是：1）使用超过2，500，000人的EHR数据来调查关系 神经精神疾病和多种这些疾病共有的共病表型之间的关系 2）使用新的PrediXcan方法来鉴定基因， 表达与神经精神疾病显著相关，神经精神疾病加 BioVU中超过120，000个样本的共患病或共患病;以及3）优先考虑以下基因： 使用改进的网络和途径分析进行验证，然后通过实验验证基因 与神经精神和共病表型有关。

Abstract: We propose in this application to use truly unique resources available to the Vanderbilt University research community to identify and characterize genetic risk factors for neuropsychiatric disorders. Our overarching hypothesis is that co-morbid phenotypes that cut across neuropsychiatric disorders can be used to identify more homogeneous genetic risk factors that will also be cross-cutting for neuropsychiatric diseases. To address this hypothesis, we will harness the long-standing strengths in neuroscience at Vanderbilt including extensive expertise in conducting in vivo and in vitro experimental validation studies, the strong team of investigators with long-standing research programs in key co-morbid phenotypes and neuropsychiatric disease, and our track record in developing and applying novel integrative approaches for genome investigation. The clinical data warehouse at Vanderbilt is called the Synthetic Derivative (SD), and contains continuously updated electronic health records (EHR) on more than 2,500,000 individuals. DNA samples are available on more than 217,000 of the individuals in the SD through BioVU, the biobank at Vanderbilt University. Individuals with more longitudinal data some going back as long as 20-30 years have been prioritized for genome investigation, and genome interrogation (GWAS or whole genome sequencing) will be available on > 120,000 of these subjects in 2018. The SD provides unprecedented power for characterizing cross-cutting comorbidities for neuropsychiatric disorders, and the large number of BioVU samples with genome interrogation coupled with the novel analytic approaches we have devised to optimize genome investigations in BioVU create a dynamic engine for discovery research. Our specific aims are to: 1) Use EHR data on more than 2,500,000 individuals to investigate the relationship between neuropsychiatric disorders and comorbid phenotypes shared among multiple of these disorders; 2) Use the novel PrediXcan approach to identify genes for which genetically predicted expression is significantly associated with neuropsychiatric disease, neuropsychiatric disease plus comorbidity, or comorbidity for more than 120,000 samples in BioVU; and 3) Prioritize genes for validation using improved network and pathway analyses, and then experimentally validate genes implicated in neuropsychiatric and comorbid phenotypes.