A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders

项目摘要 几十年来，被诊断为精神疾病的发病率一直在增加， 留下数以百万计的受苦受难者。尽管遗传率很高，但它们的潜在分子 机制仍然难以捉摸。大多数风险基因座位于非编码的基因组元件中，没有 对蛋白质产品的直接影响。全面的功能注释和变体影响 量化对于提供新的分子洞察力和发现治疗靶点至关重要。 新测序技术的最新进展和社区共享的努力 基因组数据提供了前所未有的机会来了解基因变异是如何起作用的 到精神疾病。该应用程序描述了综合战略的发展和 将新的检测方法(如STARR-SEQ)与种群规模相结合的机器学习方法 人类前额叶皮质基因调控语法的基因组图谱研究 并优先考虑精神疾病中的基因变异。具体地说，我们将(1)剖析顺时态- 利用种群规模的表观遗传学数据构建PFC的调控格局，(2)构建多个 利用染色质将远端顺式调控元件与基因连接起来建立基因调控网络模型 协变性分析，(3)整合遗传、表观遗传和转录数据以确定关键 导致精神障碍的转录因子和变种。与现有的不同 专注于一个基因组的努力，这项拟议的工作提出了一种真正新颖的大数据方法 通过将基因调控建模和疾病风险因素研究相结合 来自数百个个体的异质多组学资料。由此产生的全面 顺式调控元件的列表将扩大人类已知功能区的数量 大脑至少有一个命令。我们将以Web服务的形式发布我们的方法和资源， 分布式开源软件和注释数据库，这也将使其他 研究人员探索神经精神障碍的基因基础。 除了科学内容外，这项申请还提出了一项全面的培训 培养计算基因组学和神经遗传学方面的独立研究人员的计划。 培训将在耶鲁大学(系)进行。分子生物物理学和 生物化学)在Mark Gerstein教授(功能基因组学)、Nenad教授的指导下 Sestan教授(神经遗传学)和赵宏宇教授(统计遗传学和机器学习)。一个 由经验丰富的精神病专家和数据科学家组成的委员会也将提供建议 在科学研究和职业发展上都是如此。

PROJECT ABSTRACT The incidence of diagnosed psychiatric disorders has been increasing for decades, leaving millions of afflicted individuals. Despite the high heritability, their underlying molecular mechanisms remain elusive. Most risk loci are located in noncoding genomic elements without direct effects on protein products. Comprehensive functional annotation and variant impact quantification are essential to provide new molecular insights and discover therapeutic targets. Recent advances in novel sequencing technologies and community efforts to share genomic data provide unprecedented opportunities to understand how genetic variants contribute to psychiatric diseases. This application describes the development of integrative strategies and machine learning methods to combine novel assays (such as STARR-seq) with population-scale genomic profiles to elucidate the genetic regulatory grammar in the human prefrontal cortex (PFC) and to prioritize genetic variants in psychiatric disorders. Specifically, we will (1) dissect the cis- regulatory landscape of the PFC using population-scale epigenetics data, (2) construct multi- model gene regulatory networks by linking distal cis-regulatory elements to genes using chromatin co-variability analyses, (3) integrate genetic, epigenetic, and transcriptional data to identify key transcription factors and variants that contribute to psychiatric disorders. Distinct from existing efforts focusing on one genome, this proposed work presents a truly novel big-data approach for both modeling gene regulation and investigating disease-risk factors by incorporating heterogeneous multi-omics profiles from hundreds of individuals. The resultant comprehensive list of cis-regulatory elements will expand the number of known functional regions in the human brain by at least an order. We will release our methods and resources in the form of web services, distributed open-source software, and annotation databases, which will also benefit other investigators exploring the genetic underpinnings of neuropsychiatric disorders. In addition to its scientific content, this application proposes a comprehensive training program for preparing an independent investigator in computational genomics and neurogenetics. This training will take place at Yale University (in the Dept. of Molecular Biophysics and Biochemistry) under the mentorship of Prof. Mark Gerstein (functional genomics), Prof. Nenad Sestan (neurogenetics), and Prof. Hongyu Zhao (statistical genetics and machine learning). A committee of experienced psychiatric disease experts and data scientists will also provide advice on both scientific research and career development.