2/3 Building Integrative CNS Networks for Genomic Analysis of Autism

 描述(申请人提供)：大规模的基因组研究已经开始阐明基因对主要精神疾病的贡献。在自闭症谱系障碍(ASD)中，罕见的大效应变异为理解其神经生物学基础和了解疾病机制中的趋同和分歧提供了新的因果锚。这些发现与其他精神疾病一样，也强调了极端的遗传异质性。我们和其他人已经证明，基因和蛋白质网络为在统一的生物学背景下理解不同种类的精神疾病遗传风险提供了一个组织框架。来自人脑的大规模基因组和表观遗传学数据的出现，以及对自闭症和其他精神疾病所涉及的遗传变异的日益了解，加上方法学的进步，为全面综合分析提供了前所未有的机会。我们聚集了一个研究人员的协作小组，每个人都具有对理解精神疾病至关重要的不同领域的专业知识，这些领域以前从未合并过，以开发一个整合基因组网络分析的框架。这里提出的工作代表了一个雄心勃勃的多PI项目(加州大学洛杉矶分校/华盛顿大学、麻省理工学院/哈佛大学和约翰·霍普金斯大学)，该项目汇集了3名主要研究人员和合作者，他们拥有强大的出版记录和使用最先进和新颖的方法执行这项工作所需的所有方法的专业知识。通过密切合作，我们的目标是开发一个全面的框架，并测试大脑中基因表达和蛋白质相互作用(PPI)网络与遗传和表观遗传数据网络的最佳集成方法，这些网络将使用实验数据进行迭代改进。我们将通过严格结合ASD和对照大脑的多个转录数据集，构建代表成年和发育中多个大脑区域的网络，开发和验证基因网络中剪接和表达水平整合的方法(目标1)。这些网络将被改进，以告知组织特异性PPI推断，并通过实验组织特异性PPI(目标2)进行验证。我们将通过整合遗传和表观遗传学数据，进一步确定因果驱动因素，确定QTL对RNA、剪接和蛋白质水平的影响(目标3)。我们将通过实验验证对网络中枢和ASD相关基因的假定因果驱动因素子集的网络监管预测(目标4)。此外，我们将使用我们的网络来预测高可能性风险基因，其与ASD的关系将使用ASD和相关精神疾病队列的大规模测序数据进行评估，以及我们自己通过多重反转探针(MIP)测序进行的重点实验验证(AIM 4)。这些目标的完成将导致更有效和更全面的中枢神经系统网络，从而显着促进我们对ASD相关变异和致病神经生物学途径的理解。按照我们的惯例，我们的数据、网络和所有代码将以基于网络的格式免费提供，以最大限度地对社区有用。

 DESCRIPTION (provided by applicant):Large-scale genomic investigations have begun to illuminate the genetic contributions to major psychiatric illnesses. In autism spectrum disorder (ASD), rare large effect variants provide novel causal anchors to understand its neurobiological basis, and to understand convergence and divergence in disease mechanisms. These findings, not unlike other psychiatric disorders, also emphasize extreme genetic heterogeneity. We and others have shown that gene and protein networks provide an organizing framework for understanding heterogeneous psychiatric disease genetic risk in a unified biological context. The emergence of large-scale genomic and epigenetic data from human brain, and growing knowledge of genetic variation involved in ASD and other psychiatric diseases, coupled with advances in methodology, provides an unprecedented opportunity for comprehensive integrative analyses. We bring together a collaborative group of investigators, each with distinct areas of expertise critical for understanding psychiatric diseases, that have not been combined before, to develop a framework for integrative genomic network analysis. The work proposed here represents an ambitious multi-PI project (UCLA/UW, MGH/Harvard, and Johns Hopkins) that brings together 3 principal investigators and collaborators with strong publication records and expertise in all approaches necessary to perform this work using state of the art and novel methodologies. Through close collaboration we aim to develop a comprehensive framework and test optimal methods for integration of gene expression and protein-protein interaction (PPI) networks in the brain with genetic and epigenetic data - networks that will be iteratively refined using experimental data. We will construct networks representing multiple brain regions in adulthood and development through rigorous combination of multiple transcriptomic data sets from ASD and control brain, developing and validating methods for integration of splicing and expression levels within gene networks (Aim 1). These networks will be refined to inform tissue specific PPI inference, validated via experimental tissue-specific PPI (Aim 2). We will further identify causal drivers by integration of genetic and epigenetic data, identifying QTL effects on RNA, splicing and protein levels (Aim 3). We will experimentally validate network regulatory predictions for a subset of putative causal drivers prioritizing network hubs and ASD associated genes (Aim 4). In addition, we will use our networks to predict high likelihood risk genes, whose relationship to ASD will be assessed using data from large-scale sequencing in ASD and related psychiatric disease cohorts, as well as our own focused experimental validation via multiplex inversion probe (MIPs) sequencing (Aim 4). Completion of these aims will lead to more valid and comprehensive CNS networks thereby significantly advancing our understanding of ASD associated variants and causal neurobiological pathways. As is our usual practice, our data, networks, and all code will be made freely available in a web-based format to be of maximum utility to the community.