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3/3 Building integrative CNS networks for genomic analysis of autism

3/3 构建用于自闭症基因组分析的综合 CNS 网络

基本信息

批准号：
9906910
负责人：
Alexis Battle
金额：
$ 24.62万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-01 至 2021-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9906910
关键词：
Address Adult Affect Alternative Splicing Area Biochemical Pathway Biological Biology Brain Brain Diseases Brain region Candidate Disease Gene Code Collaborations Communities Coupled DNA Sequence Alteration Data Data Set Data Sources Development Disease Disease Pathway Ensure Epigenetic Process Face Family Functional disorder Gene Expression Gene Proteins Genes Genetic Genetic Heterogeneity Genetic Risk Genetic Transcription Genetic Variation Genomics Heritability Human Human Genetics Investigation Knowledge Laboratories Large-Scale Sequencing Link Mental disorders Methodology Methods MicroRNAs Network-based Neuraxis Neurobiology Online Systems Pathway Analysis Pathway interactions Population Population Genetics Principal Investigator Privatization Probability Process Proteins Proteome Proteomics Public Health Publications Quantitative Trait Loci RNA Splicing Records Regulation Research Personnel Specificity Structure Testing Time Tissues Untranslated RNA Validation Variant Work autism spectrum disorder base case control causal variant cohort data integration epigenetic variation genetic association genetic risk factor genetic variant genome-wide human data insight interest large scale data learning network mind control multidisciplinary neuropsychiatric disorder novel protein protein interaction public health relevance relating to nervous system risk variant theories transcription factor transcriptome transcriptome sequencing transcriptomics

项目摘要

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.

描述（由申请人提供）：大规模的基因组研究已经开始阐明主要精神疾病的遗传贡献。在自闭症谱系障碍（ASD）中，罕见的大效应变异提供了新的因果锚点，以了解其神经生物学基础，并了解疾病机制的收敛和发散。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。我们和其他人已经表明，基因和蛋白质网络提供了一个组织框架，在统一的生物学背景下理解异质性精神疾病的遗传风险。来自人脑的大规模基因组和表观遗传数据的出现，以及对ASD和其他精神疾病所涉及的遗传变异的不断了解，再加上方法学的进步，为全面的综合分析提供了前所未有的机会。我们汇集了一组合作的研究人员，每个人都有不同的专业领域，对理解精神疾病至关重要，以前没有结合起来，开发一个综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（UCLA/UW，MGH/哈佛和约翰霍普金斯），该项目汇集了3名主要研究者和合作者，他们具有强大的出版记录和使用最先进和新颖方法进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，并测试最佳方法，将大脑中的基因表达和蛋白质-蛋白质相互作用（PPI）网络与遗传和表观遗传数据整合在一起-这些网络将使用实验数据进行迭代优化。我们将通过严格结合ASD和对照大脑的多个转录组数据集，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内剪接和表达水平整合的方法（目标1）。将对这些网络进行改进，以告知组织特异性PPI推断，并通过实验性组织特异性PPI进行验证（目标2）。我们将通过整合遗传和表观遗传数据，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网络监管预测的一个子集的假定的因果驱动程序优先网络枢纽和ASD相关基因（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倒置探针（MIP）测序进行的集中实验验证（Aim 4）。这些目标的完成将导致更有效和更全面的CNS网络，从而显着推进我们对ASD相关变异和因果神经生物学通路的理解。作为我们的惯例，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，以便为社区提供最大的效用。