3/3 Multidimensional investigation of the etiology of autism spectrum disorder

 描述(由申请人提供)：自闭症谱系障碍(ASD)的特征是社交障碍和受限或重复的行为或兴趣。基因组技术的应用导致了许多ASD潜在基因的识别，为评估这些风险基因对ASD病因的洞察力提供了机会。在这个建议中，我们的目标是：1)生成ASD相关基因的列表；2)在生物数据中识别这些基因之间的汇聚点(例如，基因调控和表达)；3)在模型系统中验证这些汇聚点。由于ASD是一种人类神经发育障碍，我们将优先考虑从人脑组织纵向收集的跨发育的生物数据。在我们之前的工作中，我们已经证明从头突变，特别是拷贝数变异(CNV)和功能丧失(LoF)点突变，与ASD密切相关。此外，这些突变在病例中聚集在ASD危险基因和基因座上，但在对照中不聚集。通过比较这些突变在病例和对照之间的分布，我们可以确定代表ASD危险基因座的突变聚集点(例如，位于500kbp16p11.2基因座的CNV和位于CHD8基因的LOF)。我们开发了一个统计框架来评估这种聚集性，并纳入了来自遗传变异和病例对照数据的证据。这个框架被称为传输和从头关联测试(TADA)。在目标1中，我们将进一步开发这项测试，将所有可用的CNV、外显子组、基因组和靶向测序数据合并到单个ASD基因列表中，按ASD关联程度进行排序。以前，我们使用前九个ASD风险基因作为基因共表达网络的种子，并通过它们整合120个独立的ASD风险基因的能力来评估这些网络的有效性。通过将共表达输入数据限制在狭窄的发育窗口和特定的大脑区域，我们可以识别具有最大丰富的时空网络，例如胎儿中期发育的前额叶皮质。在目标2中，我们提出了类似的方法，但使用了我们团队开发的DAW(检测网络关联)方法。曙光像以前一样使用共表达数据的窄窗口，但能够纳入来自其他数据集的证据，如基因调控和蛋白质相互作用(PPI)。通过向黎明网络播种最高置信度的基因，我们将评估最能预测其他ASD基因的时空网络。ASD表现出明显的性别偏见，暗示ASD的病因和性二型因素之间存在交互作用。在我们识别发育中的人脑中的性二型转录本的工作的基础上，我们将在黎明识别的特定网络中测试它们的丰富。为了验证ASD相关网络，在目标3中，我们将识别最能代表每个网络的基因，并评估它是否也干扰了网络中的其他基因。我们将使用CRISPR/Cas9在小鼠和人类来源的IPSCs中破坏每个基因，并使用RNA-Seq评估被破坏的基因。

 DESCRIPTION (provided by applicant): Autism Spectrum Disorder (ASD) is characterized by impairments in social communication and restricted or repetitive behavior or interests. The application of genomic technologies has led to the identification of many of the genes underlying ASD, presenting the opportunity to assess the insight these risk genes can give into the etiology of ASD. In this proposal we aim to: 1) Generate a list of ASD-associated genes; 2) Identify points of convergence between these genes in biological data (e.g. gene regulation and expression); and 3) Validate these points of convergence in model systems. Since ASD is a human neurodevelopmental disorder we will prioritize biological data that is collected longitudinally across development from human brain tissue. In our prior work we have demonstrated that de novo mutations, specifically copy number variants (CNVs) and loss of function (LoF) point mutations, are strongly associated with ASD. Furthermore, these mutations cluster at ASD risk genes and loci in cases but not in controls. By comparing the distribution of these mutations between cases and controls we can identify the points of mutational clustering that represent ASD risk loci (e.g. CNVs at the 500kbp 16p11.2 locus and LoFs at the gene CHD8). We have developed a statistical framework to assess this clustering as well as incorporating evidence from inherited variants and case-control data. This framework is called the Transmitted and De novo Associated Test (TADA). In Aim 1 we will develop this test further to incorporate all the available CNV, exome, genome, and targeted sequencing data into a single ASD gene list, ranked by the degree of ASD association. Previously we used the top nine ASD risk genes as seeds for gene co-expression networks and assessed the validity of these networks by their ability to incorporate 120 independent ASD risk genes. By limiting the co- expression input data to narrow windows of development and specific brain regions we could identify the spatiotemporal networks with the greatest enrichment, for example pre-frontal cortex in mid-fetal development. In Aim 2, we propose a similar approach, but using the DAWN (Detecting Association With Networks) method developed by our group. DAWN uses the narrow windows of co-expression data as before, but is able to incorporate evidence from other datasets such as gene regulation, and protein-protein interaction (PPI). By seeding the DAWN networks with the highest confidence genes we will assess the spatiotemporal networks that best predict other ASD genes. ASD shows a significant sex bias implicating an interaction between ASD etiology and sexually dimorphic factors. Building on our work of identifying sexually dimorphic transcripts in the developing human brain we will test their enrichment within specific networks identified by DAWN. To validate the ASD-associated networks, in Aim 3 we will identify the gene that best represents each network and assess if disrupting it also disrupts the other genes within the network. We will disrupt each gene using CRISPR/Cas9 in both mice and human-derived iPSCs and assess the genes disrupted using RNA-Seq.