Rapid Phenotyping for Rare Variant Discovery in Autism

描述(由申请人提供)：自闭症谱系障碍(ASD)是一组高度异质性的神经发育障碍。虽然强有力的家族性证据支持自闭症谱系障碍(ASD)的病因学有很大的遗传贡献，但只在所有病例中的一小部分发现了特定的遗传异常。目前的关键限制是相对缺乏可用于基因分析的家系。为了全面描述自闭症的异质性遗传成分，包括识别罕见和常见的变异，总体样本量需要比目前正在研究的样本量大几个数量级。这将需要快速和可扩展的受试者评估范例，以避免基于临床的时间密集型行为表型，这是一个限速步骤。这里提出的项目是利用基于网络的招募、网络实施和验证的表型测量以及分布式血液采集的综合力量来建立受影响的自闭症儿童的具有成本效益的招募。根据我们的初步分析，我们评估了在互动自闭症网络(IAN)内实施的基于网络的自闭症表型鉴定方法的准确性，我们在IAN中建立了一个功能齐全的自动化方案，从已经登记和新登记的家庭中招募人员，以电子方式同意他们，并指示他们在家附近1,600个签约抽血点之一采集血液。已经与伊恩合作启动了这一招募程序，我们预计，我们每年招募400个单纯型四联家庭的计划将在筹资期间的头三年(具体目标1)很快实现。使用收集的1200个四分体家族的样本集，我们将执行高度敏感的靶向测序，以在先前研究(特定目标2)中富含与自闭症相关的基因的基因集中搜索罕见的风险变异和罕见的CNV。虽然这里提出的样本量仍然不足以检测所有罕见的风险变量，但由于使用了父母和未受影响的兄弟姐妹的严格控制，通过统计稳健的分析策略实现了筛查新的罕见突变的更大能力。此外，为了传达ASD的异质性，我们将对以基因为中心的亚组和以表型为中心的亚组进行探索性子集分析(具体目标3)。在整个招募过程中，我们将仔细监测伊恩人口，以确保没有观察到明显的表型变化。这里发现的变异不仅将验证先前与自闭症相关的基因发现，还将导致识别新的突变和扩大基因列表。我们希望最终建立一种环境，在这种环境中，相对于一套全面的遗传风险因素，所有受影响儿童的基因构成都是已知的，这样就可以对具有已知遗传风险因素的个人进行更深层次的表型努力。所有收集的样本和序列数据将与其他研究人员共享，大大增加了可用于自闭症遗传学研究的样本量。

DESCRIPTION (provided by applicant): Autism spectrum disorder (ASD) is a highly heterogeneous group of neuro-developmental disorders. While strong familial evidence supports a substantial genetic contribution to the etiology of autism spectrum disorders (ASD), specific genetic abnormalities have been identified in only a small minority of all cases. The key limitation currently is the relative lack of families available for genetic analysis. In order to comprehensively delineate the heterogeneous genetic components of autism including the identification of rare and common variants, overall sample sizes an order of magnitude larger than those currently under study are critically needed. This will require rapid and scalable subject assessment paradigms that obviate clinic-based time- intensive behavioral phenotyping, which is a rate-limiting step. The project proposed here is to use the combined power of web based recruiting, web-implemented and validated phenotypic measurements, and distributed blood collection to establish a cost-effective recruitment of affected children with ASD. Based on our preliminary analysis where we assessed the accuracy of a web-based approach to autism phenotyping implemented within the Interactive Autism Network (IAN), we have set up a fully functional and automated protocol in IAN to recruit from already registered and newly registered families, consent them electronically and instruct them for blood collection at one of the 1,600 contracted blood draw sites near their home. This recruitment procedure is already initiated in collaboration with IAN and we expect that our plan of recruiting 400 simplex tetrad families per year will be readily met in the first three year of the funding period (specific aim 1). Using the collected sample set of 1200 tetrad families, we will perform highly sensitive targeted sequencing to search for rare risk variants and rare CNVs within a gene set that is enriched for genes associated with autism from previous studies (specific aim 2). Although the sample size proposed here is still not large enough to detect all rare risk variants, because of the stringent control using parents and the unaffected siblings, increased power to screen for novel rare mutations is achieved with statistically robust analytical strategy. Additionally, to convey the heterogeneous nature of ASD, we will implement exploratory subset analysis of both the gene-centric and the phenotypic-centric subgroups (specific aim 3). Throughout the recruitment process, we will carefully monitor the IAN population to ensure that there is no significant phenotype shifts observed. The variants found here will not only validate the previous genetic findings associated with autism but also result in identifying novel mutations and expanding the gene list. We would hope to eventually establish an environment in which all affected children's genetic makeup is known, relative to a comprehensive set of genetic risk factors, so that deeper phenotypic efforts can be applied to individuals with known genetic risk factors. All of the samples collected and the sequence data will be shared with other investigators, substantially increasing the sample size available for autism genetic studies.