Population-Based Autism Genetics & Environment Study

描述（由申请人提供）：虽然在了解自闭症的风险结构方面取得了很大进展，但关于自闭症的遗传和非遗传风险的性质仍然存在未解之谜。其中许多问题可以通过具有详细人口统计和环境信息的以人口为基础的流行病学样本得到最好的解决。迄今为止，几乎所有关于自闭症病因的研究都依赖于方便样本，这些样本在捕捉遗传风险，甚至可能更严重的是环境风险方面存在偏见。基于流行病学的样本为确定自闭症的遗传和非遗传原因提供了独特的资源，同时允许对归因于每种风险来源的人群风险进行精确估计。瑞典得益于集中的医疗系统，该系统一直是精神疾病，特别是精神分裂症和双相情感障碍的大规模流行病学研究的基础。在我们看来，这一建议的意义在于一个独特的、基于人群的流行病学样本的价值，以这样一种方式分析，以解决自闭症的几个突出问题。这些包括：1)更好地估计自闭症的遗传性和环境；2)评估自闭症中危险性CNV的复发率；3)发现罕见的自闭症单核苷酸变异；4)剖析与自闭症相关的非遗传发现的潜在机制，并发现新的环境关联；5)交叉障碍分析，以更好地了解自闭症和精神分裂症的共同责任。目的是：1)确定至少1300例自闭症患者和1000例额外对照者并建立生物库，开发ASD的国际资源，并评估选定的、假定的风险因素；2)使用高密度SNP阵列（包括密集外显子组覆盖）对所有样品进行基因分型，并使用全外显子组方法对所有三组进行测序；3)在整合关键环境变量的情况下，利用新方法评估遗传和新生变异在自闭症中的作用，并评估自闭症中罕见的常存变异。在以后的几年里，自闭症风险和精神分裂症风险之间的关系将被评估。在我们看来，拟议中的研究是创新的，因为它以一种流行病学上有效的方式确定了自闭症样本，目标是基因同质的人群，其中精神分裂症和双相情感障碍样本已经收集。该提案还在使用新方法来估计遗传能力和识别罕见的、长期存在的自闭症风险变异方面具有创新性，同时为自闭症的遗传和环境提供了一个综合模型。最后，在我们看来，这个应用是创新的，因为它为理解自闭症和精神分裂症的共同风险提供了基础，利用同质群体来更好地识别共同风险。这是新的和实质性的

DESCRIPTION (provided by applicant): While there has been great progress in understanding the risk architecture of autism, there are still unanswered questions about the nature of the genetic and non-genetic risk for autism. Many of these questions can be best addressed with a population-based epidemiological sample with detailed demographic and environmental information. To date, almost all studies on the etiology of autism relied on convenience samples, which are subject to biases in capturing genetic and, possibly even more so, environmental risk. Epidemiologically based samples provide a unique resource to identify genetic and non-genetic causes of autism, while allowing for a precise estimate of risk in the population attributed to each source of risk. Sweden benefits from a centralized medical system that has been the foundation of large-scale epidemiological studies in psychiatric disorders, particularly schizophrenia and bipolar disorder. In our opinion, the significance of this proposal lies in the value of a unique, population-based epidemiological sample, analyzed in such a way as to address several outstanding issues in autism. These include: 1) better estimates of heritability and environment in autism; 2) assessing the rate of recurrent risk CNV in autism; 3) discovery of rare standing single nucleotide variation in autism; 4) dissection of mechanisms underlying the association of non-genetic findings with autism-and the discovery of novel environmental associations; and, 5) cross-disorder analyses to better understand shared liability to autism and schizophrenia. The aims are: 1) To ascertain and biobank at least 1300 cases with autistic disorder and 1000 additional controls, to develop an international resource for ASD, and to assess selected, putative risk factors; 2) To genotype all samples using high-density SNP arrays, including dense exome coverage, and sequence all trios using whole-exome approaches; and, 3) To use novel methods to assess the role of inherited and de novo variants in autism and to evaluate rare standing variation in autism, while integrating key environmental variables. In later years the relationship between autism risk and risk for schizophrenia will be assessed. The proposed research is innovative, in our opinion, because it ascertains autism samples in an epidemiologically valid manner, targeting a genetically homogenous population, for which schizophrenia and bipolar samples have already been collected. The proposal is also innovative in the use of novel methods to estimate heritability and to identify rare, standing variation conferring risk to autism, while providing an integrated model for genetics and environment in autism. Finally, the application is innovative, in our opinion, in that it provides he groundwork for understanding shared risk across autism and schizophrenia, making use of a homogenous group to have better power to identify shared risk. This new and substantively different approach to studying autism, compared to studies carried out in convenience samples, addresses many of the open questions in autism research and provides a path towards a better understanding of the risk factors for autism and ultimately to better interventions in autism.