Population-Based Autism Genetics & Environment Study

描述（由申请人提供）： 虽然在理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回答的问题。其中许多问题最好通过具有详细人口和环境信息的基于人口的流行病学样本来解决。到目前为止，几乎所有关于自闭症病因学的研究都依赖于方便的样本，这些样本在捕获遗传风险方面存在偏见，甚至可能更严重，环境风险。基于流行病学的样本提供了一个独特的资源，以确定自闭症的遗传和非遗传原因，同时允许精确估计归因于每种风险来源的人群风险。瑞典受益于集中的医疗系统，该系统是对精神疾病，特别是精神分裂症和双相情感障碍进行大规模流行病学研究的基础。我们认为，这项建议的意义在于一个独特的，以人口为基础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悬而未决的问题。其中包括：1）更好地估计自闭症的遗传性和环境; 2）评估自闭症复发风险CNV的比率; 3）发现自闭症中罕见的单核苷酸变异; 4）解剖非遗传发现与自闭症相关的机制-以及发现新的环境关联; 5）交叉障碍分析，以更好地理解自闭症和精神分裂症的共同责任。其目标是：1）确定并建立至少1300例自闭症患者和1000例其他对照的生物样本库，开发ASD的国际资源，并评估选定的推定风险因素; 2）使用高密度SNP阵列对所有样本进行基因分型，包括密集外显子组覆盖，并使用全外显子组方法对所有三联体进行测序; 3）使用新的方法来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设变异，同时整合关键的环境变量。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，这项拟议中的研究是创新的，因为它以流行病学有效的方式收集了自闭症样本，针对遗传同质的人群，已经收集了精神分裂症和双相情感障碍的样本。该提案还创新性地使用了新方法来估计遗传性，并确定了罕见的，常设变异赋予自闭症的风险，同时提供了一个 自闭症遗传学和环境的综合模型。最后，应用创新，在 我们的观点是，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。这种新的和实质上不同的方法来研究自闭症，相比于在方便的样本中进行的研究，解决了自闭症研究中的许多悬而未决的问题，并提供了一条更好地了解自闭症风险因素的道路，并最终更好地干预自闭症。

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 nongenetic 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 the 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.