Population-Based Autism Genetics and Environment Study

项目总结/摘要 虽然在理解自闭症的风险结构方面取得了很大进展，但仍然存在一些问题。 关于自闭症的遗传和非遗传风险的性质的未回答的问题。许多这些 最好用基于人口统计学的流行病学样本来解决问题， 环境信息。到目前为止，几乎所有关于自闭症病因学的研究都依赖于便利性， 这些样本在捕捉遗传风险方面存在偏差，甚至可能存在环境风险。 基于流行病学的样本提供了一个独特的资源，以确定遗传和非遗传性的原因， 自闭症，同时允许精确估计归因于每种风险来源的人群风险。瑞典 受益于一个集中的医疗系统，这是大规模流行病学研究的基础 尤其是精神分裂症和躁郁症。在我们看来， 这项建议的价值在于一个独特的、以人口为基础的流行病学样本，分析的方式是 解决自闭症中几个悬而未决的问题。这些措施包括：1）更好地估计遗传力和环境 自闭症的研究2)评估孤独症中CNV复发风险的比率？3)发现罕见的单核苷酸 自闭症的变异4)剖析非遗传发现与自闭症之间联系的机制， 以及新型环境关联的发现？5）交叉性精神障碍分析，以更好地了解 都有自闭症和精神分裂症的倾向目的是：1）确定和生物库至少1300例 与自闭症障碍和1000个额外的控制，开发一个国际资源的ASD，并评估 选定的、推定的风险因素？2)使用高密度SNP阵列对所有样本进行基因分型，包括高密度SNP阵列。 外显子组覆盖率，并使用全外显子组方法对所有三联体进行测序？3）采用新的方法， 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 精神分裂症将被评估。在我们看来，这项研究是创新的，因为它保留了 自闭症样本在流行病学上有效的方式，针对遗传同质的人群，其中 精神分裂症和躁郁症的样本已经收集。该提案还创新地使用了 新的方法来估计遗传性，并确定罕见的，长期的遗传变异赋予自闭症的风险，而 为自闭症的遗传学和环境提供了一个综合模型。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， 利用同质化群体，更有能力识别共同风险。这一新的和实质性的 与在方便样本中进行的研究相比，研究自闭症的不同方法， 自闭症研究中的许多开放性问题，并提供了一条更好地了解风险的途径。 自闭症的因素，并最终更好地干预自闭症。

Project Summary/Abstract 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 non­genetic 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 non­genetic 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 large­scale 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 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.