Genome-wide Environment Interaction Study for Autism: The SEED study

描述（由申请人提供）：在NIH机构间自闭症协调委员会（IACC）的自闭症谱系障碍（ASD）研究战略计划以及IOM最近的自闭症与环境报告中，提高我们对基因-环境相互作用（GxE）作为自闭症病因机制的理解已被确定为研究重点。然而，关于自闭症中GxE的实证研究仍然很少。虽然有几条证据支持其普遍的合理性，但自闭症的GxE研究面临几个关键障碍：1)缺乏结合遗传和暴露数据的流行病学数据集；2)缺乏体内GxE的机制数据/假设来指导流行病学分析（此类数据增加了测试的GxE关联真实的先验概率）；3)传统方法检验GxE的统计能力较低。在这里，我们提出了一个病例对照分析GxE旨在克服这些障碍。探索早期发展研究（SEED）是一项多地点ASD病例对照调查，招募了900名3-5岁的ASD儿童，900名典型发育对照组和900名非自闭症发育障碍儿童。生物样本的DNA和暴露信息在广泛的假设是可用的。因此，SEED在一个大的流行病学样本中汇集了遗传和环境数据。为了克服上述强调的其他障碍，我们建议扩展GWAS方法，在被称为基因-环境相互作用研究（GEWIS）的研究中考虑GxE。为了做到这一点，我们将完成前500例病例和500例典型发育对照的GEWIS基因分型，在这2年的资助时间表中获得数据，开发与产前暴露相关的暴露变量，并使用最近提出的新统计技术来搜索环境异质性背景下的基因。我们的具体目标包括：(1)利用Illumina 1M-Duo SNP面板和两种互补的分析方法，确定与母亲行为（怀孕期间吸烟和饮酒）、感染和母亲用药相关的暴露类别中对ASD的影响可能不同的SNP：在异质性背景下的遗传关联的2-df检验，通过利用对照中GxE的独立性来有效估计GxE相互作用效应的经验贝叶斯方法。(2)整合上述方法的SNP发现，通过剂量-反应和特异性分析进一步表征暴露相互作用，并在其他ASD数据集中进行复制分析。这项工作将加速未来在我们的全套SEED参与者和其他自闭症数据集中围绕基因-环境相互作用的努力。

DESCRIPTION (provided by applicant): Improving our understanding of gene-environment interaction (GxE) as an etiologic mechanism in autism has been identified as a research priority in the NIH Inter-Agency Autism Coordinating Committee's (IACC's) Strategic Plan for Autism Spectrum Disorders (ASD) Research as well as in the IOM's recent Report on Autism and the Environment. However, empirical investigations of GxE in autism remain scarce. Although several lines of evidence support its general plausibility, GxE research in autism faces several key obstacles: 1) lack of epidemiologic datasets uniting genetic and exposure data; 2) absence of mechanistic data/hypotheses on GxE in vivo to guide epidemiologic analyses (such data increases the prior probability that tested GxE associations are real); and 3) low statistical power for testing GxE using conventional methods. Here we propose a case-control analysis of GxE aimed at surmounting these obstacles. The Study to Explore Early Development (SEED) is a multisite ASD case-control investigation recruiting 900 3-5 year old children with ASDs, 900 typically developing controls, and 900 children with non-autism developmental impairments. Biosamples for DNA and exposure information across a wide range of hypotheses are available. SEED therefore brings together genetic and environmental data in a large epidemiologic sample. To surmount the other obstacles highlighted above, we propose extending the GWAS approach to consider GxE in what has been referred to as a gene-environment-wide interaction study (GEWIS). To do this we will complete GEWIS genotyping on the first 500 cases and 500 typically developing controls with data available during this 2-year grant timetable, develop summary exposure variables related to prenatal exposures, and use recently proposed novel statistical techniques to search for genes in the context of heterogeneity by environment. Our specific aims include: (1) identify SNPs whose effects on ASD may vary across exposure categories related to maternal behaviors (smoking and alcohol use during pregnancy), infection, and maternal medication use using the Illumina 1M-Duo SNP panel and two complementary analysis methods: a 2-df test for genetic association in the context of heterogeneity, an empirical Bayes approach to efficiently estimate GxE interaction effects by exploiting GxE independence in controls. (2) Integrate SNP findings from the above approaches, further characterize exposure interactions via dose-response and specificity analyses, and pursue replication analyses in other ASD data sets. This work will accelerate future efforts centered around gene-environment interaction both in our full set of SEED participants and in other data sets for autism. PUBLIC HEALTH RELEVANCE: We plan to carry out a multi-center case-control study of 500 cases and 500 controls from the SEED study to identify genes whose effects on ASD may vary across exposure categories related to pre-natal infection, maternal medication use, or smoking and drinking during pregnancy.