Detecting GxE Interactions in Genome-wide Association Studies

描述（由申请人提供）：许多常见的复杂性状被认为是基因、环境因素及其相互作用共同作用的结果。了解遗传多态性与环境暴露之间的关系有助于确定人群中的高风险亚群，并更好地了解复杂疾病的途径机制。然而，大多数在全基因组关联研究（GWAS）中寻找新基因的研究者没有考虑基因-环境（GxE）的相互作用。这部分是由于目前缺乏有效的统计方法和软件来检测大量遗传数据中的相互作用。在这个项目中，我们将开发新的和强大的方法来检测参与GxE相互作用的基因，包括高效的筛选技术和贝叶斯方法。这些方法将适用于疾病（如癌症、哮喘）和定量（如胆固醇、肺功能）结果，其形式适用于病例对照和病例-父母三人组设计。我们还将开发利用来自财团设置的GWA数据来检测GxE相互作用的方法。在单一研究和联合研究中，我们将确定这些方法相对于标准测试提供了更好的发现相互作用的能力，同时也控制了假阳性率。我们还将开发新的用户友好的软件来分析GWAS中的GxE相互作用，并将通过我们的网站免费发布这些程序。

DESCRIPTION (provided by applicant): Many common complex traits are believed to be a result of the combined effect of genes, environmental factors and their interactions. Understanding the relationship between genetic polymorphisms and environmental exposures can help to identify high risk subgroups in the population and provide a better understanding of pathway mechanisms for complex diseases. However, most investigators conducting a search for new genes in a genome-wide association study (GWAS) do not consider gene- environment (GxE) interactions. This is in part due to a current lack of efficient statistical methods and software designed to detect interactions in high-volume genetic data. In this project, we will develop new and powerful methods for the detection of genes involved in a GxE interaction, including efficient screening techniques and Bayesian methods. These methods will be applicable to both disease (e.g. cancer, asthma) and quantitative (e.g. cholesterol, lung function) outcomes, with forms applicable to case- control and case-parent trio designs. We will also develop approaches for the detection GxE interactions using GWA data from a consortium setting. In both the single-study and consortium settings, we will establish that these methods provide improved power for finding interactions relative to standard tests, while also controlling the false positive rate. We will also develop new user-friendly software for the analysis of GxE interactions in a GWAS, and will freely distribute these programs via our website.