Integration of Omic Data in the Analysis of Gene x Environment Interaction

项目3：基因x环境互作分析中基因组数据的整合 摘要 包括基因表达、代谢组、甲基化、 和微生物组，为发现新的基因环境(G×E)和 影响癌症和其他复杂性状的体型×E交互作用。例如，Figi结直肠 癌症联盟已经在这两个正常组织上产生了转录(基因表达)数据 和结肠有机化合物提示大肠G×E和Expression×E相互作用的发现 超过130,000个病例和对照的样本中的癌症，以及关于已确定风险的暴露数据 因素包括烟草、酒精、肥胖和红肉。这一多民族群体包括超过 对215,000名受试者进行了长达30年的跟踪调查，包括生物标志物、代谢组学和微生物组数据 适用于乳房和结直肠癌的精选样本和嵌套式病例对照样本 癌症。除了潜在地提高识别新交互的能力外，使用 基因组数据有望告知基因和暴露的生物机制 影响一种特殊的特征。该项目将开发两种利用OMIC的新方法 在全基因组扫描中识别交互作用的数据。第一个(目标1)考虑了一个因素 时间(例如，一个SNP，一个基因)，并使用新的两步筛选/检测方法来发现 G×E或OMIC×E交互作用。第二种方法(目标2)是考虑SNPs的联合模型 和基因组数据，使用新的分层建模技术来指导 G×E和OMIC×E相互作用的发现。对于目标1和目标2，我们将考虑不同的 可能提供的暴露数据类型，从简单的是/否指标 使用统计模型构建的综合暴露措施的调查问卷，带有或 没有相关的基因组数据。目标3将侧重于将目标1和目标2中的方法应用于几个 癌症相关数据资源，包括FIGI和MEC等流行病学调查 以及一项临床试验，检查结直肠癌患者治疗结果的改良剂。 总体而言，该项目将开发统计方法，以使用综合经济学和 提高识别新G×E和OMIC×E能力的环境暴露方法 以及告知这些因素影响风险的生物学机制 或癌症的预后。我们将利用我们在几项癌症相关研究上的合作来 指导我们的方法开发过程，设计真实的模拟研究来评估 方法，并确保我们开发的方法被转化为实际数据应用程序。

Project 3: Integration of Omic Data in the Analysis of Gene x Environment Interaction Abstract The availability of high-volume ‘omic’ data, including gene expression, metabolome, methylation, and microbiome, provides exciting opportunities to identify novel gene-environment (G×E) and omic × E interactions affecting cancer and other complex traits. For example, the FIGI colorectal cancer consortium has generated transcriptomic (gene expression) data on both normal tissue and colon organoids to inform the discovery of G×E and expression × E interactions for colorectal cancer in a sample of over 130,000 cases and controls, with exposure data on established risk factors including tobacco, alcohol, obesity, and red meat. The multi-ethnic cohort includes over 215,000 subjects followed for up to 30 years, with biomarkers, metabolomic, and microbiome data available on selected subsamples and nested case-control samples of breast and colorectal cancer. In addition to potentially improving power for identifying novel interactions, the use of omic data holds promise to inform the biological mechanisms by which genes and exposures affect a particular trait. This project will develop two types of novel methods that leverage omic data to identify interactions in a genomewide scan. The first (Aim 1) considers one factor at a time (e.g. one SNP, one gene) and uses novel two-step screening/testing methods to discover G×E or omic × E interactions. The second (Aim 2) approach is a joint model considering SNPs and omic data simultaneously, using novel hierarchical modeling techniques to guide the discovery of G×E and omic × E interactions. For both Aims 1 and 2, we will consider the various types of exposure data that may be available, ranging from simple yes/no indicators from questionnaires to integrated exposure measures constructed using statistical models, with or without relevant omic data. Aim 3 will focus on applying the methods from Aims 1 and 2 to several cancer-related data resources, including epidemiological investigations such as FIGI and MEC and a clinical trial examining modifiers of treatment outcomes in colorectal cancer patients. Overall, this project will develop statistical methods to use both integrative omic and environmental exposure approaches to improve power for identifying novel G×E and omic × E interactions as well as to inform the biological mechanism by which these factors affect the risk or prognosis of cancer. We will leverage our collaborations on several cancer-related studies to guide our methods development process, to design realistic simulation studies for evaluating the methods, and to assure that methods we develop are translated into real-data applications.