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

项目3：组学数据在基因x环境相互作用分析中的整合 摘要 大量“组学”数据的可用性，包括基因表达、代谢组、甲基化、 和微生物组，提供了令人兴奋的机会来识别新的基因环境（G×E）和 组学 × E 相互作用影响癌症和其他复杂特征。例如，FIGI 结直肠 癌症联盟已生成两种正常组织的转录组（基因表达）数据 和结肠类器官，为结直肠 G×E 和表达 × E 相互作用的发现提供信息 超过 130,000 例病例和对照样本中的癌症，以及已确定风险的暴露数据 因素包括烟草、酒精、肥胖和红肉。多种族群体包括超过 对 215,000 名受试者进行长达 30 年的跟踪调查，并提供生物标志物、代谢组学和微生物组数据 适用于乳腺癌和结直肠癌的选定子样本和嵌套病例对照样本 癌症。除了潜在地提高识别新交互的能力之外，使用 组学数据有望揭示基因和暴露的生物机制 影响特定的特质。该项目将开发两种利用组学的新颖方法 用于识别全基因组扫描中相互作用的数据。第一个（目标 1）考虑一个因素 时间（例如一个 SNP、一个基因）并使用新颖的两步筛选/测试方法来发现 G×E 或组学×E 相互作用。第二种（目标 2）方法是考虑 SNP 的联合模型 同时使用新颖的分层建模技术来指导组学数据 G×E 和 omic × E 相互作用的发现。对于目标 1 和 2，我们将考虑各种因素 可能提供的暴露数据类型，包括简单的是/否指标 使用统计模型构建的综合暴露测量问卷，带有或 没有相关的组学数据。目标 3 将侧重于将目标 1 和 2 的方法应用到多个领域 癌症相关数据资源，包括FIGI和MEC等流行病学调查 以及一项检查结直肠癌患者治疗结果修正因素的临床试验。 总体而言，该项目将开发统计方法来使用综合组学和 环境暴露方法可提高识别新型 G×E 和 omic × E 的能力 相互作用以及告知这些因素影响风险的生物机制 or prognosis of cancer.我们将利用我们在多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转化为实际数据应用。

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.