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

组学数据在基因 x 环境相互作用分析中的整合

• 批准号: 10707459
• 负责人: William JAMES GAUDERMAN
• 金额: $ 28.22万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707459
• 关键词: Accounting; Address; Affect; Alcohols; Biological; Biological Markers; Cancer Etiology; Cancer Prognosis; Clinical Research; Clinical Trials; Code; Collaborations; Colon; Colorectal Cancer; Complex; Data; Data Analyses; Dimensions; Environment; Environmental Exposure; Gene Expression; Genes; Genome Scan; Genomics; Genotype; Joints; Linkage Disequilibrium; Malignant Neoplasms; Malignant neoplasm of ovary; Malignant neoplasm of prostate; Measures; Meat; Methods; Methylation; Modeling; Molecular; Normal tissue morphology; Obesity; Organoids; Outcome; Pathway interactions; Patient-Focused Outcomes; Process; Questionnaires; Research Design; Research Personnel; Resources; Risk; Risk Factors; Sampling; Software Tools; Source; Statistical Methods; Statistical Models; Step Tests; Structure; Techniques; Testing; Time; Tobacco; Translating; Treatment outcome; Work; cancer therapy; cancer type; case control; clinical investigation; cohort; colon cancer patients; data resource; design; empowerment; epidemiology study; gene discovery; gene environment interaction; guided inquiry; high dimensionality; improved; malignant breast neoplasm; metabolome; metabolomics; method development; microbiome; multi-ethnic; novel; screening; simulation; trait; transcriptome; transcriptomics; user friendly software

Project 2: 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.

项目2：在基因X环境互动分析中的OMIC数据集成 抽象的 高量“ OMIC”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型基因环境（G×E）和 影响癌症和其他复杂性状的OMIC×E相互作用。例如，图型大肠 癌症联盟已经在两个正常组织上产生了转录组（基因表达）数据 和结肠类器官，以告知G×E的发现和表达式×E相互作用 在超过130,000例病例和对照的样本中癌症，并有有关既定风险的暴露数据 包括烟草，酒精，肥胖和红肉在内的因素。多民族队列包括 215,000名受试者最多30年，具有生物标志物，代谢组和微生物组数据 可在选定的子样本和乳房和大肠样品的嵌套病例对照样本上可用 癌症。除了有可能提高识别新型相互作用的能力之外 OMIC数据有望告知基因和暴露的生物学机制 影响特定的特征。该项目将开发两种新型方法，以利用OMIC 数据以识别全基因组扫描中的相互作用。第一个（AIM 1）认为一个因素 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OMIC×E相互作用。第二种（AIM 2）方法是考虑SNP的联合模型 简单地使用新颖的分层建模技术来指导 发现G×E和Omic×E相互作用。对于两个目标1和2，我们将考虑各种 可能可用的曝光数据类型，从简单的是/否指示器 使用统计模型，与或 没有相关的OMIC数据。 AIM 3将专注于将目标1和2的方法应用于几个 与癌症相关的数据资源，包括流行病学研究，例如FIGI和MEC 以及检查结直肠癌患者治疗结果改良剂的临床试验。 总体而言，该项目将开发统计方法，同时使用集成的OMIC和 环境暴露方法，以提高识别新型G×E和Omic×E的功率 相互作用以及这些因素影响风险的生物学机制 或癌症的预后。我们将利用几项与癌症有关的研究的合作来 指导我们的方法开发过程，设计现实的模拟研究以评估 方法，并确保我们开发的方法转化为真实数据应用程序。