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：基因与环境相互作用分析中的Omic数据整合 摘要 大量的“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，以确定新的基因环境（G×E）， 影响癌症和其他复杂性状的组学× E相互作用。例如，FIGI结肠直肠 癌症联盟已经生成了两种正常组织的转录组学（基因表达）数据， 和结肠类器官，以发现结直肠癌的G×E和表达× E相互作用 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 包括烟草、酒精、肥胖和红肉。多族裔群体包括 215，000名受试者随访长达30年，包括生物标志物，代谢组学和微生物组学数据 乳腺和结直肠的选定子样本和巢式病例对照样本可用 癌除了潜在地提高识别新相互作用的能力之外， 组学数据有望为基因和暴露的生物机制提供信息， 影响一个特定的特征。本项目将开发两种利用组学的新方法 数据来识别全基因组扫描中的相互作用。第一个（目标1）考虑一个因素， 时间（例如一个SNP，一个基因），并使用新的两步筛选/测试方法来发现 G×E或omic × E相互作用。第二种（目标2）方法是考虑SNP的联合模型 和omic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2，我们将考虑各种 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测量问卷， 没有相关的omic数据。目标3将侧重于将目标1和2中的方法应用于若干领域， 癌症相关数据资源，包括流行病学调查，如FIGI和MEC 以及一项检查结直肠癌患者治疗结果修饰剂的临床试验。 总的来说，这个项目将开发统计方法， 环境暴露方法提高识别新G×E和组学× 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 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， 指导我们的方法开发过程，设计逼真的模拟研究，以评估 方法，并确保我们开发的方法转化为实际数据应用。