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

基本信息

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
项目类别：
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

项目摘要

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：组学数据整合在基因x环境相互作用分析中抽象的大量“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。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多种族人群包括烟草、酒精、肥胖和红肉等因素。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除了潜在地提高识别新相互作用的能力之外，使用组学数据有望揭示基因和暴露的生物机制该项目将开发两种利用组学的新方法。第一个目标（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 omic × 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 和组学×E 相互作用的发现对于目标 1 和 2，我们将考虑各种不同的情况。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，带有或在没有相关组学数据的情况下，目标 3 将侧重于将目标 1 和 2 中的方法应用于几个方面。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我们将利用我们在多项癌症相关研究上的合作来实现这一目标。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。