Bayesian Hierarchical Risk Models: Nutrition, Genes, & Environment Interactions

贝叶斯分层风险模型：营养、基因、

• 批准号: 7631262
• 负责人: MICHAEL D SWARTZ
• 金额: $ 11.95万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7631262
• 关键词: Accounting; Affect; Age; Agreement; Bayesian Method; Biological Availability; Cancer Biology; Cancer Etiology; Case-Control Studies; Data; Data Sources; Development; Diet; Diet Habits; Dietary Component; Dietary Factors; Dietary Practices; Discipline; Elements; Environmental Risk Factor; Epidemiologist; Epidemiology; Ethnic Origin; Folate; Food; Funding; Gender; Genes; Genetic; Genetic Models; Goals; Grant; Individual; Intake; Interdisciplinary Study; Intervention; Knowledge; Literature; Lung; Malignant Neoplasms; Malignant neoplasm of lung; Mentors; Methodology; Modeling; Molecular; Nutrient; Nutritional; Odds Ratio; Pathway interactions; Pattern; Physical activity; Predisposition; Prevention strategy; Probability; Procedures; Process; Research; Research Personnel; Research Project Grants; Review Literature; Risk; Risk Factors; Smoker; Smoking; Smoking Status; Source; Statistical Methods; Statistical Models; Suggestion; Techniques; Training; Uncertainty; Variant; Weight; absorption; base; cancer epidemiology; cancer prevention; cancer risk; career development; case control; flexibility; folic acid metabolism; gene environment interaction; genetic epidemiology; genetic risk factor; mathematical model; method development; nutrition; nutritional epidemiology; programs; skills

DESCRIPTION (provided by applicant): My goal is to become an independent researcher in methodological development across the disciplines of statistical genetics, epidemiology, and nutritional epidemiology. Specifically, I want to focus on using Bayesian hierarchical models to model cancer risk while accounting for the uncertainty surrounding genetic, epidemiological and nutritional data and their interactions. This goal builds upon my previous statistical training, bayesian modeling and statistical genetics, but requires training in nutrition and nutritional epidemiology, cancer biology, and general epidemiology. At the end of this training period, I will be an established researcher in the nutrition and genetics of cancer prevention. To attain these goals, I have developed a comprehensive educational plan, including expert mentors consisting of a statistical geneticist, an epidemiologist specializing in lung cancer, a nutritional epidemiologist and a Bayesian statistician. The research I have proposed both reinforces my training and provides original, cutting edge methodologies to the field of cancer prevention. The research plan focuses on the following specific aims: 1) To perform a literature review of the current information about dietary patterns, environmental and genetic risk factors that influence the folate metabolism pathways as they relate to lung cancer risk. This review will further my knowledge in the field, and provide the field with a concentrated review of risk factors for lung cancer that relate to diet, folate intake and folate metabolism. 2) To develop Bayesian Hierarchical models to simultaneously identify diet and physical activity patterns and dietary components that associate with lung cancer risk. Using sophisticated models to identify dietary patterns consider nutrients as they naturally occur and are ingested as food. Additionally, we will build models that jointly consider genes, nutritional factors and environmental factors. 3) To develop Bayesian hierarchical models to identify genes and gene by diet interactions involved with folate metabolism that affect lung cancer risk. Bayesian hierarchical models provide sophisticated machinery to investigate interactions that may occur with or without related main effects. The Bayesian methodologies proposed consider sources of variation that frequentist models do not, and can provide more comprehensive risk models. The models and techniques developed can be applied to multiple cancers.

描述(由申请者提供)：我的目标是成为统计遗传学、流行病学和营养流行病学领域方法学发展的独立研究员。具体地说，我想把重点放在使用贝叶斯分层模型来模拟癌症风险，同时考虑到围绕遗传、流行病学和营养数据及其相互作用的不确定性。这个目标建立在我之前的统计培训、贝叶斯建模和统计遗传学的基础上，但需要营养和营养流行病学、癌症生物学和一般流行病学的培训。在这次培训结束时，我将成为癌症预防的营养和遗传学方面的一名老牌研究员。为了实现这些目标，我制定了一项全面的教育计划，其中包括由一名统计遗传学家、一名肺癌专业流行病学家、一名营养流行病学家和一名贝叶斯统计学家组成的专家导师。我提出的研究既加强了我的培训，又为癌症预防领域提供了原创的、尖端的方法。该研究计划集中于以下具体目标：1)对目前关于饮食模式、环境和遗传风险因素的文献进行综述，这些因素影响叶酸代谢途径，因为它们与肺癌风险有关。这篇综述将加深我在该领域的知识，并为该领域提供与饮食、叶酸摄入和叶酸代谢有关的肺癌危险因素的集中审查。2)开发贝叶斯分层模型，以同时确定与肺癌风险相关的饮食和体力活动模式以及饮食成分。使用复杂的模型来确定饮食模式，将营养素视为自然产生并被摄取为食物。此外，我们将建立联合考虑基因、营养因素和环境因素的模型。3)建立贝叶斯分层模型，通过与叶酸代谢有关的饮食交互作用识别影响肺癌风险的基因和基因。贝叶斯分层模型提供了复杂的机制来研究在有或没有相关主效应的情况下可能发生的相互作用。所提出的贝叶斯方法考虑了频数模型没有考虑到的变异来源，并且可以提供更全面的风险模型。所开发的模型和技术可以应用于多种癌症。