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

描述（由申请人提供）：我的目标是成为一名独立的研究人员，从事统计遗传学、流行病学和营养流行病学等学科的方法学发展。具体来说，我想把重点放在使用贝叶斯层次模型来模拟癌症风险，同时考虑围绕遗传、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训练、贝叶斯建模和统计遗传学的基础上，但需要营养学和营养流行病学、癌症生物学和普通流行病学方面的培训。在培训结束后，我将成为一名在癌症预防营养学和遗传学方面有建制的研究员。为了实现这些目标，我制定了一个全面的教育计划，包括一个统计遗传学家，一个专门研究肺癌的流行病学家，一个营养流行病学家和一个贝叶斯统计学家的专家导师。我提出的研究既加强了我的训练，又为癌症预防领域提供了原创的、前沿的方法。本研究计划的具体目标如下：1)对影响叶酸代谢途径与肺癌风险相关的饮食模式、环境和遗传风险因素的现有信息进行文献综述。这篇综述将进一步加深我在这一领域的知识，并为该领域提供一个与饮食、叶酸摄入和叶酸代谢有关的肺癌危险因素的集中综述。2)建立贝叶斯层次模型，同时识别与肺癌风险相关的饮食和身体活动模式以及饮食成分。使用复杂的模型来确定饮食模式，考虑营养物质的自然产生和作为食物摄入。此外，我们将建立共同考虑基因、营养因素和环境因素的模型。3)建立贝叶斯层次模型，确定叶酸代谢影响肺癌风险的基因和基因通过饮食相互作用。贝叶斯层次模型提供了复杂的机制来研究有或没有相关主效应可能发生的相互作用。提出的贝叶斯方法考虑了频率模型没有考虑的变异来源，并能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。

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.