Synergism of Gene and Environment in Cancer Studies: A New Bayesian Approach

描述（由申请人提供）： 在过去的十年中，统计规划和推理技术在为医学和健康科学中的复杂问题提供解决方案方面的重要性日益增加。两个科学团队目前主导着临床医学和公共卫生：分子生物学方法，重点是遗传学，以及定量方法，重点是流行病学。这些领域的发展共同为癌症的研究做出了根本性的贡献。这种应用在于癌症研究中人类遗传学、流行病学和统计学的新界面。病例对照研究越来越多地用于研究疾病与候选基因之间的关联。然而，除了一些罕见的疾病，如可能是单一基因产物缺乏的结果的亨廷顿或泰萨克斯病，大多数常见的人类疾病如癌症具有涉及许多遗传和环境因素的复杂相互作用的多因素病因学。通过临床和流行病学研究来识别和表征这种复杂的基因-环境相互作用，人们有更多的机会研究复杂疾病的病因，诊断，预后和治疗。在基因-环境与疾病相关性的病例对照研究中，当遗传和环境暴露在潜在人群中可以被假设为独立时，人们可以利用这种独立性来获得比传统逻辑回归分析更有效的估计技术。许多经典的病例对照分析结果，假设协变量分布是非参数的，不成立下的一个约束空间的暴露分布。然而，现代回顾性方法的效率提高是以缺乏鲁棒性为代价的，因为在违反基因-环境独立性假设的情况下，在回顾性估计中引入了大的偏差。本研究应用的主要目标是找到一个自然的分析工具，以解决模型规格的困境，现代回顾性分析的基因-环境相互作用的研究，在三种常用的流行病学设计。我们将问题的贝叶斯框架，采用不确定性的假设约束的基因环境的独立性在一个自然的数据自适应的方式。初步结果表明，所提出的估计仍然能够保持有吸引力的效率属性，而不依赖于无法验证的模型约束。流行病学家经常苦恼是否使用病例对照或病例仅估计基因-环境相互作用的一个给定的研究，目前的应用程序试图解决这个问题，在一个新的贝叶斯框架。所开发的方法可常规应用于基因-环境相互作用的各种流行病学研究。

DESCRIPTION (provided by applicant): The past decade has witnessed the growing importance of statistical planning and inferential techniques in providing solutions to complex problems in medical and health sciences. Two scientific teams are currently dominating clinical medicine and public health: the molecular biology approach with an emphasis on genetics, and the quantitative approach with an emphasis on epidemiology. The developments in these areas jointly are making fundamental contributions to the study of cancer. This application lies in that new interface of human genetics, epidemiology and statistics in cancer research. Case-control studies are being increasingly used for studying the association between a disease and a candidate gene. However, except for some rare diseases, such as Huntington or Tay Sachs disease which may be the result of a deficiency of a single gene product, most common human diseases like cancer have a multifactorial etiology involving complex interplay of many genetic and environ- mental factors. By identifying and characterizing such complicated gene-environment interactions through clinical and epidemiological studies, one has more opportunities to study etiology, diagnosis, prognosis and treatment of complex diseases. In case-control studies of gene-environment association with disease, when genetic and environmental exposures can be assumed to be independent in the underlying population, one may exploit the independence in order to derive more efficient estimation techniques than the traditional logistic regression analysis. Many of the classical results for case- control analysis, which assume the covariate distribution to be non-parametric, do not hold under a constrained space of exposure distributions. However, the gain in efficiency of modern retrospective methods comes at the cost of lack of robustness, since large biases are introduced in the retrospective estimates under violation of the gene-environment independence assumption. The main objective of this research application is to find a natural analytical tool to solve the model specification dilemma of modern retrospective analysis of studies of gene-environment interaction, under three commonly used epidemiological designs. We posit the problem in a Bayesian framework that incorporates uncertainty regarding the assumed constraint of gene-environment independence in a natural data adaptive way. Preliminary results indicate that the proposed estimator is still able to maintain attractive efficiency properties, without relying on unverifiable model constraints. Epidemiologists have often anguished whether to use the case-control or the case-only estimator of gene-environment interaction for a given study, and the current application tries to resolve the question in a novel Bayesian framework. The methods developed may be routinely applied to various epidemiological studies of gene-environment interaction.