Statistical Methods for Gene-Gene and Gene-Environment *

描述(由申请人提供)： 随着人类基因组计划的完成，确定基因如何与其他基因以及与环境因素相互作用产生疾病，这是一项重要的流行病学和公共卫生研究重点，现在已经成为可能。复杂的人类疾病，如癌症，往往是遗传易感因素和可能可改变的环境因素相互作用的结果。病例对照研究在流行病学中被广泛用于研究疾病与潜在的遗传和环境风险因素之间的联系。然而，对选择合适的对照组的担忧导致了替代流行病学研究设计的发展。一种特殊的方法，仅病例设计，已被证明是一种在遗传和环境因素独立的假设下检验基因-环境相互作用的有效和有效的方法。然而，仅基于案例数据来检验风险因素独立性的假设是不可能的。 因此，这一建议的目的是开发新的统计工具，在只研究设计而不假设基因与环境之间的独立性或基因之间的连锁平衡的情况下，将有助于研究基因与环境之间的相互作用。特别是，我们提出了一种基于贝叶斯似然的方法，使用了不需要假设风险因素独立的马尔可夫链蒙特卡罗技术。我们计划检验所提出的方法的稳健性。该提案中开发的方法将应用于通过M.D.安德森癌症中心的胸外科诊所登记的1700多名组织学确诊的肺癌患者的一系列数据，这项正在进行的肺癌遗传易感性研究。

DESCRIPTION (provided by applicant): With the completion of the Human Genome Project, determining how genes interact with other genes and with environmental factors to produce diseases, an important epidemiologic and public health research priority, is now feasible. Complex human diseases such as cancer are often the result of the interaction of genetic susceptibility factors and possibly modifiable environmental factors. Case-control studies are widely used in epidemiology for studying associations between diseases and potential genetic and environmental risk factors. However, concerns over the selection of an appropriate control groups have led to the development of alternative epidemiologic study designs. One particular approach, the case only design, has been shown to be an efficient and valid method for testing for gene-environment interactions under the assumption of independence between the genetic and environment factor. However, it is impossible to test the assumption of independence of risk factors based on the case only data. Therefore, the objective of this proposal is to develop novel statistical tools that will be useful in studying the interactions among genes and the environment in the case only study design without the assumption of independence between gene and environment or linkage equilibrium between genes. Particularly, we are proposing a Bayesian likelihood-based approach, using a Markov chain Monte Carlo technique that does not require the assumption of independence of risk factors. We plan to examine the robustness of the proposed method. The methods developed in this proposal will be applied to data from a series of more than 1700 comprehensively characterized patients with histologically confirmed lung cancer enrolled through the thoracic surgery clinics at M.D. Anderson Cancer Center in an ongoing study of genetic susceptibility to lung cancer.