Statistical Methods for Gene Interactions in Case*

案例*中基因相互作用的统计方法

• 批准号: 6743824
• 负责人: Sanjay Shete
• 金额: $ 7.5万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-30 至 2005-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6743824
• 关键词: computer program /software; computer system design /evaluation; gene environment interaction; gene interaction; genetic susceptibility; human data; lung neoplasms; mathematical model; method development; statistics /biometry

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.

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和环境因素相互作用以产生疾病，这是流行病学和公共卫生研究的一个重要优先事项，现在已经可行。复杂的人类疾病，如癌症，往往是遗传易感性因素和可能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广泛用于研究疾病与潜在遗传和环境风险因素之间的关联。然而，对选择适当的对照组的关注导致了替代流行病学研究设计的发展。一个特殊的方法，只有情况下的设计，已被证明是一个有效的和有效的方法来测试基因与环境之间的相互作用的假设下，遗传和环境因素之间的独立性。然而，不可能基于仅病例数据来检验风险因素独立性的假设。 因此，本提案的目的是开发新的统计工具，这将是有用的，在研究基因和环境之间的相互作用的情况下，只有研究设计没有假设基因和环境之间的独立性或基因之间的连锁平衡。特别是，我们提出了一个贝叶斯似然为基础的方法，使用马尔可夫链蒙特卡罗技术，不需要假设的独立风险因素。我们计划检查所提出的方法的鲁棒性。本提案中开发的方法将应用于一系列1700多例组织学确诊的肺癌患者的数据，这些患者是通过MD胸外科诊所招募的。安德森癌症中心正在进行的肺癌遗传易感性研究。