STATISTICAL METHODS FOR GENE/ENVIRONMENT INTERACTIONS AND GENETIC SUSCEPTIBILITY

该项目旨在开发新的统计工具，用于评估基因， 环境相互作用和遗传易感性。 工作已经开始， 两个方面：（1）改进流行病学研究设计， 基因型和环境暴露的独立性，和（2）统计学 基因型和环境相互作用的参数化模型 因素吝啬地提高数据分析师的能力，以检测这种 交互. 基因分型对照受试者可能不可行，因为他们可能 不愿意捐献组织或可能担心隐私 遗传信息。 在基因型 和暴露在研究人群中是独立的，我们发现， 不对对照进行基因分型的研究设计可以使用较少的受试者 与早期的设计相比， 暴露效应和基因型-暴露交互效应。 然而，在这方面， 基因型效应不能在没有外部信息的情况下进行评估， 基因在人群中的普遍性。 当研究具有两个以上等位基因的基因时， 相互作用可能很难检测，因为描述它们需要 很多参数。 我们已经提出了一种方法来参数化相互作用 这将使调查人员能够检测到相互作用 致癌物质代谢基因的影响， 传统方法。

This project seeks to develop new statistical tools for evaluating gene- environment interactions and genetic susceptibility. Work has begun in two areas: (1) improved epidemiologic study designs that exploit the independence of genotype and environmental exposure, and (2) statistical modeling that parameterizes interactions of genotype and environmental factors parsimoniously to enhance a data analyst's ability to detect such interactions. Genotyping control subjects may not be feasible because they may be reluctant to contribute tissue or may have concerns about privacy with genetic information. Under the often-plausible assumption that genotype and exposure are independent in the study populations, we found that study designs where controls are not genotyped can use fewer subjects than earlier designs to achieve the same precision in estimating both exposure effects and genotype-exposure interaction effects. However, genotype effects cannot be assessed without external information about gene prevalence in the population. When studying genes with more than two alleles, genotype-environment interactions can be difficult to detect because describing them requires many parameters. We have proposed a way to parameterize interactions parsimoniously that will allow investigators to detect interaction effects of carcinogen metabolism genes that would be missed with traditional methods.