Methods to Identify Genetic Markers That Interact with Multiple Environmental Exp

描述（由申请人提供）：常见的，复杂的疾病，如癌症，心脏病和2型糖尿病是多因素的：这些疾病的风险取决于多种遗传和环境因素。然而，到目前为止，寻找与疾病风险相关的遗传标记的流行病学研究已经考虑了单个遗传因素的影响，平均超过所有其他因素。如果遗传变异的影响在由环境暴露定义的阶层中不同，那么边际方法可能无法检测到变异。在某些情况下，通过测量暴露量来考虑可能的遗传效应修改可以提高研究人员检测疾病遗传标记的能力。大多数通过考虑基因-环境相互作用来提高检测致病基因座的能力的方法都集中在单个基因座和单个环境暴露上。当一个以上的曝光可能会修改与遗传效应，这些方法迫使调查人员猜测先验的曝光是最有可能的修改器或测试个人曝光系列，导致多个测试惩罚的增加和权力的损失。我们提出了两个分析框架，灵活地模拟遗传标记和多个测量暴露之间的相互作用。我们假设，这些方法将比集中在一个单一的曝光，或考虑多个曝光单独，在一系列的测试更强大。我们的目标的成功完成将为研究人员提供工具，以提高他们识别与复杂人类特征相关的遗传变异的能力，这反过来又会导致更好地了解人类疾病的多因素机制，并潜在地改善风险预测和预防计划。 公共卫生相关性：常见的复杂疾病，如癌症，心脏病和2型糖尿病是多因素的：这些疾病的风险取决于多种遗传和环境因素。我们建议开发新的统计方法，以提高研究人员的能力，以确定遗传风险位点的影响可能被掩盖或放大，在特定的环境。我们的方法可用于加深对人类疾病的多因素机制的理解，并改善风险预测和预防计划。

DESCRIPTION (provided by applicant): Common, complex diseases like cancer, heart disease and type 2 diabetes are multifactorial: risk for these diseases depends on multiple genetic and environmental factors. To date, however, epidemiological studies searching for genetic markers associated with disease risk have considered the effect of a single genetic factor, averaged over all other factors. If the effect of a genetic variant differs across strata defined by environmental exposures, then it is possible that the marginal approach will fail to detect the variant. In some situations, considering possible genetic effect modification by measured exposures can increase researchers' ability to detect genetic markers of disease. Most of the proposed methods for improving power to detect causal loci by considering gene-environment interaction focus on a single locus and a single environmental exposure. When more than one exposure might modify with a genetic effect, these methods force investigators either to guess a priori which exposure is the most likely modifier or to test individual exposures in series, leading to an increase in multiple testing penalty and a loss of power. We propose two analytic frameworks that flexibly model the interactions between a genetic marker and multiple measured exposures. We hypothesize that these approaches will be more powerful than tests that focus on a single exposure, or consider multiple exposures individually, in series. Successful completion of our aims will provide researchers with tools to improve their ability to identify genetic variants associated with complex human traits, which will in turn lead to a better understanding of the multifactorial mechanisms underlying human disease and potential improvements in risk prediction and prevention programs. PUBLIC HEALTH RELEVANCE: Common, complex diseases like cancer, heart disease and type 2 diabetes are multifactorial: risk for these diseases depends on multiple genetic and environmental factors. We propose to develop new statistical methods to improve researchers' ability to identify genetic risk loci whose effect may be masked or amplified in particular environments. Our methods can be used to deepen understanding of the multifactorial mechanisms underlying human disease and improve in risk prediction and prevention programs.