Methods to Identify Genetic Markers That Interact with Multiple Environmental Exp

识别与多种环境实验相互作用的遗传标记的方法

基本信息

批准号：
8218719
负责人：
PETER KRAFT
金额：
$ 19.38万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-02-17 至 2014-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8218719
关键词：
Address Breast Collaborations Complex Coronary heart disease Data Data Set Disease Ensure Environment Environmental Exposure Environmental Risk Factor Epidemiologic Studies Genes Genetic Genetic Markers Genetic Risk Heart Diseases Human Individual Joints Lead Malignant Neoplasms Masks Measures Methods Modeling Modification Molecular Epidemiology Non-Insulin-Dependent Diabetes Mellitus Outcome Prevention program Public Health Schools Research Personnel Risk Risk Factors Series Staging Statistical Methods Structure Testing Variant cancer type case control clinically relevant cohort design disorder risk gene environment interaction genetic epidemiology genetic variant genome wide association study human disease improved interest lead series performance tests programs simulation software development statistics tool trait vector web page

项目摘要

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.

描述（由申请人提供）：常见的复杂疾病，例如癌症，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，迄今为止，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，平均所有其他因素。如果遗传变异的影响在环境暴露定义的地层之间有所不同，那么边际方法可能无法检测到变体。在某些情况下，考虑可能通过测量的暴露来修饰可能的遗传效应可以提高研究人员检测疾病遗传标记的能力。通过考虑基因 - 环境相互作用的重点关注单个基因座和单个环境暴露，大多数提出的改善功率来检测因果基因座的方法。当多次接触可能会因遗传效应而改变时，这些方法迫使研究人员猜测最有可能的修饰符是最有可能的修饰符或串联测试个体暴露，从而导致多重测试惩罚增加和权力损失。我们提出了两个分析框架，这些框架灵活地模拟了遗传标记和多个测得的暴露之间的相互作用。我们假设这些方法将比关注单个暴露的测试更强大，或者以串联考虑多个暴露。成功完成我们的目标将为研究人员提供工具，以提高其识别与复杂人类特征相关的遗传变异的能力，这反过来又可以更好地了解人类疾病的多因素机制以及风险预测和预防计划的潜在改善。公共卫生相关性：常见的复杂疾病，例如癌症，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，以提高研究人员识别遗传风险基因座的能力，这些遗传风险基因座的作用可能会在特定环境中掩盖或放大。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。