Liability threshold modeling of genes and environment in case-control studies

病例对照研究中基因和环境的责任阈值模型

• 批准号: 8217393
• 负责人: ALKES L PRICE
• 金额: $ 16.99万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8217393
• 关键词: Accounting; Biological; Case-Control Studies; Computer software; Data; Data Set; Diet; Disease; Disease model; Environment; Environmental Exposure; Environmental Risk Factor; Exposure to; Fibrinogen; Gender; Genes; Genetic; Genetic Risk; Goals; Heritability; Individual; Joints; Left; Letters; Mediating; Methodology; Methods; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Odds Ratio; Performance; Physical activity; Predisposition; Prevalence; Publications; Relative (related person); Research; Rheumatoid Arthritis; Risk; Sampling; Signal Transduction; Smoking; Source; Specific qualifier value; Statistical Methods; Testing; Variant; Work; abstracting; base; case control; disorder risk; gene environment interaction; genetic risk factor; genome wide association study; novel; programs; simulation; success; trait

DESCRIPTION (provided by applicant): Liability threshold modeling of genes and environment in case-control studies Abstract Gene-environment interaction, which we define as the joint effect of genes and environment that cannot be explained by their independent marginal effects, is broadly recognized as one of the potential sources of missing heritability in genome-wide association studies. Indeed, even if genetic effects are not biologically mediated by environmental factors, one can expect to see higher genetic risks (higher SNP odds ratios) in disease cases carrying lower risks from environmental factors as compared to disease cases carrying higher environmental risks. Our work has provided compelling evidence of this type of interaction in our applied work on type 2 diabetes. The current proposal focuses on this type of interaction, motivated by the established idea that the main goal of studying gene-environment interaction is not to identify interactions per se, but rather to identify genes that would not be identified by standard marginal tests. Surprisingly, despite the huge potential for improvement in power, methods that optimally account for this type of gene-environment interaction have yet to be applied to case-control studies. In particular, as we show below, standard approaches such as using environmental risk factors as covariates, as well as previously developed statistical tests for gene-environment interaction, all fail to capture the available increase in statistical power. In this proposal, we will develop methods based on liability threshold modeling that attain superior power in the presence of interaction effects of this type. We will apply these methods to large type 2 diabetes and rheumatoid arthritis data sets involving tens of thousands of samples. PUBLIC HEALTH RELEVANCE: Susceptibility to type 2 diabetes, rheumatoid arthritis, and a wide range of other diseases is known to be due to a combination of genetic and environmental factors, but association studies have had only partial success in identifying the underlying genetic risk variants-thus the search continues. Because disease may be due to either genetic or environmental factors, diseased individuals with low environmental risks are likely to harbor increased genetic risk relative to diseased individuals with high environmental risks. In this proposal, we develop and apply new methodology to exploit this statistical gene-environment interaction in order to identify genetic risk factors with increased statistical power.

描述（由申请人提供）：病例对照研究中基因和环境的责任阈值建模摘要基因环境相互作用，我们将其定义为基因和环境的关节效应，无法通过其独立的边际影响来解释，被广泛认为是基因组全基因组范围内缺失的潜在遗传性的潜在来源之一。的确，即使遗传作用不是由环境因素在生物学上介导的，与承受较高环境风险的疾病病例相比，在疾病病例中，人们可能会看到较高的遗传风险（SNP优势比较高）。我们的工作为我们在2型糖尿病的应用工作中提供了令人信服的证据。当前的提案着重于这种类型的相互作用，这是出于既定观念的促进，即研究基因 - 环境相互作用的主要目标不是确定相互作用本身，而是要识别未通过标准边缘测试来识别的基因。令人惊讶的是，尽管功率提高了巨大的潜力，但最佳地说明这种基因环境相互作用的方法尚未应用于病例对照研究。特别是，正如我们在下面显示的那样，将使用环境风险因素（例如协变量）以及先前开发的基因环境相互作用的统计检验等标准方法都无法捕获统计能力的可用增加。在此提案中，我们将基于责任阈值建模开发方法，这些方法在存在这种类型的相互作用效应的情况下实现了卓越的功率。我们将将这些方法应用于大型2型糖尿病和类风湿关节炎数据集，涉及数以万计的样本。 公共卫生相关性：对2型糖尿病，类风湿关节炎以及多种其他疾病的敏感性是由于遗传和环境因素的结合，但是关联研究在识别基本的遗传风险变异方面仅取得了部分成功，而搜索的搜索仍在继续。由于疾病可能是由于遗传因素或环境因素引起的，因此与具有高环境风险的患病患者相比，患病的患者可能会增加遗传风险。在此提案中，我们开发和应用新方法来利用这种统计基因 - 环境相互作用，以确定统计能力增加的遗传风险因素。