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

描述(申请人提供)：病例对照研究中基因和环境的易感性阈值建模摘要基因与环境的相互作用，我们将其定义为基因和环境的联合效应，不能用它们的独立边际效应来解释，被广泛认为是全基因组关联研究中遗漏遗传性的潜在来源之一。事实上，即使遗传效应不受环境因素的生物调节，人们也可以预期，与环境因素风险较高的疾病病例相比，环境因素风险较低的疾病病例的遗传风险更高(SNP赔率比更高)。我们的工作提供了令人信服的证据，证明在我们对2型糖尿病的应用工作中存在这种类型的相互作用。目前的建议侧重于这种类型的相互作用，其动机是既定的想法，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，而是确定标准边际检验无法确定的基因。令人惊讶的是，尽管在能力方面有巨大的改进潜力，但对这种类型的基因-环境相互作用进行最佳解释的方法尚未应用于病例对照研究。特别是，如下所示，标准方法，如使用环境风险因素作为协变量，以及以前开发的基因-环境相互作用的统计检验，都无法捕捉到统计能力的现有增长。在这个提案中，我们将开发基于责任阈值建模的方法，在存在这种类型的交互影响的情况下获得更高的权力。我们将把这些方法应用于涉及数万个样本的大型2型糖尿病和类风湿性关节炎数据集。 公共卫生相关性：众所周知，2型糖尿病、类风湿性关节炎和一系列其他疾病的易感性是由遗传和环境因素共同作用的，但关联研究在识别潜在的遗传风险变量方面只取得了部分成功--因此研究仍在继续。由于疾病可能是由遗传或环境因素引起的，与环境风险较高的疾病个体相比，环境风险较低的患病个体可能具有更高的遗传风险。在这项建议中，我们开发和应用新的方法来利用这种统计基因-环境相互作用，以增加统计能力来识别遗传风险因素。

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.