Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA.
Hum Genet. 2013 Dec;132(12):1413-25. doi: 10.1007/s00439-013-1350-z. Epub 2013 Aug 24.
The genetic influences on complex disease traits generally depend on the joint effects of multiple genetic variants, environmental factors, as well as their interplays. Gene × environment (G × E) interactions play vital roles in determining an individual's disease risk, but the underlying genetic machinery is poorly understood. Traditional analysis assuming linear relationship between genetic and environmental factors, along with their interactions, is commonly pursued under the regression-based framework to examine G × E interactions. This assumption, however, could be violated due to nonlinear responses of genetic variants to environmental stimuli. As an extension to our previous work on continuous traits, we proposed a flexible varying-coefficient model for the detection of nonlinear G × E interaction with binary disease traits. Varying coefficients were approximated by a non-parametric regression function through which one can assess the nonlinear response of genetic factors to environmental changes. A group of statistical tests were proposed to elucidate various mechanisms of G × E interaction. The utility of the proposed method was illustrated via simulation and real data analysis with application to type 2 diabetes.
复杂疾病性状的遗传影响通常取决于多个遗传变异、环境因素以及它们相互作用的联合效应。基因与环境（G×E）相互作用在决定个体疾病风险方面起着至关重要的作用，但潜在的遗传机制仍知之甚少。传统的分析方法假设遗传和环境因素以及它们的相互作用之间存在线性关系，通常在基于回归的框架下进行，以研究 G×E 相互作用。然而，由于遗传变异对环境刺激的非线性反应，这种假设可能会被违反。作为我们之前关于连续性状研究的扩展，我们提出了一种用于检测二分类疾病性状非线性 G×E 相互作用的灵活变系数模型。通过非参数回归函数来逼近变系数，通过该函数可以评估遗传因素对环境变化的非线性反应。提出了一组统计检验来阐明 G×E 相互作用的各种机制。通过模拟和实际数据分析来说明所提出方法的有效性，并将其应用于 2 型糖尿病。
