Most human traits are influenced by the interplay between genetic and environmental factors. Many statistical methods have been proposed to screen for gene-environment interaction (GxE) in the post genome-wide association study era. However, most of the existing methods assume a linear interaction between genetic and environmental factors toward phenotypic variations, which diminishes statistical power in the case of nonlinear GxE. In this paper, we present a flexible statistical procedure to detect GxE regardless of whether the underlying relationship is linear or not. By modeling the joint genetic and GxE effects as a varying-coefficient function of the environmental factor, the proposed model is able to capture dynamic trajectories of GxE. We employ a likelihood ratio test with a fast Monte Carlo algorithm for hypothesis testing. Simulations were conducted to evaluate validity and power of the proposed model in various settings. Real data analysis was performed to illustrate its power, in particular, in the case of nonlinear GxE.

大多数人类特征都受到遗传因素和环境因素之间相互作用的影响。在后全基因组关联研究时代，人们提出了许多统计方法来筛选基因-环境相互作用（GxE）。然而，大多数现有方法都假设遗传和环境因素之间对表型变异存在线性相互作用，这会降低非线性 GxE 情况下的统计功效。在本文中，我们提出了一种灵活的统计程序来检测 GxE，无论潜在关系是否是线性的。通过将遗传和 GxE 的联合效应建模为环境因素的变系数函数，所提出的模型能够捕获 GxE 的动态轨迹。我们采用似然比检验和快速蒙特卡罗算法进行假设检验。进行模拟以评估所提出的模型在各种设置下的有效性和功效。通过实际数据分析来说明其威力，特别是在非线性 GxE 的情况下。