Partial linear varying multi-index coefficient model for integrative gene-environment interactions.
Partial linear varying multi-index coefficient model for integrative gene-environment interactions.
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DOI：
10.5705/ss.202015.0114
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发表时间：
2016-07
影响因子：
1.4
通讯作者：
Li R
中科院分区：
文献类型：
--
作者：
Liu X;Cui Y;Li R
基因-环境（G×E）相互作用在许多复杂疾病中起着关键作用。越来越多的流行病学研究表明，多种环境暴露对疾病风险的综合影响。然而，当考虑G×E相互作用时，还没有开发出适当的统计模型来对这种综合效应进行严格的评估。在本文中，我们提出了一个偏线性变多指数系数模型（PLVMICM）来评估多种环境因素如何共同作用来改变复杂疾病的个体遗传风险。我们的模型将变指标系数模型作为特例，其中离散变量作为线性部分被接受。因此，PLVMICM允许人们同时研究基因与连续环境之间的非线性相互作用效应以及基因与离散环境之间的线性相互作用。导出了估计参数参数的轮廓法和估计非线性相互作用函数的b样条反拟核方法。在一定的正则性条件下，建立了参数估计和非参数估计的相合性和渐近正态性。对参数系数和非参数函数进行假设检验。结果表明，检验参数系数和非参数函数的统计量渐近服从不同自由度的χ2分布。通过大量的仿真和案例研究证明了该方法的实用性。
Gene-environment (G×E) interactions play key roles in many complex diseases. An increasing number of epidemiological studies have shown the combined effect of multiple environmental exposures on disease risk. However, no appropriate statistical models have been developed to conduct a rigorous assessment of such combined effects when G×E interactions are considered. In this paper, we propose a partial linear varying multi-index coefficient model (PLVMICM) to assess how multiple environmental factors act jointly to modify individual genetic risk on complex disease. Our model includes the varying-index coefficient model as a special case, where discrete variables are admitted as the linear part. Thus PLVMICM allows one to study nonlinear interaction effects between genes and continuous environments as well as linear interactions between genes and discrete environments, simultaneously. We derive a profile method to estimate parametric parameters and a B-spline backfitted kernel method to estimate nonlinear interaction functions. Consistency and asymptotic normality of the parametric and nonparametric estimates are established under some regularity conditions. Hypothesis testing for the parametric coefficients and nonparametric functions are conducted. Results show that the statistics for testing the parametric coefficients and the non-parametric functions asymptotically follow a χ2-distribution with different degrees of freedom. The utility of the method is demonstrated through extensive simulations and a case study.