Mediation analysis has been a useful tool for investigating the effect of mediators that lie in the path from the independent variable to the outcome. With the increasing dimensionality of mediators such as in (epi)genomics studies, high-dimensional mediation model is needed. In this work, we focus on epigenetic studies with the goal to identify important DNA methylations that act as mediators between an exposure disease outcome. Specifically, we focus on gene-based high-dimensional mediation analysis implemented with kernel principal component analysis to capture potential nonlinear mediation effect. We first review the current high-dimensional mediation models and then propose two gene-based analytical approaches: gene-based high-dimensional mediation analysis based on linearity assumption between mediators and outcome (gHMA-L) and gene-based high-dimensional mediation analysis based on nonlinearity assumption (gHMA-NL). Since the underlying true mediation relationship is unknown in practice, we further propose an omnibus test of gene-based high-dimensional mediation analysis (gHMA-O) by combing gHMA-L and gHMA-NL. Extensive simulation studies show that gHMA-L performs better under the model linear assumption and gHMA-NL does better under the model nonlinear assumption, while gHMA-O is a more powerful and robust method by combining the two. We apply the proposed methods to two datasets to investigate genes whose methylation levels act as important mediators in the relationship: (1) between alcohol consumption and epithelial ovarian cancer risk using data from the Mayo Clinic Ovarian Cancer Case-Control Study and (2) between childhood maltreatment and comorbid post-traumatic stress disorder and depression in adulthood using data from the Gray Trauma Project.

中文翻译：

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表观遗传学研究中基于基因的中介分析。

中介分析是研究从自变量到结果的路径中中介效应的有用工具。随着（表观）基因组学研究等中介因素维度的不断增加，需要高维中介模型。在这项工作中，我们专注于表观遗传学研究，目的是确定重要的 DNA 甲基化，这些甲基化在暴露性疾病结果之间充当中介。具体来说，我们专注于通过核主成分分析实现的基于基因的高维中介分析，以捕获潜在的非线性中介效应。我们首先回顾了当前的高维中介模型，然后提出了两种基于基因的分析方法：基于中介和结果之间线性假设的基于基因的高维中介分析（gHMA-L）和基于基因的高维中介分析基于非线性假设（gHMA-NL）。由于潜在的真实中介关系在实践中是未知的，因此我们通过结合 gHMA-L 和 gHMA-NL 进一步提出了基于基因的高维中介分析（gHMA-O）的综合测试。大量的仿真研究表明，gHMA-L 在模型线性假设下表现更好，gHMA-NL 在模型非线性假设下表现更好，而 gHMA-O 将两者结合起来是一种更强大、更鲁棒的方法。我们将所提出的方法应用于两个数据集，以研究甲基化水平在以下关系中充当重要调节因素的基因：（1）使用梅奥诊所卵巢癌病例对照研究的数据，饮酒与上皮性卵巢癌风险之间；以及（2）使用灰色创伤项目的数据，研究儿童期虐待以及成年期共病创伤后应激障碍和抑郁症。