TESTING FOR THE CAUSAL MEDIATION EFFECTS OF MULTIPLE MEDIATORS USING THE KERNEL MACHINE DIFFERENCE METHOD IN GENOME-WIDE EPIGENETIC STUDIES.

The rapid growth of high-throughput genomic and epigenomic data enables exploration of biological mechanisms underlying diseases causing processes beyond traditional association studies. Using the causal mediation analysis framework, we develop the kernel machine difference (KMD) method, which provides a testing procedure for detecting the mediation effects of a set of mediators, for example, the DNA methylation probes within a region or a gene. Our method extends the difference method in single mediator analysis to jointly model the mediatory role of the methylation of multiple neighboring probes, as they often work together in a collaborative fashion. Kernel machine regression is employed to accommodate flexible parametric and nonparametric effects of multiple mediators on the outcome and to allow for robust testing for the joint natural indirect effect (NIE). The proposed testing procedure does not require explicit modeling of the dependence of multiple mediators on exposure and confounders and the correlation among multiple mediators. It hence provides a robust and computationally efficient tool, especially for genomic regions with moderate to high-dimensional probes. We evaluate the performance of the proposed test using extensive simulations and demonstrate its gain in robustness and power when the effects are nonlinear. We apply the proposed test to the analysis of the epigenome-wide Normative Aging Study (NAS) to investigate the mediatory role of DNA methylation in the causal pathway between smoking behavior and lung function.