Gene set mediation analysis in high-dimensional epigenetic studies

Abstract

Mediation analysis has significantly enhanced the elucidation of underlying epigenetic mechanisms by identifying mediators that influence the effect of an exposure on an outcome. In epigenomics research, the exponential increase in the dimensionality of mediators necessitates the development of advanced high-dimensional mediation models. Here we introduce a novel Gene Set High-Dimensional Mediation Analysis (gHDMA) model to understand the gene-level mediation role of DNA methylations, building upon our previous HDMA method. We begin our analysis by applying functional principal component analysis to capture gene variation signals across regions, thereby enhancing the model's statistical power. Following this, we employ the Sure Independence Screening method, based on Wilks' $\varLambda$-test, to efficiently reduce the dimensionality to a more manageable level, enabling high-dimensional inference. Our model is distinguished by its minimal reliance on stringent assumptions about the relationship between mediators and the outcome. Extensive simulation studies, by varying the type of basis function, the nature of the outcome variable, and the dimensionality of the gene region, demonstrate the robust performance of the gHDMA model. A corroborative case study further validates the methodology's efficacy. The gHDMA model emerges as a powerful analytical tool, with an enhanced capacity to identify genes characterized by hypermethylation or hypomethylation that mediate the effects of exposure on biological outcomes.