A multiple-testing procedure for high-dimensional mediation hypotheses.

ABSTRACT

Mediation analysis is of rising interest in epidemiology and clinical trials. Among existing methods, the joint significance (JS) test yields an overly conservative type I error rate and low power, particularly for high-dimensional mediation hypotheses. In this article we develop a multiple-testing procedure that accurately controls the family-wise error rate (FWER) and the false discovery rate (FDR) when testing high-dimensional mediation hypotheses. The core of our procedure is based on estimating the proportions of component null hypotheses and the underlying mixture null distribution of p-values. Theoretical developments and simulation experiments prove that the proposed procedure effectively controls FWER and FDR. Two mediation analyses on DNA methylation and cancer research are presented assessing the mediation role of DNA methylation in genLetic regulation of gene expression in primary prostate cancer samples; exploring the possibility of DNA methylation mediating the effect of exercise on prostate cancer progression. Results of data examples include wellL-behaved quantile-quantile plots and improved power to detect novel mediation relationships. An R package HDMT implementing the proposed procedure is freely accessible in CRAN.