Large-Scale Hypothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies

In genome-wide epigenetic studies, it is of great scientific interest to assess whether the effect of an exposure on a clinical outcome is mediated through DNA methylations. However, statistical inference for causal mediation effects is challenged by the fact that one needs to test a large number of composite null hypotheses across the whole epigenome. Two popular tests, the Wald-type Sobel’s test and the joint significant test using the traditional null distribution are underpowered and thus can miss important scientific discoveries. In this article, we show that the null distribution of Sobel’s test is not the standard normal distribution and the null distribution of the joint significant test is not uniform under the composite null of no mediation effect, especially in finite samples and under the singular point null case that the exposure has no effect on the mediator and the mediator has no effect on the outcome. Our results explain why these two tests are underpowered, and more importantly motivate us to develop a more powerful divide-aggregate composite-null test (DACT) for the composite null hypothesis of no mediation effect by leveraging epigenome-wide data. We adopted Efron’s empirical null framework for assessing statistical significance of the DACT test. We showed analytically that the proposed DACT method had improved power, and could well control Type I error rate. Our extensive simulation studies showed that, in finite samples, the DACT method properly controlled the Type I error rate and outperformed Sobel’s test and the joint significance test for detecting mediation effects. We applied the DACT method to the U.S. Department of Veterans Affairs Normative Aging Study, an ongoing prospective cohort study which included men who were aged 21 to 80 years at entry. We identified multiple DNA methylation CpG sites that might mediate the effect of smoking on lung function with effect sizes ranging from –0.18 to –0.79 and false discovery rate controlled at the level 0.05, including the CpG sites in the genes AHRR and F2RL3. Our sensitivity analysis found small residual correlations (less than 0.01) of the error terms between the outcome and mediator regressions, suggesting that our results are robust to unmeasured confounding factors.

Short Bio

Dr. Zhonghua Liu is currently assistant professor in the Department of Biostatistics at Columbia University since August 2022. He obtained his doctorate in 2015 from Harvard University. He later worked at Morgan Stanley in New York City and then moved to University of Hong Kong as an assistant professor in the Department of Statistics and Actuarial Science in 2018. His current interests include causal inference and statistical genetics/genomics.