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

The 2021 Bernard G. Greenberg Distinguished Lecture Series

Featuring Professor Xihong Lin, Harvard University

Lecture 2: May 20, 3-4 p.m.
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 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. Dr. Lin will 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. The 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. The research team adopted Efron’s empirical null framework for assessing statistical significance of the DACT test. They showed analytically that the proposed DACT method had improved power, and could well control type I error rate. The 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. They applied the DACT method to the Normative Aging Genome-wide Epigenetic Study to study the mediation effects of smoking, DNA methylations and lung function.

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