报告简介  Abstract

In genome-wide epigenetic studies, it is often of scientific interest to  assess whether the effect of an exposure on a clinical outcome is mediated through DNA methylation. 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 genome. In this talk,  we first study the theoretical properties of the commonly used methods for testing for causal mediation effects, Sobel's test and the joint significance test. We show the joint significance test is the likelihood ratio test for the composite null hypothesis of no mediation effect. Both Sobel's test and the joint significance test follow non-standard distributions, and they are overly conservative for testing mediation effects and yield invalid inference in genome-wide epigenetic studies. We propose a novel Divide-Aggregate Composite-null Test (DACT) for the composite null  hypothesis  of no mediation effect in genome-wide analysis.  We  show that the DACT method provides valid statistical inference  and boosts power for testing mediation effects across the genome. We propose a correction procedure to improve the DACT method using Efron's empirical null method when the exposure-mediator or/and the mediator-outcome association signals are not sparse. Our extensive simulation studies show that the DACT method properly controls type I error rates and outperforms the  Sobel's and the joint significance tests for genome-wide causal mediation analysis. We applied the DACT method to the Normative Aging Study to identify putative DNA methylation  sites that mediate the effect of smoking on lung function. We also developed a computationally-efficient  R package DACT for public use.

嘉宾简介  About the Speaker     

Zhonghua Liu is an Assistant Professor at Department of Statistics and Actuarial Science, The University of Hong Kong. His Research interests are Statistical inference for massive data. Big Data Analytics. Causal Inference and Mediation Analysis. Mixed Models. Biostatistics.

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