Statistics Seminar (2019-04)
Topic: Large-Scale Mediation Effect Signal Detection in Genome-wide Epigenetic Studies
Speaker: Zhonghua Liu, University of Hong Kong
Time: Thursday, Mar 28, 14:00-15:00
Place: Room 217, Guanghua Building 2
In genome-wide epigenetic studies, it is often of scientific interest assessing the mediator role of DNA methylation in the causal pathway from an exposure to a clinical outcome. Mediation analysis is commonly used to answer this question. This is often done via fitting two regression models: the mediator model and the outcome model, and then the product of coefficient method to integrate information from these two models and performing hypothesis testing using Sobel's test. Another popular test is the joint significance test which declares the presence of a mediation effect if the effect of the exposure on mediator and the effect of mediator on outcome are both statistically significant. In this paper, we show that both the Sobel's test and the joint significance test are overly conservative for the detection of mediation effect in genome-wide epigenetic studies. The null hypothesis of no mediation effect is composite, and it is therefore challenging to perform large-scale hypothesis testing to detect mediation effects. We propose a novel divide-aggregate test (DAT) for the composite null hypothesis for the detection of mediation effects in genome-wide epigenetic studies. We first divide the composite null parameter space into three disjoint parts, each with a separate testing procedure. The DAT is then obtained by aggregating the statistical evidence via a weighted average of the three parts with the weights estimated as the proportion of true nulls based on the p-values from the mediator and outcome regression models. We further show that the DAT can outperform the Sobel's test and the joint significance test for the detection of mediation effects in genome-wide epigenetic studies. A fast Monte Carlo correction method is also proposed for computing the p-value of the DAT method. We show via simulation studies that the DAT method controls type I error rates and outperforms the Sobel's and the joint significance tests. We applied the DAT method to the Normative Aging Study to identify putative DNA methylation sites that mediate the effect of smoking on lung function.
Dr. Zhonghua Liu is currently assistant professor in the department of statistics and actuarial science at the University of Hong Kong. Before joining HKU, Dr. Liu worked at Morgan Stanley Global headquarter in New York City for about two years.
He obtained his doctorate in biostatistics supervised by Professor Xihong Lin from Harvard University in 2015 and then did a one-year postdoc at Harvard University with Prof. Xihong Lin. Dr. Liu's current primary research interests are developing statistical methods for high-throughput genetic/genomic data.
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