陽明交通大學公衛所 Faculty Seminar

時間(Time)：114年3月3日(一) 12:10PM – 13:10PM

地點(Location)：醫學二館221教室 (Rm221, Medical Building II)

講者(Speaker)： 賴恩語博士（中央研究院統計科學研究所）

Dr. En-Yu Lai, Institute of Statistical Science, Academia Sinica

講題(Title)：

Causal Inference and Genome-wide Multimediator Analyses（ 因果推論及全基因組多重中介分析）

摘要(Abstract) ：

Mediation analysis is performed to evaluate the effects of a hypothetical causal mechanism that marks the progression from an exposure, through mediators, to an outcome.

In the age of high-throughput technologies, it has become routine to assess numerous potential mechanisms at the genome or proteome scales. Alongside this, the necessity to address issues related to multiple testing has also arisen.

In a sparse scenario where only a few genes or proteins are causally involved, conventional methods for assessing mediation effects lose statistical power because the composite null distribution behind this experiment can not be attained.

The power loss hence decreases the true mechanisms identified after multiple testing corrections.

To fairly delineate a uniform distribution under the composite null, Huang (2019, AoAS) proposed the composite test to provide adjusted p-values for single-mediator analyses.

Our contribution is to extend the method to multi-mediator analyses, which are commonly encountered in genomic studies and also flexible to various biological interests.

Using the generalized Berk-Jones statistics with the composite test, we proposed a multivariate approach that favors dense and diverse mediation effects, a decorrelation approach that favors sparse and consistent effects, and a hybrid approach that captures the edges of both approaches. Our analysis suite has been implemented as an R package MACtest. The utility is demonstrated by analyzing the lung adenocarcinoma datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium. 

We further investigate the genes and networks whose expression may be regulated by smoking-induced epigenetic aberrations.