时 间：2021年12月9日（周四）10：30-11：30
地 点：线上：Zoom ID: 87968213598密码：267603
题 目：Heterogeneous Mediation Analysis on Epigenomic PTSD and Traumatic Stress in an African American Cohort
主讲人：Peiyong Qu Chancellor’s Professor,University of California Irvine
主持人：周勇 教授
摘 要：
DNA methylation (DNAm) has been suggested to play a critical role in post-traumatic stress disorder (PTSD), through mediating the relationship between trauma and PTSD. However, this underlying mechanism of PTSD for African Americans still remains unknown. To fill this gap, in this talk, we investigate how DNAm mediates the effects of traumatic experiences on PTSD symptoms in the Detroit Neighborhood Health Study (DNHS) (2008–2013) which involves primarily African Americans adults. To achieve this, we develop a new mediation analysis approach for high-dimensional potential DNAm mediators. A key novelty of our method is that we consider heterogeneity in mediation effects across sub-populations. Specifically, mediators in different sub-populations could have opposite effects on the outcome, and thus could be difficult to identify under a traditional homogeneous model framework. In contrast, the proposed method can estimate heterogeneous mediation effects and identifies subpopulations in which individuals share similar effects. Simulation studies demonstrate that the proposed method outperforms existing methods for both homogeneous and heterogeneous data. We also present our mediation analysis results of a dataset with 125 participants and more than 450, 000 CpG sites from the DNHS preprocessed via an expression quantitative trait methylation (eQTM) analysis. The proposed method finds three subgroups of subjects and identifies DNAm mediators corresponding to genes such as FKBP5 and NFATC1 which have been linked to PTSD symptoms in literature. Our finding could be useful in future finer-grained investigation of PTSD mechanism within race and in the development of new treatments for PTSD.
报告人简介：
Chancellor’s Professor, Department of Statistics, University of California Irvine
Ph.D., Statistics, the Pennsylvania State University
Qu’s research focuses on solving fundamental issues regarding structured and unstructured large-scale data, and developing cutting-edge statistical methods and theory in machine learning and algorithms on personalized medicine, text mining, recommender systems, medical imaging data and network data analyses for complex heterogeneous data. The newly developed methods are able to extract essential and relevant information from large volume high-dimensional data. Her research has impacts in many fields such as biomedical studies, genomic research, public health research, social and political sciences.
Before she joins the UC Irvine, Dr. Qu is Data Science Founder Professor of Statistics, and the Director of the Illinois Statistics Office at the University of Illinois at Urbana-Champaign. She was awarded as Brad and Karen Smith Professorial Scholar by the College of LAS at UIUC, a recipient of the NSF Career award in 2004-2009, and is a Fellow of the Institute of Mathematical Statistics and a Fellow of the American Statistical Association.