统计学系系列讲座之327期

时　间：2018年12月21日（星期五）10:00-11:00

地　点：史带楼301室

主持人：张新生 教授 复旦大学管理学院统计学系

主　题：Tree-based Rare Variants Analyses

主讲人：Professor Heping Zhang（张和平）

Department of Biostatistics, Yale School of Public Health. Yale University, USA.

简　介：Dr. Zhang published over 250 research articles and monographs in theory and applications of statistical methods and in several areas of biomedical research including epidemiology, genetics, child and women health, mental health, substance use, and reproductive medicine. He directed a training program in mental health research that was funded by the NIMH. He directs the Collaborative Center for Statistics in Science that coordinates the Reproductive Medicine Network to evaluate treatment effectiveness for infertility. He is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics. He was named the 2008 Myrto Lefokopoulou distinguished lecturer by Harvard School of Public Health and a Medallion Lecturer by the Institute of Mathematical Statistics. In 2011, he received the Royan International Award on Reproductive Health.

摘　要：

Since the development of next generation sequencing (NGS) technology, researchers have been extending their efforts on genome-wide association studies (GWAS) from common variants to rare variants to find the missing inheritance. Although various statistical methods have been proposed to analyze rare variants data, they generally face difficulties for complex disease models involving multiple genes. In this paper, we propose a tree-based method that adopts a non-parametric disease model and is capable of exploring gene-gene interactions. We found that our method outperforms the sequence kernel association test (SKAT) in most of our simulation scenarios, and by notable margins in some cases. By applying the tree-based method to the Study of Addiction: Genetics and Environment (SAGE) data, we successfully detected gene CTNNA2 and its 44 specific variants that increase the risk of alcoholism in women. This gene has not been detected in the SAGE data. Post hoc literature search also supports the role of CTNNA2 as a likely risk gene for alcohol addiction. This finding suggests that our tree-based method can be effective in dissecting genetic variants for complex diseases using rare variants data.

This is a joint work with Chi Song at Ohio State University.

 统计学系 

2018-12-13