Time:10:00-11:00am, Mar. 27th, 2019, Wednesday
Venue: RoomA1514, Science Building, North Zhongshan Road Campus
Spreker: tenure track Associate Professor Mengyun Wu, School of Statistics and Management, Shanghai University of Finance and Economics
Abstract:For the etiology, progression, and treatment of complex diseases, gene-environment (G-E) interactions have important implications beyond the main G and E effects. G-E interaction analysis can be more challenging with the higher dimensionality and need for accommodating the “main effects, interactions” hierarchy. In the recent literature, an array of novel methods, many of which are based on the penalization technique, have been developed. In most of these studies, however, the structures of G measurements, for example the adjacency structure of SNPs (attributable to their physical adjacency on the chromosomes) and network structure of gene expressions (attributable to their coordinated biological functions and correlated measurements), have not been well accommodated. In this study, we develop the structured G-E interaction analysis, where such structures are accommodated using penalization for both the main G effects and interactions. Penalization is also applied for regularized estimation and selection. The proposed structured interaction analysis can be effectively realized. It is shown to have the consistency properties under high dimensional settings. Simulations and the analysis of GENEVA diabetes data with SNP measurements and TCGA melanoma data with gene expression measurements demonstrate its competitive practical performance.
Speaker’s Bio：吴梦云，上海财经大学统计与管理学院副教授。2013年获得中山大学概率论与数理统计博士学位，并于2016年8月至2018年7月在耶鲁大学生物统计系进行博士后研究。主要研究方向为高维数据变量选择、网络模型及整合分析等。目前，已在The Annals of Applied Statistics、Statistics in Medicine、Briefings in Bioinformatics、Genetic Epidemiology、Genomics等期刊发表多篇学术论文。入选上海市晨光计划，主持国家自然科学青年基金项目，以及全国统计科学研究重大项目。
