报告题目: Detecting gene-gene interactions in genome-wide case-control studies 

报告人：余维川教授（香港科技大学） 

时 间：2011年4月6日（星期三）上午10：00 

地 点：行政楼2号会议室 

Abstract: 

Gene-gene interactions have long been recognized to be fundamentally important to understand genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this talk, we introduce a new method, named ‘BOolean Operation based Screening and Testing’(BOOST). To discover unknown gene-gene interactions that underlie complex diseases, BOOST allows examining all pair-wise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hours on a standard 3.0 GHz desktop with 4G memory running Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, while both data sets share a very similar hit region in the WTCCC report. BOOST has also identified many undiscovered interactions between genes in the major histocompatibility complex (MHC) region in the type 1 diabetes data set. In the coming era of large-scale interaction mapping in genome-wide case-control studies, our method can serve as a computationally and statistically useful tool. 

Biography: 

Weichuan Yu received his Ph.D. degree in Computer Vision and Image Analysis from University Kiel, Germany in 2001. He was a postdoctoral associate at Yale University from 2001 to 2004 and a research faculty member in the Center for Statistical Genomics and Proteomics at Yale University from 2004 to 2006. He has been an assistant professor in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology since August, 2006. 

He is interested in computational analysis problems with biological and medical applications. He has published papers on a variety of topics including bioinformatics, computational biology, biomedical imaging, signal processing, pattern recognition and computer vision. His long term goal is to develop mathematical, computational, and statistical methods to address challenges in biological and medical data analysis.