Detecting genetic interactions in complex diseases

Speaker:	Dr. Xiang WAN
		Department of Electronic and Computer Engineering
		Hong Kong University of Science and Technology

Title:		"Detecting genetic interactions in complex diseases"

Date:		Thursday, 2 December 2010

Time:		2:00pm - 3:00pm

Venue:		Lecture Theatre F (near lifts 25/26), HKUST

Abstract:

Genetic interactions have long been recognized as fundamentally important
to understand genetic causes of complex diseases. The task of identifying
genetic interactions in large-scale studies often takes months to
complete, as it is computationally and methodologically challenging. In
this talk, I will first review the existing methods and discuss their pros
and cons. After that, I will present a fast approach to detecting genetic
interactions in complex diseases. It is a two-stage (screening and
testing) search method. In the screening stage, we use a non-iterative
method to approximate the interaction effects of all SNP pairs and select
those passing the specified threshold. In the testing stage, we further
use the classical likelihood ratio test to measure the interaction effects
of the selected SNP pairs. The experiments on the large-scale data sets
indicate that the two-stage search method can identify interaction
patterns much faster than most currently available methods. The
applications on the type 1 diabetes data set and the rheumatoid arthritis
data set draw some interesting insights.


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Biography:

Xiang Wan earned a Master of Science and a Ph.D. in computing science from
the University of Alberta, Canada. He had previously worked as a
post-doctoral fellow in Bioinformatics Lab at the University of British
Columbia in 2007. He is now a research associate of the ECE department at
the Hong Kong University of Science and Technology. His research focuses
on Bioinformatics, with an emphasis on detection of genetic patterns in
complex diseases using statistics and heuristic search methodology. His
work in this area has been published in a variety of academic journals,
including American Journal of Human Genetics, Nature Genetics,
Bioinformatics, BMC Bioinformatics, IEEE/ACM Transactions on Computational
Biology and Bioinformatics, Neuroinformatics, among others.