This article is part of the supplement: Genetic Analysis Workshop 16
Detecting susceptibility genes for rheumatoid arthritis based on a novel sliding-window approach
1 Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA
2 Mathematical Sciences, Heilongjiang University, Harbin 150080, PR China
BMC Proceedings 2009, 3(Suppl 7):S14 doi:Published: 15 December 2009
With the recent rapid improvements in high-throughout genotyping techniques, researchers are facing a very challenging task of large-scale genetic association analysis, especially at the whole-genome level, without an optimal solution. In this study, we propose a new approach for genetic association analysis based on a variable-sized sliding-window framework. This approach employs principal component analysis to find the optimal window size. Using the bisection algorithm in window size searching, the proposed method tackles the exhaustive computation problem. It is more efficient and effective than currently available approaches. We conduct the genome-wide association study in Genetic Analysis Workshop 16 (GAW16) Problem 1 data using the proposed method. Our method successfully identified several susceptibility genes that have been reported by other researchers and additional candidate genes for follow-up studies.