A Variable-Sized Sliding-Window Approach for Genetic Association Studies via Principal Component Analysis


★ Corresponding author: Shuanglin Zhang, Ph.D., Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931. Tel: (906) 487-2146; Fax: (906) 487-3133; E-mail: shuzhang@mtu.edu


Recently with the rapid improvements in high-throughout genotyping techniques, researchers are facing the very challenging task of analysing large-scale genetic associations, especially at the whole-genome level, without an optimal solution. In this study, we propose a new approach for genetic association analysis that is based on a variable-sized sliding-window framework and employs principal component analysis to find the optimum window size. With the help of the bisection algorithm in window-size searching, our method is more computationally efficient than available approaches. We evaluate the performance of the proposed method by comparing it with two other methods—a single-marker method and a variable-length Markov chain method. We demonstrate that, in most cases, the proposed method out-performs the other two methods. Furthermore, since the proposed method is based on genotype data, it does not require any computationally intensive phasing program to account for uncertain haplotype phase.