A variable-sized sliding-window approach for genetic association studies via principal component analysis.
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.
Tang
Rui
R
Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA.
Feng
Tao
T
Sha
Qiuying
Q
Zhang
Shuanglin
S
D000465:Algorithms; D056726:Genetic Association Studies / Q000379:methods*; D005838:Genotype; D006801:Humans; D008390:Markov Chains; D025341:Principal Component Analysis