A variable-sized sliding-window approach for genetic association studies via principal component analysis
Document Type
Article
Publication Date
11-1-2009
Abstract
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. © 2009 The Authors. Journal compilation © 2009 Blackwell Publishing Ltd/University College London.
Publication Title
Annals of Human Genetics
Recommended Citation
Tang, R.,
Feng, T.,
Sha, Q.,
&
Zhang, S.
(2009).
A variable-sized sliding-window approach for genetic association studies via principal component analysis.
Annals of Human Genetics,
73(6), 631-637.
http://doi.org/10.1111/j.1469-1809.2009.00543.x
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/11361
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