A Variable-sized Sliding-window Approach for Genetic Association Studies Via Principal Component Analysis

Overview

Journal Ann Hum Genet

Date 2009 Sep 9

PMID 19735491

Citations 16

Authors

Rui Tang

Tao Feng

Qiuying Sha

Shuanglin Zhang

Affiliations

Soon will be listed here.

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.

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