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

A Variable‐Sized Sliding‐Window Approach for Genetic Association Studies via Principal Component... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Human Genetics Wiley

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

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References (37)

Publisher
Wiley
Copyright
Copyright © 2009 Wiley Subscription Services
ISSN
0003-4800
eISSN
1469-1809
DOI
10.1111/j.1469-1809.2009.00543.x
pmid
19735491
Publisher site
See Article on Publisher Site

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.

Journal

Annals of Human GeneticsWiley

Published: Jan 1, 2009

Keywords: ; ; ;

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