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A Variable‐Sized Sliding‐Window Approach for Genetic Association Studies via Principal Component Analysis
Author(s) -
Tang Rui,
Feng Tao,
Sha Qiuying,
Zhang Shuanglin
Publication year - 2009
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/j.1469-1809.2009.00543.x
Subject(s) - principal component analysis , sliding window protocol , computer science , window (computing) , variable (mathematics) , markov chain , phaser , bisection method , component (thermodynamics) , genetic algorithm , data mining , algorithm , artificial intelligence , mathematics , machine learning , engineering , mathematical analysis , physics , electrical engineering , thermodynamics , operating system
Summary 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.