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CLC number: Q39; O213

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Received: 2008-12-16

Revision Accepted: 2009-07-08

Crosschecked: 2009-09-08

Cited: 2

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE B 2009 Vol.10 No.10 P.721-730


Power analysis of principal components regression in genetic association studies

Author(s):  Yan-feng SHEN, Jun ZHU

Affiliation(s):  Department of Mathematics, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   jzhu@zju.edu.cn

Key Words:  Complex trait, Association study, Principal components, Power

Yan-feng SHEN, Jun ZHU. Power analysis of principal components regression in genetic association studies[J]. Journal of Zhejiang University Science B, 2009, 10(10): 721-730.

@article{title="Power analysis of principal components regression in genetic association studies",
author="Yan-feng SHEN, Jun ZHU",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Power analysis of principal components regression in genetic association studies
%A Yan-feng SHEN
%A Jun ZHU
%J Journal of Zhejiang University SCIENCE B
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%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0830866

T1 - Power analysis of principal components regression in genetic association studies
A1 - Yan-feng SHEN
A1 - Jun ZHU
J0 - Journal of Zhejiang University Science B
VL - 10
IS - 10
SP - 721
EP - 730
%@ 1673-1581
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0830866

Association analysis provides an opportunity to find genetic variants underlying complex traits. A principal components regression (PCR)-based approach was shown to outperform some competing approaches. However, a limitation of this method is that the principal components (PCs) selected from single nucleotide polymorphisms (SNPs) may be unrelated to the phenotype. In this article, we investigate the theoretical properties of such a method in more detail. We first derive the exact power function of the test based on PCR, and hence clarify the relationship between the test power and the degrees of freedom (DF). Next, we extend the PCR test to a general weighted PCs test, which provides a unified framework for understanding the properties of some related statistics. We then compare the performance of these tests. We also introduce several data-driven adaptive alternatives to overcome difficulties in the PCR approach. Finally, we illustrate our results using simulations based on real genotype data. Simulation study shows the risk of using the unsupervised rule to determine the number of PCs, and demonstrates that there is no single uniformly powerful method for detecting genetic variants.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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