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Bioinformatics Advance Access published online on October 30, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn558
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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Bayesian Robust Analysis for Genetic Architecture of Quantitative Traits

Runqing Yang 1,2, Xin Wang 1, Jian Li 2 and Hongwen Deng 2,*

1School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai 200240, People's Republic of China.
2School of Medicine, University of Missouri – Kansas City, Kansas City, MO 64108, USA.

*To whom correspondence should be addressed. Hongwen Deng


  Abstract

Motivation: In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may affect the accuracy of QTL detection and lead to detection of spurious QTLs. To improve the robustness of QTL mapping methods, we replaced the normal distribution for residuals in multiple interacting QTL models with the normal/independent distributions that are a class of symmetric and long-tailed distributions and are able to accommodate residual outliers. Subsequently, we developed a Bayesian robust analysis strategy for dissecting genetic architecture of quantitative traits and for mapping genome-wide interacting QTLs in line crosses.

Results: Through computer simulations, we showed that our strategy had a similar power for QTL detection compared to traditional methods assuming normal-distributed traits, but had a substantially increased power for non-normal phenotypes. When this strategy was applied to a group of traits associated with physical-chemical characteristics and quality in rice, more main and epistatic QTLs were detected than traditional Bayesian model analyses under the normal assumption

Associate Editor: Dr. Alex Bateman

The two authors have equally contributed.

Received on July 14, 2008; revised on October 11, 2008; accepted on October 24, 2008

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