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Open Access Highly Accessed Methodology article

Multitrait analysis of quantitative trait loci using Bayesian composite space approach

Ming Fang1*, Dan Jiang2, Li Jun Pu1, Hui Jiang Gao34, Peng Ji5, Hong Yi Wang5 and Run Qing Yang6

Author Affiliations

1 Life Science College, Heilongjiang August First Land Reclamation University, Daqing, 163319, PR China

2 College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, PR China

3 College of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, PR China

4 College Animal Science and Technology, China Agricultural University, Beijing, 100094, PR China

5 College of Plant Science and Technology, Heilongjiang August First Land Reclamation University, Daqing, 163319, PR China

6 School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, 201101, PR China

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BMC Genetics 2008, 9:48  doi:10.1186/1471-2156-9-48

Published: 18 July 2008

Abstract

Background

Multitrait analysis of quantitative trait loci can capture the maximum information of experiment. The maximum-likelihood approach and the least-square approach have been developed to jointly analyze multiple traits, but it is difficult for them to include multiple QTL simultaneously into one model.

Results

In this article, we have successfully extended Bayesian composite space approach, which is an efficient model selection method that can easily handle multiple QTL, to multitrait mapping of QTL. There are many statistical innovations of the proposed method compared with Bayesian single trait analysis. The first is that the parameters for all traits are updated jointly by vector or matrix; secondly, for QTL in the same interval that control different traits, the correlation between QTL genotypes is taken into account; thirdly, the information about the relationship of residual error between the traits is also made good use of. The superiority of the new method over separate analysis was demonstrated by both simulated and real data. The computing program was written in FORTRAN and it can be available for request.

Conclusion

The results suggest that the developed new method is more powerful than separate analysis.