Methodology article

Multitrait analysis of quantitative trait loci using Bayesian composite space approach

Ming Fang¹ , Dan Jiang² , Li Jun Pu¹ , Hui Jiang Gao^3,4 , Peng Ji⁵ , Hong Yi Wang⁵ and Run Qing Yang⁶

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

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

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

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

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

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

author email corresponding author email

BMC Genetics 2008, 9:48doi: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.