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This article is part of the supplement: Proceedings of the 15th European workshop on QTL mapping and marker assisted selection (QTLMAS)

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Comparison of five methods for genomic breeding value estimation for the common dataset of the 15th QTL-MAS Workshop

Chong-Long Wang12, Pei-Pei Ma1, Zhe Zhang1, Xiang-Dong Ding1, Jian-Feng Liu1, Wei-Xuan Fu1, Zi-Qing Weng1 and Qin Zhang1*

Author affiliations

1 Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China

2 Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei 230031, China

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Citation and License

BMC Proceedings 2012, 6(Suppl 2):S13  doi:10.1186/1753-6561-6-S2-S13

Published: 21 May 2012

Abstract

Background

Genomic breeding value estimation is the key step in genomic selection. Among many approaches, BLUP methods and Bayesian methods are most commonly used for estimating genomic breeding values. Here, we applied two BLUP methods, TABLUP and GBLUP, and three Bayesian methods, BayesA, BayesB and BayesCπ, to the common dataset provided by the 15th QTL-MAS Workshop to evaluate and compare their predictive performances.

Results

For the 1000 progenies without phenotypic values, the correlations between GEBVs by different methods ranged from 0.812 (GBLUP and BayesCπ) to 0.997 (TABLUP and BayesB). The accuracies of GEBVs (measured as correlations between true breeding values (TBVs) and GEBVs) were from 0.774 (GBLUP) to 0.938 (BayesCπ) and the biases of GEBVs (measure as regressions of TBVs on GEBVs) were from 1.033 (TABLUP) to 1.648 (GBLUP). The three Bayesian methods and TABLUP had similar accuracy and bias.

Conclusions

BayesA, BayesB, BayesCπ and TABLUP performed similarly and satisfactorily and remarkably outperformed GBLUP for genomic breeding value estimation in this dataset. TABLUP is a promising method for genomic breeding value estimation because of its easy computation of reliabilities of GEBVs and its easy extension to real life conditions such as multiple traits and consideration of individuals without genotypes.