[Bayesian methods for genomic breeding value estimation].
Estimation of genomic breeding values is the key step in genomic selection. The successful application of genomic selection depends on the accuracy of genomic estimated breeding values, which is mostly determined by the estimation method. Bayes-type and BLUP-type methods are the two main methods which have been widely studied and used. Here, we systematically introduce the currently proposed Bayesian methods, and summarize their effectiveness and improvements. Results from both simulated and real data showed that the accuracies of Bayesian methods are higher than those of BLUP methods, especially for the traits which are influenced by QTL with large effect. Because the theories and computation of Bayesian methods are relatively complicated, their use in practical breeding is less common than BLUP methods. However, with the development of fast algorithms and the improvement of computer hardware, the computational problem of Bayesian methods is expected to be solved. In addition, further studies on the genetic architecture of traits will provide Bayesian methods more accurate prior information, which will make their advantage in accuracy of genomic estimated breeding values more prominent. Therefore, the application of Bayesian methods will be more extensive.
1. Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei 230031, China; 2. College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Chonglong
C
Wang
College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Xiangdong
X
Ding
College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Jianfeng
J
Liu
College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China.
Zongjun
Z
Yin
College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Qin
Q
Zhang
D001499:Bayes Theorem; D001947:Breeding / Q000379:methods*; D023281:Genomics / Q000379:methods*; D008957:Models, Genetic