Received date: 2013-06-07
Revised date: 2013-12-12
Online published: 2014-01-25
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.
Chonglong Wang, Xiangdong Ding, Jianfeng Liu, Zongjun Yin, Qin Zhang . Bayesian methods for genomic breeding value estimation[J]. Hereditas(Beijing), 2014 , 36(2) : 111 -118 . DOI: 10.3724/SP.J.1005.2014.0111
[1] Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001, 157(4): 1819–1829. <\p>
[2] Solberg TR, Sonesson AK, Woolliams JA, Meuwissen THE. Reducing dimensionality for prediction of ge-nome-wide breeding values. Genet Sel Evol, 2009, 41(1): 29. <\p>
[3] VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci, 2008, 91(11): 4414–4423. <\p>
[4] Zhang Z, Liu J, Ding X, Bijma P, de Koning DJ, Zhang Q. Best linear unbiased prediction of genomic breeding val-ues using a trait-specific marker-derived relationship ma-trix. PLoS ONE, 2010, 5(9): e12648. <\p>
[5] Habier D, Fernando RL, Kizilkaya K, Garrick DJ. Exten-sion of the Bayesian alphabet for genomic selection. BMC Bioinformatics, 2011, 12(1): 186. <\p>
[6] Verbyla KL, Hayes BJ, Bowman PJ, Goddard ME. Accu-racy of genomic selection using stochastic search variable selection in Australian Holstein Friesian dairy cattle. Genet Res (Camb), 2009, 91(5): 307–311. <\p>
[7] Yi N, Xu S. Bayesian LASSO for quantitative trait loci mapping. Genetics, 2008, 179(2): 1045–1055. <\p>
[8] Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol, 2005, 67(2): 301–320. <\p>
[9] Gianola D, Fernando RL, Stella A. Genomic-assisted pre-diction of genetic value with semiparametric procedures. Genetics, 2006, 173(3): 1761–1776. <\p>
[10] Long N, Gianola D, Rosa GJM, Weigel KA, Avendano S. Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. J Anim Breed Genet, 2007, 124(6): 377–389. <\p>
[11] Sun XC, Habier D, Fernando RL, Garrick DJ, Dekkers JCM. Genomic breeding value prediction and QTL map-ping of QTLMAS2010 data using Bayesian Methods. BMC Proceedings, 2011, 5(Suppl. 3): S13. <\p>
[12] 刘小磊, 杨松柏, Max F Rothschild, ZHANG Zhi-Wu, 樊斌. 利用紧缩线性模型和贝叶斯模型对猪总产仔数和产活仔数性状的全基因组关联研究. 遗传, 2012, 34(10): 1261–1270. <\p>
[13] Fernando RL, Habier D, Stricker C, Dekkers JCM, Totir LR. Genomic selection. Acta Agric Scand A Anim Sci, 2007, 57(4): 192–195. <\p>
[14] Gianola D, de los Campos G, Hill WG, Manfredi E, Fer-nando R. Additive genetic variability and the Bayesian alphabet. Genetics, 2009, 183(1): 347–363. <\p>
[15] Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Series B Stat Methodol, 1996, 58(1): 267–288. <\p>
[16] Yuan M, Lin Y. Efficient empirical Bayes variable selec-tion and estimation in linear models. J Am Stat Assoc, 2005, 100(472): 1215–1225. <\p>
[17] Park T, Casella G. The Bayesian Lasso. Technical report. Gainesville, FL: University of Florida, 2008. <\p>
[18] Usai MG, Goddard ME, Hayes BJ. LASSO with cross- validation for genomic selection. Genet Res (Camb), 2009, 91(6): 427–436. <\p>
[19] Lund MS, Sahana G, de Koning DJ, Su G, Carlborg O. Comparison of analyses of the QTLMAS XII common dataset. I: Genomic selection. BMC Proceedings, 2009, 3(Suppl. 1): S1. <\p>
[20] Szydlowski M, Paczyńska P. QTLMAS 2010: simulated dataset. BMC Proceedings, 2011, 5(Suppl. 3):S3. <\p>
[21] Bastiaansen JWM, Bink MCAM, Coster A, Maliepaard C, Calus MPL. Comparison of analyses of the QTLMAS XIII common dataset. I: genomic selection. BMC Proceedings, 2010, 4(Suppl. 1): S1. <\p>
[22] Pszczola M, Strabel T, Wolc A, Mucha S, Szydlowski M. Comparison of analyses of the QTLMAS XIV common dataset. I: genomic selection. BMC Proceedings, 2011, 5(Suppl. 3): S1. <\p>
[23] Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA. The impact of genetic architecture on Genome-wide evaluation methods. Genetics, 2010
/
| 〈 |
|
〉 |