基因组育种值估计的贝叶斯方法研究进展
收稿日期: 2013-06-07
修回日期: 2013-12-12
网络出版日期: 2014-01-25
基金资助
农业部948计划(编号：2011-G2A), 教育部博士学科点专项科研基金项目(编号：20110008110001), 国家高技术研究发展计划(863计划)项目(编号：2011AA100302), 国家自然科学基金项目(编号：31371258, 31171200, 31272418), 国家农业科技成果转化资金项目(编号：2011GB2C300017), 国家生猪产业技术体系(编号：CARS-36), 科技富民强县专项行动计划, 黎平黄牛品种资源保护与开发利用研究(编号：黔农育专字(2010)016号), 安徽省现代农业项目, 安徽省生猪产业技术体系, 安徽省农业科学院成果推广项目(编号：13E0403), 安徽省农业科学院院长杰出青年创新基金项目(编号：13B0405)和安徽省农业科学院科技创新团队建设项目(编号：13C0405)资助
Bayesian methods for genomic breeding value estimation
Received date: 2013-06-07
Revised date: 2013-12-12
Online published: 2014-01-25
基因组育种值估计是基因组选择的重要环节, 基因组育种值的准确性是基因组选择成功应用的关键, 而其准确性在很大程度上取决于估计方法。目前研究和应用最多的基因组育种值估计方法是贝叶斯(Bayes)和最佳线性无偏预测(BLUP)两大类方法。文章系统介绍了目前已提出的各种Bayes方法, 并总结了该类方法的估计效果和各方面的改进。模拟数据和实际数据研究结果都表明, Bayes类方法估计基因组育种值的准确性优于BLUP类方法, 特别对于存在较大效应QTL的性状其优势更明显。由于Bayes方法的理论和计算过程相对复杂, 目前其在实际育种中的运用不如BLUP类方法普遍, 但随着快速算法的开发和计算机硬件的改进, 计算问题有望得到解决; 另外, 随着对基因组和性状遗传结构研究的深入开展, 能为Bayes方法提供更为准确的先验信息, 从而使Bayes方法估计基因组育种值准确性的优势更加突出, 应用将会更加广泛。
王重龙, 丁向东, 刘剑锋, 殷宗俊, 张勤 . 基因组育种值估计的贝叶斯方法研究进展[J]. 遗传, 2014 , 36(2) : 111 -118 . DOI: 10.3724/SP.J.1005.2014.0111
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] 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
/
| 〈 |
|
〉 |
www.chinagene.cn
备案号：京ICP备09063187号-4
总访问:,今日访问:,当前在线:
