Accuracy of genomic breeding values in multi-breed dairy cattle populations.
Accuracy of genomic breeding values in multi-breed dairy cattle populations.
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DOI：
10.1186/1297-9686-41-51
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发表时间：
2009-11-24
期刊：
影响因子：
--
通讯作者：
Goddard ME
中科院分区：
文献类型：
--
作者：
Hayes BJ;Bowman PJ;Chamberlain AC;Verbyla K;Goddard ME
基因组选择实验的两个关键发现是：1）所用的参考群体必须非常大，以随后预测准确的基因组估计育种值（GEBV），和2）预测方程在一个品种不能预测准确的GEBV时，适用于其他品种。这两个发现都是一个问题的品种，在参考人群中的个人数量是有限的。多品种参考群体是一个潜在的解决方案，在这里，我们调查的准确性GEBV在荷斯坦奶牛和泽西奶牛时，参考群体是单品种或多品种。的准确性，获得作为一个函数的元素的反系数矩阵和实现的准确性GEBV。最佳线性无偏预测与多品种基因组关系矩阵（GBLUP）和两个贝叶斯方法（BAYESA和BAYES_SSVS），估计个人SNP的影响，分别预测GEBV为400和77年轻的荷斯坦和泽西公牛，从参考群体的781和287荷斯坦和泽西公牛，分别。使用了39，048个SNP标记的基因型。参考群体中的表型是生产性状的去回归育种值。对于GBLUP方法，将从系数矩阵的逆的对角线计算的预期精度与实现的精度进行比较。当使用GBLUP时，从逆系数矩阵的元素的函数的预期准确度与从单品种群体中的GEBV和EBV之间的相关性计算的实现准确度相当好地一致，但在多品种群体中不一致。当使用贝叶斯方法时，当使用多品种参考群体时，GEBV的实现准确度比使用纯品种参考时高13%。然而，没有一致的增加，在各性状的准确性。使用基因组关系矩阵预测基因组育种值是实施基因组选择的有吸引力的方法，因为可以容易地导出GEBV的预期精度。然而，在多品种群体中，贝叶斯方法对某些性状具有更高的准确性。最后，多品种参考群体将是精细定位QTL的宝贵资源。
Two key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. Both findings are a problem for breeds where the number of individuals in the reference population is limited. A multi-breed reference population is a potential solution, and here we investigate the accuracies of GEBV in Holstein dairy cattle and Jersey dairy cattle when the reference population is single breed or multi-breed. The accuracies were obtained both as a function of elements of the inverse coefficient matrix and from the realised accuracies of GEBV. Best linear unbiased prediction with a multi-breed genomic relationship matrix (GBLUP) and two Bayesian methods (BAYESA and BAYES_SSVS) which estimate individual SNP effects were used to predict GEBV for 400 and 77 young Holstein and Jersey bulls respectively, from a reference population of 781 and 287 Holstein and Jersey bulls, respectively. Genotypes of 39,048 SNP markers were used. Phenotypes in the reference population were de-regressed breeding values for production traits. For the GBLUP method, expected accuracies calculated from the diagonal of the inverse of coefficient matrix were compared to realised accuracies. When GBLUP was used, expected accuracies from a function of elements of the inverse coefficient matrix agreed reasonably well with realised accuracies calculated from the correlation between GEBV and EBV in single breed populations, but not in multi-breed populations. When the Bayesian methods were used, realised accuracies of GEBV were up to 13% higher when the multi-breed reference population was used than when a pure breed reference was used. However no consistent increase in accuracy across traits was obtained. Predicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived. However in multi-breed populations, Bayesian approaches give higher accuracies for some traits. Finally, multi-breed reference populations will be a valuable resource to fine map QTL.
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影响因子：
3.3
作者：
Habier, D.;Fernando, R. L.;Dekkers, J. C. M.
通讯作者：
Dekkers, J. C. M.
DOI：
10.1073/pnas.0308518100
发表时间：
2004-02-24
影响因子：
11.1
作者：
Grisart, B;Farnir, F;Georges, M
通讯作者：
Georges, M
影响因子：
1.5
作者：
Goddard, Mike
通讯作者：
Goddard, Mike
影响因子：
3.3
作者：
de Roos, A. P. W.;Hayes, B. J.;Goddard, M. E.
通讯作者：
Goddard, M. E.
影响因子：
1.5
作者：
Hayes, B. J.;Visscher, P. M.;Goddard, M. E.
通讯作者：
Goddard, M. E.