Department of Agriculture, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, T6G 2P5, Canada.
J Anim Sci. 2011 Nov;89(11):3353-61. doi: 10.2527/jas.2010-3361. Epub 2011 Jun 3.
The benefit of using genomic breeding values (GEBV) in predicting ADG, DMI, and residual feed intake for an admixed population was investigated. Phenotypic data consisting of individual daily feed intake measurements for 721 beef cattle steers tested over 5 yr was available for analysis. The animals used were an admixed population of spring-born steers, progeny of a cross between 3 sire breeds and a composite dam line. Training and validation data sets were defined by randomly splitting the data into training and testing data sets based on sire family so that there was no overlap of sires in the 2 sets. The random split was replicated to obtain 5 separate data sets. Two methods (BayesB and random regression BLUP) were used to estimate marker effects and to define marker panels and ultimately the GEBV. The accuracy of prediction (the correlation between the phenotypes and GEBV) was compared between SNP panels. Accuracy for all traits was low, ranging from 0.223 to 0.479 for marker panels with 200 SNP, and 0.114 to 0.246 for marker panels with 37,959 SNP, depending on the genomic selection method used. This was less than accuracies observed for polygenic EBV accuracies, which ranged from 0.504 to 0.602. The results obtained from this study demonstrate that the utility of genetic markers for genomic prediction of residual feed intake in beef cattle may be suboptimal. Differences in accuracy were observed between sire breeds when the random regression BLUP method was used, which may imply that the correlations obtained by this method were confounded by the ability of the selected SNP to trace breed differences. This may also suggest that prediction equations derived from such an admixed population may be useful only in populations of similar composition. Given the sample size used in this study, there is a need for increased feed intake testing if substantially greater accuracies are to be achieved.
利用基因组育种值（GEBV）预测杂种群体的 ADG、DMI 和剩余采食量的优势进行了研究。分析中使用了 721 头肉牛公牛在 5 年内进行的个体每日采食量测量的表型数据。所使用的动物是春季出生的公牛的杂种群体，是 3 个父本品种杂交和一个综合母本系的后代。根据父本家族将数据随机分为训练和测试数据集来定义训练和验证数据集，因此 2 个数据集之间没有父本重叠。随机分割重复了 5 次，以获得 5 个独立的数据集。使用两种方法（BayesB 和随机回归 BLUP）估计标记效应并定义标记面板，最终定义 GEBV。比较了 SNP 面板之间预测的准确性（表型与 GEBV 之间的相关性）。对于具有 200 个 SNP 的标记面板，所有性状的准确性都较低，范围为 0.223 到 0.479，对于具有 37,959 个 SNP 的标记面板，准确性范围为 0.114 到 0.246，这取决于所使用的基因组选择方法。这低于多基因 EBV 准确性的准确性，范围为 0.504 到 0.602。本研究的结果表明，遗传标记用于预测肉牛剩余采食量的基因组可能不是最优的。当使用随机回归 BLUP 方法时，观察到父本品种之间的准确性存在差异，这可能意味着该方法获得的相关性受到所选 SNP 追踪品种差异的能力的干扰。这也可能表明，从这种杂种群体中得出的预测方程可能仅在类似组成的群体中有用。考虑到本研究中使用的样本量，如果要实现更高的准确性，需要增加采食量测试。
