Vallée A, van Arendonk J A M, Bovenhuis H
Gènes Diffusion, 3595 route de Tournai, CS70023, 59501 Douai Cedex, France Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH, Wageningen, The Netherlands
Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH, Wageningen, The Netherlands.
J Anim Sci. 2014 Oct;92(10):4342-8. doi: 10.2527/jas.2014-8109. Epub 2014 Aug 22.
Charolais bulls are selected for their crossbreed performance when mated to Montbéliard or Holstein dams. To implement genomic prediction, one could build a reference population for each crossbred population independently. An alternative could be to combine both crossbred populations into a single reference population to increase size and accuracy of prediction. The objective of this study was to investigate the accuracy of genomic prediction by combining different crossbred populations. Three scenarios were considered: 1) using 1 crossbred population as reference to predict phenotype of animals from the same crossbred population, 2) combining the 2 crossbred populations into 1 reference to predict phenotype of animals from 1 crossbred population, and 3) using 1 crossbred population as reference to predict phenotype of animals from the other crossbred population. Traits studied were bone thinness, height, and muscular development. Phenotypes and 45,117 SNP genotypes were available for 1,764 Montbéliard × Charolais calves and 447 Holstein × Charolais calves. The population was randomly spilt into 10 subgroups, which were assigned to the validation one by one. To allow fair comparison between scenarios, size of the reference population was kept constant for all scenarios. Breeding values were estimated with BLUP and genomic BLUP. Accuracy of prediction was calculated as the correlation between the EBV and the phenotypic values of the calves in the validation divided by the square root of the heritability. Genomic BLUP showed higher accuracies (between 0.281 and 0.473) than BLUP (between 0.197 and 0.452). Accuracies tended to be highest when prediction was within 1 crossbred population, intermediate when populations were combined into the reference population, and lowest when prediction was across populations. Decrease in accuracy from a prediction within 1 population to a prediction across populations was more pronounced for bone thinness (-27%) and height (-29%) than for muscular development (-14%). Genetic correlation between the 2 crossbred populations was estimated using pedigree relationships. It was 0.70 for bone thinness, 0.80 for height, and 0.99 for muscular development. Genetic correlation indicates the expected gain in accuracy of prediction when combining different populations into 1 reference population. The larger the genetic correlation is, the larger the benefit is to combine populations for genomic prediction.
夏洛莱公牛与蒙贝利亚尔或荷斯坦母牛杂交时，会根据其杂交性能进行选择。为实施基因组预测，可以为每个杂交群体独立构建一个参考群体。另一种方法是将两个杂交群体合并为一个单一的参考群体，以增加预测的规模和准确性。本研究的目的是调查通过合并不同杂交群体进行基因组预测的准确性。考虑了三种情况：1）使用一个杂交群体作为参考来预测来自同一杂交群体的动物的表型，2）将两个杂交群体合并为一个参考群体来预测来自一个杂交群体的动物的表型，3）使用一个杂交群体作为参考来预测来自另一个杂交群体的动物的表型。研究的性状有骨瘦度、身高和肌肉发育。有1764头蒙贝利亚尔×夏洛莱犊牛和447头荷斯坦×夏洛莱犊牛的表型和45117个单核苷酸多态性（SNP）基因型数据。该群体被随机分成10个亚组，并逐一分配到验证组。为了在不同情况之间进行公平比较，所有情况下参考群体的规模保持不变。育种值用最佳线性无偏预测（BLUP）和基因组最佳线性无偏预测（GBLUP）进行估计。预测准确性通过验证组中犊牛的估计育种值（EBV）与表型值之间的相关性除以遗传力的平方根来计算。基因组最佳线性无偏预测显示出比最佳线性无偏预测更高的准确性（在0.281至0.473之间）（最佳线性无偏预测在0.197至0.452之间）。当在一个杂交群体内进行预测时，准确性往往最高，当群体合并到参考群体中时准确性居中，而当跨群体进行预测时准确性最低。从一个群体内预测到跨群体预测时，骨瘦度（-27%）和身高（-29%）的准确性下降比肌肉发育（-14%）更为明显。利用系谱关系估计了两个杂交群体之间的遗传相关性。骨瘦度的遗传相关性为0.70，身高为0.80，肌肉发育为0.99。遗传相关性表明将不同群体合并为一个参考群体时预测准确性的预期提高。遗传相关性越大，为基因组预测合并群体的益处就越大。
