International Livestock Research Institute, Box 30709-01001 Nairobi, Kenya; Scotland's Rural College, Easter Bush, Midlothian, EH25 9RG, United Kingdom.
International Livestock Research Institute, Box 30709-01001 Nairobi, Kenya.
J Dairy Sci. 2021 Nov;104(11):11779-11789. doi: 10.3168/jds.2020-20052. Epub 2021 Aug 5.
Selection based on genomic predictions has become the method of choice for genetic improvement in dairy cattle. This offers huge opportunity for developing countries with little or no pedigree data, and preliminary studies have shown promising results. The African Dairy Genetic Gains (ADGG) project initiated a digital system of dairy performance data collection, accompanied by genotyping in Tanzania in 2016. Currently, ADGG has the largest body of dairy performance data generated in East Africa from a smallholder dairy system. This study examines the use of genomic best linear unbiased prediction (GBLUP) and single-step (ss)GBLUP for the estimation of genetic parameters and accuracy of genomic prediction for daily milk yield and body weight in Tanzania. The estimates of heritability for daily milk yield from GBLUP and ssGBLUP were essentially the same, at 0.12 ± 0.03. The heritability estimates for daily milk yield averaged over the whole lactation from random regression model (RRM) GBLUP or ssGBLUP were 0.22 and 0.24, respectively. The heritability of body weight from GBLUP was 0.24 ± 04 but was 0.22 ± 04 from the ssGBLUP analysis. Accuracy of genomic prediction for milk yield from a forward validation was 0.57 for GBLUP based on fixed regression model or 0.55 from an RRM. Corresponding estimates from ssGBLUP were 0.59 and 0.53, respectively. Accuracy for body weight, however, was much higher at 0.83 from GBLUP and 0.77 for ssGBLUP. The moderate to high levels of accuracy of genomic prediction (0.53-0.83) obtained for milk yield and body weight indicate that selection on the basis of genomic prediction is feasible in smallholder dairy systems and most probably the only initial possible pathway to implementing sustained genetic improvement programs in such systems.
基于基因组预测的选择已成为奶牛遗传改良的首选方法。这为 pedigree 数据很少或没有的发展中国家提供了巨大的机会，初步研究结果也很有前景。非洲奶牛遗传增益（ADGG）项目于 2016 年在坦桑尼亚启动了一个奶牛性能数据收集的数字系统，并进行了基因分型。目前，ADGG 拥有来自东非小农系统的最大的奶牛性能数据集。本研究检验了基因组最佳线性无偏预测（GBLUP）和一步（ss）GBLUP 在估计遗传参数和预测坦桑尼亚日奶产量和体重的基因组预测准确性中的应用。GBLUP 和 ssGBLUP 对日奶产量的遗传力估计基本相同，为 0.12±0.03。随机回归模型（RRM）GBLUP 或 ssGBLUP 对日奶产量的整个泌乳期的遗传力估计平均值分别为 0.22 和 0.24。GBLUP 对体重的遗传力为 0.24±04，但 ssGBLUP 分析为 0.22±04。基于固定回归模型的 GBLUP 的正向验证的牛奶产量的基因组预测准确性为 0.57，基于 RRM 的为 0.55。ssGBLUP 的相应估计值分别为 0.59 和 0.53。然而，体重的准确性要高得多，GBLUP 为 0.83，ssGBLUP 为 0.77。获得的奶产量和体重的基因组预测准确性（0.53-0.83）处于中等至高水平，这表明在小农奶牛系统中，基于基因组预测的选择是可行的，很可能是在这些系统中实施可持续遗传改良计划的唯一初始可能途径。
