Instituto Nacional de Investigación Agropecuaria, INIA Uruguay, Canelones, Uruguay.
J Anim Breed Genet. 2023 Sep;140(5):508-518. doi: 10.1111/jbg.12775. Epub 2023 Apr 25.
Selection for feed efficiency is the goal for many genetic breeding programs in beef cattle. Residual feed intake has been included in genetic evaluations to reduce feed intake without compromising performance traits as liveweight, body gain or carcass traits. However, measuring feed intake is expensive, and only a small percentage of selection candidates are phenotyped. Genomic selection has become a very important tool to achieve effective genetic progress in these traits. Another effective strategy has been the implementation of multi-trait prediction using easily recordable predictor traits on both reference animals and candidates without phenotypes, and this could be another inexpensive way to increase accuracy. The objective of this work was to analyse and compare the prediction ability of two alternative different approaches to predict GEBVs for RFI. The population of inference was Hereford bulls in Uruguay that were genotyped candidates for to selection. The first model was the conventional univariate model for RFI and the second model was a multi-trait model which included a predictor trait (weaning weight, WW), in addition to the traits used in the first one (dry matter intake, metabolic mid test weight, average daily gain and ultrasound back fat) (DMI, MWT, ADG, UBF, respectively). GEBVs from the multi-trait model were combined using selection index theory to derive RFI values. All analyses were performed using ssGBLUP procedure. The prediction ability of both models was tested using two validation strategies (30 different replicates of random groups of animals and validation across 9 different feed intake tests). The prediction quality was assessed by the following parameters: bias, dispersion, ratio of accuracies and the relative increase in accuracy by adding phenotypic information. All parameters showed that the univariate model outperforms the multi-trait model, regardless of the validation strategy considered. These results indicate that including WW as a proxy trait in a multi-trait analysis does not improve the prediction ability when all animals to be predicted are genotyped.
选择饲料效率是许多肉牛遗传育种计划的目标。残留饲料摄入量已被纳入遗传评估中，以减少饲料摄入量，而不影响性能特征，如活重、体增重或胴体特征。然而，测量饲料摄入量是昂贵的，只有一小部分选择候选者被表型化。基因组选择已成为实现这些特征有效遗传进展的非常重要的工具。另一个有效的策略是使用参考动物和候选者上易于记录的预测因子性状来实施多性状预测，而无需表型，这可能是另一种增加准确性的廉价方法。本工作的目的是分析和比较两种替代方法预测 RFI 的 GEBV 的预测能力。推断群体是乌拉圭的赫里福德公牛，这些公牛是为选择而进行基因分型的候选者。第一个模型是 RFI 的传统单变量模型，第二个模型是多性状模型，除了第一个模型（干物质摄入量、代谢中期体重、平均日增重和超声背膘）使用的性状外，还包括一个预测性状（断奶体重，WW）（DMI、MWT、ADG、UBF 分别）。使用选择指数理论将多性状模型的 GEBVs 组合起来，以得出 RFI 值。所有分析均使用 ssGBLUP 程序进行。使用两种验证策略（30 种不同的动物随机组的重复和跨 9 种不同饲料摄入量测试的验证）测试了两种模型的预测能力。通过以下参数评估预测质量：偏差、分散、准确度比和添加表型信息的准确度相对增加。所有参数表明，无论考虑哪种验证策略，单变量模型的表现都优于多性状模型。这些结果表明，当所有要预测的动物都进行基因分型时，将 WW 作为多性状分析中的代理性状并不能提高预测能力。
