Improving feedlot performance, carcase weight and quality is a primary goal of the beef industry worldwide. Here, we used data from 3408 Australian Angus steers from seven years of birth (YOB) cohorts (2011–2017) with a minimal level of sire linkage and that were genotyped for 45,152 SNPs. Phenotypic records included two feedlot and five carcase traits, namely average daily gain (ADG), average daily dry matter intake (DMI), carcase weight (CWT), carcase eye muscle area (EMA), carcase Meat Standard Australia marbling score (MBL), carcase ossification score (OSS) and carcase subcutaneous rib fat depth (RIB). Using a 7-way cross-validation based on YOB cohorts, we tested the quality of genomic predictions using the linear regression (LR) method compared to the traditional method (Pearson’s correlation between the genomic estimated breeding value (GEBV) and its associated adjusted phenotype divided by the square root of heritability); explored the factors, such as heritability, validation cohort, and phenotype that affect estimates of accuracy, bias, and dispersion calculated with the LR method; and suggested a novel interpretation for translating differences in accuracy into phenotypic differences, based on GEBV quartiles (Q1Q4). Heritability (h2) estimates were generally moderate to high (from 0.29 for ADG to 0.53 for CWT). We found a strong correlation (0.73, P-value < 0.001) between accuracies using the traditional method and those using the LR method, although the LR method was less affected by random variation within and across years and showed a better ability to discriminate between extreme GEBV quartiles. We confirmed that bias of GEBV was not significantly affected by h2, validation cohort or trait. Similarly, validation cohort was not a significant source of variation for any of the GEBV quality metrics. Finally, we observed that the phenotypic differences were larger for higher accuracies. Our estimates of h2 and GEBV quality metrics suggest a potential for accurate genomic selection of Australian Angus for feedlot performance and carcase traits. In addition, the Q1Q4 measure presented here easily translates into possible gains of genomic selection in terms of phenotypic differences and thus provides a more tangible output for commercial beef cattle producers.

中文翻译：

---

澳大利亚安格斯阉牛饲养场和胴体性状基因组预测的偏差、分散和准确性

提高饲养场性能、胴体重量和质量是全球牛肉行业的主要目标。在这里，我们使用了来自 7 年出生 (YOB) 队列（2011-2017 年）的 3408 只澳大利亚安格斯公牛的数据，它们的父系连锁水平最低，并且对 45,152 个 SNP 进行了基因分型。表型记录包括两个饲养场和五个胴体特征，即平均日增重 (ADG)、平均每日干物质摄入量 (DMI)、胴体重量 (CWT)、胴体眼肌面积 (EMA)、胴体肉类标准澳大利亚大理石花纹评分 (MBL) , 胴体骨化评分 (OSS) 和胴体皮下肋骨脂肪深度 (RIB)。使用基于 YOB 队列的 7 向交叉验证，我们使用线性回归 (LR) 方法与传统方法（基因组估计育种值 (GEBV) 与其相关调整表型之间的 Pearson 相关性除以遗传力的平方根）测试了基因组预测的质量；探索了影响使用 LR 方法计算的准确性、偏差和离散度估计值的因素，例如遗传力、验证队列和表型；并基于 GEBV 四分位数 (Q1Q4) 提出了一种将准确性差异转化为表型差异的新解释。遗传力 (h2) 估计值通常为中到高（从 ADG 的 0.29 到 CWT 的 0.53）。我们发现使用传统方法的准确度与使用 LR 方法的准确度之间存在很强的相关性（0.73，P 值 < 0.001），尽管 LR 方法受年内和年间随机变化的影响较小，并且显示出更好的区分极端 GEBV 四分位数的能力。我们确认 GEBV 的偏差不受 h2、验证队列或特征的显着影响。同样，验证队列不是任何 GEBV 质量指标的重要变异来源。最后，我们观察到表型差异越大，准确度越高。我们对 h2 和 GEBV 质量指标的估计表明，澳大利亚安格斯有可能针对饲养场性能和胴体特征进行准确的基因组选择。此外，此处介绍的 Q1Q4 措施很容易转化为基因组选择在表型差异方面的可能收益，从而为商业肉牛生产商提供更切实的产出。