CSIRO, Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Rd., St Lucia, Brisbane, QLD, 4067, Australia.
CSIRO, Agriculture and Food, F.D. McMaster Laboratory, Chiswick, New England Highway, Armidale, NSW, 2350, Australia.
Genet Sel Evol. 2021 Sep 26;53(1):77. doi: 10.1186/s12711-021-00673-8.
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 (h) 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 h, 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 h 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.
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，我们使用了来自七个出生年份（YOB）队列（2011-2017 年）的 3408 头澳大利亚安格斯阉牛的数据，这些数据具有最低水平的父系联系，并对 45152 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性状，即平均日增重（ADG）、平均日干物质摄入量（DMI）、胴体重（CWT）、胴体眼肌面积（EMA）、澳大利亚肉品标准大理石花纹评分（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 种交叉验证方法，我们使用线性回归（LR）方法测试了基因组预测的质量，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皮尔逊相关系数除以遗传力的平方根）进行了比较；探索了遗传力、验证队列和表型等因素对使用 LR 方法计算的准确性、偏差和分散的估计值的影响；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（h）估计值通常为中等至高（从 ADG 的 0.29 到 CWT 的 0.53）。我们发现，使用传统方法和 LR 方法的准确性之间存在很强的相关性（0.73，P 值<0.001），尽管 LR 方法受年内和年际随机变异的影响较小，并且能够更好地区分极端 GEBV 四分位数。我们证实 GEBV 的偏差不受 h、验证队列或性状的显著影响。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，准确性越高，表型差异越大。
我们对 h 和 GEBV 质量指标的估计表明，对澳大利亚安格斯进行饲养场性能和胴体性状的基因组选择具有潜在的准确性。此外，本文提出的 Q1Q4 措施很容易转化为基于表型差异的基因组选择的可能收益，从而为商业肉牛生产者提供更切实的产出。
