In this study, we aimed to assess the value of genotyping DNA pools as a strategy to generate accurate and cost-effective genomic estimated breeding values (GEBV) of sires in multi-sire mating systems. In order to do that, we used phenotypic records of 2,436 Australian Angus cattle from 174 sires, including yearling weight (YWT; N = 1,589 records), coat score (COAT; N = 2,026 records), and Meat Standards Australia marbling score (MARB; N = 1,304 records). Phenotypes were adjusted for fixed effects and age at measurement and pools of 2, 5, 10, 15, 20, and 25 animals were explored. Pools were created either by phenotype or at random. When pools were created at random, 10 replicates were examined to provide a measure of sampling variation. The relative accuracy of each pooling strategy was measured by the Pearson correlation coefficient between the sire’s GEBV with pooled progeny and the GEBV using individually genotyped progeny. Random pools allow the computation of sire GEBV that are, on average, moderately correlated (i.e., r > 0.5 at pool sizes [PS] ≤ 10) with those obtained without pooling. However, for pools assigned at random, the difference between the best and the worst relative accuracy obtained out of the 10 replicates was as high as 0.41 for YWT, 0.36 for COAT, and 0.61 for MARB. This uncertainty associated with the relative accuracy of GEBV makes randomly assigning animals to pools an unreliable approach. In contrast, pooling by phenotype allowed the estimation of sires’ GEBV with a relative accuracy ≥ 0.9 at PS < 10 for all three phenotypes. Moreover, even with larger PS, the lowest relative accuracy obtained was 0.88 (YWT, PS = 20). In agreement with results using simulated data, we conclude that pooling by phenotype is a robust approach to implementing genomic evaluation using commercial herd data, and PS larger than 10 individuals can be considered.

中文翻译：

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在计算机上验证了安格斯牛基因组评估的联合基因分型策略。

在这项研究中，我们旨在评估基因分型DNA库的价值，以此作为在多父系交配系统中生成准确且具有成本效益的父系基因组估计育种值（GEBV）的策略。为此，我们使用了来自174个父本的2,436头澳大利亚安格斯牛的表型记录，包括一岁体重（YWT ; N = 1,589记录），外套得分（COAT ; N = 2,026记录）和澳大利亚肉类标准大理石花纹得分（MARB）。 ; N= 1,304条记录）。调整表型的固定作用和测量时的年龄，并研究2、5、10、15、20和25只动物的库。通过表型或随机创建库。当随机创建库时，将检查10个重复样本以提供抽样变化的度量。每种合并策略的相对准确度是通过父系具有合并后代的GEBV与使用单独基因型后代的GEBV之间的皮尔逊相关系数来衡量的。随机池允许计算平均具有相关性的父亲GEBV（即，池大小时r > 0.5 [ PS]≤10），而没有合并的结果。但是，对于随机分配的库，从10个重复中获得的最佳相对准确度和最差相对准确度之间的差异对于YWT高达0.41，对于COAT高达0.36，对于MARB高达0.61。与GEBV的相对准确性相关的不确定性使得将动物随机分配到池中是不可靠的方法。相比之下，按表型合并可以对所有三种表型在PS <10的情况下以相对准确度≥0.9估算父系的GEBV。此外，即使具有较大的PS，所获得的最低相对精度也为0.88（YWT，PS = 20）。与使用模拟数据得到的结果一致，我们得出结论，通过表型合并是使用商业畜群数据实施基因组评估的可靠方法，可以考虑PS大于10个人。