Economically relevant traits are routinely collected within the commercial segments of the beef industry but are rarely included in genetic evaluations because of unknown pedigrees. Individual relationships could be resurrected with genomics, but this would be costly; therefore, pooling DNA and phenotypic data provide a cost-effective solution. Pedigree, phenotypic, and genomic data were simulated for a beef cattle population consisting of 15 generations. Genotypes mimicked a 50k marker panel (841 quantitative trait loci were located across the genome, approximately once per 3 Mb) and the phenotype was moderately heritable. Individuals from generation 15 were included in pools (observed genotype and phenotype were mean values of a group). Estimated breeding values (EBV) were generated from a single-step genomic best linear unbiased prediction model. The effects of pooling strategy (random and minimizing or uniformly maximizing phenotypic variation within pools), pool size (1, 2, 10, 20, 50, 100, or no data from generation 15), and generational gaps of genotyping on EBV accuracy (correlation of EBV with true breeding values) were quantified. Greatest EBV accuracies of sires and dams were observed when there was no gap between genotyped parents and pooled offspring. The EBV accuracies resulting from pools were usually greater than no data from generation 15 regardless of sire or dam genotyping. Minimizing phenotypic variation increased EBV accuracy by 8% and 9% over random pooling and uniformly maximizing phenotypic variation, respectively. A pool size of 2 was the only scenario that did not significantly decrease EBV accuracy compared with individual data when pools were formed randomly or by uniformly maximizing phenotypic variation (P > 0.05). Pool sizes of 2, 10, 20, or 50 did not generally lead to statistical differences in EBV accuracy than individual data when pools were constructed to minimize phenotypic variation (P > 0.05). Largest numerical increases in EBV accuracy resulting from pooling compared with no data from generation 15 were seen with sires with prior low EBV accuracy (those born in generation 14). Pooling of any size led to larger EBV accuracies of the pools than individual data when minimizing phenotypic variation. Resulting EBV for the pools could be used to inform management decisions of those pools. Pooled genotyping to garner commercial-level phenotypes for genetic evaluations seems plausible although differences exist depending on pool size and pool formation strategy.

中文翻译：

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在单步基因组最佳线性无偏预测框架中使用合并数据进行基因组预测。

经济相关的性状通常在牛肉行业的商业领域内收集，但由于谱系未知，很少包含在基因评估中。个人关系可以通过基因组学来复活，但这会造成高昂的代价。因此，合并DNA和表型数据可提供一种经济高效的解决方案。模拟了由15个世代组成的肉牛种群的谱系，表型和基因组数据。基因型模仿了一个50k的标记组（整个基因组中定位了841个定量性状基因座，大约每3 Mb定位一次），并且该表型具有中等遗传性。来自第15代的个体包括在库中（观察到的基因型和表型是一组的平均值）。从单步基因组最佳线性无偏预测模型生成估计育种值（EBV）。合并策略的影响（随机和最小化或最大程度地最大化集合内的表型变异），集合大小（第15、1、2、10、20、50、100或无数据来自第15代）以及基因型的代际差距对EBV准确性的影响EBV与真实育种值的相关性被量化。当基因型父母和合并后代之间没有差距时，可以观察到最大的父亲和母亲的EBV准确性。不论父本或大坝的基因分型，由库产生的EBV准确性通常大于第15代的数据。与随机汇总相比，将表型变异最小化可将EBV准确性分别提高8％和9％，并使表型变异均匀最大化。当个体随机形成或通过均匀地最大化表型变异而形成个体集合时，个体大小与个体数据相比，个体大小只有2个不会显着降低EBV准确性（P> 0.05）。当构建池以最小化表型变化时，池大小为2、10、20或50时，与单个数据相比，通常不会导致EBV准确性的统计差异（P> 0.05）。与以前的EBV精度较低的父亲（出生于14代的父亲）相比，合并产生的EBV准确性的数值增加最大，而没有15代的数据。当最小化表型变异时，任何大小的合并都比单个数据导致更大的EBV准确性。池的结果EBV可用于通知那些池的管理决策。