Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k single nucleotide polymorphism (SNP) panels. After imputation and quality control, 61,666 SNPs were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent single nucleotide polymorphisms (SNPs) across all chromosomes were 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help us to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

中文翻译：

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巴西安格斯牛基因组预测的发展，结合了美国相关公牛的基因型

基因组预测已成为遗传改良计划的新标准，目前，希望将该技术用于巴西安格斯牛的评估。因此，本研究的主要目的是评估使用来自美国安格斯 (AA) 公牛的单步基因组 BLUP (ssGBLUP) 评估年轻巴西安格斯 (BA) 公牛和小母牛的 12 个常规记录性状的可行性。第二个目标是获得巴西安格斯种群中有效种群大小 (Ne) 和连锁不平衡 (LD) 的估计值。该数据集包含属于 Promebo 育种计划的多达 277,661 只动物的表型信息，362,900 只动物的谱系，其中 1,386 只动物进行了 50k、77k 和 150k 单核苷酸多态性 (SNP) 面板的基因分型。在插补和质量控制之后，有 61,666 个 SNP 可用于分析。此外，从 AA 协会数据库中检索了 332 头在巴西广泛使用的美国安格斯 (AA) 公牛的基因型，用于基因组预测。双变量动物模型用于估计方差分量、传统 EBV 和基因组 EBV (GEBV)。使用线性回归方法 (LR) 进行验证，使用 2013 年至 2015 年间出生的年轻基因型动物，在缩减数据集中没有表型，在完整数据集中有记录。验证动物被进一步分为 BA 和 AA 公牛的后代，以评估它们的后代是否会因包括来自 AA 公牛的基因型而受益。Ne 是 254 基于血统和 197 基于 LD, 所有染色体的平均 LD (±SD) 和相邻单核苷酸多态性 (SNP) 之间的距离分别为 0.27 (±0.27) 和 40743.68 bp。ssGBLUP 的预测准确度在所有性状上都优于 BLUP，BA 幼公牛和小母牛的准确度平均提高了 16%。所有性状的 GEBV 预测准确度范围从 0.37（断奶体重和蜱计数的总母体）到 0.54（一岁早熟），离散度（LR 系数）在 0.92 和 1.06 之间波动。与仅使用 BA 参考种群相比，包含来自 AA 的基因型公牛平均提高了 2% 的 GEBV 准确度。我们的研究表明，基因组信息可以帮助我们提高 GEBV 的准确性，从而提高巴西安格斯种群的遗传进展。