Cesarani A, Lourenco D, Tsuruta S, Legarra A, Nicolazzi E L, VanRaden P M, Misztal I
Department of Animal and Dairy Science, University of Georgia, Athens 30602.
Department of Animal and Dairy Science, University of Georgia, Athens 30602.
J Dairy Sci. 2022 Jun;105(6):5141-5152. doi: 10.3168/jds.2021-21505. Epub 2022 Mar 10.
Official multibreed genomic evaluations for dairy cattle in the United States are based on multibreed BLUP evaluation followed by single-breed estimation of SNP effects. Single-step genomic BLUP (ssGBLUP) allows the straight computation of genomic (G)EBV in a multibreed context. This work aimed to develop ssGBLUP multibreed genomic predictions for US dairy cattle using the algorithm for proven and young (APY) to compute the inverse of the genomic relationship matrix. Only purebred Ayrshire (AY), Brown Swiss (BS), Guernsey (GU), Holstein (HO), and Jersey (JE) animals were considered. A 3-trait model with milk (MY), fat (FY), and protein (PY) yields was applied using about 45 million phenotypes recorded from January 2000 to June 2020. The whole data set included about 29.5 million animals, of which almost 4 million were genotyped. All the effects in the model were breed specific, and breed was also considered as fixed unknown parent groups. Evaluations were done for (1) each single breed separately (single); (2) HO and JE together (HO_JE); (3) AY, BS, and GU together (AY_BS_GU); (4) all the 5 breeds together (5_BREEDS). Initially, 15k core animals were used in APY for AY_BS_GU and 5_BREEDS, but larger core sets with more animals from the least represented breeds were also tested. The HO_JE evaluation had a fixed set of 30k core animals, with an equal representation of the 2 breeds, whereas HO and JE single-breed analysis involved 15k core animals. Validation for cows was based on correlations between adjusted phenotypes and (G)EBV, whereas for bulls on the regression of daughter yield deviations on (G)EBV. Because breed was correctly considered in the model, BLUP results for single and multibreed analyses were the same. Under ssGBLUP, predictability and reliability for AY, BS, and GU were on average 7% and 2% lower in 5_BREEDS compared with single-breed evaluations, respectively. However, validation parameters for these 3 breeds became better than in the single-breed evaluations when 45k animals were included in the core set for 5_BREEDS. Evaluations for Holsteins were more stable across scenarios because of the greatest number of genotyped animals and amount of data. Combining AY, BS, and GU into one evaluation resulted in predictions similar to the ones from single breed, especially when using about 30k core animals in APY. The results showed that single-step large-scale multibreed evaluations are computationally feasible, but fine tuning is needed to avoid a reduction in reliability when numerically dominant breeds are combined. Having evaluations for AY, BS, and GU separated from HO and JE may reduce inflation of GEBV for the first 3 breeds.
美国奶牛的官方多品种基因组评估基于多品种最佳线性无偏预测（BLUP）评估，随后进行单品种单核苷酸多态性（SNP）效应估计。单步基因组BLUP（ssGBLUP）允许在多品种背景下直接计算基因组（G）估计育种值（EBV）。本研究旨在利用经产和青年动物算法（APY）计算基因组关系矩阵的逆矩阵，开发美国奶牛的ssGBLUP多品种基因组预测方法。仅考虑纯种爱尔夏牛（AY）、瑞士褐牛（BS）、格恩西牛（GU）、荷斯坦牛（HO）和娟姗牛（JE）。使用2000年1月至2020年6月记录的约4500万个表型数据，应用包含产奶量（MY）、乳脂量（FY）和乳蛋白量（PY）的三性状模型。整个数据集包含约2950万头动物，其中近400万头进行了基因分型。模型中的所有效应都是品种特异性的，品种也被视为固定的未知亲本组。评估分别针对以下情况进行：（1）每个单一品种（单品种）；（2）HO和JE一起（HO_JE）；（3）AY、BS和GU一起（AY_BS_GU）；（4）所有5个品种一起（5个品种）。最初，在APY中，AY_BS_GU和5个品种使用15000头核心动物，但也测试了包含更多来自代表性不足品种动物的更大核心群体。HO_JE评估有一组固定的30000头核心动物，两个品种的代表性相同，而HO和JE单品种分析涉及15000头核心动物。母牛的验证基于调整后的表型与（G）EBV之间的相关性，而公牛的验证基于女儿产量偏差对（G）EBV的回归。由于模型中正确考虑了品种，单品种和多品种分析的BLUP结果相同。在ssGBLUP下，与单品种评估相比，5个品种中AY、BS和GU的预测性和可靠性平均分别低7%和2%。然而，当5个品种的核心群体中包含45000头动物时，这3个品种的验证参数比单品种评估更好。由于基因分型动物数量最多和数据量最大，荷斯坦牛的评估在不同情况下更稳定。将AY、BS和GU合并为一次评估得到的预测结果与单品种评估相似，特别是在APY中使用约30000头核心动物时。结果表明，单步大规模多品种评估在计算上是可行的，但需要进行微调以避免在合并数量占主导的品种时可靠性降低。将AY、BS和GU的评估与HO和JE分开，可能会减少前3个品种的GEBV膨胀。
