de Oliveira H R, Silva F F, Brito L F, Guarini A R, Jamrozik J, Schenkel F S
Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada.
Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
J Anim Breed Genet. 2018 Apr;135(2):97-106. doi: 10.1111/jbg.12317. Epub 2018 Feb 26.
We aimed to investigate the performance of three deregression methods (VanRaden, VR; Wiggans, WG; and Garrick, GR) of cows' and bulls' breeding values to be used as pseudophenotypes in the genomic evaluation of test-day dairy production traits. Three scenarios were considered within each deregression method: (i) including only animals with reliability of estimated breeding value (REL ) higher than the average of parent reliability (REL ) in the training and validation populations; (ii) including only animals with REL higher than 0.50 in the training and REL higher than REL in the validation population; and (iii) including only animals with REL higher than 0.50 in both training and validation populations. Individual random regression coefficients of lactation curves were predicted using the genomic best linear unbiased prediction (GBLUP), considering either unweighted or weighted residual variances based on effective records contributions. In summary, VR and WG deregression methods seemed more appropriate for genomic prediction of test-day traits without need for weighting in the genomic analysis, unless large differences in REL between training population animals exist.
我们旨在研究三种奶牛和公牛育种值去回归方法（范拉登法，VR；威根斯法，WG；以及加里克法，GR）作为测试日奶牛生产性状基因组评估中的伪表型的性能。在每种去回归方法中考虑了三种情况：（i）在训练和验证群体中仅纳入估计育种值可靠性（REL）高于亲本可靠性平均值的动物；（ii）在训练群体中仅纳入REL高于0.50且在验证群体中REL高于亲本可靠性平均值的动物；以及（iii）在训练和验证群体中仅纳入REL高于0.50的动物。使用基因组最佳线性无偏预测（GBLUP）预测泌乳曲线的个体随机回归系数，基于有效记录贡献考虑未加权或加权残差方差。总之，VR和WG去回归方法似乎更适合测试日性状的基因组预测，在基因组分析中无需加权，除非训练群体动物的REL存在较大差异。
