The objectives of this study were (1) to evaluate the computational feasibility of the multitrait test-day single-step SNP-BLUP (ssSNP-BLUP) model using phenotypic records of genotyped and nongenotyped animals, and (2) to compare accuracies (coefficient of determination; R²) and bias of genomic estimated breeding values (GEBV) and de-regressed proofs as response variables in 3 Australian dairy cattle breeds (i.e., Holstein, Jersey, and Red breeds). Additive genomic random regression coefficients for milk, fat, protein yield and somatic cell score were predicted in the first, second, and third lactation. The predicted coefficients were used to derive 305-d GEBV and were compared with the traditional parent averages obtained from a BLUP model without genomic information. Cow fertility traits were evaluated from the 5-trait repeatability model (i.e., calving interval, days from calving to first service, pregnancy diagnosis, first service nonreturn rate, and lactation length). The de-regressed proofs were only for calving interval. Our results showed that ssSNP-BLUP using multitrait test-day model increased reliability and reduced bias of breeding values of young animals when compared with parent average from traditional BLUP in Australian Holsten, Jersey, and Red breeds. The use of a custom selection of approximately 46,000 SNP (custom XT SNP list) increased the reliability of GEBV compared with the results obtained using the commercial Illumina 50K chip (Illumina, San Diego, CA). The use of the second preconditioner substantially improved the convergence rate of the preconditioned conjugate gradient method, but further work is needed to improve the efficiency of the computation of the Kronecker matrix product by vector. Application of ssSNP-BLUP to multitrait random regression models is computationally feasible.

这项研究的目的是（1）使用基因型和非基因型动物的表型记录评估多特征测试日单步SNP-BLUP（ssSNP-BLUP）模型的计算可行性，以及（2）比较准确性（系数的决心; R ²）和基因组估计育种值（GEBV）的偏倚以及递减的证明作为3个澳大利亚奶牛品种（即Holstein，Jersey和Red品种）的响应变量。在第一次，第二次和第三次泌乳中，预测了牛奶，脂肪，蛋白质产量和体细胞评分的累加基因组随机回归系数。预测系数用于推导305-d GEBV，并将其与从BLUP模型获得的传统母体平均值进行比较，而没有基因组信息。从五性重复性模型（即产犊间隔，从产犊到首次服役的天数，妊娠诊断，首次服役不退率和泌乳时间）评估母牛的生育力特征。递减的证明仅用于产犊间隔。我们的结果表明，与澳大利亚Holsten，Jersey和Red品种的传统BLUP亲本平均值相比，使用多特征测试日模型的ssSNP-BLUP提高了可靠性，并减少了幼小动物育种值的偏差。与使用商业Illumina 50K芯片（Illumina，圣地亚哥，加利福尼亚）获得的结果相比，使用大约46,000 SNP（定制XT SNP列表）的定制选择提高了GEBV的可靠性。使用第二个预处理器可以大大提高预处理共轭梯度法的收敛速度，但是还需要进一步的工作来提高通过矢量计算Kronecker矩阵乘积的效率。ssSNP-BLUP在多特征随机回归模型中的应用在计算上是可行的。