Centre for Genetic Improvement of Livestock, Department of Animal and Poultry Science, University of Guelph, Ontario, Canada.
J Dairy Sci. 2010 Mar;93(3):1216-33. doi: 10.3168/jds.2009-2585.
Multiple-trait random regression animal models with simultaneous and recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test day were fitted to Canadian Holstein data. All models included fixed herd test-day effects and fixed regressions within region-age at calving-season of calving classes, and animal additive genetic and permanent environmental regressions with random coefficients. Regressions were Legendre polynomials of order 4 on a scale from 5 to 305 d in milk (DIM). Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Heterogeneity of structural coefficients was modeled across (the first 3 lactations) and within (4 DIM intervals) lactation. Model comparisons in terms of Bayes factors indicated the superiority of simultaneous models over the standard multiple-trait model and recursive parameterizations. A moderate heterogeneous (both across- and within-lactation) negative effect of SCS on milk yield (from -0.36 for 116 to 265 DIM in lactation 1 to -0.81 for 5 to 45 DIM in lactation 3) and a smaller positive reciprocal effect of SCS on milk yield (from 0.007 for 5 to 45 DIM in lactation 2 to 0.023 for 46 to 115 DIM in lactation 3) were estimated in the most plausible specification. No noticeable differences among models were detected for genetic and environmental variances and genetic parameters for the first 2 regression coefficients. The curves of genetic and permanent environmental variances, heritabilities, and genetic and phenotypic correlations between milk yield and SCS on a daily basis were different for different models. Rankings of bulls and cows for 305-d milk yield, average daily SCS, and milk lactation persistency remained the same among models. No apparent benefits are expected from fitting causal phenotypic relationships between milk yield and SCS on the same test day in the random regression test-day model for genetic evaluation purposes.
基于同一泌乳日，采用同时和递归链接的方法，针对产奶量和体细胞评分（SCS）构建多性状随机回归动物模型，对加拿大荷斯坦奶牛数据进行拟合。所有模型均包含固定的群体产奶日效应和固定回归，回归包括区域-产犊季节-产犊胎次、动物加性遗传和永久环境的回归，且采用随机系数。SCS 与产奶量（5 至 305 天泌乳日，DIM）的回归为 Legendre 多项式，阶数为 4。采用 Gibbs 抽样的贝叶斯方法进行模型参数估计。跨泌乳阶段（前 3 个泌乳期）和泌乳阶段内（4 个 DIM 间隔）对结构系数的异质性进行建模。根据贝叶斯因子对模型进行比较，结果表明同时模型优于标准多性状模型和递归参数化模型。SCS 对产奶量的负向影响（从第 1 个泌乳期的 116 至 265 DIM 时的-0.36 到第 3 个泌乳期的 5 至 45 DIM 时的-0.81）以及 SCS 对产奶量的正向互作效应（从第 2 个泌乳期的 5 至 45 DIM 时的 0.007 到第 3 个泌乳期的 46 至 115 DIM 时的 0.023）呈中等异质性（在泌乳阶段内和泌乳阶段间均存在），在最合理的指定中进行了估计。在不同模型中，前 2 个回归系数的遗传和环境方差以及遗传参数之间没有明显差异。不同模型中，产奶量和 SCS 的遗传和永久环境方差、遗传率以及遗传和表型相关性的曲线存在差异。在不同模型中，305 天产奶量、平均每日 SCS、泌乳持久性的公牛和奶牛的排名保持不变。在随机回归产奶日模型中，从遗传评估的角度来看，在同一产奶日拟合产奶量和 SCS 之间的因果表型关系，预计不会带来明显的益处。
