Lassen J, Sørensen M K, Madsen P, Ducrocq V
Department of Genetics and Biotechnology, Danish Institute of Agricultural Sciences, P.O. Box 50, DK-8830 Tjele, Denmark.
J Dairy Sci. 2007 Jun;90(6):3002-11. doi: 10.3168/jds.2006-430.
Three different models for prediction of breeding values were compared in a stochastic simulation study of a dairy cattle population of 100,000 cows. The simulation was done in 2 steps. The first step involved 15 yr of selection using breeding values obtained in a univariate model for production and a trivariate model for mastitis occurrence, udder depth, and somatic cell score, in which production and mastitis occurrence were included in the breeding goal. This was done to create an initial population that had already been under selection. The second step consisted of 20 replicates of 4 different scenarios set up to make it possible to compare the different models. Two scenarios were based on univariate evaluations and one for udder health traits on trivariate evaluations, with 2 different breeding goals. In another scenario, an approximate multitrait model using preadjusted data in a 2-step procedure was used and in the last scenario, a complete linear multitrait model was carried out. Differences in genetic response in total merit over the last 15 yr of selection were compared and used to rank the models. The linear multitrait model gave the highest regression coefficient of true genetic values on year (3.073 +/- 0.069 in economic units), and this was significantly better than for the approximate multitrait model (2.819 +/- 0.047), which again was significantly better than for the univariate approach (2.672 +/- 0.060). The linear multitrait model cannot be applied to nearly the same number of traits as the approximate model. Therefore, the approximate model with developments handling breeding values from more complex models than presented in this paper is an option of choice in countries providing total merit indices that combine many traits because it does not neglect correlations between these traits.
在一个拥有10万头奶牛的奶牛群体的随机模拟研究中，对三种不同的育种值预测模型进行了比较。模拟分两步进行。第一步涉及使用单变量模型得出的生产育种值以及用于乳腺炎发病率、乳房深度和体细胞评分的三变量模型，对15年的选择进行模拟，其中生产和乳腺炎发病率被纳入育种目标。这样做是为了创建一个已经经过选择的初始群体。第二步包括对4种不同情景进行20次重复设置，以便能够比较不同模型。两种情景基于单变量评估，一种针对乳房健康性状的三变量评估，有2种不同的育种目标。在另一种情景中，使用了一种在两步程序中使用预调整数据的近似多性状模型，在最后一种情景中，实施了一个完整的线性多性状模型。比较了在最后15年选择中总价值的遗传反应差异，并用于对模型进行排名。线性多性状模型给出了真实遗传值对年份的最高回归系数（经济单位为3.073±0.069），这明显优于近似多性状模型（2.819±0.047），而近似多性状模型又明显优于单变量方法（2.672±0.060）。线性多性状模型不能应用于与近似模型几乎相同数量的性状。因此，对于提供综合许多性状的总价值指数的国家来说，具有比本文所展示的更复杂模型的育种值处理方法的近似模型是一个选择，因为它不会忽略这些性状之间的相关性。
