Veerkamp R F, Goddard M E
Animal Genetics and Breeding Unit, University of New England, Armidale, New South Wales, Australia.
J Dairy Sci. 1998 Jun;81(6):1690-701. doi: 10.3168/jds.S0022-0302(98)75736-4.
Multiple-trait BLUP evaluations of test day records require a large number of genetic parameters. This study estimated covariances with a reduced model that included covariance functions in two dimensions (stage of lactation and herd production level) and all three yield traits. Records came from all six states in Australia, were evenly distributed across the herd production levels, but decreased with increasing lactation stage from 9693 records for the 1st mo of lactation to 4199 records for the 10th mo. Using the variance component estimation package and a bivariate animal model, 1176 genetic (co)variances and 312 environmental (co)variances were estimated for 48 traits (1, 4, 7, and 10 mo of lactation; herd production levels of < 20, 20 to 22, 22 to 24, > 24 kg of milk/d; and milk, fat, and protein yields). The genetic (co)variances could be predicted by a multiplicative model that included 1) a term dependent on which yields (milk, fat, or protein) were involved in the covariance, 2) the covariance functions for month of lactation and herd production level, and 3) a covariance function for the interaction between these. This model required only 27 parameters instead of the 1176 (co)variances. For the environmental (co)variances, a model was fitted that contained several additional covariance functions. This model reduced the number of parameters from 312 to 71. For the same trait at the same production level, genetic correlations between test days ranged from 0.59 to 1, and environmental correlations ranged from 0.17 to 0.48. Genetic correlations between milk and fat, milk and protein, and fat and protein were 0.38, 0.83, 0.59, respectively, and correlations between the herd production levels ranged from 0.79 to 0.97. Failure to consider herd production level in a test day model evaluation might result, for instance, in overweighting of early lactation information from high production herds compared with information coming from bulls tested across all production levels.
对测定日记录进行多性状最佳线性无偏预测（BLUP）评估需要大量遗传参数。本研究使用一个简化模型估计协方差，该模型包括二维（泌乳阶段和牛群生产水平）协方差函数以及所有三个产量性状。记录来自澳大利亚的所有六个州，在牛群生产水平上均匀分布，但随着泌乳阶段的增加而减少，从泌乳第1个月的9693条记录降至第10个月的4199条记录。使用方差分量估计软件包和二元动物模型，对48个性状（泌乳1、4、7和10个月；牛奶日产量<20、20至22、22至24、>24千克的牛群生产水平；以及牛奶、脂肪和蛋白质产量）估计了1176个遗传（协）方差和312个环境（协）方差。遗传（协）方差可以通过一个乘法模型预测，该模型包括：1）一个取决于协方差中涉及哪些产量（牛奶、脂肪或蛋白质）的项；2）泌乳月份和牛群生产水平的协方差函数；3）这些因素之间相互作用的协方差函数。该模型仅需要27个参数，而不是1176个（协）方差。对于环境（协）方差，拟合了一个包含几个额外协方差函数的模型。该模型将参数数量从312个减少到71个。对于相同生产水平下的相同性状，测定日之间的遗传相关性在0.59至1之间，环境相关性在0.17至0.48之间。牛奶与脂肪、牛奶与蛋白质、脂肪与蛋白质之间的遗传相关性分别为0.38、0.83、0.59，牛群生产水平之间的相关性在0.79至0.97之间。例如，在测定日模型评估中未考虑牛群生产水平可能导致高估高产牛群早期泌乳信息，而低估所有生产水平下公牛的测试信息。
