Sire Evaluation by Best Linear Unbiased Prediction for Categorically Scored Type Traits

Best linear unbiased prediction to predict category frequencies of future progeny of a sire for type traits scored in mutually exclusive categories is described. The method accounts for automatic covariances among categories and is comparable to prediction for multiple traits. The method does not require linearity of measurements and also allows nonlinear economic values to be assigned to each category after frequencies are predicted. Evaluations were for 12 descriptive type traits for 712 Brown Swiss bulls having daughters in more than one herd. Problems in obtaining solutions to the mixed-model equations for multiple traits are discussed. I N T R O D U C T I O N Genetic evaluations for type characteristics have changed rapidly in the last few years after many years of little change. Workers at Virginia Tech (23, 24) in cooperation with the Holstein Association developed herdmate procedures for Holstein data. Even more recently the United States Department of Agriculture (USDA) (4,14) has adapted best linear unbiased prediction (BLUP) procedures (5, 6, 7, 8, 18, 19, 20) for Jersey, Guernsey, and Holstein records. A BLUP system was begun in Canada for Holsteins (16,17). These evaluations have been for final score and score card traits that have been recorded on a linear scale. Analysis of descriptive traits has been complicated by their categorical and nonlinear scoring. The National Association of Animal Breeders (11) has been instrumental in attempting to standardize a linear method of scoring descriptive traits. Another method of analysis Received December 14, 1979. 1980 J Dairy Sci 63:1328-1333 L. D. V A N VLECK and P. J. K A R N E R Department of Animal Science Cornell University Ithaca, NY 14853 of categorically scored traits is to treat each category as a separate subtrait and utilize the covariance structure of the subtraits to predict frequencies of future progeny for each category (1, 2, 3, 15). The covariance structure can be utilized and, perhaps more important, nonlinear economic values can be assigned to predicted frequencies in the categories. The purpose of this paper is to describe a best linear unbiased prediction procedure which was used for categorically scored type traits recorded by the Brown Swiss Cattle Breeders' Association.