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

Abstract

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.

Successive Overrelaxation, Block Iteration, and Method of Conjugate Gradients for Solving Equations for Multiple Trait Evaluation of Sires

“…Two sets of equations for sire evaluation (12) were chosen for categorically scored traits of front end (5 categories = 4 subtraits) and stature (3 categories = 2 subtraits) corresponding to traits with low and moderately high herit- i (e,e).S/(r,r).S < C.…”

“…Prediction of category frequencies for traits such as calving difficulty and type traits by best linear unbiased prediction is a special form of multiple trait evaluation of sires. Such sets of equations were available from analysis of Brown Swiss type data (12). Equations for a test set of data and for the complete data set were available for multiple subtraits of two traits, the first having three categories (equivalent to two traits) and the second having five categories (equivalent to four traits).…”