Genetic evaluation of multi-breed beef cattle

Three alternative methods for measuring the degree of connectedness among test groups (TG), including variance of estimated differences between TG effects (VED), connectedness rating (CR), and total number of direct genetic links between TG due to common sires and dams (GLT), which could be routinely used in genetic evaluation programs, were evaluated. Data were consecutive weights of bulls tested in central evaluation stations in Ontario, Canada. The Prediction error variance of differences in estimated breeding values of bulls from different TG (PEVD) was assumed the most adequate measure of connectedness and results from VED, CR, and GLT were compared relative to PEVD. Average PEVD of pairs of TG can be more accurately predicted on the basis of GLT than on the basis of either VED or CR. Average PEVD of each TG with all other test groups can be more accurately predicted on the basis of either CR or GLT.; The GLT, which is not excessively computing demanding, was used to identify a set of connected contemporary groups including both purebred and crossbred animals from beef herds in Ontario. Estimates of variance components, breed additive genetic changes, direct and maternal breed, dominance, and epistatic loss genetic effects on pre-weaning weight gain (PWG) were obtained. Both direct and maternal dominance effects were assumed proportional to breed heterozygosity and showed favourable effects on PWG. Direct epistatic loss reduced the performance of the animals, whereas maternal epistatic loss did not significantly affect the PWG. Breeds ranked similarly to what was expected, but estimates were highly unstable, with high standard errors, possibly due to multicollinearity, which can result in inaccurate across-breed estimated breeding values.; A framework using ridge regression methods was developed to obtain more stable estimates of direct and maternal breed, dominance, and epistatic loss effects on PWG when multicollinearity is of concern. Two generalized methods were applied in the choice of the ridge parameter. Once the choice of the ridge parameter was made, its reliability and validity were evaluated through bootstrap resampling procedures. Mean squared error of prediction (MSEP) of both ridge regression methods were 3% lower than the MSEP from ordinary least squares. Ridge regression methods were effective in reducing the multicollinearity involving predictor variables of breed effects.