Scope for a random regression model in genetic evaluation of beef cattle for growth

Abstract

Potential improvement in accuracy of genetic evaluation of beef cattle for growth from replacing the current multi-trait (MT) model comprising birth, weaning, yearling and final weights as separate traits, with a random regression (RR) model analysis is examined by simulation. Maintaining the original data and pedigree structure for three beef cattle data sets, data were simulated assuming a cubic regression on polynomials of age for direct and maternal, genetic and permanent environmental effects and heterogeneous measurement error variances. Ages at weighing from birth to 730 days were considered. Data set I represented records from an experimental herd with monthly weighing of animals. Data sets II and III were field data, selecting a subset of herds in which at least 50% of animals had four or more weights recorded and records for all herds for a small breed, respectively. Simulated records were analysed fitting a MT model and RR models. Field data sets were expanded by adding a fictitious weight approximately 3 months after the original records. Accuracy of genetic evaluation was calculated as correlation between true and estimated values for each analysis. For the same subset of data, accuracies from a RR analysis were consistently higher than for a MT analysis, due to more appropriate modelling of variances and genetic parameters. Using all records available, RR accuracies for breeding value estimates for 200, 400 and 600 days in data set I were 0.023–0.034 or 4.3–5.9% higher than for MT. Corresponding gains were 3.1–3.6% for data set II and 1.5–1.7% for data set III. Expanding the field data sets by 100%, increased RR accuracies by 0.027–0.038 over those from MT analyses. While small in absolute terms, this was equivalent to a proportional increase of 5.7–8.3%. Results showed that substantial benefits could be obtained from the implementation of a RR model, if additional weight records were collected.