Comparison of genomic prediction methods for evaluation of adaptation and productive efficiency traits in Braford and Hereford cattle

Abstract

Various strategies to the use of genetic markers in genetic evaluations have been proposed and substantial genetic progress has been achieved in livestock breeding programs by incorporating genomic information. However, the implementation of genomic selection in beef cattle breeds, especially in composites, adapted to tropical and sub-tropical conditions, is well behind other cattle breeds. Alternative methods to perform genomic predictions for novel adaptation and productive efficiency traits in Brazilian Hereford and Braford need to be further investigated in order to improve the competitiveness of these beef cattle populations. Pedigree, phenotypes and a total of 3,680 genotypes (2,997 Braford and 683 Hereford) were used in this study to assess the predictive ability of genomic predictions for tick resistance, prepuce (navel) score, hair length, ocular pigmentation score, birth weight, pre- and post-weaning weight gain, scrotal circumference, body conformation, precocity, muscularity, and body size. Accuracies of Direct Genomic Values (DGVs) across all scenarios ranged from 0.08 to 0.66, and accuracies of Genomic Estimated Breeding Values (GEBVs) ranged from 0.08 to 0.81. In general, there were no statistical differences (P>0.05) between the DGVs and GEBVs accuracies, and the regression coefficients were not statistically different from 1 (i.e. not biased) for the large majority of traits and scenarios. Our findings indicate that accurate breeding values can be obtained at an early age for various adaptation and productive efficiency traits in Hereford and Braford. In addition, both DGVs and GEBVs predicted by single-step and two-step GBLUP methods produced similar accuracy and amount of bias. Considering the implementation's feasibility, the use the single-step GBLUP method for genetic evaluation of adaptation and productive efficiency traits in Braford and Hereford cattle is recommended.