Abstract:
Traditionally, the selection of beef cattle was based on the quantitative animal breeding theory and principles. The development of estimated breeding values (EBVs) resulted in accelerated genetic progress in most traits of economic importance. The advent of molecular technology, completion of the bovine genome sequence and single nucleotide polymorphism (SNP) marker discoveries facilitated the use of genomic selection as a selection tool which increased breeding value accuracies. In South Africa, the Beef Genomic Programme enabled the establishment of a reference population for the Bonsmara breed facilitated by large datasets containing performance data due to mandatory performance recording in the breed and availability of biological samples for genotyping. In 2017, the first genomic enhanced breeding values (GEBVs) were made available to breeders. This study aimed to assess the accuracies of EBVs and GEBVs in the selection of Bonsmara cattle for growth traits. The study was conducted in two parts; the dataset for analysis I and II consisted of 4128 and 4189 genotypes, respectively and 2 018 052 phenotypic records. In analysis I, EBVs and GEBVs were estimated for 4128 animals and were correlated to determine if including genomic information in the breeding value estimates influenced the ranking of the animals and breeding value accuracies. In analysis II, a forward validation scheme was applied using validation populations, which consisted of the youngest 500 animals with phenotypic and genotypic information for each trait. Traditional parental averages (TPAs) without SNP information of the parents, genomic-based parental averages (GPAs) with SNP information of the parents, parental averages with genomic information (PAGs) which include SNP information of the parents and the animals themselves, EBVs and GEBVs were estimated. These breeding values were correlated to determine the predictive ability of the breeding value models. In analysis I, the ranking of the animals based on the GEBVs differed from the EBV rankings. The increase in the average GEBV accuracy was between 2.7% to 5.3% compared to the EBVs. In analysis II, the predictive ability of the GEBV models were 8.4% to 78.1% higher compared to the TPA models for all the traits. Additionally, the predictive ability of the PAG models were 5.3% to 11.0% higher compared to the TPA and GPA models for height, direct and maternal weaning weight. The results indicated that genomic information plays an important role in the breeding value estimation and should be included in routine genomic evaluations for growth traits in the Bonsmara breed. This study confirmed the value of genomic information in the breeding value estimation for Bonsmara cattle.
