Genomics will improve the efficiency of beef cattle genetic improvement programs through the incorporation of genomic predictions into traditional genetic evaluations. The global dairy cattle breeding industry has been changed considerably in the last year through the implementation of genomic selection. Now proven to work in dairy cattle breeding, the challenge remains for the beef industry to successfully implement this technology. The primary challenge in beef cattle is the required resource population that relates genomic profile to phenotypic performance, which is quite large and its establishment will require collaboration or a significant investment by any one enterprise. Another challenge in beef cattle is the requirement for genomic predictions to function across breeds, which will require denser marker panels. Opportunities to increase genetic progress include increased accuracy of selection, reduced generation interval, increased selection intensity and better utilization of limited recording capacity, such as individual feed intake, along with opportunities to genetically change novel traits. Implementation of a low density panel at the commercial level will allow informative decisions based on genetic potential at all levels of the production chain. This reduced panel will include predictive SNP based on fine QTL mapping efforts, combined with additional SNP to enable imputation of genotypes from a high density SNP panel, when combined with high density genotypes of key ancestors, such as sires. With electronic recording in cattle, a single genotyping event on each animal would provide information throughout the beef production chain, which will create the incentive for genetic change. Genomics will create new opportunities for reproductive technologies such as embryo transfer as elite females will be identified with increased accuracy. Potential changes to the structure of the breeding industry are discussed including changes to recording strategies and the development of novel beef products.
breeding strategies; DNA; genomic selection; phenotype; recording; single nucleotide polymorphisms