ARS | Publication request: Opportunities and challenges from the use of genomic selection for beef cattle breeding in Latin America

Submitted to: Animal Frontiers
Publication Type: Review Article
Publication Acceptance Date: November 16, 2011
Publication Date: January 20, 2012
Citation: Montaldo, H.H., Casas, E., Sterman Ferraz, J.B., Vega-Murillo, V.E., Roman-Ponce, S.I. 2012. Opportunities and challenges from the use of genomic selection for beef cattle breeding in Latin America. Animal Frontiers. 2(1):23-29.

Technical Abstract: The beef cattle production in Latin America in very important on a worldwide scale and for several regional countries. The region accounts for 29% of the world cattle population and beef production. Genomic selection allows the estimation of breeding values in animals for young animals from DNA samples through the use of panels of SNP (a type of DNA genetic marker). Relatively more accurate evaluations of young animals may increase the yearling rates of improvement for economically important traits. In order to implement these evaluations, the effects of the SNP on the traits need to be estimated in a training population. The cost of running a training population depends on the number and type of measured traits and also on the number of phenotypes and genotypes. Several beef cattle populations at the Latin American level undergo traditional genetic programs for genetic evaluation using measurements and pedigree information, but opportunities exist for increasing the rates of improvement using genomic selection. Not all populations are suitable for short-term implementation of the technique because of a small number of sires with genetic evaluations and small numbers of progeny per sire. Another short-term consideration is cost, but genotyping costs are decreasing. Longer term considerations for using genomic selection may be to increase the competitive position of a breed in the market, selecting for a larger number of traits more related to the economic performance of the animals under specific environments, and to detect the genes associated to variation in productivity so that genetic improvement may be more efficient to increase productivity.