Value of genomics in breeding objectives for beef cattle

Abstract

The objective of this research was to discern the contribution of genomic information to multiple-trait breeding objectives and thus understand the economic value of that information. True genetic values were simulated for each of n, possibly correlated, traits. These true genetic values, combined with uncorrelated random noise, resulted in both genomic and phenotypic estimated breeding values, EBVg and EBVp, respectively. The separate EBV were then merged (blended) as a function of their respective accuracies to produce a unified EBV for each of the n traits. Finally, for each simulated animal (N = 10000), the sum of products of economic weights and EBV was calculated to predict the economic value (net merit) of the individual. Accuracies of the EBV for the individual traits and net merit were calculated as correlations between predicted and true values. Predicted responses to selection for individual traits included in the breeding objectives were enhanced from 9% to 76% with the greatest benefit accorded to those economically relevant traits that are recorded after selection decisions are made at one year of age, measured less frequently in national cattle evaluation, or often predicted using information from indicator traits. Combining the EBV to predict net merit for terminal and maternal breeding objectives resulted in predicted increases in selection response due to incorporation of genomic information of 27% and 57%, respectively. The results are interpreted to suggest that the economic benefit to be derived from selection based on a multiple-trait economic breeding objective, which is predicted using genomically enhanced EBV, can substantially exceed the present day cost of genotyping the candidates for selection.