Evaluation of a genomic-enhanced sorting system for feeder cattle

Citation

Akanno, E.C., Ekine-Dzivenu, C., Chen, L., Vinsky, M., Abo-Ismail, M.K., Macneil, M.D., Plastow, G., Basarab, J., Li, C., Fitzsimmons, C. (2019). Evaluation of a genomic-enhanced sorting system for feeder cattle. Journal of Animal Science, [online] 97(3), 1066-1075. http://dx.doi.org/10.1093/jas/skz026

Plain language summary

This study evaluated the use of molecular breeding values (MBVs) for carcass traits to sort steers into quality grid and lean meat yield groups to test genomics applications in marker-assisted management. Angus, Charolais, and crossbred steers were sorted in silico into four MBV groups namely Quality (high lean meat yield, high marbling), Lean (high lean meat yield), Marbling (high marbling) and Other. Carcass phenotypes on the steers were then collected at slaughter and evaluated for consistency with the assigned MBV groups. Results showed that on average, Quality and Marbling groups had greater back-fat and more marbling while Lean group had leaner carcasses. Carcass weights were similar across MBV groups. Within MBV groups, decreases in variability were observed for most traits suggesting improvement in carcass uniformity. Greater than 70% of the steers in Quality, Lean and Marbling groups met the Quality Grid and Y1-LMY target for Angus and Charolais but not for crossbred steers. The accuracy of MBV prediction ranged from 0.43 - 0.59 indicating that up to 35% of the observed carcass trait variability can be predicted, which suggests utility of MBV as a marker-assisted management tool to sort feeder cattle into uniform carcass endpoint groups under similar environmental and management conditions Further investigation is warranted to evaluate the performance of feeder cattle sorted based on MBV and managed for different carcass endpoints as well as the cost-benefit implications for feedlot operations. This research is important in particular to the beef feedlot industry as the suggested improvement in carcass uniformity stemming marker-assisted management can reduce inefficiencies in the performance variability of large groups of cattle by grouping them more efficiently based upon their genetic potential, which would consequently lead to improved management of these animals to a more predictable and consistent endpoint.

Abstract

This study evaluated the use of molecular breeding values (MBVs) for carcass traits to sort steers into quality grid and lean meat yield (LMY) groups. A discovery set of 2,609 animals with genotypes and carcass phenotypes was used to predict MBVs for LMY and marbling score (MBS) for 299 Angus, 181 Charolais, and 638 Kinsella Composite steers using genomic best linear unbiased prediction. Steers were sorted in silico into four MBV groups namely Quality (with MBVs greater than the mean for LMY and MBS), Lean (with MBVs greater than the mean for LMY but less than or equal to the mean for MBS), Marbling (with MBVs greater than the mean for MBS but less than or equal to the mean for LMY), and Other (with MBVs lower than the mean for LMY and MBS). Carcass phenotypes on the steers were then collected at slaughter and evaluated for consistency with the assigned MBV groups using descriptive statistics and least square analysis. Accuracy of MBV predictions was assessed by Pearson's correlation between predicted MBV and adjusted phenotype divided by the square root of trait heritability. Genomic breed compositions were predicted for all steers to correct for possible population stratification and breed effects in the test model. The number of steers that met the expected carcass outcome was counted to produce actual percentages for each MBV group. Results showed that on average, Quality and Marbling groups had greater back-fat and more marbling across the three populations while Lean group had leaner carcasses. Carcass weights were similar across MBV groups. Within MBV groups, decreases in variability were observed for most traits suggesting improvement in carcass uniformity. Greater than 70% of the steers in Quality, Lean, and Marbling groups met the Quality Grid and Y1-LMY target for Angus and Charolais but not for Kinsella composite. The accuracy of MBV prediction ranged from 0.43 to 0.59 indicating that up to 35% of the observed carcass trait variability can be predicted, which suggests utility of MBV as a marker-assisted management tool to sort feeder cattle into uniform carcass endpoint groups under similar environmental and management conditions. Further investigation is warranted to evaluate the performance of feeder cattle sorted based on MBV and managed for different carcass endpoints as well as the cost-benefit implications for feedlot operations.

Publication date

2019-03-01