Genomic estimated breeding values for bovine respiratory disease resistance in Angus feedlot cattle

Bovine respiratory disease (BRD) causes major losses in feedlot cattle worldwide. A genetic component for BRD resistance in feedlot cattle and calves has been reported in a number of studies, with heritabilities ranging from 0.04 to 0.2. These results suggest selection could be used to reduce the incidence of BRD. Genomic selection could be an attractive approach for breeding for BRD resistance, given the phenotype is not likely to be recorded on breeding animals. In this study, we derived GEBVs for BRD resistance and assessed their accuracy in a reasonably large data set recorded for feedlot treatment of BRD (1213 Angus steers, in two feedlots). In fivefold cross validation, genomic predictions were moderately accurate (0.23 ± 0.01) when a BayesR approach was used. Expansion of this approach to include more animals and a diversity of breeds is recommended to successfully develop a GEBV for BRD resistance in feedlots for the beef industry.

Bias, dispersion, and accuracy of genomic predictions for feedlot and carcase traits in Australian Angus steers

Background

Improving feedlot performance, carcase weight and quality is a primary goal of the beef industry worldwide. Here, we used data from 3408 Australian Angus steers from seven years of birth (YOB) cohorts (2011–2017) with a minimal level of sire linkage and that were genotyped for 45,152 SNPs. Phenotypic records included two feedlot and five carcase traits, namely average daily gain (ADG), average daily dry matter intake (DMI), carcase weight (CWT), carcase eye muscle area (EMA), carcase Meat Standard Australia marbling score (MBL), carcase ossification score (OSS) and carcase subcutaneous rib fat depth (RIB). Using a 7-way cross-validation based on YOB cohorts, we tested the quality of genomic predictions using the linear regression (LR) method compared to the traditional method (Pearson’s correlation between the genomic estimated breeding value (GEBV) and its associated adjusted phenotype divided by the square root of heritability); explored the factors, such as heritability, validation cohort, and phenotype that affect estimates of accuracy, bias, and dispersion calculated with the LR method; and suggested a novel interpretation for translating differences in accuracy into phenotypic differences, based on GEBV quartiles (Q1Q4).

Results

Heritability (h²) estimates were generally moderate to high (from 0.29 for ADG to 0.53 for CWT). We found a strong correlation (0.73, P-value < 0.001) between accuracies using the traditional method and those using the LR method, although the LR method was less affected by random variation within and across years and showed a better ability to discriminate between extreme GEBV quartiles. We confirmed that bias of GEBV was not significantly affected by h², validation cohort or trait. Similarly, validation cohort was not a significant source of variation for any of the GEBV quality metrics. Finally, we observed that the phenotypic differences were larger for higher accuracies.

Conclusions

Our estimates of h² and GEBV quality metrics suggest a potential for accurate genomic selection of Australian Angus for feedlot performance and carcase traits. In addition, the Q1Q4 measure presented here easily translates into possible gains of genomic selection in terms of phenotypic differences and thus provides a more tangible output for commercial beef cattle producers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00673-8.