Interpretive Summary: Genetic parameters, such as heritability and genetic correlations, are used to predict responses and correlated responses to selection. The (co)variance components used to obtain these parameters are also incorporated into genetic evaluation systems to predict the genetic merit of animals for selection. Accurate estimates of these are essential to insure appropriate predictions of response to selection and genetic merit. This research compares analytical approaches to appropriately deal with the presence of outlier observation. The data used for this research were carcass trait observations collected on animals from a large commercial beef cow / calf operation located in northwest Nebraska. The results confirmed that the assumption of normally distributed residuals is not adequate for the analysis of these growth traits and that the parameters estimated from the analyses using Heavy-tail densities are most appropriate for these data.

Technical Abstract:
Assumptions of normality of residuals for carcass evaluation may make inferences vulnerable to the presence of outliers but heavy-tail densities are viable alternatives to normal distributions and provide robustness against unusual or outlying observations when used to model the densities of residual effects. We compare estimates of genetic parameters by fitting Multivariate Normal (MN) or heavy-tail distributions (Multivariate Student’s-t and Multivariate Slash, MSt and MS) for residuals in data of hot carcass weight (HCW), longissimus muscle area (REA) and 12th to 13th Rib Fat (FAT) traits in beef cattle using 2,476 records from 2007 to 2008 from a large commercial operation in Nebraska. Model comparisons using deviance information criteria (DIC) favored MSt over MS and MN models respectively. The posterior means (and 95% posterior probability intervals, PPI) of v for the MSt and MS models were 5.90 ± 0.86 (4.37, 7.70) and 2.02 ± 0.17 (1.70, 2.38), respectively. Smaller values of posterior densities of v for MSt and MS models confirm that the assumption of normally distributed residuals is not adequate for the analysis of the dataset. Posterior mean (PM) and posterior median (PD) estimates of direct genetic variances were variable with MSt having the highest mean value followed by MS and MN respectively. Posterior inferences on genetic variance were however comparable among the models for FAT. Posterior inference on additive heritabilities for HCW, REA and FAT using MN, MSt, and MS models indicated similar and moderate heritability comparable to the literature. Posterior means of genetic correlations for carcass traits were variable but positive except for between REA and FAT which showed an antagonistic relationship. Our results show that PM for HCW from MN and MSt models did not overlap. We have demonstrated that genetic evaluation and selection strategies will be sensitive to the assumed model for residuals.