Location: Genetics, Breeding, & Animal Health
Title: Application of multivariate heavy-tailed distributions to residuals in the estimation of genetic parameters of growth traits in beef cattle Authors
|Peters, S -|
|Kizilkaya, Kadir -|
|Garrick, Dorian -|
|Fernando, R -|
|DE Donato, M -|
|Hussain, T -|
|Imumorin, Ikhide -|
Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 23, 2013
Publication Date: April 1, 2013
Repository URL: http://handle.nal.usda.gov/10113/56122
Citation: Peters, S.O., Kizilkaya, K., Garrick, D.J., Fernando, R.L., Pollak, E.J., De Donato, M., Hussain, T., Imumorin, I.G. 2013. Application of multivariate heavy-tailed distributions to residuals in the estimation of genetic parameters of growth traits in beef cattle. Journal of Animal Science. 91(4):1552-1561. 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 growth trait observations collected on 17,019 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 in most animal breeding applications may make inferences vulnerable to the presence of outliers. 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. Our objective is to 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 body birth weight (BBW), weaning (WW) and yearling (YW) weight traits in beef cattle. A total of 17,019 weight records for BBW, WW and YW from 1998 through 2010 from a large commercial cow/calf operation in the sand hills of Nebraska were analyzed. Models included fixed effects of contemporary group and sire breed, whereas animal and maternal effects were random and the degrees of freedom (v) was treated as unknown for MSt and MS. 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 5.28 (4.80, 5.85) and 1.88 (1.76, 2.00), 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 BBW, WW and YW datasets. Posterior mean (PM) and posterior median (PD) estimates of direct and maternal genetic variances were the same and posterior densities of these parameters were found to be symmetric. The 95% PPI estimates from MN and MSt models for BBW did not overlap, which indicates significant difference between PM estimates from MN or MSt models. The observed antagonistic relationship between additive direct and additive maternal effects indicated that genetic evaluation and selection strategies will be sensitive to the assumed model for residuals.