Estimation of genetic parameters for weaning weight of Awassi lambs by using classical and Bayesian methods

Abstract

The aim of this study was to estimate variance components and genetic parameters with six different animal models and two approaches (Bayesian and classical) on weaning weight (WW) of Awassi lambs. For this purpose, the data were obtained from Sheep and Goat Breeders' Associations of Osmaniye in Turkey. The data of 4971 progenies (from 80 rams and 1917 ewes) born between 2012 and 2016 raised under traditional conditions were evaluated. Year/season, sex, birth type, dam age, and flock size were fixed effects. All these effects except birth type were found statistically significant (P < 0.01). The overall least squares mean of weaning weight (WW) was obtained as 17.93 +/- 0.05 kg. Variance components and genetic parameters were estimated by MCMC algorithms with R (for Bayesian approach) and by REML procedure with MTDFREML (for classical approach) programs. The Akaike information criterion (AIC), the log likelihood function (-2logL), and deviance information criterion (DIC) values were taken as criteria to choose the best model. Direct heritabilities of WW ranged from 0.20 to 0.35 across the models. The genetic correlation between additive genetic effect and maternal effect ranged from 0.00 to 1.00. The results were found similar across methodologies and maternal additive genetic variance resulted in lower than direct additive genetic variance. According to this study, both approaches are suitable for estimation of genetic parameters in the case of low sample size. However, the Bayesian approach, becoming increasingly popular, may be feasible to estimate variancecovariance components and genetic parameters.