bayesian inference of (co) variance components and genetic parameters for economic traits in iranian holsteins via gibbs sampling

the aim of this study was using bayesian approach via gibbs sampling (gs) for estimating genetic parameters of production, reproduction and health traits in iranian holstein cows. data consisted of 320666 first- lactation records of holstein cows from 7696 sires and 260302 dams collected by the animal breeding center of iran from year 1991 to 2010. (co) variance components were estimated using a multi-trait animal model analyzed via gibbs sampling. after convergence, the highest posterior density region of heritability for milk (my305), fat (fy305), protein (py305), age at first calving (afc), calving interval (ci) and somatic cell score (scs) were 0.255-0.275, 0.195-0.215, 0.195-0.225, 0.260-0.275, 0.065-0.080 and 0.055-0.075, respectively. genetic correlations ranged from -0.121 (between fy305 and afc) to 0.914 (between my305 and py305) and for phonotypic correlations, it was from -0.083 (between my305 and scs) to 0.929 (between my305 and py305. the result of this study showed that production traits and afc have enough genetic variation to develop breeding programs. the estimated genetic correlations suggest that milk production traits and ci would be affected if increasing milk production is the selection goal. the high genetic correlation between ci with scs suggests that increasing calving interval trait result in an increased scs.