REML (restricted maximum likelihood) has become the standard method of variance component estimation in
animal breeding. Inference in Bayesian animal models is typically based upon
Markov chain Monte Carlo (MCMC) methods, which are generally flexible but time-consuming.
Recently, a new Bayesian computational method, integrated nested
Laplace approximation (INLA), has been introduced for making fast
non-sampling-based Bayesian inference for hierarchical latent Gaussian
models. This paper is concerned with the comparison of estimates provided by
three representative programs (ASReml, WinBUGS and the R package AnimalINLA)
of the corresponding methods (REML, MCMC and INLA), with a view to their
applicability for the typical animal breeder. Gaussian and binary as well
as simulated data were used to assess the relative efficiency of the methods.
Analysis of 2319 records of body weight at 35 days of age from a broiler
line suggested a purely additive animal model, in which the heritability
estimates ranged from 0.31 to 0.34 for the Gaussian trait and from 0.19 to
0.36 for the binary trait, depending on the estimation method. Although in
need of further development, AnimalINLA seems a fast program for Bayesian
modeling, particularly suitable for the inference of Gaussian traits, while
WinBUGS appeared to successfully accommodate a complicated structure between
the random effects. However, ASReml remains the best practical choice for
the serious animal breeder
