Estimation of the environmental dam-offspring correlation in beef cattle

A long standing controversy in animal breeding is related to the strong negative estimates of the direct-maternal genetic correlation obtained when fitting data on maternally influenced traits. In this article, we focused on a model that introduces a new correlation parameter among dam-offspring records. The extant theory allows estimation of the parameter when dams have at most a single offspring. Our goal was to develop an inferential procedure in a more general setting. To do so, we applied a Bayesian approach and we showed that the estimation could be accomplished by introducing a Markov chain Monte Carlo (MCMC) step embedded into a regular Gibbs sampler program. The procedure was implemented by means of an MCMC algorithm known as the Griddy-Gibbs sampler, and a Fortran 90 library was created to accomplish the task. The computer program is available from http://www.agro.uba.ar/catedras/mg_animal/software/RDBLK. With this tool at hand, we applied the inferential procedure to weaning weight records on beef cattle calves from an Argentinean Hereford herd, and we estimated the marginal distribution of the environmental dam-offspring correlation parameter. The distribution was unimodal and symmetric with a mean value of -0.14 (±0.03) and a 95% high posterior density interval between -0.20 and -0.07, indicating that the model placed a huge mass on negative values of the parameter. Noticeably, the magnitude of the direct-maternal genetic correlation diminished from -0.61 to -0.37 with respect to the standard maternal animal model. This result reinforces the idea that environmental covariances among dam-offspring records may bias the estimate of the direct-maternal genetic correlation.