A Genetic Analysis of Mortality in Pigs

Abstract

An analysis of mortality is undertaken in two breeds of pigs: Danish Landrace and Yorkshire. Zeroinflated and standard versions of hierarchical Poisson, binomial, and negative binomial Bayesian models were fitted using Markov chain Monte Carlo (MCMC). The objectives of the study were to investigate whether there is support for genetic variation for mortality and to study the quality of fit and predictive properties of the various models. In both breeds, the model that provided the best fit to the data was the standard binomial hierarchical model. The model that performed best in terms of the ability to predict the distribution of stillbirths was the hierarchical zero-inflated negative binomial model. The best fit of the binomial hierarchical model and of the zero-inflated hierarchical negative binomial model was obtained when genetic variation was included as a parameter. For the hierarchical binomial model, the estimate of the posterior mean of the additive genetic variance (posterior standard deviation in brackets) at the level of the logit of the probability of a stillbirth was 0:173 0:039 ð Þin Landrace and 0:202 0:048 ð Þin Yorkshire. The implications of these results from a breeding perspective are briefly discussed.

Joint Analysis of Binomial and Continuous Traits with a Recursive Model: A Case Study Using Mortality and Litter Size of Pigs

“…In previous work we performed genetic analyses of count data using a number of discrete models (Varona and Sorensen 2010) with an illustration using mortality in pigs. Mortality data show overdispersion, due to a high proportion of litter records with an absence of mortality and heterogeneity induced by covariation among observations.…”

“…The model that showed the best global fit was a hierarchical binomial logit model and was therefore chosen in this work. In contrast with the models implemented by Varona and Sorensen (2010), in this work the logit of the probability of mortality is assumed to be functionally related to litter size. Both linear and nonlinear functions at the level of the logit were studied and the results indicate that in Landrace, the linear relationship leads to the best global fit.…”

“…These studies have assumed normality of the sampling model for mortality (e.g., Van Arendonk et al 1996), based inferences on a variety of threshold models (e.g., Roehe and Kalm 2000;Arango et al 2006), or implemented mixed models for count data (Varona and Sorensen 2010). Mortality data, regarded as a trait of the mother, show typically a large proportion of "zeros" (many litters do not have stillborn piglets).…”

“…Mortality data, regarded as a trait of the mother, show typically a large proportion of "zeros" (many litters do not have stillborn piglets). The study of Varona and Sorensen (2010) included a variety of models that accounted for this feature of the data and concluded that the best fit was achieved with a hierarchical binomial logit mixed model. In this work we extend this model in two directions.…”

Cytoplasmic line effects for birth weight and preweaning growth traits in the Asturiana de los Valles beef cattle breed

“…Model comparison was computed by the logarithm of the conditional predictive ordinate (logCPO) (Pettit and Young, 1990;Varona and Sorensen, 2010;Varona et al, 1997) for each observation. CPO is a cross-validated predictive approach i.e., predictive distributions conditioned on the observed data with a single data point deleted.…”