Abstract
With the increasing use of survival models in animal breeding to address the genetic aspects of mainly longevity of livestock but also disease traits, the need for methods to infer genetic correlations and to do multivariate evaluations of survival traits and other types of traits has become increasingly important. In this study we derived and implemented a bivariate quantitative genetic model for a linear Gaussian and a survival trait that are genetically and environmentally correlated. For the survival trait, we considered the Weibull log-normal animal frailty model. A Bayesian approach using Gibbs sampling was adopted. Model parameters were inferred from their marginal posterior distributions. The required fully conditional posterior distributions were derived and issues on implementation are discussed. The two Weibull baseline parameters were updated jointly using a Metropolis-Hasting step. The remaining model parameters with non-normalized fully conditional distributions were updated univariately using adaptive rejection sampling. Simulation results showed that the estimated marginal posterior distributions covered well and placed high density to the true parameter values used in the simulation of data. In conclusion, the proposed method allows inferring additive genetic and environmental correlations, and doing multivariate genetic evaluation of a linear Gaussian trait and a survival trait.
Highlights
In recent years, several breeding organizations have implemented a routine genetic evaluation of sires for longevity of dairy cows
The focus was on validating the estimation of parameters under conditions where all model assumptions were satisfied, and not on issues related to the robustness of the model
In this study we describe a Gibbs sampler for joint Bayesian analysis of a linear Gaussian trait and a survival trait
Summary
Several breeding organizations have implemented a routine genetic evaluation of sires for longevity of dairy cows. For genetic evaluation of animals based on several traits, a multivariate analysis is advantageous both. Korsgaard in increasing the efficiency with which animals are ranked for selection, and in providing information about the genetic correlation between traits [10, 23, 25] The latter measures the extent to which different traits are controlled by the same genes and provides important information about how selective breeding is expected to lead to correlated and not necessarily favorable responses in different traits. The bias introduced by artificial selection for one or more of the traits considered will be avoided in a multivariate analysis as opposed to the corresponding univariate analyses
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have