A bivariate quantitative genetic model for a linear Gaussian trait and a survival trait

Area of Science:

• Animal breeding and genetics
• Quantitative genetics
• Statistical modeling

Background:

• Survival models are increasingly used in animal breeding for traits like longevity and disease resistance.
• There is a growing need for methods to estimate genetic correlations and perform multivariate evaluations involving survival traits.

Purpose of the Study:

• To derive and implement a bivariate quantitative genetic model for a linear Gaussian trait and a survival trait.
• To estimate genetic and environmental correlations between these traits.
• To facilitate multivariate genetic evaluations in animal populations.

Main Methods:

• A bivariate quantitative genetic model was developed, incorporating a Weibull log-normal animal frailty model for the survival trait.
• A Bayesian approach using Gibbs sampling was adopted for parameter inference.
• Metropolis-Hasting and adaptive rejection sampling techniques were used for updating model parameters.

Main Results:

• The developed model successfully infers additive genetic and environmental correlations.
• Simulation results demonstrated that the estimated marginal posterior distributions accurately reflected the true parameter values.
• The method allows for joint genetic evaluation of linear and survival traits.

Conclusions:

• The proposed bivariate model provides a robust framework for analyzing correlated linear and survival traits in animal breeding.
• This approach advances the genetic assessment of complex traits, including livestock longevity and disease resilience.
• The method facilitates more comprehensive genetic evaluations by integrating diverse trait types.