Multi-breed genetic evaluation in a Gelbvieh population

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Beef Cattle Breeding

Background:

• Accurate genetic evaluation is crucial for improving growth traits in beef cattle.
• Existing models often focus on single breeds, limiting cross-breed genetic insights.
• Incorporating breed-specific effects like heterosis and breed-of-founder is complex in multi-breed scenarios.

Purpose of the Study:

• To develop and present a multi-breed genetic evaluation model for beef cattle growth traits.
• To integrate direct and maternal genetic effects, heterosis, and breed-of-founder effects.
• To apply a Bayesian approach that balances prior literature estimates with dataset information.

Main Methods:

• A multi-breed genetic model was formulated, including fixed effects, random direct and maternal genetic effects, and maternal permanent environmental effects.
• Direct and maternal heterosis and breed-of-founder (BOF) x generation group effects were fitted using a Bayesian framework.
• The model was applied to American Gelbvieh Association data, with analyses varying the weights of prior literature versus dataset information.

Main Results:

• Significant differences in heterosis, BOF x generation group effects, and predicted breeding values were observed across breeds based on weighting strategies.
• The choice of weighting prior literature estimates versus data-derived estimates substantially influenced specific genetic parameter estimations.
• Within-breed rankings of predicted breeding values demonstrated relative robustness despite variations in prior information weighting.

Conclusions:

• The developed multi-breed model effectively incorporates complex genetic effects for improved beef cattle evaluations.
• Weighting prior information significantly impacts specific genetic parameter estimates, highlighting the importance of model parameterization.
• The model provides a robust framework for multi-breed genetic evaluations, with stable within-breed rankings offering reliable selection criteria.