Bayesian single-step genomic evaluations combining local and foreign information in Walloon Holsteins

Area of Science:

• Animal Genetics and Breeding
• Genomic Evaluation
• Dairy Cattle Improvement

Background:

• Small dairy cattle populations often rely on imported bulls and foreign genetic evaluations.
• Existing single-step Genomic BLUP (ssGBLUP) methods primarily use local data, limiting the incorporation of international information.
• Holstein breeders in Wallonia require advanced genetic evaluation services.

Purpose of the Study:

• To develop and implement an alternative method for Walloon genomic evaluations that incorporates foreign data.
• To improve the accuracy of genomically enhanced breeding values (GEBV) in small dairy cattle populations.
• To address the limitations of ssGBLUP in utilizing international Multiple Across Country Evaluation (MACE) results.

Main Methods:

• Developed and implemented a single-step Genomic Bayesian Prediction (ssGBayes) method.
• ssGBayes approximates equations using estimated breeding values (EBV) and reliabilities (REL) without explicit deregresssion.
• Combined local Walloon EBV/REL with foreign MACE EBV/REL, accounting for potential double-counting of information.

Main Results:

• The ssGBayes method computed more accurate predictions for all animal categories.
• Genotyped animals with low Walloon reliabilities showed an average increase of 0.38 in trait reliability due to MACE data inclusion.
• The Walloon genomic evaluation system successfully passed Interbull GEBV tests and was implemented for routine evaluations.

Conclusions:

• The ssGBayes methodology effectively integrates local and foreign data for more accurate genomic predictions.
• This approach is particularly beneficial for small dairy cattle populations seeking to leverage international genetic information.
• The developed system offers a valuable tool for enhancing genetic evaluations in Wallonia and similar populations globally.