Multi-Breed Genomic Predictions for Average Daily Gain in Three Italian Beef Cattle Breeds
Daniele Colombi1, Renzo Bonifazi2, Fiorella Sbarra3
1Dipartimento di Scienze Agrarie, Alimentari e Ambientali, University of Perugia, Perugia, Italy.
Summary
Genomic data and multi-breed models significantly improve genomic prediction accuracy for average daily gain in Italian beef cattle. Combining data across breeds and including correlated traits enhances genetic selection, especially for breeds with limited data.
Area of Science:
- Animal Genetics and Breeding
- Quantitative Genetics
- Genomic Prediction
Background:
- Italian autochthonous beef cattle breeds (Marchigiana, Chianina, Romagnola) are selected for meat production.
- Genomic data and multi-breed (MB) models can enhance prediction accuracy, particularly with limited within-breed data.
- Accurate genomic predictions are crucial for efficient genetic improvement in livestock.
Purpose of the Study:
- To evaluate and compare the accuracies of genomic predictions for average daily gain (ADG) in three Italian beef cattle breeds.
- To assess the impact of single-breed vs. multi-breed models and single-trait vs. multi-trait approaches.
- To determine the benefits of incorporating genomic data for genetic selection in local cattle populations.
Main Methods:
- Utilized phenotypes from 5303 young bulls, 23,793 pedigree records, and 4593 genotypes.
- Implemented pedigree Best Linear Unbiased Prediction (pBLUP) and single-step Genomic BLUP (ssGBLUP) models.
- Compared single-trait/single-breed, single-trait/multi-breed, and multi-trait/multi-breed evaluation scenarios, including correlated traits like weight and muscularity.
Main Results:
- Genomic data incorporation improved prediction accuracies by an average of 5% in ssGBLUP models compared to pBLUP.
- Single-trait multi-breed models increased accuracy by an average of 4% for breeds with lower ADG heritability.
- Multi-trait models including weight and muscularity further enhanced prediction accuracies beyond ADG-only models.
Conclusions:
- Multi-breed genomic models are effective in boosting prediction accuracy for average daily gain in Italian beef cattle.
- Leveraging data across breeds and incorporating correlated traits is beneficial for genetic improvement, especially for traits with low heritability or limited data.
- These findings support the application of genomic predictions for enhancing genetic gain in local livestock populations facing data limitations.
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