Genomic prediction for Nordic Red Cattle using one-step and selection index blending

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Genomic Selection

Background:

• Accurate breeding values are crucial for genetic improvement in livestock.
• Genomic selection offers potential to enhance prediction accuracy over traditional methods.
• Evaluating different genomic prediction models is essential for practical application.

Purpose of the Study:

• To compare the accuracy of direct genomic breeding values (DGV) and genomic enhanced breeding values (GEBV) using different blending approaches.
• To assess the reliability of genomic predictions in Nordic Red Cattle.
• To determine the optimal method for incorporating genomic information into genetic evaluations.

Main Methods:

• Genomic BLUP model for DGV calculation.
• One-step and selection index blending approaches for GEBV estimation.
• Validation using deregressed proofs (DRP) from a test data set of young bulls.
• Analysis of 15 traits in 6,631 Nordic Red Cattle bulls, with 4,408 genotyped.

Main Results:

• Direct genomic breeding values (DGV) were 11.0 percentage points more reliable than conventional pedigree index for bulls without daughter records.
• Selection index blending yielded an additional 0.9% gain in reliability.
• One-step blending achieved a 1.3% gain by integrating genotyped and nongenotyped bulls.
• Weighting factors influenced GEBV variation more than reliability.

Conclusions:

• Genomic selection substantially enhances the accuracy of pre-selection for young bulls in Nordic Red Cattle.
• The one-step blending approach is a viable and effective method for predicting GEBV in routine genetic evaluations.
• Integrating genomic data improves prediction accuracy, facilitating more efficient genetic gain.