Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey

Area of Science:

• Animal Genetics and Breeding
• Quantitative Genetics
• Genomic Prediction

Background:

• Small reference populations limit genomic prediction accuracy in numerically small cattle breeds like Danish Jersey.
• Accurate genomic prediction is crucial for efficient genetic improvement and selection in livestock populations.

Purpose of the Study:

• To investigate methods for improving genomic prediction accuracy in Danish Jersey cattle.
• To assess the impact of expanding the reference population by including international data and female animals.

Main Methods:

• Genomic Best Linear Unbiased Prediction (GBLUP) model used for breeding value prediction.
• De-regressed proofs served as response variables for validation.
• Two validation strategies were employed: on bulls and on cows, with varying reference population compositions.

Main Results:

• Including North American Jersey bulls improved genomic prediction reliability for six of eight traits in bulls (average gain of 3%).
• In cows, including US bulls increased reliability by 6.6%, while including Danish cows boosted it by 8.2% (averaged over six traits).
• The combined inclusion of US bulls and Danish cows yielded the largest gain (10.5%), demonstrating synergistic benefits.

Conclusions:

• Sharing reference data across populations and incorporating female animals are effective strategies to enhance genomic prediction reliability.
• Genomic selection is a viable and promising tool for genetic improvement in numerically small cattle breeds.
• The findings support international data sharing and broader reference population composition for robust genomic evaluations.