Enhancing Genomic Prediction Accuracy for Body Conformation Traits in Korean Holstein Cattle

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Dairy Cattle Breeding

Background:

• The Holstein breed is crucial for Korean dairy production.
• Accurate genomic prediction of body conformation traits is vital for genetic improvement in cattle.

Purpose of the Study:

• To evaluate genomic prediction accuracy for 24 body conformation traits in Korean Holstein cattle.
• To assess the impact of different π levels in Bayesian methods (BayesB and BayesC) on prediction accuracy.
• To investigate the effect of incorporating parent average into deregressed estimated breeding values.

Main Methods:

• Bayesian methods (BayesB and BayesC) were applied with varying π levels (0.75, 0.90, 0.99, 0.995).
• Deregressed estimated breeding values, including parent average, were used as the response variable.
• Genome-wide association studies (GWAS) were conducted to identify significant genomic regions.

Main Results:

• Prediction accuracy generally increased with higher π levels for specific traits, varying by Bayesian method.
• The highest accuracy was observed for rear teat angle when parent average was included.
• Incorporating parent average into deregressed estimated breeding values enhanced genomic prediction accuracy.
• Eighteen significant genomic window regions were identified through GWAS.

Conclusions:

• Genomic selection for body conformation traits in Korean Holsteins can be enhanced by optimizing Bayesian method parameters and incorporating parental information.
• The study provides insights into improving genomic prediction models for dairy cattle.
• Identified genomic regions offer targets for future fine mapping and causal mutation discovery.