Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Breeding Program Optimization

Background:

• Conventional dairy cattle breeding relies on phenotyping for selection accuracy.
• Genomic selection (GS) has increased genetic gain but faces implementation challenges in small or resource-limited populations due to a lack of training animals and funds.
• Optimizing resource allocation between phenotyping and genotyping is crucial for maximizing genetic gain.

Purpose of the Study:

• To evaluate the impact of reallocating resources from phenotyping to genotyping on genetic gain in dairy cattle breeding.
• To assess the effectiveness of genomic selection in small populations under varying resource allocation scenarios.
• To determine if genomic selection can be implemented without increasing overall investment.

Main Methods:

• Simulated a case study of a small dairy population using various scenarios.
• Compared conventional progeny testing with genomic selection scenarios.
• Varied the number of phenotypic records per lactation and investment in genotyping.
• Tested scenarios with different relative costs of phenotyping to genotyping and with/without an initial training population.

Main Results:

• Reallocating resources from phenotyping to genotyping increased genetic gain compared to conventional selection.
• Increased genotyping led to higher genetic gain, even with reduced phenotyping.
• High-genotyping scenarios demonstrated potential resource savings.
• Genomic selection improved accuracy for young and proven animals.

Conclusions:

• Optimizing investment in phenotyping and genotyping is key to maximizing return on investment in dairy breeding programs.
• Genomic selection can be implemented by dairy breeding programs using conventional progeny testing with repeated milk records without increasing investment.
• Resource reallocation towards genotyping offers a viable strategy for enhancing genetic gain, particularly in resource-constrained settings.