Genomic selection in dairy cattle simulated populations.

Area of Science:

• Animal Breeding and Genetics
• Quantitative Genetics
• Agricultural Economics

Background:

• Genomic selection offers substantial potential for enhancing genetic gain in domestic animals.
• However, the implementation of genomic selection is often associated with high costs.
• Evaluating the economic impact is crucial for its successful adoption in breeding programs.

Purpose of the Study:

• To assess the economic viability of implementing genomic selection in a simulated dairy cattle population.
• To compare the effectiveness of different selection strategies, including genomic estimated breeding values (GEBV).
• To determine the genetic gain rates and inbreeding levels associated with each strategy.

Main Methods:

• Simulated genomic and phenotypic data using QMSim software with realistic genetic parameters.
• Modeled a dairy cattle population over multiple generations with selection for milk yield.
• Compared four selection strategies: random selection (RS), phenotypic selection (PS), BLUP, and genomic BLUP (GBlup/GBlupi) with varying marker densities.

Main Results:

• Genomic selection strategies (GBlup/GBlupi) and BLUP demonstrated superior genetic gain rates compared to RS and PS.
• Genomic selection effectively reduced the average inbreeding coefficient within the population.
• Only BLUP and GBlup/GBlupi strategies proved economically viable under the specified discount rate.

Conclusions:

• Genomic information aids in achieving higher genetic gain and lower inbreeding in dairy cattle breeding.
• Economic analysis confirms the feasibility of BLUP and genomic selection when integrated with milk recording and genetic evaluation.
• The findings support the strategic implementation of genomic selection for sustainable dairy cattle improvement.