Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program

Area of Science:

• Plant breeding
• Quantitative genetics
• Genomic selection

Background:

• Genomic selection offers rapid genetic improvement but risks long-term additive genetic variance reduction.
• Balancing short-term genetic gain with long-term genetic variance is crucial for sustainable breeding programs.

Purpose of the Study:

• To develop an integrated index selection approach within the genomic inferred cross-selection (GCS) framework.
• To maximize genetic gain across multiple traits while retaining additive genetic variance.

Main Methods:

• Utilized a stochastic simulated recurrent breeding program over 40 years.
• Evaluated different GCS methods, including posterior mean variance, usefulness criterion, and others.
• Assessed the impact of parent, cross, and progeny numbers on genetic gain.

Main Results:

• The posterior mean variance approach consistently enhanced genetic gain compared to other methods.
• Identified optimal crosses that maximize progeny performance and maintain genetic variance.
• Provided strategies for optimizing parent, cross, and progeny numbers for maximal short- and long-term gain.

Conclusions:

• The integrated index selection within GCS effectively balances genetic gain and variance.
• Posterior mean variance is a superior method for enhancing genetic gain in breeding programs.
• Optimizing breeding program parameters is key to maximizing both short- and long-term genetic gain.