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

Abstract

The major drawback to the implementation of genomic selection in a breeding program is the reduction of additive genetic variance in the long term, primarily due to the Bulmer effect. Increasing genetic gain and retaining additive genetic variance requires optimizing the trade-off between the two competing factors. Our approach integrated index selection in the genomic infer cross-selection (GCS) methods. With this strategy, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-year period, we evaluated different GCS metrics with other factors, such as the number of parents, crosses, and progenies per cross, that influence genetic gain in a breeding program. Across all breeding scenarios, the posterior mean-variance consistently enhances genetic gain when compared to other metrics such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progenies per cross that maximizes short- and long-term genetic gain in a breeding program.