Optimizing progeny allocation strategies in breeding schemes while updating genomic prediction models

Genomic selection has revolutionized breeding by enabling early identification of superior individuals using genome-wide markers. Over the past decade, new selection and mating strategies leveraging optimization methods have been introduced to improve decision-making in breeding programs. However, optimizing breeding remains challenging when the positions and effects of quantitative trait loci are unknown. We developed a framework that optimizes breeding strategies while updating genomic prediction models during recurrent selection programs. In these programs, selection, crossing, and progeny allocation are repeated across generations under limited population sizes. By updating prediction models at intermediate generations, we re-optimized how progeny are allocated among crosses, i.e., the number of progeny assigned to each cross under a fixed total population size, based on newly accumulated data. Our simulations compared this approach with equal allocation and optimal cross selection methods across various conditions. The optimized allocation strategy significantly outperformed other approaches under moderate to low selection intensities, particularly when combined with model updates. While genetic gains plateaued without updates, our approach with updates enabled continuous improvement through the fourth generation of recurrent selection. The framework showed robustness across different conditions and maintained genetic diversity, confirming its effectiveness under estimated marker effects intended for real-world breeding programs.