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Peer-reviewedCC BYBreeding science

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

  • Kosuke Hamazaki1
  • Koji Tsuda1,2
  • Hiroyoshi Iwata3
  1. 1RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, 178-4-4 Wakashiba, Kashiwa, Chiba 277-0871, Japan.
  2. 2Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan.
  3. 3Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan.
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{hamazaki2026optimizing,
  title = {{Optimizing progeny allocation strategies in breeding schemes while updating genomic prediction models.}},
  author = {Kosuke Hamazaki and Koji Tsuda and Hiroyoshi Iwata},
  journal = {Breeding science},
  year = {2026},
  month = jun,
  doi = {10.1270/jsbbs.25056},
  url = {https://europepmc.org/article/MED/42936430},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Optimizing progeny allocation strategies in breeding schemes while updating genomic prediction models.
AU  - Kosuke Hamazaki
AU  - Koji Tsuda
AU  - Hiroyoshi Iwata
PY  - 2026
DA  - 2026/06/25
JO  - Breeding science
DO  - 10.1270/jsbbs.25056
UR  - https://europepmc.org/article/MED/42936430
AB  - 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.
SN  - 1344-7610
SN  - 1347-3735
ER  -