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Genomic prediction models based on a large-scale recombinant population allow quick breeding of high-yield rice

Sakai, T.; Abe, A.; Takagi, H.; Fujioka, T.; Oota, Y.; Nakajo, S.; Yaegashi, H.; Oikawa, K.; Utsushi, H.; Ito, K.; Natsume, S.; Shimizu, M.; Takeda, T.; Terauchi, R.

2025-09-15 genetics
10.1101/2025.09.14.676066 bioRxiv
Show abstract

Genomics-based breeding is a promising approach to generate crops with high yield and quality in a short timeframe. However, this approach is currently not suitable for simultaneously controlling multiple traits, necessitating development of a more efficient methodology. Here, we present a quick breeding strategy employing interpretable genomic prediction models generated using a large-scale recombinant population, which enables optimizing multiple traits in cultivars. To validate our strategy, we developed a nested association mapping population in rice (Oryza sativa) and generated its associated genomic models, which allowed rapid improvement of high-yielding rice cultivars. Our genomic breeding strategy provides a general framework to quickly breed high-yielding cultivars capable of coping with the challenges caused by a rapidly changing environment.

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