Genotype-Driven Phenotype Prediction in Onion Breeding: Machine Learning Models for Enhanced Bulb Weight Selection

Junhwa Choi, Sunghyun Cho, Subin Choi, Myunghee Jung, Yu-jin Lim, Eunchae Lee, Jaewon Lim, Han Yong Park () and Younhee Shin ()
Additional contact information
Junhwa Choi: Institute of Breeding Research, MIRACLE Co., Ltd., Jeju 63022, Republic of Korea
Sunghyun Cho: Research and Development Center, Insilicogen Inc., 13, Yongin-si 16954, Republic of Korea
Subin Choi: Department of Bioresource Engineering, Sejong University, Seoul 05006, Republic of Korea
Myunghee Jung: Research and Development Center, Insilicogen Inc., 13, Yongin-si 16954, Republic of Korea
Yu-jin Lim: Research and Development Center, Insilicogen Inc., 13, Yongin-si 16954, Republic of Korea
Eunchae Lee: Research and Development Center, Insilicogen Inc., 13, Yongin-si 16954, Republic of Korea
Jaewon Lim: Research and Development Center, Insilicogen Inc., 13, Yongin-si 16954, Republic of Korea
Han Yong Park: Department of Bioresource Engineering, Sejong University, Seoul 05006, Republic of Korea
Younhee Shin: Research and Development Center, Insilicogen Inc., 13, Yongin-si 16954, Republic of Korea

Agriculture, 2024, vol. 14, issue 12, 1-16

Abstract: Onions ( Allium cepa L.) are a globally significant horticultural crop, ranking second only to tomatoes in terms of cultivation and consumption. However, due to the crop’s complex genome structure, lengthy growth cycle, self-incompatibility, and susceptibility to disease, onion breeding is challenging. To address these issues, we implemented digital breeding techniques utilizing genomic data from 98 elite onion lines. We identified 51,499 high-quality variants and employed these data to construct a genomic estimated breeding value (GEBV) model and apply machine learning methods for bulb weight prediction. Validation with 260 new individuals revealed that the machine learning model achieved an accuracy of 83.2% and required only thirty-nine SNPs. Subsequent in silico crossbreeding simulations indicated that offspring from the top 5% of elite lines exhibited the highest bulb weights, aligning with traditional phenotypic selection methods. This approach demonstrates that early-stage selection based on genotypic information followed by crossbreeding can achieve economically viable breeding results. This methodology is not restricted to bulb weight and can be applied to various horticultural traits, significantly improving the efficiency of onion breeding through advanced digital technologies. The integration of genomic data, machine learning, and computer simulations provides a powerful framework for data-driven breeding strategies, accelerating the development of superior onion varieties to meet global demand.

Keywords: Allium cepa; bulb weight; digital breeding; horticultural crops; machine learning; onion (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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