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Xu, Y., Yang, W., Qiu, J., Zhou, K., Yu, G., Zhang, Y., Wang, X., Jiao, Y., Wang, X., Hu, S., Zhang, X., Li, P., Lu, Y., Chen, R., Tao, T., Yang, Z., Xu, Y., & Xu, C. (2025). Metabolic marker-assisted genomic prediction improves hybrid breeding. Plant Communications, 6(3), 101199. https://doi.org/10.1016/j.xplc.2024.101199
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Abstract
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Hybrid breeding is widely acknowledged as the most effective method for increasing crop yield, particularly in maize and rice. However, a major challenge in hybrid breeding is the selection of desirable combinations from the vast pool of potential crosses. Genomic selection (GS) has emerged as a powerful tool to tackle this challenge, but its success in practical breeding depends on prediction accuracy. Several strategies have been explored to enhance prediction accuracy for complex traits, such as the incorporation of functional markers and multi-omics data. Metabolome-wide association studies (MWAS) help to identify metabolites that are closely linked to phenotypes, known as metabolic markers. However, the use of preselected metabolic markers from parental lines to predict hybrid performance has not yet been explored. In this study, we developed a novel approach called metabolic marker-assisted genomic prediction (MM_GP), which incorporates significant metabolites identified from MWAS into GS models to improve the accuracy of genomic hybrid prediction. In maize and rice hybrid populations, MM_GP outperformed genomic prediction (GP) for all traits, regardless of the method used (genomic best linear unbiased prediction or extreme gradient boosting). On average, MM_GP demonstrated 4.6% and 13.6% higher predictive abilities than GP for maize and rice, respectively. MM_GP could also match or even surpass the predictive ability of M_GP (integrated genomic-metabolomic prediction) for most traits. In maize, the integration of only six metabolic markers significantly associated with multiple traits resulted in 5.0% and 3.1% higher average predictive ability compared with GP and M_GP, respectively. With advances in high-throughput metabolomics technologies and prediction models, this approach holds great promise for revolutionizing genomic hybrid breeding by enhancing its accuracy and efficiency.
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Journal
Plant Communications
Journal volume
6
Journal issue
3
Article number
101199
Place of Publication
United States of America
Publisher
Cell Press
Plant Communications Shanghai Editorial
Plant Communications Shanghai Editorial
Donor or Funder
Key Research and Development Program
Natural Science Foundation of China
Jiangsu Province Agricultural Science and Technology Independent Innovation
Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
Seed Industry Revitalization Project of Jiangsu Province
Jiangsu Provincial Key Research and Development Program
Shenzhen Science and Technology Innovation Program
S&T Program of Hebei
Shanghai Agricultural Science and Technology Innovation Program
Shandong Provincial Technology Innovation Program
Qing Lan Project of Jiangsu Province
Yangzhou University High-end Talent Support Program
Natural Science Foundation of China
Jiangsu Province Agricultural Science and Technology Independent Innovation
Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
Seed Industry Revitalization Project of Jiangsu Province
Jiangsu Provincial Key Research and Development Program
Shenzhen Science and Technology Innovation Program
S&T Program of Hebei
Shanghai Agricultural Science and Technology Innovation Program
Shandong Provincial Technology Innovation Program
Qing Lan Project of Jiangsu Province
Yangzhou University High-end Talent Support Program
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Breeding for Tomorrow