Xu Yang, Yang Wenyan, Qiu Jie, Zhou Kai, Yu Guangning, Zhang Yuxiang, Wang Xin, Jiao Yuxin, Wang Xinyi, Hu Shujun, Zhang Xuecai, Li Pengcheng, Lu Yue, Chen Rujia, Tao Tianyun, Yang Zefeng, Xu Yunbi, Xu Chenwu
Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Zhongshan Biological Breeding Laboratory/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou 225009, China.
Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China.
Plant Commun. 2025 Mar 10;6(3):101199. doi: 10.1016/j.xplc.2024.101199. Epub 2024 Nov 29.
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.
杂交育种被广泛认为是提高作物产量的最有效方法，尤其是在玉米和水稻中。然而，杂交育种的一个主要挑战是从大量潜在杂交组合中选择理想的组合。基因组选择（GS）已成为应对这一挑战的有力工具，但其在实际育种中的成功取决于预测准确性。人们已经探索了几种策略来提高复杂性状的预测准确性，例如纳入功能标记和多组学数据。全代谢组关联研究（MWAS）有助于识别与表型密切相关的代谢物，即代谢标记。然而，尚未探索使用来自亲本系的预选代谢标记来预测杂种性能。在本研究中，我们开发了一种名为代谢标记辅助基因组预测（MM_GP）的新方法，该方法将从MWAS中鉴定出的重要代谢物纳入GS模型，以提高基因组杂种预测的准确性。在玉米和水稻杂交群体中，无论使用何种方法（基因组最佳线性无偏预测或极端梯度提升），MM_GP在所有性状上均优于基因组预测（GP）。平均而言，MM_GP在玉米和水稻中的预测能力分别比GP高4.6%和13.6%。对于大多数性状，MM_GP的预测能力也可以与M_GP（整合基因组-代谢组预测）相匹配甚至超越。在玉米中，仅整合六个与多个性状显著相关的代谢标记，其平均预测能力分别比GP和M_GP高5.0%和3.1%。随着高通量代谢组学技术和预测模型的发展，这种方法有望通过提高基因组杂交育种的准确性和效率来彻底改变该领域。
