Metabolic marker-assisted genomic prediction improves hybrid breeding.

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 selecting desirable combinations from a 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 the prediction accuracy for complex traits, such as incorporating functional markers and multi-omics data. Metabolome-wide association studies (MWAS) help identify metabolites closely linked to phenotype, 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) that 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 GP for all traits, regardless of the methods used (GBLUP or XGBoost). On average, MM_GP yielded 4.6% and 13.6% higher predictive abilities compared to GP in maize and rice, respectively. Additionally, MM_GP could match or even surpass the predictive ability of M_GP (integrated genomic-metabolomic prediction) for most traits. Notably, integrating only six metabolic markers significantly related to multiple traits resulted in a 5.0% and 3.1% higher average predictive ability than GP and M_GP in maize, respectively. With the advancement of high-throughput metabolomics technologies and prediction models, this approach holds great promise to revolutionize genomic hybrid breeding by enhancing its accuracy and efficiency.