TY  - JA
AU  - Yang Xu
AU  - Wenyan Yang
AU  - Jie Qiu
AU  - Kai Zhou
AU  - Guangning Yu
AU  - Yuxiang Zhang
AU  - Xin Wang
AU  - Yuxin Jiao
AU  - Xinyi Wang
AU  - Shujun Hu
AU  - Xuecai Zhang
AU  - Pengcheng Li
AU  - Yue Lu
AU  - Rujia Chen
AU  - Tianyun Tao
AU  - Zefeng Yang
AU  - Yunbi Xu
AU  - Chenwu Xu
TI  - Metabolic marker-assisted genomic prediction improves hybrid breeding
SN  - 2590-3462
PY  - 2025///
CY  - United States of America
PB  - Cell Press, Plant Communications Shanghai Editorial
KW  - Genomics
KW  - AGROVOC
KW  - Forecasting
KW  - Hybrids
KW  - Breeding
KW  - Metabolome
KW  - Association mapping
KW  - Marker-assisted selection
KW  - Maize
KW  - Genome-wide association studies
N1  - Peer review; Open access
N2  - 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
UR  - https://hdl.handle.net/10883/35473
T2  - Plant Communications
DO  - https://doi.org/10.1016/j.xplc.2024.101199
ER  -