biostudies-literatureDan ZOpen Research Fund of State Key Laboratory of Hybrid RiceNational Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Rice Industry Technology System906-913https://www.ebi.ac.uk/biostudies/studies/S-EPMC6587747biostudies-literatureUnknown17(5)Marker-based prediction holds great promise for improving current plant and animal breeding efficiencies. However, the predictabilities of complex traits are always severely affected by negative factors, including distant relatedness, environmental discrepancies, unknown population structures, and indeterminate numbers of predictive variables. In this study, we utilised two independent F₁ hybrid populations in the years 2012 and 2015 to predict rice thousand grain weight (TGW) using parental untargeted metabolite profiles with a partial least squares regression method. A stable predictive model for TGW was built based on hybrids from the population in 2012 (r = 0.75) but failed to properly predict TGW for hybrids from the population in 2015 (r = 0.27). After integrating hybrids Plant biotechnology journalA metabolome-based core hybridisation strategy for the prediction of rice grain weight across environments.PMC658774731771746CARS-01-072017YFD0100400Dan ZHuang JHu JZhu YHuang WChen YYao GXu YfalseA metabolome-based core hybridisation strategy for the prediction of rice grain weight across environments.Marker-based prediction holds great promise for improving current plant and animal breeding efficiencies. However, the predictabilities of complex traits are always severely affected by negative factors, including distant relatedness, environmental discrepancies, unknown population structures, and indeterminate numbers of predictive variables. In this study, we utilised two independent F₁ hybrid populations in the years 2012 and 2015 to predict rice thousand grain weight (TGW) using parental untargeted metabolite profiles with a partial least squares regression method. A stable predictive model for TGW was built based on hybrids from the population in 2012 (r = 0.75) but failed to properly predict TGW for hybrids from the population in 2015 (r = 0.27). After integrating hybrids 2019-01-01T00:00:00Z2019 May2025-04-22T00:08:29.572Z2019-07-24T07:25:30ZS-EPMC65877473032148210.1111/pbi.13024