Abstract
SummaryMarker‐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 from both populations into the training set, the TGW of hybrids could be predicted but was largely dependent on population structures. Then, core hybrids from each population were determined by principal component analysis and the TGW of hybrids in both environments were successfully predicted (r > 0.60). Moreover, adjusting the population structures and numbers of predictive analytes increased TGW predictability for hybrids in 2015 (r = 0.72). Our study demonstrates that the TGW of F1 hybrids across environments can be accurately predicted based on parental untargeted metabolite profiles with a core hybridisation strategy in rice. Metabolic biomarkers identified from early developmental stage tissues, which are grown under experimental conditions, may represent a workable approach towards the robust prediction of major agronomic traits for climate‐adaptive varieties.
Highlights
The utilisation of hybrid vigour in crops and livestock has achieved substantial production improvement over the past decades
18 representative indica and japonica (Oryza sativa L. ssp.) were the parents, and a complete diallel cross-design was adopted in the hybridisation procedure
The selected 18 inbred lines, which consisted of typical indica, intermediate types and typical japonica, had distinct grain performance (Figure 1a) and different thousand grain weight (TGW) values (Figure 1b)
Summary
The utilisation of hybrid vigour in crops and livestock has achieved substantial production improvement over the past decades. Randomness in breeding programmes severely affects breeding efficiency, and much room for improvement exists in the current pace of hybrid breeding. Marker-assisted selection and genomic selection are expected to improve breeding efficiency, and different types of biomarkers are used to predict hybrid performances. Several obstacles must be overcome before the markers can be appropriately applied to accelerate breeding programmes. The first obstacle is the relatedness between training and validation sets in the cross-validation procedure (Schulthess et al, 2017; Wray et al, 2013). Similar findings are reported in the prediction of yield and disease resistance in hybrid wheat (Gowda et al, 2014b; Zhao et al, 2015). Distant relatedness between test and estimation sets can severely decrease prediction accuracy
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