A metabolome‐based core hybridisation strategy for the prediction of rice grain weight across environments

Abstract

Summary 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 F 1 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 F 1 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.

Metabolome-based prediction of yield heterosis contributes to the breeding of elite rice

“…A correlation analysis between the observed and predicted BPH-YPPs indicated that the predictability of BPH-YPP for Pop2015 was further improved (Fig 3C; r = 0.44, P = 0.001). Therefore, for predicting component traits (e.g., grain weight) (Dan et al, 2019), the dissection of population structures as the initial step in building predictive models is better than the screening of predictive variables.…”

“…Details of the plant materials have been presented previously (Dan et al, 2019). Briefly, two hybrid populations were used to explore predictive models of heterosis for rice grain yield.…”

“…The metabolome is a bridge between the genome and phenome, and metabolite levels have close and comprehensive connections with polygenic complex traits (Schauer et al, 2006; Riedelsheimer et al, 2012a, 2012b). Metabolic prediction of agronomic traits has been preliminarily explored in maize and rice (Dan et al, 2016; Xu et al, 2016; de Abreu et al, 2017), and metabolic analytes detected in young rice seedlings can predict grain weight in hybrids from different populations (Dan et al, 2019). However, the accurate prediction of yield heterosis remains a challenge because the mechanisms of yield heterosis are highly complex and the corresponding component traits (grain weight, grain number per panicle, tiller number, and seed setting rate) are polygenic.…”

A Simplified Method to Isolate Rice Mitochondria

“…Generally, those dysfunctions resulted from the disorders on the transcriptional, post-transcriptional and translational regulations. Many groups have devoted to the plant mitochondrial omics studies, and the interaction between nuclear and mitochondrion has contributed to the speciation and cytoplasmic male sterility which could be applied to explore the crop hybrid vigor [15][16][17][18][19][20][21][22].…”

A simplified method to isolate rice mitochondria

“…Generally, these dysfunctions result from disorders of transcriptional, post-transcriptional and translational regulation. Many groups have performed plant mitochondrial omics studies, and the interaction between the nucleus and the mitochondrion has contributed to speciation and cytoplasmic male sterility, which could be applied to explore crop hybrid vigor [15][16][17][18][19][20][21][22].…”