Metabolomic prediction of yield in hybrid rice.
Rice (Oryza sativa) provides a staple food source for more than 50% of the world's population. An increase in yield can significantly contribute to global food security. Hybrid breeding can potentially help to meet this goal because hybrid rice often shows a considerable increase in yield when compared with pure-bred cultivars. We recently developed a marker-guided prediction method for hybrid yield and showed a substantial increase in yield through genomic hybrid breeding. We now have transcriptomic and metabolomic data as potential resources for prediction. Using six prediction methods, including least absolute shrinkage and selection operator (LASSO), best linear unbiased prediction (BLUP), stochastic search variable selection, partial least squares, and support vector machines using the radial basis function and polynomial kernel function, we found that the predictability of hybrid yield can be further increased using these omic data. LASSO and BLUP are the most efficient methods for yield prediction. For high heritability traits, genomic data remain the most efficient predictors. When metabolomic data are used, the predictability of hybrid yield is almost doubled compared with genomic prediction. Of the 21 945 potential hybrids derived from 210 recombinant inbred lines, selection of the top 10 hybrids predicted from metabolites would lead to a ~30% increase in yield. We hypothesize that each metabolite represents a biologically built-in genetic network for yield; thus, using metabolites for prediction is equivalent to using information integrated from these hidden genetic networks for yield prediction.
Department of Botany and Plant Sciences, University of California, Riverside, CA, 92507, USA.
Shizhong
S
Xu
Department of Botany and Plant Sciences, University of California, Riverside, CA, 92507, USA.
Yang
Y
Xu
National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China.
Liang
L
Gong
National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China.
Qifa
Q
Zhang
D001947:Breeding; D053263:Gene Regulatory Networks; D018745:Genome, Plant / Q000235:genetics; D006824:Hybridization, Genetic; D055432:Metabolomics; D012275:Oryza / Q000235:genetics / Q000378:metabolism*; D010940:Plant Proteins / Q000235:genetics / Q000378:metabolism
D010940:Plant Proteins
genomic prediction; hybrid breeding; metabolites; rice; transcripts