Exploring molecular backgrounds of quality traits in rice by predictive models based on high-coverage metabolomics.
BACKGROUND
Increasing awareness of limitations to natural resources has set high expectations for plant science to deliver efficient crops with increased yields, improved stress tolerance, and tailored composition. Collections of representative varieties are a valuable resource for compiling broad breeding germplasms that can satisfy these diverse needs.


RESULTS
Here we show that the untargeted high-coverage metabolomic characterization of such core collections is a powerful approach for studying the molecular backgrounds of quality traits and for constructing predictive metabolome-trait models. We profiled the metabolic composition of kernels from field-grown plants of the rice diversity research set using 4 complementary analytical platforms. We found that the metabolite profiles were correlated with both the overall population structure and fine-grained genetic diversity. Multivariate regression analysis showed that 10 of the 17 studied quality traits could be predicted from the metabolic composition independently of the population structure. Furthermore, the model of amylose ratio could be validated using external varieties grown in an independent experiment.


CONCLUSIONS
Our results demonstrate the utility of metabolomics for linking traits with quantitative molecular data. This opens up new opportunities for trait prediction and construction of tailored germplasms to support modern plant breeding.
RIKEN Plant Science Center, Tsurumi-ku, Suehiro-cho, 1-7-22 Yokohama, Kanagawa 230-0045, Japan.
Henning
H
Redestig
Miyako
M
Kusano
Kaworu
K
Ebana
Makoto
M
Kobayashi
Akira
A
Oikawa
Yozo
Y
Okazaki
Fumio
F
Matsuda
Masanori
M
Arita
Naoko
N
Fujita
Kazuki
K
Saito
D001947:Breeding / Q000379:methods*; D014644:Genetic Variation; D005828:Genetics, Population; D055432:Metabolomics / Q000379:methods*; D008957:Models, Genetic; D012275:Oryza / Q000235:genetics* / Q000378:metabolism*; D010641:Phenotype; D012044:Regression Analysis