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Roots are the only organ system to uptake water and nutrients from the soil. The root system is crucial for plants to survive and adapt to environmental stresses. Therefore, the root system architecture (RSA) is an important breeding target for developing climate-resilient rice. Since the rice genome has been completely sequenced, many genes for root development have been cloned and characterized. In addition, with the advances in technologies related to omics analysis, such as high-throughput sequencing, transcriptome analysis of roots has also progressed. In contrast, high-throughput root phenotyping has not been established not only in rice but also in whole plants because roots are hidden underground. This deficiency represents a bottleneck for utilizing an integrated multi-omics approach for molecular breeding of RSA. We first summarized previous transcriptome analyses for root development under various abiotic stresses such as drought, salinity, and heat, and assessed the current status of root phenotyping technology and modeling in rice. This knowledge allowed us to contemplate the possibility of applying an integrated multi-omics dataset from RSA to molecular breeding of climate-resilient rice.

Root is the only organ to uptake water and nutrients from soil. Root system is crucial for plants to survive and/or adapt to environmental stresses, therefore, root system architecture (RSA) is an important breeding target for developing climate-resilient rice. Since the rice genome has been completely sequenced, many genes for root development were cloned and characterized so far. In addition, with the advances in technologies related to omics analysis such as high-throughput sequencer, transcriptome analysis of roots has also been progressed. In contrast, high-throughput root phenotyping has not been established in not only rice but also whole plants because root is hidden underground. This should be a bottleneck for utilizing multi-omics integrated approach for molecular breeding of RSA. We first summarize previous transcriptome analysis for root development under various abiotic stresses such as drought, salinity, heat etc. and overview current status of root phenotyping technology and modelling in rice. These knowledges would allow us to contemplate a possibility of applying of integrated multi-omics data of RSA to molecular breeding of climate-resilient rice.

The stele (root vascular cylinder) in plants plays an important role in the transport of water and nutrients from the root to the shoot. A quantitative trait locus (QTL) on rice chromosome 9 that controls stele transversal area (STA) was previously detected in an F3 mapping population derived from a cross between the lowland cultivar ‘IR64’, with a small STA, and the upland cultivar ‘Kinandang Patong’, with a large STA. To identify the gene(s) underlying this QTL, we undertook fine mapping of the locus. We screened eight plants from BC2F3 lines in which recombination occurred near the QTL. Progeny testing of BC2F4 plants was used to determine the genotype classes for the QTL in each BC2F3 line. Accordingly, the STA QTL Sta1 (Stele Transversal Area 1) was mapped between the InDel markers ID07_12 and ID07_14. A candidate genomic region for Sta1 was defined more precisely between markers RM566 and RM24334, which delimit a 359-kb interval in the reference cultivar ‘Nipponbare’. A line homozygous for the ‘Kinandang Patong’ allele of Sta1 had an STA approximately 28.4% larger than that of ‘IR64’. However, Sta1 did not influence maximum or total root length, suggesting that this QTL specifically controls STA.

Although grain shape is an important cereal breeding target, it has only been evaluated using simple measurements, e.g., the length:width ratio. We used elliptic Fourier analysis to evaluate grain shape variation and conducted whole genome association mapping of grain shape using a germplasm collection of Asian cultivated rice. The first principal component of elliptic Fourier descriptors, accounting for over 90% of the total variation, was associated with the length:width ratio of brown rice. That component was the most significant among the first ten components: the length:width ratio is the major genetic variation of rice grain shape. Bayesian multilocus association mapping detected five significant markers for this component; three might be linked to previously reported quantitative trait loci. Allelic effects of significant markers were visualized using inverse Fourier transformation, showing that the allele of a Japonica variety “Nipponbare” produced plumper grains in four of five significant markers. For the second to tenth principal components, varietal effects were significant (P < 0.001), although most accounted for less than 1% of the total variation in elliptic Fourier descriptors. Association mapping detected at least one quantitative trait locus in six of these nine components: breeding programs can improve the heritable shape characteristics associated with these components. For association mapping, elliptic Fourier analysis’ accuracy and high throughput are suitable; it is readily applicable to cereal crops because it is not based on rice-specific morphological characteristics but rather on universal shape descriptors that can delineate any closed contour.