Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice.

Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.

Remote-Sensing-Combined Haplotype Analysis Using Multi-Parental Advanced Generation Inter-Cross Lines Reveals Phenology QTLs for Canopy Height in Rice.

High-throughput phenotyping systems with unmanned aerial vehicles (UAVs) enable observation of crop lines in the field. In this study, we show the ability of time-course monitoring of canopy height (CH) to identify quantitative trait loci (QTLs) and to characterise their pleiotropic effect on various traits. We generated a digital surface model from low-altitude UAV-captured colour digital images and investigated CH data of rice multi-parental advanced generation inter-cross (MAGIC) lines from tillering and heading to maturation. Genome-wide association studies (GWASs) using the CH data and haplotype information of the MAGIC lines revealed 11 QTLs for CH. Each QTL showed haplotype effects on different features of CH such as stage-specificity and constancy. Haplotype analysis revealed relationships at the QTL level between CH and, vegetation fraction and leaf colour [derived from UAV red-green-blue (RGB) data], and CH and yield-related traits. Noticeably, haplotypes with canopy lowering effects at qCH1-4, qCH2, and qCH10-2 increased the ratio of panicle weight to leaf and stem weight, suggesting biomass allocation to grain yield or others through growth regulation of CH. Allele mining using gene information with eight founders of the MAGIC lines revealed the possibility that qCH1-4 contains multiple alleles of semi-dwarf 1 (sd1), the IR-8 allele of which significantly contributed to the "green revolution" in rice. This use of remote-sensing-derived phenotyping data into genetics using the MAGIC lines gives insight into how rice plants grow, develop, and produce grains in phenology and provides information on effective haplotypes for breeding with ideal plant architecture and grain yield.

Haplotype analysis from unmanned aerial vehicle imagery of rice MAGIC population for the trait dissection of biomass and plant architecture.

Unmanned aerial vehicles (UAVs) are popular tools for high-throughput phenotyping of crops in the field. However, their use for evaluation of individual lines is limited in crop breeding because research on what the UAV image data represent is still developing. Here, we investigated the connection between shoot biomass of rice plants and the vegetation fraction (VF) estimated from high-resolution orthomosaic images taken by a UAV 10 m above a field during the vegetative stage. Haplotype-based genome-wide association studies of multi-parental advanced generation inter-cross (MAGIC) lines revealed four quantitative trait loci (QTLs) for VF. VF was correlated with shoot biomass, but the haplotype effect on VF was better correlated with that on shoot biomass at these QTLs. Further genetic characterization revealed the relationships between these QTLs and plant spreading habit, final shoot biomass and panicle weight. Thus, genetic analysis using high-throughput phenotyping data derived from low-altitude, high-resolution UAV images during early stages of rice growing in the field provides insights into plant growth, architecture, final biomass, and yield.

Surveillance of panicle positions by unmanned aerial vehicle to reveal morphological features of rice.

Rice plant architecture affects biomass and grain yield. Thus, it is important to select rice genotypes with ideal plant architecture. High-throughput phenotyping by use of an unmanned aerial vehicle (UAV) allows all lines in a field to be observed in less time than with traditional procedures. However, discrimination of plants in dense plantings is difficult, especially during the reproductive stage, because leaves and panicles overlap. Here, we developed an original method that relies on using UAV to identify panicle positions for dissecting plant architecture and to distinguish rice lines by detecting red flags attached to panicle bases. The plant architecture of recombinant inbred lines derived from Japanese cultivars 'Hokuriku 193' and 'Mizuhochikara', which differ in plant architecture, was assessed using a commercial camera-UAV system. Orthomosaics were made from UAV digital images. The center of plants was plotted on the image during the vegetative stage. The horizontal distance from the center to the red flag during the reproductive stage was used as the panicle position (PP). The red flags enabled us to recognize the positions of the panicles at a rate of 92%. The PP phenotype was related to but was not identical with the phenotypes of the panicle base angle, leaf sheath angle, and score of spreading habit. These results indicate that PP on orthomosaics could be used as an index of plant architecture under field conditions.

A follow-up study for biomass yield QTLs in rice.

The biomass yield (plant weight) of rice fluctuates from year to year. In a previous study, we demonstrated that six quantitative trait loci (QTLs) contribute to the variation in the plant weight of recombinant inbred lines (RILs) of high-yielding Japanese rice cultivars. However, it remains unclear whether the effects of those QTLs are stable over multiple years. Therefore, we evaluated the effect of the alleles on the plant weight of RILs over multiple years, including a change of fertilization level (i.e., in different environments). Even though the biomass yields of all RILs fluctuated among environments, RILs that were selected on the basis of the genotypes of the detected QTLs had a stable rank order of plant weight that corresponded to their genotypes. This multiple-environment experiment reveals the highly significant contribution of both genotypic and environmental variances to the observed variance in plant weight. A marginally significant QTL-environment interaction was detected at only one of the six QTLs, with a subtle contribution. These results support the idea that the biomass yield of rice can be improved through QTL-based allele selection.

Discovery of QTL Alleles for Grain Shape in the Japan-MAGIC Rice Population Using Haplotype Information.

A majority of traits are determined by multiple quantitative trait loci (QTL) that can have pleiotropic effects. A multi-parent advanced generation inter-cross (MAGIC) population is well suited for genetically analyzing the effects of multiple QTL on traits of interest because it contains a higher number of QTL alleles than a biparental population. We previously produced the JAPAN-MAGIC (JAM) population, derived from eight rice (Oryza sativa L.) cultivars with high yield and biomass in Japan, and developed the method of genome-wide association study (GWAS) using haplotype information on the JAM lines. This method was effective for identifying major genes such as Waxy for eating quality and Sd1 for culm length. Here, we show that haplotype-based GWAS is also effective for the evaluation of multiple QTL with small effects on rice grain shape in the JAM lines. Although both the haplotype- and SNP-based GWAS identified multiple QTL for grain length and width, the sum of the estimated trait values of each allele for the QTL detected by haplotype-based GWAS had higher correlation with observed values than those detected by SNP-based GWAS, indicating high-accuracy QTL detection in the haplotype-based GWAS. Furthermore, the study revealed pleiotropic effects of some QTL regions in regulation of grain shape, suggesting that the haplotype-based GWAS using the JAM lines is an effective means to evaluate the main and side effects of haplotypes at each QTL. Information on the pleiotropic effects of haplotypes on various traits will be useful for designing ideal lines in a breeding program.

Haplotype-based allele mining in the Japan-MAGIC rice population.

Multi-parent advanced generation inter-cross (MAGIC) lines have broader genetic variation than bi-parental recombinant inbred lines. Genome-wide association study (GWAS) using high number of DNA polymorphisms such as single-nucleotide polymorphisms (SNPs) is a popular tool for allele mining in MAGIC populations, in which the associations of phenotypes with SNPs are investigated; however, the effects of haplotypes from multiple founders on phenotypes are not considered. Here, we describe an improved method of allele mining using the newly developed Japan-MAGIC (JAM) population, which is derived from eight high-yielding rice cultivars in Japan. To obtain information on the haplotypes in the JAM lines, we predicted the haplotype blocks in the whole chromosomes using 16,345 SNPs identified via genotyping-by-sequencing analysis. Using haplotype-based GWAS, we clearly detected the loci controlling the glutinous endosperm and culm length traits. Information on the alleles of the eight founders, which was based on the effects of mutations revealed by the analysis of next-generation sequencing data, was used to narrow down the candidate genes and reveal the associations between alleles and phenotypes. The haplotype-based allele mining (HAM) proposed in this study is a promising approach to the detection of allelic variation in genes controlling agronomic traits in MAGIC populations.

Improvement of Rice Biomass Yield through QTL-Based Selection.

Biomass yield of rice (Oryza sativa L.) is an important breeding target, yet it is not easy to improve because the trait is complex and phenotyping is laborious. Using progeny derived from a cross between two high-yielding Japanese cultivars, we evaluated whether quantitative trait locus (QTL)-based selection can improve biomass yield. As a measure of biomass yield, we used plant weight (aboveground parts only), which included grain weight and stem and leaf weight. We measured these and related traits in recombinant inbred lines. Phenotypic values for these traits showed a continuous distribution with transgressive segregation, suggesting that selection can affect plant weight in the progeny. Four significant QTLs were mapped for plant weight, three for grain weight, and five for stem and leaf weight (at α = 0.05); some of them overlapped. Multiple regression analysis showed that about 43% of the phenotypic variance of plant weight was significantly explained (P < 0.0001) by six of the QTLs. From F2 plants derived from the same parental cross as the recombinant inbred lines, we divergently selected lines that carried alleles with positive or negative additive effects at these QTLs, and performed successive selfing. In the resulting F6 lines and parents, plant weight significantly differed among the genotypes (at α = 0.05). These results demonstrate that QTL-based selection is effective in improving rice biomass yield.

Genomic regions involved in yield potential detected by genome-wide association analysis in Japanese high-yielding rice cultivars.

BACKGROUND: High-yielding cultivars of rice (Oryza sativa L.) have been developed in Japan from crosses between overseas indica and domestic japonica cultivars. Recently, next-generation sequencing technology and high-throughput genotyping systems have shown many single-nucleotide polymorphisms (SNPs) that are proving useful for detailed analysis of genome composition. These SNPs can be used in genome-wide association studies to detect candidate genome regions associated with economically important traits. In this study, we used a custom SNP set to identify introgressed chromosomal regions in a set of high-yielding Japanese rice cultivars, and we performed an association study to identify genome regions associated with yield. RESULTS: An informative set of 1152 SNPs was established by screening 14 high-yielding or primary ancestral cultivars for 5760 validated SNPs. Analysis of the population structure of high-yielding cultivars showed three genome types: japonica-type, indica-type and a mixture of the two. SNP allele frequencies showed several regions derived predominantly from one of the two parental genome types. Distinct regions skewed for the presence of parental alleles were observed on chromosomes 1, 2, 7, 8, 11 and 12 (indica) and on chromosomes 1, 2 and 6 (japonica). A possible relationship between these introgressed regions and six yield traits (blast susceptibility, heading date, length of unhusked seeds, number of panicles, surface area of unhusked seeds and 1000-grain weight) was detected in eight genome regions dominated by alleles of one parental origin. Two of these regions were near Ghd7, a heading date locus, and Pi-ta, a blast resistance locus. The allele types (i.e., japonica or indica) of significant SNPs coincided with those previously reported for candidate genes Ghd7 and Pi-ta. CONCLUSIONS: Introgression breeding is an established strategy for the accumulation of QTLs and genes controlling high yield. Our custom SNP set is an effective tool for the identification of introgressed genome regions from a particular genetic background. This study demonstrates that changes in genome structure occurred during artificial selection for high yield, and provides information on several genomic regions associated with yield performance.

Genetic characterization of rainfed upland New Rice for Africa (NERICA) varieties.

A total of 18 rainfed upland New Rice for Africa (NERICA) varieties were categorized as the heavy panicle and low tillering types and early heading, in compared with 32 different varieties. These chromosome components were clarified using 243 SSR markers which showed polymorphism among NERICA varieties and their parents, CG 14 (O. glaberrima Steud.) and one of the recurrent parents, WAB-56-104 (O. sativa L.). NERICA varieties were classified into three groups, which corresponded with these parents' continuation including two exceptions, NERICAs 14 and 17, by a cluster analysis using polymorphism data of SSR markers and 14 differential markers among them were selected to classify NERICA varieties. However, three groups: NERICAs, 3 and 4, NERICAs, 8, 9 and 11 and NERICAs, 15 and 16 were not distinguishable. Association analysis was carried out for characterization of NERICA varieties by using SSR markers genotype and phenotype of agronomic traits. A total of 131 quantitative trait loci between SSR markers and 11 agronomic traits were detected. The characteristics of early maturity and heavy panicle of upland NERICA varieties were succeeded from Asian rice varieties and the characteristics of high dry matter production and late heading were introduced from CG 14 and the other varieties.