Degenerate oligonucleotide primer MIG-seq: an effective PCR-based method for high-throughput genotyping.

Next-generation sequencing (NGS) library construction often involves using restriction enzymes to decrease genome complexity, enabling versatile polymorphism detection in plants. However, plant leaves frequently contain impurities, such as polyphenols, necessitating DNA purification before enzymatic reactions. To overcome this problem, we developed a PCR-based method for expeditious NGS library preparation, offering flexibility in number of detected polymorphisms. By substituting a segment of the simple sequence repeat sequence in the MIG-seq primer set (MIG-seq being a PCR method enabling library construction with low-quality DNA) with degenerate oligonucleotides, we introduced variability in detectable polymorphisms across various crops. This innovation, named degenerate oligonucleotide primer MIG-seq (dpMIG-seq), enabled a streamlined protocol for constructing dpMIG-seq libraries from unpurified DNA, which was implemented stably in several crop species, including fruit trees. Furthermore, dpMIG-seq facilitated efficient lineage selection in wheat and enabled linkage map construction and quantitative trait loci analysis in tomato, rice, and soybean without necessitating DNA concentration adjustments. These findings underscore the potential of the dpMIG-seq protocol for advancing genetic analyses across diverse plant species.

Integrated genome-wide differentiation and association analyses identify causal genes underlying breeding-selected grain quality traits in japonica rice.

Improving grain quality is a primary objective in contemporary rice breeding. Japanese modern rice breeding has developed two different types of rice, eating and sake-brewing rice, with different grain characteristics, indicating the selection of variant gene alleles during the breeding process. Given the critical importance of promptly and efficiently identifying genes selected in past breeding for future molecular breeding, we conducted genome scans for divergence, genome-wide association studies, and map-based cloning. Consequently, we successfully identified two genes, OsMnS and OsWOX9D, both contributing to rice grain traits. OsMnS encodes a mannan synthase that increases the white core frequency in the endosperm, a desirable trait for sake brewing but decreases the grain appearance quality. OsWOX9D encodes a grass-specific homeobox-containing transcription factor, which enhances grain width for better sake brewing. Furthermore, haplotype analysis revealed that their defective alleles were selected in East Asia, but not Europe, during modern improvement. In addition, our analyses indicate that a reduction in grain mannan content during African rice domestication may also be caused a defective OsMnS allele due to breeding selection. This study not only reveals the delicate balance between grain appearance quality and nutrition in rice but also provides a new strategy for isolating causal genes underlying complex traits, based on the concept of "breeding-assisted genomics" in plants.

High-Density Linkage Maps from Japanese Rice japonica Recombinant Inbred Lines Using Genotyping by Random Amplicon Sequencing-Direct (GRAS-Di).

The genetic dissection of agronomically important traits in closely related Japanese rice cultivars is still in its infancy mainly because of the narrow genetic diversity within japonica rice cultivars. In an attempt to unveil potential polymorphism between closely related Japanese rice cultivars, we used a next-generation-sequencing-based genotyping method: genotyping by random amplicon sequencing-direct (GRAS-Di) to develop genetic linkage maps. In this study, four recombinant inbred line (RIL) populations and their parents were used. A final RIL number of 190 for RIL71, 96 for RIL98, 95 for RIL16, and 94 for RIL91 derived from crosses between a common leading Japanese rice cultivar Koshihikari and Yamadanishiki, Taichung 65, Fujisaka 5, and Futaba, respectively, and the parent plants were subjected to GRAS-Di library construction and sequencing. Approximately 438.7 Mbp, 440 Mbp, 403.1 Mbp, and 392 Mbp called bases covering 97.5%, 97.3%, 98.3%, and 96.1%, respectively, of the estimated rice genome sequence at average depth of 1× were generated. Analysis of genotypic data identified 1050, 1285, 1708, and 1704 markers for each of the above RIL populations, respectively. Markers generated by GRAS-Di were organized into linkage maps and compared with those generated by GoldenGate SNP assay of the same RIL populations; the average genetic distance between markers showed a clear decrease in the four RIL populations when we integrated markers of both linkage maps. Genetic studies using these markers successfully localized five QTLs associated with heading date on chromosomes 3, 6, and 7 and which previously were identified as Hd1, Hd2, Hd6, Hd16, and Hd17. Therefore, GRAS-Di technology provided a low cost and efficient genotyping to overcome the narrow genetic diversity in closely related Japanese rice cultivars and enabled us to generate a high density linkage map in this germplasm.

Identification of Novel Quantitative Trait Loci for Culm Thickness of Rice Derived from Strong-Culm Landrace in Japan, Omachi.

Increasing the lodging resistance of rice through genetic improvement has been an important target in breeding. To further enhance the lodging resistance of high-yielding rice varieties amidst climate change, it is necessary to not only shorten culms but strengthen them as well. A landrace rice variety, Omachi, which was established more than 100 years ago, has the largest culm diameter and bending moment at breaking in the basal internodes among 135 temperate japonica accessions. Using unused alleles in such a landrace is an effective way to strengthen the culm. In this study, we performed quantitative trait locus (QTL) analysis to identify the genetic factors of culm strength of Omachi using recombinant inbred lines (RILs) derived from a cross between Omachi and Koshihikari, a standard variety in Japan. We identified three QTLs for the culm diameter of the 5th internode on chromosomes 3 (qCD3) and 7 (qCD7-1, qCD7-2). Among them, qCD7-2 was verified by QTL analysis using the F2 population derived from a cross between one of the RILs and Koshihikari. RNA-seq analysis of shoot apex raised 10 candidate genes underlying the region of qCD7-2. The increase in culm strength by accumulating Omachi alleles of qCD3, qCD7-1 and qCD7-2 was 25.0% in 2020. These QTLs for culm diameter pleiotropically increased spikelet number per panicle but did not affect days to heading or culm length. These results suggest that the Omachi alleles of qCD3, qCD7-1 and qCD7-2 are useful for breeding to increase lodging resistance and yield.

Identification of a unique allele in the quantitative trait locus for crown root number in japonica rice from Japan using genome-wide association studies.

To explore the genetic resources that could be utilized to help improve root system architecture phenotypes in rice (Oryza sativa), we have conducted genome-wide association studies to investigate maximum root length and crown root number in 135 10-day-old Japanese rice accessions grown hydroponically. We identified a quantitative trait locus for crown root number at approximately 32.7 Mbp on chromosome 4 and designated it qNCR1 (quantitative trait locus for Number of Crown Root 1). A linkage disequilibrium map around qNCR1 suggested that three candidate genes are involved in crown root number: a cullin (LOC_Os04g55030), a gibberellin 20 oxidase 8 (LOC_Os04g55070), and a cyclic nucleotide-gated ion channel (LOC_Os04g55080). The combination of haplotypes for each gene was designated as a haploblock, and haploblocks 1, 2, and 3 were defined. Compared to haploblock 1, the accessions with haploblocks 2 and 3 had fewer crown roots; approximately 5% and 10% reductions in 10-day-old plants and 15% and 25% reductions in 42-day-old plants, respectively. A Japanese leading variety Koshihikari and its progenies harbored haploblock 3. Their crown root number could potentially be improved using haploblocks 1 and 2.

Genetic Background Negates Improvements in Rice Flour Characteristics and Food Processing Properties Caused by a Mutant Allele of the PDIL1-1 Seed Storage Protein Gene.

Phenotypic differences among breeding lines that introduce the same superior gene allele can be a barrier to effective development of cultivars with desirable traits in some crop species. For example, a deficient mutation of the Protein Disulfide Isomerase Like 1-1 (PDIL1-1) gene can cause accumulation of glutelin seed storage protein precursors in rice endosperm, and improves rice flour characteristics and food processing properties. However, the gene must be expressed to be useful. A deficient mutant allele of PDIL1-1 was introduced into two rice cultivars with different genetic backgrounds (Koshihikari and Oonari). The grain components, agronomic traits, and rice flour and food processing properties of the resulting lines were evaluated. The two breeding lines had similar seed storage protein accumulation, amylose content, and low-molecular-weight metabolites. However, only the Koshihikari breeding line had high flour quality and was highly suitable for rice bread, noodles, and sponge cake, evidence of the formation of high-molecular-weight protein complexes in the endosperm. Transcriptome analysis revealed that mRNA levels of fourteen PDI, Ero1, and BiP genes were increased in the Koshihikari breeding line, whereas this change was not observed in the Oonari breeding line. We elucidated part of the molecular basis of the phenotypic differences between two breeding lines possessing the same mutant allele in different genetic backgrounds. The results suggest that certain genetic backgrounds can negate the beneficial effect of the PDIL1-1 mutant allele. Better understanding of the molecular basis for such interactions may accelerate future breeding of novel rice cultivars to meet the strong demand for gluten-free foods.

JNETS clinical practice guidelines for gastroenteropancreatic neuroendocrine neoplasms: diagnosis, treatment, and follow-up: a synopsis.

Neuroendocrine neoplasms (NENs) are rare neoplasms that occur in various organs and present with diverse clinical manifestations. Pathological classification is important in the diagnosis of NENs. Treatment strategies must be selected according to the status of differentiation and malignancy by accurately determining whether the neoplasm is functioning or nonfunctioning, degree of disease progression, and presence of metastasis. The newly revised Clinical Practice Guidelines for Gastroenteropancreatic Neuroendocrine Neoplasms (GEP-NENs) comprises 5 chapters-diagnosis, pathology, surgical treatment, medical and multidisciplinary treatment, and multiple endocrine neoplasia type 1 (MEN1)/von Hippel-Lindau (VHL) disease-and includes 51 clinical questions and 19 columns. These guidelines aim to provide direction and practical clinical content for the management of GEP-NEN preferentially based on clinically useful reports. These revised guidelines also refer to the new concept of "neuroendocrine tumor" (NET) grade 3, which is based on the 2017 and 2019 WHO criteria; this includes health insurance coverage of somatostatin receptor scintigraphy for NEN, everolimus for lung and gastrointestinal NET, and lanreotide for GEP-NET. The guidelines also newly refer to the diagnosis, treatment, and surveillance of NEN associated with VHL disease and MEN1. The accuracy of these guidelines has been improved by examining and adopting new evidence obtained after the first edition was published.

Development of an Aus-Derived Nested Association Mapping (Aus-NAM) Population in Rice.

A genetic resource for studying genetic architecture of agronomic traits and environmental adaptation is essential for crop improvements. Here, we report the development of a rice nested association mapping population (aus-NAM) using 7 aus varieties as diversity donors and T65 as the common parent. Aus-NAM showed broad phenotypic variations. To test whether aus-NAM was useful for quantitative trait loci (QTL) mapping, known flowering genes (Ehd1, Hd1, and Ghd7) in rice were characterized using single-family QTL mapping, joint QTL mapping, and the methods based on genome-wide association study (GWAS). Ehd1 was detected in all the seven families and all the methods. On the other hand, Hd1 and Ghd7 were detected in some families, and joint QTL mapping and GWAS-based methods resulted in weaker and uncertain peaks. Overall, the high allelic variations in aus-NAM provide a valuable genetic resource for the rice community.

Landraces of temperate japonica rice have superior alleles for improving culm strength associated with lodging resistance.

Lodging can reduce grain yield and quality in cereal crops including rice (Oryza sativa L.). To achieve both high biomass production and lodging resistance, the breeding of new cultivars with strong culms is a promising strategy. However, little is known about the diversity of culm strength in temperate japonica rice and underlying genetic factors. Here, we report a wide variation of culm strength among 135 temperate japonica cultivars, and some landraces having the strongest culms among these cultivars. The genome-wide association study (GWAS) identified 55 quantitative trait loci for culm strength and morphological traits, and revealed several candidate genes. The superior allele of candidate gene for culm thickness, OsRLCK191, was found in many landraces but had not inherited to the modern improved cultivars. Our results suggest that landraces of temperate japonica rice have unutilized superior alleles for contributing future improvements of culm strength and lodging resistance.

Choosing the optimal population for a genome-wide association study: A simulation of whole-genome sequences from rice.

A genome-wide association study (GWAS) needs to have a suitable population. The factors that affect a GWAS (e.g. population structure, sample size, and sequence analysis and field testing costs) need to be considered. Mixed populations containing subpopulations of different genetic backgrounds may be suitable populations. We conducted simulation experiments to see if a population with high genetic diversity, such as a diversity panel, should be added to a target population, especially when the target population harbors small genetic diversity. The target population was 112 accessions of Oryza sativa L. subsp. japonica, mainly developed in Japan. We combined the target population with three populations that had higher genetic diversity. These were 100 indica accessions, 100 japonica accessions, and 100 accessions with various genetic backgrounds. The results showed that the GWAS's power with a mixed population was generally higher than with a separate population. Also, the optimal GWAS populations varied depending on the fixation index (FST ) of the quantitative trait nucleotides (QTNs) and the polymorphism of QTNs in each population. When a QTN was polymorphic in a target population, a target population combined with a higher diversity population improved the QTN's detection power. By investigating FST and the expected heterozygosity (He ) as factors influencing the detection power, we showed that single nucleotide polymorphisms with high FST or low He are less likely to be detected by GWAS with mixed populations. Sequenced or genotyped germplasm collections can improve the GWAS's detection power by using a subset of the collections with a target population.

Coupling day length data and genomic prediction tools for predicting time-related traits under complex scenarios.

Genomic selection (GS) has proven to be an efficient tool for predicting crop-rank performance of untested genotypes; however, when the traits have intermediate optima (phenology stages), this implementation might not be the most convenient. GS might deliver high-rank correlations but incurring in serious bias. Days to heading (DTH) is a crucial development stage in rice for regional adaptability with a significant impact on yield potential. The objective of this research consisted in develop a novel method that accurately predicts time-related traits such as DTH in unobserved environments. For this, we propose an implementation that incorporates day length information (DL) in the prediction process for two relevant scenarios: CV0, predicting tested genotypes in unobserved environments (C method); and CV00, predicting untested genotypes in unobserved environments (CB method). The use of DL has advantages over weather data since it can be determined in advance just by knowing the location and planting date. The proposed methods showed that DL information significantly helps to improve the predictive ability of DTH in unobserved environments. Under CV0, the C method returned a root-mean-square error (RMSE) of 3.9 days, a Pearson correlation (PC) of 0.98 and the differences between the predicted and observed environmental means (EMD) ranged between -4.95 and 4.67 days. For CV00, the CB method returned an RMSE of 7.3 days, a PC of 0.93 and the EMD ranged between -6.4 and 4.1 days while the conventional GS implementation produced an RMSE of 18.1 days, a PC of 0.41 and the EMD ranged between -31.5 and 28.7 days.

Predicting biomass of rice with intermediate traits: Modeling method combining crop growth models and genomic prediction models.

Genomic prediction (GP) is expected to become a powerful technology for accelerating the genetic improvement of complex crop traits. Several GP models have been proposed to enhance their applications in plant breeding, including environmental effects and genotype-by-environment interactions (G×E). In this study, we proposed a two-step model for plant biomass prediction wherein environmental information and growth-related traits were considered. First, the growth-related traits were predicted by GP. Second, the biomass was predicted from the GP-predicted values and environmental data using machine learning or crop growth modeling. We applied the model to a 2-year-old field trial dataset of recombinant inbred lines of japonica rice and evaluated the prediction accuracy with training and testing data by cross-validation performed over two years. Therefore, the proposed model achieved an equivalent or a higher correlation between the observed and predicted values (0.53 and 0.65 for each year, respectively) than the model in which biomass was directly predicted by GP (0.40 and 0.65 for each year, respectively). This result indicated that including growth-related traits enhanced accuracy of biomass prediction. Our findings are expected to contribute to the spread of the use of GP in crop breeding by enabling more precise prediction of environmental effects on crop traits.

Predicting Rice Heading Date Using an Integrated Approach Combining a Machine Learning Method and a Crop Growth Model.

Accurate prediction of heading date under various environmental conditions is expected to facilitate the decision-making process in cultivation management and the breeding process of new cultivars adaptable to the environment. Days to heading (DTH) is a complex trait known to be controlled by multiple genes and genotype-by-environment interactions. Crop growth models (CGMs) have been widely used to predict the phenological development of a plant in an environment; however, they usually require substantial experimental data to calibrate the parameters of the model. The parameters are mostly genotype-specific and are thus usually estimated separately for each cultivar. We propose an integrated approach that links genotype marker data with the developmental genotype-specific parameters of CGMs with a machine learning model, and allows heading date prediction of a new genotype in a new environment. To estimate the parameters, we implemented a Bayesian approach with the advanced Markov chain Monte-Carlo algorithm called the differential evolution adaptive metropolis and conducted the estimation using a large amount of data on heading date and environmental variables. The data comprised sowing and heading dates of 112 cultivars/lines tested at 7 locations for 14 years and the corresponding environmental variables (day length and daily temperature). We compared the predictive accuracy of DTH between the proposed approach, a CGM, and a single machine learning model. The results showed that the extreme learning machine (one of the implemented machine learning models) was superior to the CGM for the prediction of a tested genotype in a tested location. The proposed approach outperformed the machine learning method in the prediction of an untested genotype in an untested location. We also evaluated the potential of the proposed approach in the prediction of the distribution of DTH in 103 F2 segregation populations derived from crosses between a common parent, Koshihikari, and 103 cultivars/lines. The results showed a high correlation coefficient (ca. 0.8) of the 10, 50, and 90th percentiles of the observed and predicted distribution of DTH. In this study, the integration of a machine learning model and a CGM was better able to predict the heading date of a new rice cultivar in an untested potential environment.

Whole genome sequencing data of Oryza sativa L. cv. Takanari.

Oryza sativa L. 'Takanari' is one of the most productive indica cultivars [1,2]. Reciprocal chromosome segment substitution lines (CSSLs) derived from a cross between 'Koshihikari' and 'Takanari' are useful tools for the detection and precise mapping of target quantitative trait loci (QTL) in 'Takanari'. Although the available Os-Nipponbare-Reference-IRGSP-1.0 reference genome is available and useful for evaluating genetic diversity among japonica cultivars, it is not always useful for evaluating genetic diversity harbored by indica cultivars such as 'Takanari'. To reveal sequence variants in 'Takanari' and to exploit these variants in rice breeding programs, the whole genome of 'Takanari' was sequenced using a combination of Illumina HiSeq X Ten (20,983,495 reads and %GC 43) and PacBio (2,847,220 high-quality subreads). NGS data obtained have been deposited in the DNA Data Bank of Japan (DDBJ) under accession number DRA007557.

Validation of a quantitative trait locus for the white-core expression rate of grain on chromosome 6 in a brewing rice cultivar and development of DNA markers for marker-assisted selection.

Sake-brewing cultivars among varieties of Japanese rice (Oryza sativa L.) have traits adapted to the sake-brewing process, such as a high white-core expression rate (WCE). Our previous study detected putative quantitative trait loci (QTLs) associated with a high WCE derived from Yamadanishiki, a popular brewing rice cultivar. Because the occurrence of white-core grains depends on air temperature and the position of the grain on the panicle, phenotyping of WCE must consider these variable conditions. In this study, qWCE6, a QTL for the WCE on chromosome 6, was validated for the first time, and the phenotyping method examined for its suitability in fine-mapping. A clear tendency towards high WCE was observed in late-heading substituted lines which headed under low daily mean temperature at the experimental location. White-core grains were often expressed by the primary spikelets on the upper panicle, producing a high percentage of superior grains. The segregating population for qWCE6 in late heading revealed a distinct difference in WCE between the Koshihikari and Yamadanishiki homozygous alleles at qWCE6 as determined from that locality. Further, two insertion/deletion markers were developed for the marker-assisted selection of qWCE6. Our results will be useful for informing the breeding of sake-brewing rice cultivars.

Genetic and phenotypic assessment of sugar beet (Beta vulgaris L. subsp. vulgaris) elite inbred lines selected in Japan during the past 50 years.

Genetic diversity of Japanese sugar beet elite inbred line diversity (JSBDIV) set consisting of 63 lines was investigated using 33 cleaved amplified polymorphic sequence and 38 simple sequence repeat analyses. JSBDIV set was significantly subdivided into six (pedigree information), seven (Neighbor-Joining method) or 12 (population structure analysis) groups. The highest value of a pairwise population differentiation estimate, Φ PT value, among groups was yielded from population structure analysis with explained variation 32%. Some of the groups defined in this study exhibited close association with ancestral open-pollinated varieties (OPVs), suggesting that inter-OPV cross was rare during the establishment of JSBDIV set. On the other hand, low Φ PT values between some groups suggest that genetic backgrounds of ancestral OPVs had historically overlapped to some extent. Phenotypic traits showed significant differences both among and within groups. A nearly identical group was identified as the highest sugar content group irrespective of the grouping methods. Groups with Aphanomyces root rot resistance are associated with an OPV 'Tmm-1', suggesting it as a source of this trait. 'Tmm-1' is also associated with Cercospora leaf spot resistance, but an exceptional resistant line with no association of 'Tmm-1' supports a notion that different genetic resources exist for this trait.

QTL analysis of resistance to bacterial wilt caused by Ralstonia solanacearum in potato.

Ralstonia solanacearum causes bacterial wilt, a soil-borne disease and one of the most important maladies of potato and other Solanaceae crops. We analyzed the resistance of a potato clone to bacterial wilt by quantitative trait locus (QTL) analysis. A resistant diploid potato clone 10-03-30 was crossed with a susceptible diploid clone F1-1 to generate a diploid, two-way pseudo-testcross F1 population comprised of 94 genotypes. Dense linkage maps, containing 4,139 single nucleotide polymorphism markers with an average distance of 0.6 and 0.3 cM between markers, were constructed for both parents. The resistance level was evaluated by in vitro inoculation test with R. solanacearum (phylotype I/biovar 4/race 1). Five QTLs (qBWR-1 to -5) were identified on potato chromosomes 1, 3, 7, 10, and 11, and they explained 9.3-18.4% of the phenotypic variance. The resistant parent had resistant alleles in qBWR-2, qBWR-3, and qBWR-4 and susceptible alleles in qBWR-1 and qBWR-5. Accumulation of the resistant alleles in all five QTLs increased the level of resistance compared with that of the resistant parent. This is the first study to identify novel QTLs for bacterial wilt resistance in potato by using genome-wide markers.

Description of grain weight distribution leading to genomic selection for grain-filling characteristics in rice.

Grain-filling ability is one of the factors that controls grain yield in rice (Oryza sativa L.). We developed a method for describing grain weight distribution, which is the probability density function of single grain weight in a panicle, using 128 Japanese rice varieties. With this method, we quantitively analyzed genotypic differences in grain-filling ability and used the grain weight distribution parameters for genomic prediction subject to genetic improvement in grain yield in rice. The novel description method could represent the observed grain weight distribution with five genotype-specific parameters of a mixture of two gamma distributions. The estimated genotype-specific parameters representing the proportion of filled grains had applicability to explain the grain filling ability of genotypes comparable to that of sink-filling rate and the conventionally measured proportion of filled grains, which suggested the efficiency and flexibility of grain weight distribution parameters to handle several genotypes. We revealed that perfectly filled grains have to be prioritized over partially filled grains for the optimum allocation of the source of yield in a panicle, from the analysis for obtaining an ideal shape of grain weight distribution. We conducted genomic prediction of grain weight distribution considering five genotype-specific parameters of the distribution as phenotypes relating to grain filling ability. The proportion of filled grains, average weight of filled grains, and variance of filled grain weight, which were considered to control grain yield to a certain degree, were predicted with accuracies of 0.30, 0.28, and 0.53, respectively. The proposed description method of grain weight distribution facilitated not only the investigation of the optimum allocation of nutrients in a panicle for realizing high grain-filling ability, but also allowed genomic selection of grain weight distribution.

A novel Rice QTL qOPW11 Associated with Panicle Weight Affects Panicle and Plant Architecture.

BACKGROUND: The improvement of rice yield is a crucial global issue, but evaluating yield requires substantial efforts. Rice yield comprises the following indices: panicle number (PN), grain number per panicle (GN), 1000-grain weight, and percentage of ripened grain. To simplify measurements, we analyzed one panicle weight (OPW) as a simplified yield index that integrates GN, grain weight, and percentage of ripened grain, and verified its suitability as a proxy for GN and grain weight in particular. RESULTS: Quantitative trait locus (QTL) analysis using 190 recombinant inbred lines derived from Koshihikari (large panicle and small grain) and Yamadanishiki (small panicle and large grain), japonica cultivars detected three QTLs on chromosomes 5 (qOPW5), 7 (qOPW7) and 11 (qOPW11). Of these, qOPW5 and qOPW11 were detected over two years. qOPW5 and qOPW7 increased OPW, and qOPW11 decreased it at Yamadanishiki alleles. A chromosome segment substitution line (CSSL) with a genomic segment from Yamadanishiki substituted in the Koshihikari genetic background harboring qOPW5 increased grain weight. qOPW11 had the largest genetic effect of QTLs, which was validated using a CSSL. Substitution mapping using four CSSLs revealed that qOPW11 was located in the range of 1.46 Mb on chromosome 11. The CSSL harboring qOPW11 decreased primary and secondary branch numbers, culm length, and panicle length, and increased PN. CONCLUSIONS: In this study, three QTLs associated with OPW were detected. The CSSL with the novel and largest QTL, qOPW11, differed in some traits associated with both panicle and plant architecture, indicating different functions for the meristem in the vegetative versus the reproductive stages. qOPW5 coincided with an identified QTL for grain width and grain weight, suggesting that qOPW5 was affected by rice grain size. OPW can be considered a useful trait for efficient detection of QTLs associated with rice yield.

Ethylene-gibberellin signaling underlies adaptation of rice to periodic flooding.

Most plants do poorly when flooded. Certain rice varieties, known as deepwater rice, survive periodic flooding and consequent oxygen deficiency by activating internode growth of stems to keep above the water. Here, we identify the gibberellin biosynthesis gene, SD1 (SEMIDWARF1), whose loss-of-function allele catapulted the rice Green Revolution, as being responsible for submergence-induced internode elongation. When submerged, plants carrying the deepwater rice-specific SD1 haplotype amplify a signaling relay in which the SD1 gene is transcriptionally activated by an ethylene-responsive transcription factor, OsEIL1a. The SD1 protein directs increased synthesis of gibberellins, largely GA4, which promote internode elongation. Evolutionary analysis shows that the deepwater rice-specific haplotype was derived from standing variation in wild rice and selected for deepwater rice cultivation in Bangladesh.