Although embryo accounts for only 2–3% of the total weight of a rice grain, it is a good source of various nutrients for human health. Because enlarged embryo size causes increase of the amount of nutrients and bioactive compounds stored within rice grain, giant embryo mutants of rice (Oryza sativa L.) are excellent genetic resources for improving the nutritional value of rice grains. Three giant embryo mutants, including large embryo (le), giant embryo (ge) and super-giant embryo (ges), with variable embryo size were used in this study. We investigated whether genes controlling embryo size in these mutants (le, ge and ges) were allelic to each other. Although ge and ges was allelic to GIANT EMBRY (GE), le was not allelic to ge and ges in allelism test. The GE gene carried a unique nucleotide substitution in each of the two mutants (ge and ges), resulting in non-synonymous mutations in exon 2 of GE in both mutants. However, the GE gene of the le mutant did not carry any mutation, suggesting that the enlarged embryo phenotype of le was governed by another gene. Using map-based cloning, we mapped the LE gene to the short arm of chromosome 3. The le mutant showed mild enlargement in embryo size, which resulted from an increase in the size of scutellar parenchyma cells. The LE encodes a C3HC4-type RING finger protein and was expressed to relatively high levels in seeds at a late developmental stage. Knockdown of LE expression using RNA interference increased the embryo size of rice grains, confirming the role of LE in determining the embryo size. Overall, we identified a new gene controlling embryo size in rice. Phenotypic and molecular characterization results suggest that the le mutant will serve as a valuable resource for developing new rice cultivars with large embryos and nutrient-dense grains.

Background: Tongil (IR667-98-1-2) rice, developed in 1972, is a high-yield rice variety derived from a three-way cross between indica and japonica varieties. Tongil contributed to the self-sufficiency of staple food production in Korea during a period known as the `Korean Green Revolution'. We analyzed the nucleotide-level genome structure of Tongil rice and compared it to those of the parental varieties. Results: A total of 17.3 billion Illumina Hiseq reads, 47× genome coverage, were generated for Tongil rice. Three parental accessions of Tongil rice, two indica types and one japonica type, were also sequenced at approximately 30x genome coverage. A total of 2,149,991 SNPs were detected between Tongil and Nipponbare varieties. The average SNP frequency of Tongil was 5.77 per kb. Genome composition was determined based on SNP data by comparing Tongil with three parental genome sequences using the sliding window approach. Analyses revealed that 91.8% of the Tongil genome originated from the indica parents and 7.9% from the japonica parent. Copy numbers of SSR motifs, ORF gene distribution throughout the whole genome, gene ontology (GO) annotation, and some yield-related QTLs or gene locations were also comparatively analyzed between Tongil and parental varieties using sequence-based tools. Each genetic factor was transferred from the parents into Tongil rice in amounts that were in proportion to the whole genome composition. Conclusions: Tongil was derived from a three-way cross among two indica and one japonica varieties. Defining the genome structure of Tongil rice demonstrates that the Tongil genome is derived primarily from the indica genome with a small proportion of japonica genome introgression. Comparative gene distribution, SSR, GO, and yield-related gene analysis support the finding that the Tongil genome is primarily made up of the indica genome.

Grain size is one of the key factors determining yield and quality in rice. A large number of genes are involved in the regulation of grain size parameters such as grain length and grain width. Different alleles of these genes have different impacts on the grain size traits under their control. However, the combined influence of multiple alleles of different genes on grain size remains to be investigated. Six key genes known to influence grain size were investigated in this study: GS3, GS5, GS6, GW2, qSW5/GW5, and GW8/OsSPL16. Allele and grain measurement data were used to develop a regression equation model that can be used for molecular breeding of rice with desired grain characteristics. A total of 215 diverse rice germplasms, which originated from or were developed in 28 rice-consuming countries, were used in this study. Genotyping analysis demonstrated that a relatively small number of allele combinations were preserved in the diverse population and that these allele combinations were significantly associated with differences in grain size. Furthermore, in several cases, variation at a single gene was sufficient to influence grain size, even when the alleles of other genes remained constant. The data were used to develop a regression equation model for prediction of rice grain size, and this was tested using data from a further 34 germplasms. The model was significantly correlated with three of the four grain size-related traits examined in this study. Rice grain size is strongly influenced by specific combinations of alleles from six different genes. A regression equation model developed from allele and grain measurement data can be used in rice breeding programs for the development of new rice varieties with desired grain size and shape.

Starch is a major ingredient in rice, and the amylose content of starch significantly impacts rice quality. OsSS (starch synthase) is a gene family related to the synthesis of amylose and amylopectin, and 10 members have been reported. In the present study, a synteny analysis of a novel family member belonging to the OsSSIV subfamily that contained a starch synthase catalytic domain showed that three segmental duplications and multiple duplications were identified in rice and other species. Expression data showed that the OsSS gene family is involved in diverse expression patterns. The prediction of miRNA targets suggested that OsSS are possibly widely regulated by miRNA functions, with miR156s targeted to OsSSII-3, especially. Haplotype analysis exhibited the relationship between amylose content and diverse genotypes. These results give new insight and a theoretical basis for the improved amylose content and eating quality of rice.

Direct seeding is considered an efficient cultivation technology that reduces water use and labor costs. Mesocotyl length is one of the significant traits in cultivation; long mesocotyl is beneficial for the rate and uniformity of seedling emergence. In this study, we used a core collection of 137 rice accessions to identify quantitative trait loci (QTL) for mesocotyl elongation. A genome-wide association study (GWAS), combined with a principal component analysis (PCA) and a kinship matrix analysis, was conducted for the genotype analysis of 2 million, high-quality single nucleotide polymorphisms (SNPs). Through this GWAS analysis, 11 lead SNPs were confirmed to be associated with mesocotyl length, and a linkage disequilibrium (LD) decay analysis identified the 230 kb exploratory range for the detection of QTLs and candidate genes. Based on the gene expression database and haplotype analysis, five candidate genes (Os01g0269800, Os01g0731100, Os08g0136700, Os08g0137800, and Os08g0137900) were detected to be significantly associated with phenotypic variation. Five candidate gene expressions are reported to be associated with various plant hormone responses. Interestingly, two biotic stress response genes and two copper-containing redox proteins were detected as the candidate genes. The results of this study provide associated SNPs in candidate genes for mesocotyl length and strategies for developing direct seeding in breeding programs.

In rice there are few genetic studies reported for allelopathy traits, which signify the ability of plants to inhibit or stimulate growth of other plants in the environment, by exuding chemicals. QTL analysis for allelopathic traits were conducted with 98 F8 RILs developed from the cross between the high allelopathic parents of ‘Sathi’ and non-allelopathic parents of ‘Nong-an’. The performance of allelopathic traits were evaluated with inhibition rate on root length, shoot length, total length, root weight, shoot weight, and total weight of lettuce as a receiver plant. With 785 polymorphic DNA markers, we constructed a linkage map showing a total of 2489.75 cM genetic length and 3.17 cM of average genetic distance between each adjacent marker. QTL analysis detected on QTL regions on chromosome 8 responsible for the inhibition of shoot length and inhibition of total length. The qISL-8 explained 20.38% of the phenotypic variation for the inhibition on the shoot length. The qITL-8 explained 14.93% of the phenotypic variation for the inhibition on total length. The physical distance of the detected QTL region was 194 Kbp where 31 genes are located.