Root network structure plays a crucial role in growth and development processes in rice. Longer, more branched root structures help plants to assimilate water and nutrition from soil, support robust plant growth, and improve resilience to stresses such as disease. Understanding the molecular basis of root development through screening of root-related traits in rice germplasms is critical to future rice breeding programs. This study used a small germplasm collection of 137 rice varieties chosen from the Korean rice core set (KRICE_CORE) to identify loci linked to root development. Two million high-quality single nucleotide polymorphisms (SNPs) were used as the genotype, with maximum root length (MRL) and total root weight (TRW) in seedlings used as the phenotype. Genome-wide association study (GWAS) combined with Principal Components Analysis (PCA) and Kinship matrix analysis identified four quantitative trait loci (QTLs) on chromosomes 3, 6, and 8. Two QTLs were linked to MRL and two were related to TRW. Analysis of Linkage Disequilibrium (LD) decay identified a 230 kb exploratory range for detection of candidate root-related genes. Candidates were filtered using RNA-seq data, gene annotations, and quantitative real-time PCR (qRT-PCR), and five previously characterized genes related to root development were identified, as well as four novel candidate genes. Promoter analysis of candidate genes showed that LOC_Os03g08880 and LOC_Os06g13060 contained SNPs with the potential to impact gene expression in root-related promoter motifs. Haplotype analysis of candidate genes revealed diverse haplotypes that were significantly associated with phenotypic variation. Taken together, these results indicate that LOC_Os03g08880 and LOC_Os06g13060 are strong candidate genes for root development functions. The significant haplotypes identified in this study will be beneficial in future breeding programs for root improvement.

Salinity is one of the major constraints causing soil problems and is considered a limitation to increased rice production in rice-growing countries. This genome-wide association study (GWAS) experiment was conducted to understand the genetic basis of salt tolerance at the seedling stage in Korean rice. After 10 days of salt stress treatment, salt tolerance was evaluated with a standard evaluation system using a visual salt injury score. With 191 Korean landrace accessions and their genotypes, including 266,040 single-nucleotide polymorphisms (SNPs), using a KNU Axiom Oryza 580K Genotyping Array, GWAS was conducted to detect three QTLs with significant SNPs with a −log10(P) threshold of ≥3.66. The QTL of qSIS2, showed −log10(P) = 3.80 and the lead SNP explained 7.87% of total phenotypic variation. The QTL of qSIS4, showed −log10(P) = 4.05 and the lead SNP explained 10.53% of total phenotypic variation. The QTL of qSIS8 showed −log10(P) = 3.78 and the lead SNP explained 7.83% of total phenotypic variation. Among the annotated genes located in these three QTL regions, five genes were selected as candidates (Os04g0481600, Os04g0485300, Os04g0493000, Os04g0493300, and Os08g0390200) for salt tolerance in rice seedlings based on the gene expression database and their previously known functions.

Grain size affects the yield and quality of rice. The large grain line (LGL), showing a large grain size and japonica-like genome, was selected in the breeding field. The 94 F2 plants derived from a cross between LGL and Hanareum (a high-yielding tongil-type variety) were used for the quantitative trait loci (QTL) analysis of grain length (GL), grain width (GW), and grain thickness (GT). A linkage map of the F2 population, covering 1312 cM for all 12 chromosomes, was constructed using 123 Fluidigm SNP markers. A total of nine QTLs for the three traits were detected on chromosomes two, three, four, six, and seven. Two QTLs for GL on chromosomes two and six explained 17.3% and 16.2% of the phenotypic variation, respectively. Two QTLs were identified for GW on chromosomes two and three, and explained 24.3% and 23.5% of the phenotypic variation, respectively. The five QTLs for GT detected on chromosomes two, three, five, six and seven, explained 13.2%, 14.5%, 16.6%, 10.9%, and 10.2% of the phenotypic variation, respectively. A novel QTL for GT, qGT2, was validated on the same region of chromosome two in the selected F3 population. The QTLs identified in this study, and LGL, could be applied to the development of large-grain rice varieties.

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