In the present study, we conducted a detailed analysis of the genetic diversity and structural organization of 96 domestic Korean rice varieties (Oryza sativa L.) using 2,565 high-resolution TCS-based single nucleotide polymorphism (SNP) markers. Genetic structural variations were investigated using diversity indices, PCA, genetic similarity, and network analysis. Genetic diversity analysis revealed a significant expansion of the genetic foundation after the 1980s, marked by a sharp increase in the number of alleles (Na) from the 2000s. Despite this, high genetic homogeneity was maintained, with an average similarity of 77.7%. The observed 10% difference among same-cross varieties suggests that critical genetic variations are fixed by strong selection pressures for quality traits. Network analysis (85% similarity threshold) confirmed that the Korean rice breeding population followed a distinct core-periphery model (eight communities). The connected 84 varieties had a centrality range of 0.01 0.39. Core Variety Groups (e.g., ‘Junam’ and ‘Sindongjin’) exhibited the highest centrality (up to 0.39), indicating their extensive use as key breeding parents and their function as the central axis of the genetic network. Bridge Variety Groups (e.g., ‘Hwayeong’ and ‘Samkwang’) played an intermediary role linking clusters. Crucially, 12 ‘isolated accessions’ showed zero centrality (0.00), representing a genetic disconnect from the main pool. This quantitative network-based assessment provides essential fundamental data for breeders to select appropriate germplasms. Furthermore, the findings suggest that the current cultivar naming system, which inadequately reflects genetic relationships, requires reassessment, and that the establishment of
objective
management standards based on this research is warranted.

Early selection of grain quality traits in rice (Oryza sativa L.), is essential to improve the yield and quality of this staple crop. We analyzed four key traits—protein content, grain filling rate, height, and panicle length—in 85 Korean cultivars. Through whole- genome resequencing we identified 12,718,879 raw single nucleotide polymorphisms (SNPs); after PLINK-based quality control (bi-allelic selection, call rate≥0.90, MAF≥0.03), ~2.20 million high-quality SNPs remained for machine-learning (ML) pre-screening. To rank the features (without marker-level inference), we applied a liberal univariate PLINK case-control scan using the top and bottom 30% per trait. We also analyzed associations with a linear mixed model (GCTA v1.93.2, MLMA; fixed covariates: ecotype, PC1, PC2; random effect: GRM) to verify calibration under population structure; with n=85, no genome-wide significant hits were detected, and QQ-plots indicated adequate calibration (per-trait effective tests m≈1.54-1.57 million under stricter filters). The random forest feature importance prioritized 26, 51, 19, and 20 core SNPs for the four traits, respectively. Across the algorithms, the best models achieved mean accuracies of 81.8% (protein content), 81.0% (grain filling rate), 73.1% (height), and 94.0% (panicle length). All selected SNPs met the Fluidigm array design requirements, supporting its deployment as a compact, genotype-based panel for early selection and a practical step toward digital breeding in rice.

Male sterility is used to mass-produce F₁ hybrid seeds in pepper (Capsicum annuum L.). In particular, the development of genetic male sterility (GMS)-linked molecular markers may play a crucial role in hybrid breeding of pepper. To date, approximately 20 GMS genes have been identified in pepper. Among these, several molecular markers for the ms₃ gene have been developed in previous studies; however, they are not completely linked and thus have limitations for use in selection. Therefore, in the present study, we aimed to develop molecular markers for ms₃ selection using single-nucleotide polymorphism (SNP)-based high-resolution melting (HRM) analysis. Chi-square test was conducted using three F₂ segregating populations, and the results confirmed a 3:1 segregation ratio between male-fertile and male-sterile plants. A total of 128 primer sets were designed by selecting SNPs near the ms₃ gene, and 25 HRM markers were successfully developed. Using 420 individuals from the F₂ segregating population ‘GMS3,’ a high-density genetic linkage map of pepper chromosome 1 was constructed, with eight HRM markers found to be co-segregated with the ms₃ gene. Subsequent experiments using various plant materials validated these eight markers, ultimately identifying two HRM markers, HRM119655681 and HRM135273656, for the final selection. These two markers showed co-segregation between the phenotype and genotype of ms₃ across all plant materials used in the study. The markers developed in this study are expected to be effective for maternal line development and large-scale F₁ hybrid seed production using ms₃ in pepper.

Grape (Vitis vinifera L.) is a perennial fruit tree with high heterozygosity, consisting of 38 chromosomes (2n=38), and it takes a long time for grape seedlings to grow into fruit-bearing trees. Therefore, it is difficult to study grape genetics and breeding strategies. However, it has recently become possible to discover many SNPs through whole genome resequencing or genotyping-by-sequencing (GBS) analysis. In this study, we aimed to develop high-resolution melting (HRM) markers from the detected SNPs and construct a genetic linkage map using HRM markers. In a previous study, 2,553 SNPs were identified using GBS analysis. In this study, 1,336 SNPs were used to design primer sets for HRM analysis. The developed HRM markers were used for construction of a genetic linkage map in an F₁ segregating population consisting of 192 individuals from a cross between ‘Tano Red’ (V. labrusca×V. vinifera) and ‘Ruby Seedless’ (V. vinifera). A total of 805 polymorphic HRM markers were developed, of which 363 were mapped onto the genetic linkage map of grape, with a total length of 1,453.5 cM consisting of 19 chromosomes. This SNP-based genetic linkage map and HRM markers can be used for QTL identification and marker development for important fruit traits of grape.

The bulb onion (Allium cepa L.), one of the most important vegetables worldwide, contains various functional compounds such as quercetin, allicin, and flavonoids. Red onions are rich in anthocyanins, a flavonoid that is a functional phytochemical with antioxidative and anticancer activities. In the previous study, two quantitative trait loci (QTLs) (qAC4.1 and qAC4.2) controlling the anthocyanin content were identified on chromosome 4 in an F₂ population derived from a cross between A. cepa ‘SP3B’ and ‘H6’. In this study, we developed single nucleotide polymorphism-based high-resolution melting (HRM) markers linked to QTLs qAC4.1 and qAC4.2. In addition, we constructed a new genetic linkage map of chromosome 4 using HRM markers and performed a QTL analysis. The QTL qAC4.1 was false, while qAC4.2 was a major QTL. The QTL peak position, logarithm of the odds value, and phenotypic variance explained of qAC4.2 was 53.6 cM, 7.45, and 22.51%, respectively. Four HRM markers (AC4.2_65336.1_1123-HRM, AC4.2_53230.3_454-HRM, AC4.2_11999.1_756-HRM, and AC4.2_14596.1_345-HRM) within the QTL region of qAC4.2 were developed in this study. The average anthocyanin content of B (homozygous paternal) genotypes was higher than that of A (homozygous maternal) and H (heterozygous) genotypes for all markers. Consequently, these markers will be useful for marker-assisted selection to develop onion cultivars with high anthocyanin content.

Hexaploid wheat (common wheat/bread wheat) is one of the most important cereal crops in the world and a model for research of an allopolyploid plant with a large, highly repetitive genome. In the heritability of agronomic traits, variation in gene presence/absence plays an important role. However, there have been relatively few studies on the variation in gene presence/absence in crop species, including common wheat. Recently, a reference genome sequence of common wheat has been fully annotated and published. In addition, advanced next-generation sequencing (NGS) technology provides high quality genome sequences with continually decreasing NGS prices, thereby dawning full-scale wheat functional genomic studies in other crops as well as common wheat, in spite of their large and complex genomes. In this review, we provide information about the available tools and methodologies for wheat functional genomics research supported by NGS technology. The use of the NGS and functional genomics technology is expected to be a powerful strategy to select elite lines for a number of germplasms.

It is important for radish to have late flowering characteristics especially in the case of spring and winter cultivars. To understand late flowering characteristics of radish at the molecular level the flowering time genes of two radish lines (NH-JS1 and NH-JS2) with different flowering time were compared by re-sequencing their genomes. There were a total of 872,587 SNPs and 194,637 INDELs between the two lines. The SNP density of each chromosome was relatively uniform throughout, but the region with low SNP density was found at the end of R3 and the middle of R9. To compare the flowering time genes of the two lines, we first looked for the flowering time genes in radish using Arabidopsis thaliana flowering time genes. As a result, homologs of radish were found for most flowering time genes, but FRIGIDA was not found. Among 224 radish flowering time gene-homologs found, 97 genes showed more than one sequence difference (SNP or INDEL) between the two lines, and 127 genes had no difference. In particular, no sequence differences were found in FT, CO, and FLC, core flowering time control genes. Rs350520 (FVE), Rs193800 (CURLY LEAF) and Rs255320 (ATX1) with more than 100 sequence variations were expected to have a significant effect on flowering time difference between the two lines. These results will be of great help in understanding the flowering timing difference between the two lines at the molecular level.

An allo-octoploid strawberry (Fragaria × ananassa Duch.) is one of the most important vegetable crops in Korea. However, there were few genomic researches of strawberry due to polyploidy and complexity of its genome. In this study, we aimed to construct a genetic linkage map of strawberry using single nucleotide polymorphism (SNP) markers that were developed through a next-generation sequencing (NGS) analysis. Two strawberry varieties, ‘Sulhyang’ and ‘Senga-sengana’, were used as a maternal and a paternal parent, respectively, and their F1 generation consisting of 94 individuals was used for construction of a genetic linkage map. A total of 19.0 Gbp (‘Sulhyang’) and 21.8 Gbp (‘Senga-sengana’) of genomic sequences were obtained through NGS analysis. Subsequently, approximately 87,000 SNPs were identified and 1,154 primer sets for high-resolution melting (HRM) analysis were designed through bioinformatic analysis. In result, a total of 224 polymorphic HRM markers were developed and 205 markers were mapped on the genetic linkage map of strawberry, which total length was 800.8 cM and the number of linkage groups were 30. This SNP-based genetic linkage map and the 224 SNP markers will be very helpful for the genomic and genetic researches of allo-octoploid strawberry.

As a first step of mapping genes conferring resistance to the brown planthopper, Nilaparvata lugens Stål, in Gayabyeo using a population derived from a cross between Gayabyeo and Taebaegbyeo, we performed the whole genome resequencing of these two Tongil-type rice varieties. The amount of raw sequence data was about 18.5X10⁹ bp and 17.9X10⁹ bp in Gayabyeo and Taebaegbyeo, respectively. After quality trimming and read mapping onto Nipponbare reference genome sequence, 9.3X10⁹ bp was mapped in Gayabyeo with mapping depth of 25.0X, and 9.5X10⁹ bp was mapped in Taebaegbyeo with mapping depth of 25.5X. Between Gayabyeo and Nipponbare, 1,585,880 SNPs were detected, while 1,416,898 SNPs were detected between Taebaegbyeo and Nipponbare. Between Gayabyeo and Taebaegbyeo, 284,501 SNPs were detected. Among the SNPs between Gayabyeo and Taebaegbyeo, 21.2% were in genic region and 78.8% were in intergenic region. In CDS region, 15,924 SNPs were detected, among which synonymous SNPs covered 47.3% and non-synonymous SNPs covered 52.7%. We designed Cleaved Amplified Polymorphic Sequences (CAPS) markers with SNPs in the restriction enzyme recognition sites, and 20 CAPS markers were tested. Of the 20 markers, 19 markers showed polymorphism and one marker showed monomorphism between Gayabyeo and Taebaegbyeo. It is expected that sufficient DNA markers for mapping genes with a population derived from a cross between Gayabyeo and Taebaegbyeo can be developed based on the results of the study.