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ISSN : 2287-5824(Print)
ISSN : 2287-5832(Online)
Journal of The Korean Society of Grassland and Forage Science Vol.45 No.3 pp.206-216
DOI : https://doi.org/10.5333/KGFS.2025.45.3.206

Next-Generation Sequencing–Based Development of Single Nucleotide Polymorphism Barcodes for Identification of the Cultivar ‘Kowinearly’ in Italian Ryegrass (Lolium multiflorum Lam.)

Chang-Woo Min1, Jae Hoon Woo1, Bo Ram Choi1, Yowook Song1, Jun Gyeong Choi2, Yun Ju Kang2, Ki-Won Lee1*
1Forages Production Systems Division, National Institute of Animal Science, RDA, Cheonan 31000, Republic of Korea
2BIOTO, 187, Techno 2-ro, Yuseong-gu, Daejeon 34015, Republic of Korea
* Corresponding author: Ki-Won Lee, Forages Production Systems Division, National Institute of Animal Science, RDA, Cheonan 31000, Republic of Korea. Tel: +82-41-580-6757, Fax: +82-41-580-6779, E-mail: kiwon@korea.kr
September 18, 2025 September 24, 2025 September 25, 2025

Abstract


Molecular markers have been widely applied in population genetics, diagnostic taxonomy, and genetic mapping, and they can also be used for classifying varieties of Italian ryegrass during field selection. In this study, genome-wide sequence information was generated for 10 Italian ryegrass cultivars (40 samples), including ‘Kowinearly’ (KW), using next-generation sequencing (NGS). Single nucleotide polymorphism (SNP) analysis revealed that only three SNP loci were sufficient to distinguish KW from the other cultivars. Furthermore, 21 alternative barcode sets, each consisting of three SNPs, were identified. These SNP barcode sets provide a reliable criterion for cultivar discrimination in Italian ryegrass and can contribute to the protection of domestic varieties and the advancement of the forage industry in Korea. More broadly, the development of distinguishing markers across Italian ryegrass cultivars will enhance genetic resource identification and support the breeding of high-quality new varieties.


Italian ryegrass , Kowinearly , Next-generation sequencing , Single nucleotide polymorphism marker

초록

    Ⅰ. INTRODUCTION

    Italian ryegrass (Lolium multiflorum Lam.) is one of the most important winter forages worldwide due to its high productivity, quality, and palatability for livestock (Humphreys et al., 2010). In Korea, its rapid establishment, short growing period, vigorous regrowth, and tolerance to waterlogging have made it particularly suitable for paddy fields (Cojocariu et al., 2010), establishing it as a predominant winter forage in Korea (Oh et al., 2021). However, its limited cold tolerance restricts cultivation mainly to the southern regions (Kim and Sung, 2021). This limitation has driven breeding efforts for cold tolerance and early heading, resulting in the widespread use of the cultivar ‘Kowinearly’ (Choi et al., 2006;Ji et al., 2018).

    Recently, with tariff elimination and the increasing practice of domestic seed production abroad and reimporting them, the risk of admixture has grown, underscoring the need for accurate cultivar identification. Moreover, in forage crops, key morphological descriptors used for cultivar determination are strongly influenced by environmental conditions and observer effects, and the differences among cultivars are often small. As a result, morphology-based DUS testing often lacks sufficient discriminating power, making molecular marker–based evaluation essential (Pasquali et al., 2022). Indeed, approximately 700 Italian ryegrass cultivars were listed in the OECD catalogue as of 2025 (OECD, 2025). Furthermore, extensive hybridization and limited germplasm have reduced genetic diversity, further limiting the reliability of morphology alone for discrimination.

    Advances in next-generation sequencing (NGS) now enable rapid access to genomic information, and the development of molecular markers for cultivar discrimination has been reported in many crops (Chakravarthi and Naravaneni, 2006;Park et al., 2017). Molecular markers, which detect DNA polymorphisms, allow the assessment of genetic diversity and cultivar identity, providing a faster and more accurate alternative to morphology (Collard and Mackill, 2008).

    In Italian ryegrass, most studies have focused on the development of simple sequence repeat (SSR) markers. For example, Nie et al. (2019) applied 29 SSR markers to six tetraploid cultivars, whereas Kubik et al. (2001) demonstrated that at least 15 SSR markers could identify perennial ryegrass (Lolium perenne L.) cultivars with over 99% accuracy. Although SSR markers have been effectively used for cultivar discrimination in the genus Lolium, research on single nucleotide polymorphisms (SNPs) remains limited.

    SNPs, which are based on single base substitutions, offer higher marker density and more precise detection than SSRs. Combined with automated high-throughput platforms, they enable efficient large-scale analyses (Mammadov et al., 2012;Zhang et al., 2023). These advantages make SNPs particularly valuable for cultivar identification in Italian ryegrass, where genetic diversity is relatively narrow.

    Therefore, this study aimed to investigate SNP polymorphisms in major Korean cultivars using NGS and develop a molecular marker set capable of rapidly and accurately identifying the representative cultivar ‘Kowinearly’. The results are expected to improve the efficiency of cultivar identification and provide a solid foundation for breeding research and cultivar management, thereby strengthening the competitiveness of the Korean forage industry.

    Ⅱ. MATERIALS AND METHODS

    1. Plant materials

    This study analyzed 10 Italian ryegrass (Lolium multiflorum Lam.) cultivars, comprising 40 samples. These included nine domestic cultivars released in Korea—‘Kowinearly’ (KW), ‘Greenfarm’ (GF), ‘Greencall’ (GC), ‘Kospeed’ (KS), ‘Kogreen’ (KG), ‘Kowinmaster’ (KM), ‘IR605’ (605), ‘Kowinner’ (KWR), and ‘Hwasan104’ (HS104)—as well as one foreign reference cultivar, ‘Florida80’ (FLO). All plant materials were obtained from the National Institute of Animal Science (NIAS), Department of Animal Genetic Resources, Rural Development Administration (RDA), Republic of Korea. Young and healthy leaf tissues were collected at the vegetative growth stage from multiple individuals representing each cultivar. To preserve DNA integrity, harvested leaf samples were immediately frozen in liquid nitrogen and stored at −70°C in an ultra-low temperature freezer until genomic DNA extraction.

    2. DNA extraction and library preparation

    Genomic DNA was extracted from 100 mg of leaf tissue using the CTAB method (Kidwell and Osborn, 1992). DNA quality was assessed via electrophoresis of 3 μL DNA on a 1% agarose gel at 200 V for 25 min, with a 1 kb+ ladder used as a reference. For sequencing library construction, qualified DNA samples were digested with the restriction enzyme ApeKI at 75°C for 2 h, followed by adapter and barcode ligation and pooling. Purification was performed using the QIAquick PCR Purification Kit, and amplification was carried out for 15 cycles. Library quality was verified on a 1.5% agarose gel at 200 V for 30 min, with a 100 bp ladder used to confirm the expected fragment distribution. Sequencing was conducted on an Illumina HiSeq X platform (Illumina Inc., San Diego, CA, USA) using an S4 flow cell in paired-end mode (2 × 150 bp).

    3. Preprocessing of sequencing data and SNP detection

    After demultiplexing with barcodes, adapter sequences were removed using Cutadapt (v1.8.3) (Martin, 2011). Low-quality reads with a Phred score below 20 were trimmed using Trimmomatic (v0.39) (Bolger et al., 2014), yielding highquality cleaned reads. These cleaned reads were mapped to the Lolium multiflorum reference genome, consisting of 2,545 scaffolds (175,758,422 bp) reported by Knorst et al. (2019), using the BWA-MEM algorithm (v0.7.17) (Li, 2013). SNP calling was performed with SAMtools (v0.1.16) (Li et al., 2009), and the resulting SNP loci were arranged in a matrix format (SNVs), with loci as rows and cultivars as columns. SNP filtering was applied using the following thresholds: minor allele frequency (MAF) > 25%, missing data < 20%, and reference/alternative allele frequency > 25%.

    4. Selection of cultivar-specific SNP markers

    SNP loci were annotated based on the scaffold track information reported by Knorst et al. (2019). Each SNP was coded as “a” for the reference allele, “b” for the alternative allele, “h” for heterozygous, and “–” for missing data. To identify markers specific to KW, additional filtering was applied with a missing rate < 15%. Homozygous SNPs consistently observed in all KW individuals but polymorphic in other cultivars were retained as candidate loci. From these candidates, the minimal number of SNPs required to uniquely discriminate KW was determined, and the selected SNPs were designated as the cultivar-specific barcode marker set.

    Ⅲ. RESULTS AND DISCUSSION

    1. Sequencing data generation and preprocessing

    Sequencing of the 40 Italian ryegrass samples on the Illumina HiSeq X platform generated 558,852,406 paired-end reads (151 bp), corresponding to 84,386,713,306 bp in total. After quality trimming with Trimmomatic, 515,716,846 high-quality reads were retained, yielding 61,628,597,634 bp of cleaned data (Table 1). Mapping against the 2,545 scaffolds of Lolium multiflorum resulted in 88.45% alignment (457,253,563 reads), covering a total of 13,985,967 bp with an average depth of 15.26 (Table 2).

    2. SNP discovery through bioinformatic analysis

    Using the aligned reads as input, SAMTools identified 7,002,372 SNPs across the L. multiflorum scaffolds, which were organized into a single-nucleotide variant (SNV) dataset. Successive filtering reduced the dataset substantially: with a minor allele frequency (MAF) threshold of >25%, 1,239,685 loci were retained. A missing data threshold of <20% was applied to reduce the set to 7,908 loci and an additional reference/alternative allele frequency filter (>25%) yielded 1,671 high-confidence SNP loci (Table 3).

    The removal of most SNPs at the MAF >25% stage suggests that the Korean Italian ryegrass cultivars share a high proportion of common alleles, with most variation concentrated in rare alleles. Similar patterns have been observed in other low-diversity breeding panels. For example, in a U.S. maize inbred panel, 68% of SNPs had MAF <0.1 (Romay et al., 2013). In such low-diversity populations, increasing the MAF threshold excludes rare variants and emphasizes medium- to high-frequency loci, improving marker utility. Geibel et al. (2021) also recommended selection of high-MAF SNPs for reliable marker design in restricted breeding populations.

    This reduction in genetic diversity underscores the limitations of morphology- and physiology-based discrimination, which are strongly influenced by environment and genotype–environment interactions. As Pasquali et al. (2022) highlighted, reliance on phenotypic traits alone in DUS testing risks misclassification. The SNP-based molecular markers developed in this study thus provide a more reliable framework for cultivar identification.

    3. Phylogenetic tree and PCA analysis

    The final set of 1,671 SNP loci was used to investigate the genetic relationships among the 10 Italian ryegrass cultivars (40 samples) through phylogenetic tree construction and principal component analysis (PCA) (Figs. 1 and 2). Most cultivars clustered closely, reflecting their genetic relatedness. However, in the PCA, KW showed partial separation from other cultivars along PC2 (12.0%) and PC3 (9.7%). Although the variance explained by these components was modest, the separation suggests that KW may have a distinct genetic background compared with other major domestic cultivars.

    4. Development of SNP barcode sets for cultivar discrimination

    Among the 1,671 loci, 52 SNPs were consistently detected in all four KW individuals. After excluding 28 loci with high missing rates, 24 SNPs were retained with missing data <15%, ensuring higher reliability. These SNPs exhibited an average MAF of 46.3% (range: 38.6–50%) and an average polymorphism information content (PIC) of 0.372 (range: 0.362–0.375) (Table 5). Given that SNPs are bi-allelic markers with a maximum PIC value of 0.5, markers with PIC values above 0.3 are generally considered informative (Yang et al., 2016). Accordingly, the selected SNPs were validated as effective candidates for cultivar identification.

    From these 24 loci, a barcode system was developed using combinations of three SNPs. The minimum set of three SNPs was sufficient to unambiguously distinguish KW, and an additional 21 alternative SNP combinations were also identified. For instance, in barcode set 1, three loci (LM_4600405, LM_39418557, and LM_39440022) with genotypes T, C, and A were unique to KW (Table 6). Genotypes were coded as “a” (reference allele), “b” (alternative allele), “h” (heterozygous), or “–” (missing) and converted into barcode profiles (Table 7). In this system, the “bbb” genotype pattern uniquely identified KW, clearly distinguishing it from GF, GC, KS, KG, KM, 605, KWR, HS104, and FLO.

    In a previous study, RAD-seq–based SNP markers were shown to discriminate Italian ryegrass cultivars, reinforcing the validity of our SNP barcode system (Yu et al., 2022). Although simple sequence repeat (SSR) markers remain valuable for studying population structure and complementing classical DUS testing (Nam et al., 2025), the small number of SNPs required in this study offers practical advantages for rapid and cost-effective identification. However, as Yu et al. (2022) noted, the robustness of SNP-based discrimination depends on both sample size and genetic diversity. Therefore, further validation across a broader set of cultivars and individuals is necessary to establish robust applicability.

    Ⅳ. CONCLUSIONS

    In this study, NGS of 10 Italian ryegrass cultivars (40 samples) generated genome-wide sequence information, from which SNP analysis revealed that as few as three SNP loci were sufficient to discriminate the cultivar KW. Moreover, 21 alternative three-SNP barcode sets were developed. These SNP barcodes provide an effective and reliable tool for precise cultivar discrimination, supporting the protection of domestic varieties and the advancement of the forage industry. Beyond KW, the development of SNP-based marker sets for additional Italian ryegrass cultivars will further improve the resolution of genetic resource identification and contribute to the breeding of highquality new varieties.

    Although only a single foreign reference cultivar, FLO, was included, which constrains generalizability beyond cultivars distributed in Korea, immediate applicability is largely preserved because FLO represents the majority of foreign cultivars currently distributed in Korea. Future work will expand within-cultivar sampling and increase the number of cultivars analyzed to develop SNP markers capable of distinguishing all Italian ryegrass cultivars distributed in Korea. These markers will be implemented in field-deployable assays such as KASP and HRM to ensure robust and reproducible cultivar identification.

    Ⅴ. ACKNOWLEDGMENTS

    This study was supported by the Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ01669904) and the 2025 Postdoctoral Fellowship Program of the National Institute of Animal Science funded by RDA, Republic of Korea.

    Figure

    KGFS-45-3-206_F1.jpg

    Phylogenetic tree analysis of Italian ryegrass cultivars.

    KGFS-45-3-206_F2.jpg

    PCA analysis of Italian ryegrass cultivars.

    Table

    Statistic values after pre-processing

    1 Clean reads/Raw reads (%): (Total length of clean reads / Total length of raw reads) 100.

    Statistic values of read alignment

    SNP matrix record and filter of statistic values

    SNP matrix record and filter of statistic values

    1 Number of SNPs in the cultivar discrimination barcode set: constructed using three SNP markers in combination.
    2 Number of candidate barcode sets for cultivar discrimination: total number of alternative barcode set combinations.

    Nucleotide, MAF and PIC information for 24 SNPs

    SNP nucleotide sequences including barcode set

    Barcode set for identification of Kowinearly (KW) from other Italian ryegrass cultivars

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