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ISSN : 2287-5824(Print)
ISSN : 2287-5832(Online)
Journal of The Korean Society of Grassland and Forage Science Vol.46 No.2 pp.103-113
DOI : https://doi.org/10.5333/KGFS.2026.46.2.103

Development of SNP Barcodes for the Identification of the Alfalfa Cultivar ‘Alfaking’

Chang-Woo Min1, Bo Ram Choi1, Yowook Song1, Hyung Soo Park1, Jun Gyeong Choi2, Yun Ju Kang2, Sang-Hoon Lee1, 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 34025, 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
June 23, 2026 June 25, 2026 June 25, 2026

Abstract


Alfalfa (Medicago sativa L.) is an important forage legume with high feed value and productivity. Because cultivated alfalfa is an outcrossing autotetraploid species with high heterozygosity, phenotype-based cultivar identification can be limited by environmental variation and within-cultivar genetic diversity. In this study, single nucleotide polymorphism (SNP) markers were developed for the identification of the Korean alfalfa cultivar ‘Alfaking (MSCB07)’. Newly generated genotyping-by-sequencing (GBS) datasets for ‘Vernal 25’ and ‘Common (AF)’ were analyzed together with a previously generated whole-genome sequencing (WGS) dataset of ‘Alfaking (MSCB07)’. After alignment to the reference genome and SNP filtering, 20,375 SNP loci were retained for downstream analysis. Principal component analysis and neighbor-joining tree analysis separated ‘Alfaking (MSCB07)’ from the other analyzed cultivars. Genotype pattern comparison identified two diagnostic barcode groups, and their combined profile distinguished ‘Alfaking (MSCB07)’ as “bb” among the analyzed cultivars. Finally, 54 SNP loci were selected as candidate markers for ‘Alfaking (MSCB07)’ discrimination. These results suggest that the selected SNP markers may be useful for cultivar identification, seed purity control, and cultivar protection of ‘Alfaking (MSCB07)’. Further validation with additional alfalfa cultivars and genetic resources is needed to confirm the broader applicability of these markers.


Alfalfa , Variety , NGS , SNP marker

초록

    Ⅰ. INTRODUCTION

    Alfalfa (Medicago sativa L.) is one of the most important leguminous forage crops with high protein content, high forage value, and high productivity (Appiah et al., 2024). Alfalfa is cultivated on more than 30 million ha worldwide (Li et al., 2024), and the global alfalfa market was valued at approximately USD 29.27 billion in 2025 (Fortune Business Insights, 2026).

    The alfalfa market in Korea has continued to expand with the increasing demand for high-quality forage, driven by genetic improvement of livestock and advances in feeding management (Youm, 1991;Kim, 2025). However, alfalfa is vulnerable to waterlogging and acidic soils, which limits stable production under Korean cultivation conditions (Smethurst et al., 2005;Buhaiov et al., 2018). In Korea, forage production often depends on paddy fields, where poor drainage can increase the risk of waterlogging. In addition, acidic soils are widely distributed under geological and climatic conditions characterized by acidic rock types and high precipitation, which further constrains stable alfalfa cultivation (Hong et al., 2013). Consequently, Korea remains highly dependent on imported alfalfa hay, entailing considerable foreign currency expenditures (MAFRA, 2024a). Exchange rate fluctuations, rising fuel prices, shipping delays, and abnormal weather events further increase supply instability and production costs for livestock producers (Chang et al., 2024). Under these conditions, the development of alfalfa cultivars adapted to Korean cultivation environments is important for expanding the self-sufficiency base and stabilizing forage supply. The National Institute of Animal Science, Rural Development Administration, has developed alfalfa cultivars with high adaptability to Korea through crossing and selection using genetic resources from Korea and abroad. As a result, the cultivar ‘Alfaking’ was developed (Lee et al., 2024).

    Alfalfa is a complex taxonomic group that includes diploid and tetraploid subspecies (Şakiroğlu and Brummer, 2013). In autotetraploid alfalfa, high heterozygosity caused by genome complexity and self-incompatibility (Annicchiarico et al., 2025;Medina et al., 2025), together with the strong influence of cultivation environment and growth conditions on major forage-related traits such as plant habit and yield, limits the reliability of cultivar discrimination based only on phenotype (Annicchiarico et al., 2016). As the global alfalfa seed market has expanded to approximately USD 1.12 billion (Fortune Business Insights, 2026), accurate cultivar identification technologies have become important for cultivar protection and authentication of distributed seeds (Yu and Chung, 2021). Therefore, a molecular marker-based discrimination system using DNA-level variation is needed for the protection of Korean cultivars and seed quality management.

    Various DNA-based markers, including Random Amplified Polymorphic DNA (RAPD), Inter-Simple Sequence Repeat (ISSR), and Simple Sequence Repeat (SSR) markers, have been used for genetic resource evaluation and cultivar discrimination in alfalfa (Flajoulot et al., 2005;Petolescu et al., 2024;Tucak et al., 2010). In particular, SSR markers have been reported as useful tools for evaluating genetic diversity and identifying alfalfa cultivars. Recent studies have also shown that multiple SSR markers can be used for alfalfa cultivar discrimination (Flajoulot et al., 2005;Wang et al., 2025). However, interpretation of SSR profiles in autotetraploid alfalfa can be complicated by tetrasomic inheritance. In tetraploid alfalfa, SSR alleles may be scored independently as present or absent using single-dose allele analysis, without retaining allele-dosage information (Diwan et al., 2000). Such presence/absence scoring can hinder the establishment of standardized molecular profiles for routine cultivar identification. Given the autotetraploid, outcrossing, and highly heterozygous nature of alfalfa, cultivar discrimination requires markers with high reproducibility and suitability for large-scale analysis (Semagn et al., 2014;Thomson, 2014). Single nucleotide polymorphism (SNP) markers, which are distributed at high density across the genome and are compatible with automated genotyping platforms, have been applied in alfalfa through the development of SNP arrays and mid-density genotyping platforms, demonstrating their utility for genetic structure analysis and molecular breeding (Li et al., 2014;Zhao et al., 2023).

    A previous study developed SNP barcodes for identification of the Korean-bred alfalfa cultivar ‘Alfaone (MS001)’ using a panel of 7 breeding lines and cultivars developed by the National Institute of Animal Science, with ‘Alfaking (MSCB07)’ included as one of the comparison materials (Min et al., 2026). In contrast, the present study focused on cultivar-specific identification of ‘Alfaking (MSCB07)’ using ‘Vernal 25’ and ‘Common (AF)’ as comparator cultivars. These cultivars were selected as imported seed materials relevant to the Korean alfalfa seed-distribution context, allowing the practical discrimination of ‘Alfaking’ from foreign cultivar materials that may be encountered during seed distribution. Newly generated genotyping-by-sequencing (GBS) datasets for ‘Vernal 25’ and ‘Common (AF)’ were analyzed together with the previously generated whole-genome sequencing (WGS) dataset of ‘Alfaking (MSCB07)’ to establish an Alfaking-specific SNP barcode profile.

    Accordingly, this study aimed to establish an SNP barcode profile for identification of the Korean alfalfa cultivar ‘Alfaking’. Candidate SNP loci were selected through comparative analysis of the WGS and GBS datasets, and their ability to distinguish ‘Alfaking’ from the comparator cultivars was evaluated. The selected SNP markers are expected to provide baseline information for cultivar protection, seed purity monitoring during distribution, and future alfalfa breeding programs in Korea.

    Ⅱ. MATERIALS AND METHODS

    1. Plant materials

    This study analyzed alfalfa lines and cultivars developed by the National Institute of Animal Science, Rural Development Administration in Korea. The plant materials included ‘Vernal 25’, ‘Common (AF)’, and ‘Alfaking (MSCB07)’. For ‘Vernal 25’ and ‘Common (AF)’, three biological replicates were obtained from three independently grown individual plants per cultivar. All plant materials were grown for three weeks, after which young leaves were collected from each plant. To preserve DNA integrity, the collected leaves were immediately flash-frozen in liquid nitrogen. Frozen leaf samples were stored at -70°C in an ultra-low temperature freezer until genomic DNA extraction.

    2. DNA extraction and sequencing data production

    Genomic DNA was extracted from young leaf tissues of ‘Vernal 25’ and ‘Common (AF)’ using the CTAB method. To verify DNA quality, 3 µl of extracted DNA was subjected to electrophoresis at 200 V for 25 min, and DNA integrity was confirmed using a 1 kb + ladder. The WGS dataset of ‘Alfaking (MSCB07)’ was generated in a previous SNP barcode study for alfalfa cultivar identification and was reanalyzed in the present study together with newly generated GBS datasets for ‘Vernal 25’ and ‘Common (AF)’ (Min et al., 2026).

    For ‘Vernal 25’ and ‘Common (AF)’ samples, sequencing was performed via GBS, where DNA was fragmented using the ApeKI restriction enzyme. Barcode adapters and common adapters were diluted in 50 µM TE buffer (10 mM Tris, 0.1 mM EDTA), and then mixed with barcode and common adapters using 10 × adapter buffer (500 mM NaCl, 100 mM Tris-Cl). Multiplexing PCR products were verified by agarose gel electrophoresis, purified using a QIAquick PCR Purification Kit, and then amplified restriction fragments were used to construct NGS libraries. For ‘Alfaking (MSCB07)’ sample, 100 µg of gDNA was fragmented for WGS library preparation, treated at 32°C for 15 minutes, and then fragmentation enzymes were inactivated at 65°C for 30 minutes. Adapter ligation was performed at 20°C for 15 minutes, followed by purification with a QIAquick PCR Purification Kit. PCR was conducted for 8 cycles using a GeneExplorer instrument from BIOER, and the band quality was assessed using a 100 bp ladder before NGS library construction.

    Finally, sequencing of the prepared libraries was performed at 2 × 150 bp using the Illumina HiSeq X platform.

    3. Sequencing data preprocessing and SNP analysis

    The raw sequencing data were preprocessed using two programs: SolexaQA (version 1.13) and Trimmomatic (version 0.39) (Cox et al., 2010;Bolger et al., 2014). First, low-quality sequences with a phred score below 20 and reads shorter than 25 bp were removed using DynamicTrim and LengthSort in SolexaQA to extract cleaned reads (Cox et al., 2010). Additional removal of sequences with phred quality scores below 20 was performed using Trimmomatic to obtain the final set of cleaned reads (Bolger et al., 2014). GBS data were de-multiplexed based on barcode sequences, after which barcode sequences were removed using cutadapt (version 1.8.3), and preprocessing was completed with Trimmomatic (Martin, 2011). The preprocessed cleaned reads were mapped to the Medicago sativa L. reference genome using BWA-MEM (version 0.7.17) (Li, 2013). The reference genome used was the 3.15 Gbp sequence composed of 32 chromosomes and 9,789 unscaffolded contigs, as reported by Chen et al. (2020).

    SNP extraction was performed by analyzing differences with the reference genome using SAMTools (version 0.1.16), and the varFilter program within SAMTools was used for filtering (Li et al., 2009). During the SNP filtering process, key options such as a minimum mapping quality (-Q) of 30, a minimum read depth (-d) of 3, and a maximum read depth (-D) of 611 were applied. Additional settings were used to filter out nearby SNPs or variations near In/Dels. Extracted SNPs were classified as homozygous if the read rate was 90% or higher, and heterozygous if it was between 40-60%. Finally, SNP information from samples produced by GBS and WGS was aligned based on their positions in the reference genome to create an SNP matrix. Filtering criteria included a minor allele frequency (MAF) > 5%, Missing rate < 30%, and REF/ALT frequency > 25% for SNP selection. For the GBS datasets of ‘Vernal 25’ and ‘Common (AF)’, a locus was retained as a cultivar-level consensus SNP only when an identical genotype call was observed across all three biological replicates within each cultivar.

    SNP loci were annotated using the genome feature annotation tracks associated with the Medicago sativa L. reference assembly reported by Chen et al. (2020), which comprises 32 chromosomes and 9,789 unscaffolded contigs. Each SNP was represented as “a” for the reference allele, “b” for the alternative allele, “h” for heterozygous, and “-” for missing data. Candidate loci were retained where homozygous types were observed in ‘Alfaking (MSCB07)’, while polymorphism was observed in other cultivars. From these candidates, the minimum number of SNPs required to uniquely identify ‘Alfaking (MSCB07)’ was determined, and the selected SNPs were designated as cultivar-specific markers.

    4. Phylogenetic tree and principal component analysis (PCA)

    In this study, a phylogenetic tree analysis was performed to evaluate the genetic relationships among alfalfa cultivars. The phylogenetic tree was constructed using the neighbor-joining (NJ) method (Saitou and Nei, 1987) implemented in MEGA11 (Tamura et al., 2021). Genetic distance information calculated from 20,375 SNP loci selected through the filtering process was used for the analysis. To assess the reliability of the inferred tree, bootstrap analysis was conducted with 1,000 replicates, and the values shown at each node indicate the proportion of replicate trees in which the corresponding cluster was observed. Genetic distances among samples were calculated using the maximum composite likelihood method (Tamura et al., 2004), and branch lengths were expressed in the same units as those of the genetic distances. Based on these results, the genetic relationships and population structure among alfalfa cultivars were evaluated, and the potential for cultivar discrimination was examined.

    PCA was conducted using the SNPRelate package (Zheng et al., 2012) in R, based on the same set of 20,375 SNP loci selected after filtering. Genotype data from each sample were used to derive principal components, and the contribution of each principal component to the total variation was estimated by eigen decomposition. The resulting principal component scores were used to visualize the genetic similarity and population structure among samples, thereby allowing evaluation of the genetic differentiation among alfalfa cultivars.

    Ⅲ. RESULTS AND DISCUSSION

    1. Sequencing data production and preprocessing

    For GBS, ‘Vernal 25’ and ‘Common (AF)’ were sequenced in triplicate, and the integrated raw data yielded 0.83 Gb and 0.39 Gb, respectively (Table 1). For WGS, the previously generated ‘Alfaking (MSCB07)’ dataset consisted of 32.5 Gb of raw sequence data (Table 1). Based on the 3.15 Gb reference genome reported by Chen et al. (2020), the raw sequence data corresponded to approximately 0.26-fold and 0.12-fold genome coverage for the GBS datasets of ‘Vernal 25’ and ‘Common (AF)’, respectively, and 10.3-fold genome coverage for the WGS dataset of ‘Alfaking (MSCB07)’.

    After preprocessing, more than 90% of the reads were retained in the GBS datasets, resulting in 0.59 Gb and 0.28 Gb of clean reads for ‘Vernal 25’ and ‘Common (AF)’, respectively. In the WGS dataset, 81.28% of the raw reads were retained after preprocessing, resulting in 21.45 Gb of clean reads and approximately 6.70-fold genome coverage for ‘Alfaking (MSCB07)’ (Table 1).

    2. Read mapping and SNP extraction

    For ‘Vernal 25’ and ‘Common (AF)’, cultivar-level consensus SNPs were retained only when an identical genotype call was observed across all three biological replicates. Because GBS targets restriction enzyme-associated genomic regions, fewer SNPs were obtained from GBS data than from the WGS dataset. A total of 210,415 and 167,205 SNPs were identified from ‘Vernal 25’ and ‘Common (AF)’, respectively, whereas 3,148,609 SNPs were identified from the WGS dataset of ‘Alfaking (MSCB07)’ (Table 2).

    SNP loci from the WGS and GBS datasets were aligned based on their physical positions in the reference genome, resulting in 295,021 loci shared across the datasets. After applying the filtering criteria of minor allele frequency, MAF > 5% and missing rate < 30%, 20,375 SNP loci were retained for downstream analysis (Table 3). These loci were further screened to exclude SNPs with additional sequence variation within 200 bp of the target site, thereby retaining flanking regions suitable for primer and probe design in downstream marker conversion, including quantitative polymerase chain reaction, qPCR, or Kompetitive Allele-Specific PCR, KASP assays.

    Using the 20,375 filtered SNP loci, genetic relationships among the seven analyzed samples were evaluated using neighbor-joining tree analysis and principal component analysis (PCA) (Figures 1 and 2). The first three principal components explained 60% of the total variation, with PC1, PC2, and PC3 accounting for 22%, 20%, and 18%, respectively. In the PCA, ‘Alfaking (MSCB07)’ was separated from ‘Vernal 25’ and ‘Common (AF)’, indicating that the filtered SNP set captured genetic differentiation among the analyzed cultivars. PC1 and PC2 together explained 42% of the total variation and supported the separation of the cultivar groups. Although one ‘Vernal 25’ replicate appeared relatively distant from the other ‘Vernal 25’ replicates in the PC1-PC3 projection, the three ‘Vernal 25’ replicates formed the same cluster in the neighbor-joining tree, with a bootstrap value of 98. Therefore, the apparent dispersion among ‘Vernal 25’ replicates in one PCA projection was interpreted as within-cultivar variation or variation associated with reduced GBS coverage, rather than as a failure of cultivar-level grouping. This interpretation is consistent with previous reports that cultivated alfalfa can show limited genetic differentiation among cultivars, together with substantial variation within cultivars, due to its outcrossing autotetraploid nature and synthetic breeding history (Flajoulot et al., 2005;Malmberg et al., 2026).

    3. Extraction of marker candidates for cultivar discrimination

    To select SNP loci capable of distinguishing ‘Alfaking (MSCB07)’ from the other analyzed cultivars, genotype patterns were compared across the seven analyzed samples. Candidate loci were retained when ‘Alfaking (MSCB07)’ showed a consistent alternative allele type and the other cultivars showed different genotype patterns. As a result, two diagnostic barcode groups were identified, and the combined barcode profile of ‘Alfaking (MSCB07)’ was represented as “bb” (Table 4). In contrast, ‘Vernal 25’ showed an “ab” profile, whereas ‘Common (AF)’ showed an “aa” profile. These results indicate that the selected SNP loci generated a cultivar-specific barcode profile that distinguished ‘Alfaking (MSCB07)’ from the other cultivars analyzed in this study.

    The selected SNP loci showed two levels of minor allele frequency (MAF) and polymorphic information content (PIC). Among the 54 SNP loci, 48 loci showed an allele count ratio of 6:1, resulting in MAF of 0.143 and PIC of 0.215, whereas 6 loci showed an allele count ratio of 3:4, resulting in MAF of 0.429 and PIC of 0.370. The repeated MAF and PIC values were attributed to the limited number of analyzed samples and identical reference/alternative allele count combinations across loci. Because the full PIC formula is calculated from allele frequencies, loci with the same allele count can produce identical PIC values (Botstein et al., 1980;Dou et al., 2023). For bi-allelic SNP markers, MAF values closer to 0.5 and PIC values closer to 0.375 indicate higher discriminatory information (Dou et al., 2023). In this study, the 6 SNP loci with MAF of 0.429 and PIC of 0.370 were therefore considered relatively informative markers. However, SNP loci with lower MAF and PIC values were also retained when they showed consistent diagnostic genotype patterns for ‘Alfaking (MSCB07)’. This approach is supported by previous SNP fingerprinting studies in which markers with relatively low MAF or PIC values were still retained when they contributed to cultivar or accession discrimination (Dou et al., 2023;Shen et al., 2025). Thus, the selected SNPs should be interpreted primarily as cultivar-discriminating candidate markers for ‘Alfaking (MSCB07)’, rather than as markers representing broad population-level genetic diversity.

    Several limitations should be considered when interpreting the selected marker set. The use of WGS for ‘Alfaking’ and low-coverage GBS for ‘Vernal 25’ and ‘Common (AF)’ resulted in substantial differences in sequencing coverage and genomic representation among the datasets. Although candidate loci were selected only from SNPs shared across the datasets and were further filtered using MAF and missing-rate criteria, the unequal coverage and restriction-site sampling of GBS may have influenced SNP detection and candidate-marker selection. In addition, the 54 candidate SNP loci were selected based on a single ‘Alfaking’ individual. Although these loci showed homozygous genotype calls in the analyzed individual, their stability across the cultivar has not yet been verified. Because alfalfa is an outcrossing autotetraploid species with substantial within-cultivar genetic variation, validation using multiple independent ‘Alfaking’ individuals and seed lots, together with additional domestic and foreign cultivars, will be required to assess the robustness of the barcode profile for routine cultivar identification.

    Following such validation, the SNP barcode profile developed in this study may provide a molecular reference for verifying the authenticity of seed lots labeled as ‘Alfaking’ and for monitoring potential seed admixture during seed propagation and distribution. Such marker-based verification may be particularly valuable for alfalfa because cultivar discrimination based solely on phenotypic traits is constrained by environmental effects and within-cultivar variation (Annicchiarico et al., 2016;Annicchiarico et al., 2025). Given that violations related to seed and seedling distribution continue to be detected in Korea (MAFRA, 2024b), this type of marker-based verification may contribute to more reliable seed distribution management.

    Ⅳ. CONCLUSIONS

    This study was conducted to develop SNP markers for the identification of the Korean alfalfa cultivar ‘Alfaking (MSCB07)’. GBS datasets were newly generated for ‘Vernal 25’ and ‘Common (AF)’, and a previously generated WGS dataset of ‘Alfaking (MSCB07)’ was reanalyzed for comparative SNP analysis. After read preprocessing and SNP filtering, 20,375 SNP loci were retained and used to evaluate genetic relationships among seven analyzed samples. PCA and neighbor-joining tree analysis showed that ‘Alfaking (MSCB07)’ was genetically separated from ‘Vernal 25’ and ‘Common (AF)’. Although one ‘Vernal 25’ replicate showed dispersion in one PCA projection, the ‘Vernal 25’ replicates were grouped together in the neighbor-joining tree with a bootstrap value of 98, supporting cultivar-level grouping. Based on genotype pattern comparison, two diagnostic barcode groups were identified, and the combined barcode profile of ‘Alfaking (MSCB07)’ was represented as “bb”, whereas ‘Vernal 25’ and ‘Common (AF)’ showed “ab” and “aa” profiles, respectively. A total of 54 SNP loci were selected as candidate markers for ‘Alfaking (MSCB07)’ discrimination. Among them, 48 loci showed minor allele frequency (MAF) of 0.143 and polymorphic information content (PIC) of 0.215, and 6 loci showed MAF of 0.429 and PIC of 0.370. These SNP markers can provide useful baseline information for cultivar identification, seed purity management, and cultivar protection of ‘Alfaking (MSCB07)’. Further validation using broader alfalfa germplasm will be required to confirm their wider applicability.

    Ⅴ. ACKNOWLEDGEMENTS

    This study was supported by the Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ01787801) and the 2026 Postdoctoral Fellowship Program of the National Institute of Animal Science funded by RDA, Republic of Korea.

    Figure

    KGFS-46-2-103_F1.jpg

    Phylogenetic tree analysis of alfalfa varieties.

    KGFS-46-2-103_F2.jpg

    PCA analysis of alfalfa varieties.

    Table

    Statistic value after pre-processing generated by genotyping-by-sequencing of three biological replicates of ‘Vernal 25’ and ‘Common (AF)’ and whole genome sequencing of ‘Alfaking (MSCB07)’
    1Clean reads/Raw reads (%): (Total number of clean reads / Total number of raw reads) × 100
    SNP matrix record and filtering statistics by alfalfa variety
    1Average: mean all SNPs of each alfalfa variety
    2Total: sum all SNPs of each alfalfa variety
    SNP matrix record and filtering statistics for common loci among alfalfa varieties
    1MAF (minor allele frequency) > 5%: SNP loci with a MAF greater than 5% across all samples were retained.
    2Missing rate < 30%: SNP loci with a missing rate lower than 30% across all samples were retained.
    Loci for markers to identify each alfalfa variety
    a, same nucleotide with reference genome; b, different nucleotide with reference genome.
    Nucleotide, MAF (minor allele frequency) and PIC (polymorphic information content) information about 54 SNPs for identification of three alfalfa varieties

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