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Article

Genetic Diversity Patterns Within and Among Varieties of Korean Italian Ryegrass (Lolium multiflorum) and Perennial Ryegrass (Lolium perenne) Based on Simple Sequence Repetition

by
Dong-Geon Nam
1,
Eun-Seong Baek
1,
Eun-Bin Hwang
1,
Sang-Cheol Gwak
1,
Yun-Ho Lee
2,
Seong-Woo Cho
3,
Ju-Kyung Yu
1,* and
Tae-Young Hwang
1,*
1
Department of Crop Science, College of Agriculture, Life Science and Environmental Chemistry, Chungbuk National University, Cheongju 28644, Republic of Korea
2
Crop Physiology and Production, National Institute of Crop Science, 181 Hyeoksin-ro, Iseo-myeon, Wanju-gun 55365, Republic of Korea
3
Department of Smart Agro-Industry, College of Agriculture and Life Science, Gyeongsang National University, Jinju 52725, Republic of Korea
*
Authors to whom correspondence should be addressed.
Agriculture 2025, 15(3), 244; https://doi.org/10.3390/agriculture15030244
Submission received: 13 December 2024 / Revised: 9 January 2025 / Accepted: 16 January 2025 / Published: 23 January 2025
(This article belongs to the Special Issue Advancements in Genotype Technology and Their Breeding Applications)
Browse Figures
Versions Notes

Abstract

:
Italian ryegrass (Lolium multiflorum, IRG) and perennial ryegrass (Lolium perenne L., PRG) are widely cultivated as forage grasses in Korea using heterogeneous and polycross techniques, which promote genetic diversity within varieties. However, their genetic diversity patterns in Korea remain underexplored. This study evaluated the genetic diversity of IRG (eight varieties, including one exotic) and PRG (two exotic varieties) using 66 simple sequence repeat (SSR) markers. Across 87 samples (nine IRG and two PRG varieties), 655 alleles were identified, averaging 9.9 per locus. Key genetic parameters included heterozygosity (0.399), observed heterozygosity (0.675), fixation index (0.4344), and polymorphic informative content (0.6428). The lowest within-variety genetic distance was observed in ‘Hwasan 104ho’ (0.469), while ‘IR901’ had the highest (0.571). Between varieties, the closest genetic distance was between ‘Greencall’ and ‘Greencall 2ho’ (0.542), and the furthest was between ‘Kowinmaster’ and ‘Aspire’ (0.692). Molecular variance analysis showed 90% variation within varieties and 10% among varieties. Five clusters (I–V) were identified, with cluster I primarily including diploid IRG varieties and the tetraploid ‘Hwasan 104ho.’ Structural analysis differentiated diploid from tetraploid varieties (K = 2) and further separated tetraploid IRG and PRG (K = 3). Principal component analysis confirmed these groupings, with ‘Greencall’ and ‘Greencall 2ho’ exhibiting the closest genetic distance (0.227) and ‘Greencall’ and ‘Aspire’ the furthest (0.384). These findings provide a foundational resource for marker-assisted breeding to improve agronomic traits and enhance the efficiency of ryegrass breeding programs.

1. Introduction

Italian ryegrass (Lolium multiflorum; IRG), belonging to the grass family (Poaceae), is among the many annual forage grass species cultivated in winter in Korea [1]. Native to the Mediterranean coastal region, particularly Italy, IRG is a top, gramineous grass with distinct leaf veins and a superficial gloss on the back. Thriving predominantly in warm climates, IRG exhibits high fibrous palatability, moisture tolerance, and regeneration ability. Nonetheless, it is susceptible to summer depression [2,3]. In Korea, 20 IRG varieties have been developed, with recommended local cultivars including Kowinearly, Greenfarm, and Kowinmaster and recommended foreign varieties featuring cultivars such as Florida80 and Tetraflorum [4,5]. Additionally, perennial ryegrass (Lolium perenne; PRG), also a member of the grass family (Poaceae), is among several perennial forage species cultivated in Europe [6] PRG shares similar morphological traits with IRG, such as height and flat, glossy leaves. However, efforts to improve PRG varieties are constrained by the limited diversity within its cultivars, which can be utilized for breeding in the Korean cultivation environment. Currently, research efforts to breed and cultivate IRG varieties are mainly concentrated in Korea, where IRG is often utilized in double cropping systems with rice. In contrast, PRG has not been extensively bred in Korea and is predominantly employed to create mixed pastures. Notable foreign recommendations for PRG varieties include Reville and Bastion [5].
DNA-based molecular markers offer several advantages, including independence from environmental influences and the ability to provide an impartial assessment of genetic diversity and relationality. In the case of IRG, molecular studies have utilized amplified fragment length polymorphisms (AFLPs), restriction fragment length polymorphisms (RFLPs), simple sequence repetition (SSR), and sequence tag site (STS) genetic diversity assessments within both varieties and wild species [7,8,9,10]. Similarly, investigations into PRG have employed AFLPs [11] single-nucleotide polymorphism (SNP) analysis [12] and RFLP, STS, and SSR methodologies [13,14,15,16,17,18]. Moreover, SSR markers, characterized by their codominant nature, multiple alleles, and high polymorphic information content (PIC) values, have primarily been utilized to discriminate varieties and assess the genetic diversity and flexibility of genetic resources [19,20,21,22,23,24]. Furthermore, a substantial number of expressed sequence tags (ESTs) have recently been identified, and research employing EST-SSR markers has been conducted across various species or genera within several grain crop species [25,26,27,28,29]. In particular, as EST-SSR markers are evolutionarily conserved, they harbor the potential for genetic transfer among the species involved [30].
Understanding genetic abundance and distribution is pivotal as they affect the evolutionary potential of species [31]. SSR markers offer an accessible and practical method that does not require expensive equipment. Previously, several studies have been conducted among pasture and polycross varieties [2,32,33,34]. However, studies validating the genetic diversity within varieties are lacking. In this study, 87 samples, comprising nine IRG varieties and two PRG varieties, were analyzed using 66 polymorphic SSR markers. The investigation aimed to determine the distribution of genetic diversity, both within individual varieties and across varieties. Ultimately, the findings of this study facilitate the creation of breeds exhibiting superior development compared to their parents through polycross and heterosis breeding. The results provide a database that will expedite the breeding process and enhance economic outcomes while fostering varieties’ development in the future.

2. Materials and Methods

2.1. Plant Material

This study evaluates the genetic diversity of eight IRG varieties (Hwasan 104ho, Kowinearly, Kowinmaster, Greenfarm, Greencall, Greencall 2ho, IR605, and IR901), one exotic IRG variety (Florida 80), and two perennial ryegrass varieties (Kentaur and Aspire). All seeds were provided by the National Institute of Animal Science (Jeonju, Republic of Korea), except for Aspire, which was sourced from Samoeco (Seongnam, Republic of Korea). Two PRG varieties were included in this analysis because they are gaining attention from the Korean livestock industry. This was done to better understand the genetic relationship between IRG and PRG. Thirty-two seeds per variety were cultivated in the high-tech greenhouse at Chungbuk National University for phenotype comparison. From each variety, eight individuals were selected for analysis (except Aspire, which included seven individuals) based on observed differences (Table 1).

2.2. DNA Extraction

The plants were cultivated for 60 days, and young and bright green leaves were chosen and sampled. Subsequently, these samples were ground in liquid nitrogen and processed according to the protocols outlined in the DNeasy® Plant Mini Kit (Qiagen, Hilden, Germany) The DNA extracted from the samples was quantified at 10 ng/µL using the NanoDrop One C Spectrophotometer (TermoFisher Scientific, Waltham, MA, USA).

2.3. SSR Marker Selection and Diversity Analysis

PCR amplification was performed using the AllInOneCycle™ PCR system (Bioneer Corp., Daejeon, Republic of Korea). The total volume of the reaction mixture was 10 µL, comprising 1 µL of DNA at 10 ng/µL, 5 µL of GoTaq Green Master Mix (Promega, Madison, WI, USA), and 2 µL of forward and reverse primers at 10 pmol/µL each. The markers utilized were produced by Bioneer Company (Daejeon, Republic of Korea). PCR was initiated with denaturation at 95 °C for 9 min, followed by 34 cycles under the following conditions: denaturation at 95 °C for 30 s, annealing at 50 to 60 °C for 30 s, and extension at 72 °C for 1 min. A final extension at 72 °C for 7 min concluded the process. Electrophoresis was conducted at 150 V for 50 min using a 2.5% agarose gel prepared with Tris–acetate–EDTA (TAE) buffer based on the selected markers. Subsequently, EtBr (ethidium bromide) was added to a tank of TAE buffer, and the agarose gel underwent staining for 10 min. The gel was then subjected to UV illumination through the Digital Imaging System GDS-200D (Korea Lab Tech, Seongnam, Republic of Korea) to confirm the band pattern. For a more precise determination, to obtain more accurate base pair (bp) sizes, the selected markers underwent analysis using a 96-capillary automated DNA fragment analyzer (Fragment Analyzer™ 96, Advanced Analytical Technologies, Inc., Ames, IA, USA). The required materials for this analysis included FA dsDNA gel, 5 × 930 dsDNA inlet buffer, dilution buffer 1× TE, insertion dye, markers, DNA ladder (with fragments of 35, 75, 100, 150, 200, 250, 300, 400, and 500 bp), 5 × capillary conditioning solution, capillary storage solution, and mineral oil. The database was constructed based solely on bands within the range of 35–500 bp, and analysis was performed using PROSize™ 3.0 software (Advanced Analytical Technologies, Inc., Ankeny, IA, USA).

2.4. Data Analysis

The genetic distance was analyzed using [35] method implemented in the power marker software, and the resulting data were used to construct a UPGMA dendrogram employing Nei’s genetic distance in MEGA 4.0 [35,36]. The genetic similarity was determined based on Rohlf’s simple matching (SM) coefficient using NTSYSpcver.2.1 software, wherein alleles were designated as ‘1’ or ‘0’ depending on their presence or absence [37]). The SSRs after screening were used to calculate the average number of alleles (Na), average effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), fixation index (FST), and private alleles (PAs), and analysis of molecular variance (AMOVA) was employed to assess the significance of genetic variation within and among individuals using the GenAlEx analysis ver. 6.5 package [35]. The program STRUCTURE ver. 2.3.4 was employed to detect potential sub-populations (K = 1 to K = 15) utilizing a model accommodating admixture and correlated allele frequencies. This analysis involved a burn-in of 100,000 iterations and a run length of 100,000, with 20 iterations performed thereafter [36]. The optimal number of populations corresponded to the highest value in the k graph [38]. The bulk strategy was implemented with eight individuals per group (except for Aspire, which had seven individuals), to ascertain the genetic distance between groups. The bulk strategy involved extracting DNA from individual samples and diluting it to 10 ng/μL using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The PCR conditions (Bioneer Corporation, Daejeon, Republic of Korea) were consistent with those previously described, with us utilizing 60 markers for each DNA sample. Accordingly, each DNA sample was divided into 60 aliquots, with one marker added to each aliquot for PCR amplification. The precise base pair (bp) lengths were determined using a 96-capillary automated DNA fragment analyzer (Fragment Analyzer™ 96, Advanced Analytical Technologies, Inc., Ames, IA, USA). The presence or absence of DNA fragments in the individual PCR samples was recorded as ‘1’ for a clear band and ‘0’ for no band. These binary values from eight samples per variety were grouped by variety for further analysis. Additionally, the bulk strategy was utilized in conjunction with NTSYSpcver.2.1 software [39] and GenAlExver 6.51 software [40] to construct UPGMA dendrograms and principal component analysis (PCoA) in both 2D and 3D models (i.e., with ‘1’ representing an identical band and ‘0’ representing an absent or differing band) [39].

3. Results

3.1. Polymorphism of SSR Loci

The polymorphism of 250 markers was validated on a 2.5% agarose gel, as indicated by high PIC values in a genetic diversity analysis. The sources of markers and their details are summarized in Table 2. Specifically, 66 markers (comprising 31 genomic SSR and 35 EST-SSR) were chosen based on their distinct bands and polymorphisms observed in the agarose gel (Table 3). A total of 11 Lolium varieties (87 individuals) were analyzed using 66 markers (35 EST-SSR markers and 31 genomic SSR markers), resulting in the detection of 655 alleles, with an average of 9.9 alleles per marker (Table S1). The average values for observed heterozygosity, expected heterozygosity, and fixation index (FST) were recorded at 0.399, 0.675, and 0.4344, respectively. The PIC values ranged from 0.0742 (NFFA142) to 0.8721 (LMgSSR10-10C), with an average value of 0.6428 (Figure S1a, Table 4). The number of alleles ranged from 2 (for markers NFFA100 and NFFA142) to 22 (for LMgSSR10-10C), with the majority of PIC values falling between 0.7 and 0.8 (Figure S1b).
In the case of EST-SSR markers, 281 alleles were identified, with an average of 8.0 alleles per marker. The observed heterozygosity, expected heterozygosity, and fixation index (FST) were recorded as 0.361, 0.597, and 0.442, respectively. The PIC values ranged from 0.0742 (NFFA142) to 0.8105 (for ES699684), with an average PIC value of 0.5603 (Figure S2a, Table 4). The number of alleles ranged from two (NFFA100, NFFA142) to thirteen (NFFA017), with the majority of PIC values falling between 0.6 and 0.7 (Figure S2b, Table S1). For genomic SSR, 374 alleles were identified, with an average of 12.1 alleles per marker. The observed heterozygosity, expected heterozygosity, and fixation index (FST) were recorded as 0.442, 0.764, and 0.426, respectively. PIC values ranged from 0.4782 (for DT670835) to 0.8721 (for LMgSSR10-10C), with an average PIC value of 0.7359 (Figure S3a, Table 4). The number of alleles ranged from 4 for markers (NFFA100 and NFFA142) to 22 (for NFFA017), with the majority of PIC values falling between 0.8 and 0.9 (Figure S3b, Table S1). In the variety analysis, the average number of alleles (Na) was 4.1, the average effective number of alleles (Ne) was 2.9, the average observed heterozygosity was 0.566, and the average number of private alleles was 205 (Table 5).

3.2. Genetic Diversity and Clustering Analysis

Alleles obtained from 87 individuals and 66 SSR markers were utilized to investigate genetic diversity and similarity within and between varieties (Table S2). Within varieties, the smallest genetic distance was observed in Hwasan 104ho (Table 6), with an average value of 0.469 and a maximum difference of 0.212 (Table 6 and Table S3). Conversely, the largest genetic diversity was found in IR901 (Table 6), with an average value of 0.571 and a maximum difference of 0.211 (Table 6 and Table S3). Regarding genetic diversity between varieties, Greencall and Greencall 2ho exhibited the closest similarity (Table 6), with an average value of 0.542 and a maximum difference of 0.310. In contrast, Kowinmaster (Diploid Korean variety) and Aspire (Tetraploid US imported variety) displayed the greatest genetic diversity between varieties as expected, with an average value of 0.692 and a maximum difference of 0.174 (Table 6). The genetic distance between individuals was closest in AP-1 and AP-7 at 0.361, and farthest in KW-3 and KT-4 at 0.7945, with an average distance of 0.603 across all 87 individuals (Table S2). Genetic similarity ranged from 80% to 90%, with Greenfarm exhibiting the highest similarity (86.45 ± 2.28%) and IR901 displaying the lowest (82.80 ± 2.59%) within their respective varieties (Figure 1).
An analysis of molecular variance (AMOVA) was conducted to examine the distribution of genetic differentiation within and between IRG varieties. The results of AMOVA indicated that 90% of variation was within groups, whereas 10% was among groups (Table 7). The FST value and p-value indicated that the small genetic difference between the two populations was statistically significant. Identical results were obtained when each marker type (EST-SSR, genomic SSR) was analyzed individually (Tables S4 and S5). A cluster analysis was performed to identify genetic variation within varieties and construct dendrograms (Figure 2). These clusters, created using Nei’s genetic distance for 87 individuals, enabled us to distinguish five distinct groups. Clusters I, III, IV, and V comprised IRG varieties, whereas cluster II exclusively included PRG varieties (Table S6). Mostly diploid varieties were included in cluster I. However, the tetraploid IRG variety ‘Hwasan 104ho’ could also be distinguished alongside the diploid varieties (Figure 2, Table S7). Clusters III, IV, and V consisted of four IRG varieties that are regarded as unknown outliers. IRG varieties IRG and PRG are distinct species, and this differentiation persisted even in separate analyses of the EST-SSR and genomic SSR markers (Figures S4 and S5). In a 2D principal coordinate analysis (PCoA) of genetic distances determined using the database, the variation explained by the first two axes amounted to 6.52% and 3.72%, collectively accounting for 10.24% of the total variation (Figure 3). IRG varieties were predominantly represented by the color ‘red’, whereas PRG varieties were represented by the color ‘green’, facilitating species classification. Our 2D PCoA of the 87 samples segregated them into IRG and PRG groups, thereby enabling further subdivision into diploid and tetraploid varieties. Distinct separation was observed between Kowinmaster, Florida 80, and IR901 when individual regions were examined. Additionally, Kowinearly and IR605 were included within all regions occupied by other diploid IRG varieties (Figure S6).
The population structure of the 87 individuals, representing nine IRG and two PRG varieties, was inferred using STRUCTURE v2.3.3, based on all 66 SSR markers. We computed ΔK to ascertain the optimal value of K, ranging from K = 1 to K = 15. As Lolium varieties were utilized in this study, the highest ΔK value was identified when “K = 2” (Figure 4a). Consequently, two primary sub-populations, Pop 1 and Pop 2, were observed to have the highest ΔK value. These sub-populations largely classified IRG and PRG individuals according to their diploid or tetraploid characteristics. Pop 1 (depicted in red in Figure 4b) consisted of 24 individuals, whereas Pop 2 (depicted in green in Figure 4b) included 63 individuals, all of which were of Korean origin. Distinct differentiation between tetraploid IRG and PRG appeared when “K = 3” (Figure 4c). Individuals assigned to the blue category were represented by the Hwasan 104ho variety, whereas those categorized as red mainly comprised the Kentaur and Aspire varieties.

3.3. Bulk Strategy Employed for Cluster Evaluation of Genetic Diversity by Variety

The bulk strategy requires a group of individuals of the same variety. A UPGMA phylogenetic tree was generated using the genetic distances determined with the bulk strategy. ‘Greencall’ and ‘Greencall 2ho’ were the closest varieties, with a mean genetic distance of 0.227, while ‘Greencall’ and ‘Aspire’ displayed the furthest distance, with a mean of 0.384 (Figure 5 and Figure 6, Table S8). The dendrogram derived from the bulk strategy yielded results aligned with those obtained from the structural analysis with K = 3. Specifically, Group 1 included PRG (Aspire, Kentaur) varieties, Group 2 consisted of diploid IRG varieties, and Group 3 comprised tetraploid IRG varieties. Furthermore, consistent results were observed when each marker type was analyzed separately, with the varieties segregated into three distinct groups (EST-SSR, genomic SSR) (Figures S7 and S8). In the 3D PCoA, the first three principal coordinates explained 16.6%, 11.9%, and 11.4% of the total variation, respectively, amounting to 39.9%. These findings aligned with those obtained from the structural analysis (Figure 6). Similar results were obtained when 3D PCoA was performed for each marker type (EST-SSR, genomic SSR; Figures S9 and S10). Additionally, when only the EST-SSR marker was used, the closest genetic distance was observed between Kowinmaster and Kowinearly at 0.221, whereas the farthest distance was noted between Hwasan 104ho and Greencall at 0.388 (Table S8). When only the genomic SSR marker was used, the closest genetic distance was observed between Greencall and Greencall 2ho at 0.223, whereas the farthest distance was noted between Greenfarm and Aspire at 0.436 (Table S9).

4. Discussion

Morphological features have traditionally been used to characterize and distinguish between plant varieties. However, this method may prove inaccurate due to variations in growing environments across regions, rendering it inefficient, time-consuming, and costly [47]. Over the last two decades, molecular markers and agronomic traits have been actively utilized in studies on forage grasses [48,49,50,51]. Among these markers, SSR markers have garnered attention owing to their high polymorphisms and reliable reproducibility [52]. EST-SSR markers, which target coding region DNA, offer advantages over genomic SSR markers, particularly in applications involving transcription sequences and heterologous species [22,53]. Consequently, SSR markers have become instrumental in evaluating genetic diversity and identifying diversity within various crops [54,55,56] horticultural plants [57,58,59], and forage species [60,61,62].
This study evaluated genetic variation within and between populations, encompassing 87 samples. These included eight IRG varieties developed in Korea, one exotic variety, and two PRG exotic varieties, each comprising eight individuals except for Aspire, which had seven.
This examination employed 66 SSR markers. Previous studies on genetic diversity showed ranges of 0.25–0.76 [63] and 0.019–0.236 [34]. However, our study demonstrated a higher range of genetic diversity (0.469–0.571). Currently, in Korea, the registration of new varieties under the Act on the Protection of New Varieties of Plants considers novelty (Article 17), distinctness (Article 18), uniformity (Article 19), stability (Article 20), genealogy, phenotype, and other agronomic characteristics such as morphological, physiological, and biochemical traits. This approach is crucial for enhancing reliable variety identification and the protection of breeders’ rights. Pasture breeding typically involves heterosis and polycross, resulting in a large genetic distance within varieties and low differentiation between variants [64]. Accordingly, the sample selection method and breeding techniques utilizing molecular markers serve as tools for enhancing breeding efficiency.
Previous studies have explored the clusters in dendrograms, focusing on similar characteristics such as morphology and heading date, with consideration given to the variety, breeding location, and country [65]. Our study yielded similar results in the cluster analysis, PCoA, and bulk strategy analysis concerning the genetic distance between varieties. Specifically, varieties in the same species were mainly clustered according to ploidy. Furthermore, Greencall and Greencall 2ho consistently showed similar results in our dendrogram. Whenever Greencall 2ho was present within a cluster, it was positioned under Greencall. This observation aligns with our breeding line data, which was derived from parental lines with a similar genetic background (Table S10). Furthermore, we found that Greencall and Greencall 2ho share morphological similarities, growth habits, and blooming periods. However, Greencall 2ho has a thicker stem compared to Greencall. Similarly, Kowinearly and Kowinmaster showed analogous patterns in the dendrogram, PCoA, and bulk strategy analysis. Whenever Kowinmaster was clustered, it was included in Kowinearly, and these observations were corroborated by the structural analysis. Additionally, Kowinearly and Kowinmaster were developed using five different parental lines, three of which are shared (Table S10). This explains their similar morphological characteristics, growth habits, and blooming periods. However, they differed in their blooming periods, with Kowinearly being an early-maturing variety and Kowinmaster being a mid-maturing variety [66,67]. Interestingly, in the PCoA, Kowinearly, Greenfarm, and Greencall 2ho were found to share breeding with Florida 80, indicating a shared genetic heritage. Furthermore, Greencall and Greencall 2ho utilized Kowinearly for cross-breeding, resulting in a shared genetic heritage with Kowinearly. Similarly, Greencall and Greenfarm utilized Kospeed for cross-breeding, resulting in a shared genetic heritage with Kospeed (Table S10). Structural analysis at a K value of 2 initially posed challenges in distinguishing between polyploid species such as diploids or tetraploids. However, this issue was resolved by increasing the K value to 3. The structural analysis showed results similar to those of the 2D PCoA for the 87 samples. This suggests that both structural analysis and PCoA can effectively differentiate pasture species. These analyses can be employed to confirm polyploidy between varieties or to obtain information on new varieties. It is imperative to utilize established databases, as these methods rely on standardized reference points derived from existing knowledge about varieties.
Information on the breeding system, reproductive biology, and life history of a species informs decisions regarding the number of individuals to sample within populations. This may entail sampling many individuals from a few populations or sampling fewer individuals from more populations [68]. The genetic diversity of grasses typically surpasses that of general plants such as rice [55] and soybean [56] due to grasses’ unique characteristics such as the polycross breeding methods employed and their allogamous nature, heterosis, and self-incompatibility. Research on the genetic diversity of pasture grasses, such as IRG, PRG, and orchard grass, has varied in sampling approaches, with some studies analyzing as few as 1–3 individuals per variety and others analyzing up to 100 or more individuals per variety [2,33,34,46,48,69,70]. Our study employed genetic diversity analysis and AMOVA, among other methods, with seven to eight individuals per variety, and it yielded results consistent with those of previous studies. The findings of our study suggest that utilizing molecular markers for variety breeding is both time- and cost-efficient.
The AMOVA revealed a higher variation within varieties, at 90%, than among them. Similar outcomes were observed in previous studies on pasture grasses, consistent with our findings [71,72]. Genetic variation within varieties is higher than that between one maternal and one paternal line because pasture grasses are characterized by self-incompatibility, allogamous plants, and the polycross breeding method. Consequently, the genetic distance between varieties was confirmed using a bulk strategy, which allowed us to analyze varieties exhibiting high genetic variation [73]. Furthermore, bulk strategies serve as valuable tools for handling ‘rare’ bands that arise from diversity creating within-variety variation [8,74]. In the bulk strategy analysis, eight individuals per variety (except Aspire, which comprised seven individuals) were grouped, yielding results similar to those obtained when structural analysis and 2D PCoA were employed.

5. Conclusions

This study investigated the genetic diversity and relationships among eight IRG and two PRG varieties using 66 SSR markers, revealing substantial within-variety variation (90%) as demonstrated by AMOVA. Molecular markers, including genomic SSR and EST-SSR, effectively differentiated diploid and tetraploid varieties and distinguished IRG from PRG species. Analyses such as clustering, PCoA, and structural evaluation consistently grouped varieties by species and ploidy, while highlighting close genetic relationships, such as those between Greencall and Greencall 2ho. The bulk strategy further validated these findings, confirming the genetic diversity and distinct clustering of varieties like Kowinearly and Kowinmaster, which share parental lines and traits.
Despite the increasing importation of forage grasses in Korea, the availability of high-quality domestic varieties remains limited. While traditional breeding has been pivotal in variety development, molecular marker research—widely adopted internationally—remains underutilized locally. This study highlights the potential of molecular markers and linkage maps as powerful tools to elucidate the genetic diversity and varietal characteristics of Korean forage grasses. To maximize the applicability of these findings, incorporating key agronomic traits such as biomass and cellulose content is crucial. The research team is actively preparing to evaluate the agronomic performance of these varieties, ensuring a comprehensive approach to forage grass improvement. By integrating a robust database for marker-assisted breeding with agronomic performance analysis, this research offers a pathway to enhance agronomic traits, improve breeding efficiency, and foster innovation in the Korean forage grass industry, paving the way for a more sustainable and self-sufficient future.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agriculture15030244/s1, Figure S1: Number of alleles (a) and PIC (b) per locus for 66 SSR markers; Figure S2: Number of alleles (a) and PIC (b) per locus for EST-SSR markers; Figure S3: Number of alleles (a) and PIC (b) per locus for genomic SSR markers; Figure S4: Unweighted pair group arithmetic mean (UPGMA) phylogenetic tree using 35 EST-SSR markers in 87 individual IRG and PRG varieties; Figure S5: Unweighted pair group arithmetic mean (UPGMA) phylogenetic tree using 31 genomic SSR markers in 87 individual IRG and PRG varieties; Figure S6: Plot of the 2D model of a principal coordinate analysis (PCoA) using 66 markers for individual plants of the IRG and PRG varieties: AP (Aspire), GC1 (Greencall), GC2 (Greencall 2ho), GF (Greenfarm), HS (Hwasan 104ho), IR1 (IR605), IR2 (IR901), KM (Kowinmaster), KT (Kentaur), KW (Kowinearly), and PD (Florida 80), calculated using measurements of Nei’s distance; Figure S7: UPGMA phylogenetic tree constructed using only EST-SSR markers for bulked samples of the IRG and PRG varieties: AP (Aspire), GC1 (Greencall), GC2 (Greencall 2ho), GF (Greenfarm), HS (Hwasan 104ho), IR1 (IR605), IR2 (IR901), KM (Kowinmaster), KT (Kentaur), KW (Kowinearly), and PD (Florida 80), calculated using measurements of the average genetic distance; Figure S8: UPGMA phylogenetic tree constructed using only genomic SSR markers for bulked samples of the IRG and PRG varieties: AP (Aspire), GC1 (Greencall), GC2 (Greencall 2ho), GF (Greenfarm), HS (Hwasan 104ho), IR1 (IR605), IR2 (IR901), KM (Kowinmaster), KT (Kentaur), KW (Kowinearly), and PD (Florida 80), calculated using measurements of the average genetic distance; Figure S9: Plot of the 3D model of a principal coordinate analysis (PCOA) using only EST-SSR markers for individual plants of the IRG and PRG varieties: AP (Aspire), GC1 (Greencall), GC2 (Greencall 2ho), GF (Greenfarm), HS (Hwasan 104ho), IR1 (IR605), IR2 (IR901), KM (Kowinmaster), KT (Kentaur), KW (Kowinearly), and PD (Florida 80), calculated using measurements of the average genetic distance, where the first three principal coordinates accounted for 16.1%, 13.3%, and 11.5% of the variation, respectively; Figure S10: Plot of the 3D model of a principal coordinate analysis (PCOA) using only genomic SSR markers for individual plants of the IRG and PRG varieties: AP (Aspire), GC1 (Greencall), GC2 (Greencall 2ho), GF (Greenfarm), HS (Hwasan 104ho), IR1 (IR605), IR2 (IR901), KM (Kowinmaster), KT (Kentaur), KW (Kowinearly), and PD (Florida 80), calculated using measurements of the average genetic distance, where the first three principal coordinates accounted for 17.5%, 11.9%, and 11.2% of the variation, respectively; Table S1: Comparison of usefulness between genomic SSRs and EST-SSR markers for 66 IRG and PRG accessions; Table S2: Overall data for the genetic distances of IRG and PRG; Table S3: Deviations in genetic distance; Table S4: Hierarchical partitioning of genetic variation created using an AMOVA of only EST-SSR markers; Table S5: Hierarchical partitioning of genetic variation created using an AMOVA of only genomic SSR markers; Table S6: Distribution of varieties by cluster; Table S7: Distribution of varieties by sub-cluster; Table S8: Genetic distance between varieties obtained through the bulk strategy with only EST-SSR markers; Table S9: Genetic distance between varieties obtained through the bulk strategy with only genomic SSR markers; Table S10: Breeding lines of varieties developed in Korea.

Author Contributions

Conceptualization, T.-Y.H.; Methodology, T.-Y.H. and J.-K.Y.; Formal Analysis, D.-G.N., S.-W.C. and T.-Y.H.; Investigation, D.-G.N., E.-S.B. and E.-B.H.; Resources, T.-Y.H.; Data Curation, D.-G.N. and E.-B.H.; Writing—Original Draft Preparation, D.-G.N.; Writing—Review and Editing, J.-K.Y., D.-G.N., S.-W.C. and Y.-H.L.; Visualization, D.-G.N.; Supervision, T.-Y.H. and S.-C.G.; Project Administration, T.-Y.H.; Funding Acquisition, T.-Y.H. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by the Rural Development Administration Grant (RS-2022-RD010210), Republic of Korea.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request to the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Box plot for genetic similarity (y-axis) within varieties (87 individuals). The straight line (-) inside the box indicates the median, and the ‘X’ mark within the square denotes the average value (%). Dots represent outlier samples. GF (Greenfarm); AP (Aspire); GC2 (Greencall 2ho); KM (Kowinmaster); HS (Hwasan 104ho); GC1 (Greencall); KW (Kowinearly); IR1 (IR605); KT (Kentaur); PD (Florida 80); IR2 (IR901).
Figure 1. Box plot for genetic similarity (y-axis) within varieties (87 individuals). The straight line (-) inside the box indicates the median, and the ‘X’ mark within the square denotes the average value (%). Dots represent outlier samples. GF (Greenfarm); AP (Aspire); GC2 (Greencall 2ho); KM (Kowinmaster); HS (Hwasan 104ho); GC1 (Greencall); KW (Kowinearly); IR1 (IR605); KT (Kentaur); PD (Florida 80); IR2 (IR901).
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Figure 2. Phylogenetic tree constructed using the unweighted pair group with the arithmetic mean (UPGMA) method, employing data from 66 SSR markers across 87 individual IRG varieties. AP (Aspire); GC1 (Greencall); GC2 (Greencall 2ho); GF (Greenfarm); HS (Hwasan 104ho); IR1 (IR605); IR2 (IR901); KM (Kowinmaster); KT (Kentaur); KW (Kowinearly); PD (Florida 80).
Figure 2. Phylogenetic tree constructed using the unweighted pair group with the arithmetic mean (UPGMA) method, employing data from 66 SSR markers across 87 individual IRG varieties. AP (Aspire); GC1 (Greencall); GC2 (Greencall 2ho); GF (Greenfarm); HS (Hwasan 104ho); IR1 (IR605); IR2 (IR901); KM (Kowinmaster); KT (Kentaur); KW (Kowinearly); PD (Florida 80).
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Figure 3. Plot of the 2D model of principal coordinate analysis (PCoA), exclusively utilizing genomic SSR markers for individual plants of the following Lolium varieties: AP (Aspire), GC1 (Greencall), GC2 (Greencall 2ho), GF (Greenfarm), HS (Hwasan 104ho), IR1 (IR605), IR2 (IR901), KM (Kowinmaster), KT (Kentaur), KW (Kowinearly), and PD (Florida 80). The analysis was conducted based on measurements of the average genetic distance. The first three principal coordinates accounted for 6.52%, 3.72%, and 2.87% of the variation, respectively.
Figure 3. Plot of the 2D model of principal coordinate analysis (PCoA), exclusively utilizing genomic SSR markers for individual plants of the following Lolium varieties: AP (Aspire), GC1 (Greencall), GC2 (Greencall 2ho), GF (Greenfarm), HS (Hwasan 104ho), IR1 (IR605), IR2 (IR901), KM (Kowinmaster), KT (Kentaur), KW (Kowinearly), and PD (Florida 80). The analysis was conducted based on measurements of the average genetic distance. The first three principal coordinates accounted for 6.52%, 3.72%, and 2.87% of the variation, respectively.
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Figure 4. (a) ΔK values, with the modal value indicating the true K (K = 2). (b) Model-based membership of nine IRG and two PRG varieties as determined using STRUCTURE. The colors denote model-based sub-populations: red, Pop 1; green, Pop 2. (c) Model-based membership of nine IRG and two PRG varieties as determined using STRUCTURE. The colors represent model-based sub-populations: blue, Pop 1; green, Pop 2; red, Pop 3.
Figure 4. (a) ΔK values, with the modal value indicating the true K (K = 2). (b) Model-based membership of nine IRG and two PRG varieties as determined using STRUCTURE. The colors denote model-based sub-populations: red, Pop 1; green, Pop 2. (c) Model-based membership of nine IRG and two PRG varieties as determined using STRUCTURE. The colors represent model-based sub-populations: blue, Pop 1; green, Pop 2; red, Pop 3.
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Figure 5. A UPGMA phylogenetic for bulked samples of the IRG and PRG varieties AP (Aspire), GC1 (Greencall), GC2 (Greencall 2ho), GF (Greenfarm), HS (Hwasan 104ho), IR1 (IR605), IR2 (IR901), KM (Kowinmaster), KT (Kentaur), KW (Kowinearly), and PD (Florida 80). This tree was generated based on measurements of the average genetic distance.
Figure 5. A UPGMA phylogenetic for bulked samples of the IRG and PRG varieties AP (Aspire), GC1 (Greencall), GC2 (Greencall 2ho), GF (Greenfarm), HS (Hwasan 104ho), IR1 (IR605), IR2 (IR901), KM (Kowinmaster), KT (Kentaur), KW (Kowinearly), and PD (Florida 80). This tree was generated based on measurements of the average genetic distance.
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Figure 6. Plot of the 3D model used in the principal component analysis (PCoA) for individual plants of the IRG and PRG varieties: AP (Aspire), GC1 (Greencall), GC2 (Greencall 2ho), GF (Greenfarm), HS (Hwasan 104ho), IR1 (IR605), IR2 (IR901), KM (Kowinmaster), KT (Kentaur), KW (Kowinearly), and PD (Florida 80). This analysis was conducted using measurements of the average genetic distance. Notably, the first three principal coordinates account for 16.6%, 11.9%, and 11.4% of the variation, respectively.
Figure 6. Plot of the 3D model used in the principal component analysis (PCoA) for individual plants of the IRG and PRG varieties: AP (Aspire), GC1 (Greencall), GC2 (Greencall 2ho), GF (Greenfarm), HS (Hwasan 104ho), IR1 (IR605), IR2 (IR901), KM (Kowinmaster), KT (Kentaur), KW (Kowinearly), and PD (Florida 80). This analysis was conducted using measurements of the average genetic distance. Notably, the first three principal coordinates account for 16.6%, 11.9%, and 11.4% of the variation, respectively.
Agriculture 15 00244 g006
Table 1. Information regarding Italian ryegrass (IRG) and perennial ryegrass (PRG).
Table 1. Information regarding Italian ryegrass (IRG) and perennial ryegrass (PRG).
VarietySpeciesAbbreviationPloidyPlace of Registration
Hwasan 104hoItalian ryegrassHSTetraploidKorea (2002)
KowinearlyItalian ryegrassKWDiploidKorea (2006)
KowinmasterItalian ryegrassKMDiploidKorea (2006)
GreenfarmItalian ryegrassGFDiploidKorea (2010)
GreencallItalian ryegrassGC1DiploidKorea (2017)
Greencall 2hoItalian ryegrassGC2DiploidKorea (2017)
IR605Italian ryegrassIR1DiploidKorea (2019)
IR901Italian ryegrassIR2DiploidKorea (2019)
Florida 80Italian ryegrassPDDiploidUSA
KentaurPerennial ryegrassKTTetraploidUSA
AspirePerennial ryegrassAPTetraploidUSA
Table 2. Selected polymorphic simple sequence repeat (SSR) markers.
Table 2. Selected polymorphic simple sequence repeat (SSR) markers.
Marker Type-SelectAmplifyAnalysisReference
Genomic SSRIRG373010[41]
DRG463621[41,42]
EST-SSRTF544115[43]
Cereal51144[44]
OG51248[45,46]
PRG11118[41]
Total25015666
DRG: Lolium temulentum.
Table 3. Markers used for the SSR analysis of IRG and PRG.
Table 3. Markers used for the SSR analysis of IRG and PRG.
Marker TypeSSR MarkerSequence (5′ → 3′)Sequence (3′ → 5′)Repeat MotifTemp. (°C)
EST-SSRES699118AGCACCAAATTTCCGCTCGAGGTTCTTCTCCCTC(tcgtc)452
ES699187ATCTCATCTGCAACCAATTCGTCTTCTTCGCCTTCTCG(agc)452
ES699496ACGCTCACAGAAACAGAACTACATGCCTTCCCAGAGAC(ccg)855
ES699582CTTCAGAAATGATGGGTGATAGCCCAAATGATTCACTTC(tga)852
ES699583AACTACCCTGACCTGTAAACTGTCAGGAGGGTAAATATATCCC(tgc)452
ES699684TCCGGTTCCAGTATGTTGGCAACCCTTACAGATACTGC(tg)1752
ES699689ATCTAGTTCCCTGTCGTGGGAGACATCCAGCTTACAAGG(tgta)552
ES699543GTCAGATTTACATCTATGCTCCAAGTCCGGTAGATCTGCC(tcc)452
Dg_Contig3046GACGACGAACATCTCTAATTTGGTCTTCTCCTTGTCGAGGATCTTGCGG60 °C
Dg_Contig10135ATGAGGAGGAGATAGAGAAGCTCAATCTGATGTTATTCCAAGGAAACGCTG60 °C
Dg_Contig6373ATCGAGATCAGAAGGTCAAAGAAGGGGTAGAAGCTGAAGGACCAGGAG60 °C
Dg_Contig4921AAATTTGAGAAAAGAAACGACCAGACGCATAGACATAACCGATGTAGAATC60 °C
Dg_Contig3264TTCGCTGTATCAAGTCTGAAGAACGCATCAAGAAACATTTACAGTTGGATG60 °C
Dg_Contig667GAAGTAGCCAGCGATGATGAGTATCACCTAGGCTGGATGCCGCA60 °C
H041.042TACCTGTGCGGCGATGAATCAGGAGCAGGAGAACGTGAA-59 °C
H049.050CGCAGCAAGTAGGGTTAGGACCTCGTGGTGGATCTGCAT-59 °C
NFFA017GATGGACGAAGGCTTCTTTGAGCCGAACCTGAACTCAGAC(cag)757
NFFA019TGGATTTGCAATTAGCCTCAGCTCGTGTATGGCCTTCAAT(ta)2055
NFFA024TGCCCACGAGGTCTATCTTCAGCTTCCCCTTCATTCCACT(cgc)657
NFFA029GGACGACATGTCTGTGCAGTGCCTTGTCGCTGGCTACTC(cgt)657
NFFA041TCCTGAGAGACATCGAGCAGTCAAAAGCCCAAACACTTCC(ctgat)457
NFFA047TTCCTTCCTCTTTCCCAACAATGGTCTCCCTCTGCTCGTA(cca)627
NFFA061TGGATTTGCAATTAGCCTCAGCTCGTGTATGGCCTTCAAT(ta)2055
NFFA066CTCCCCGTCCTTCCATCTCAACCTCCTCCACCATCTTG(cct)657
NFFA100AGCTGAACTATGAGGCATGTCAATCCCTTTCCAGCATTTACCTC(gca)655
NFFA132CAATAATGGAGGAAGTGGAGGACTTGGCTCTAGGATGGCTTACT(agg)655
NFFA142CTTTGGACAAGGCAATGGAATGTTGTTCTTCTGCGGGTAGTC(agg)655
NFFA142CTTTGGACAAGGCAATGGAATGTTGTTCTTCTGCGGGTAGTC(cag)755
NFFA147TGCAGTCGGTTAAGATCAAGAAAGTTGCAGTGAAGGTGCTGAAC(ctg)755
NFFA150TGCAGTCGGTTAAGATCAAGAAGCAGAGCAATGGAGAGGTC(ctg)755
NFFA151ATCTGTGCCGGAATAACAGGGCGGATTTTCTATGACTTCCAG(cgc)655
CNL 39TACCTGTGCGGCGATGAATCAGGAGCAGGAGAACGTGAA-57
CNL 55GCTGATAGCGAGGTGGGTAGCTGCCGGTTGATCTTGTTCT-55
CNL 85ACCCTAGCCCTCCGATCACACCATCTCGTACGTCTTGC-55
CNL174GGGTCGATTCTGTTCCTCAACAGTCGTCCTCGATCATGTG-55
Genomic SSRLMgSSR02-11GAGAGGGGCCAGGCGGTATTAGACGTGCCATTTTGCCTTCC(AC)1357
LMgSSR10-10CGCAAGATCAAAACCTAACACTCCCCTGAGATAGTCTCGAATAACGTTTTCC(AC)2960
LMgSSR11-06ETGATCTCACTGACTGGCCTCGGGTCACTGTGCACATACAGATGC(AC)1460
LMgSSR18-02GTGCTTCAGCCATCCAGGACTCCTTACGTGCCTACACTGATTCG(AC)1560
LMgSSR17-12ETGGCCATCATTTTGAACGCGAGGTTTATGATAAAATTAAGTGGCTTTCC(CA)1157
LMgSSR18-08CGCATCAGGGTCGATTCCTCGTTGTCCTATGCTAAAGCTGACATCC(AC)1157
LMgSSR16-04FGTGCTGATCTAATCCCCACAGCGGCACGCCAATTCATACGAG(AC)1157
LMgSSR03-04FCAGATGGGCAGTTGCCACTGGTATTGTACACACAAGCATATTGGCG(AC)1160
DT669537TTTTGCTCCCTCTTGTCCCTTGAGCTCCTTGAGGATC(gcc)652
DT669999TCACGGTTCTCTGGTCTACTACGTACATAACATCATCGCA(ag)2055
DT670318TTAGGACGACCTATAACCTCCTATGGAGCATACCCCTTCTA(tc)952
DT670547ATCCTGCTGCTGCTTATGTCAGGTACGGGTACTCCTC(cga)752
DT670835CCAATTAGATCCATCGTTGTAAGCACAAGCTAGAGGAGC(ttc)4..(ttc)552
DT671223TCTCCTTGCTCCCACCACCTCGACGTCGAACTG(tcc)752
DT671606ATACGGGACAAATAGTTACTGGACTCGTCAACACCAGTCTTC(ca)755
DT671666CACTGAATGTTCTCAGGGATCTTCAGTCTTCGGAAATGAC(tg)952
DT671892CACTAGGCCTAGGAGCACTAGGATAGGGATAAGGATGTCC(ccg)652
DT671999CTTTACAACTAGGGGGCTGTTGACATTGACACCATCAAC(ttca)552
DT672392GCTCCAAGTGAGAGAGGAGTGTATTCGTTTCTACTGCTTTG(ag)852
DT672576CAACAATATCCACTAATGGGAACCAAGATCGTCATTGAAAC(at)1252
DT672678GGCTATATGTCACCGCAGGACTTTAACTTGTTGCACTTCA(ca)1152
DT672678GGCTATATGTCACCGCAGGACTTTAACTTGTTGCACTTCA(ca)1152
DT672713AAACCCGCTCGGTCTCAACTGACCCTTGTCGCTAC(cgc)755
DT672752CCTAGCCTCCTCCTACTCATATCCTCCTCAGAACCCTG(ctc)852
DT672932TTGTTGTGCATGTTGCATATCTCTTTTCCCGAGCTG(cgc)652
DT672934CTCATCCCCTTCCTCATCCTGATCATGAGATCGAGGAT(tcc)652
DT673496CCACCACCATCCCTAAACTTGGAGAGCTTGTCCTTG(ggc)752
DT673743GGAGGAATCCACCAAGTCCATGAGAGGGAGATGGAGTA(gag)952
LTPK-008CATCTCTTCAGCCAGACAGCTACCCCCCATGCCGTTTGAGTTACC(CT)23(CT)560
LM15(DQ399083)TCATTGCCCTGACTACCAGGGCATCGCCTTTCTCAGAGTG(GT)557
LM16(DQ399083)CCATTGTGACCCCAGTAACATGCATAGACAGCCTCCTGAAT(GT)557
Table 4. Allele diversity revealed by the 66 polymorphic SSR markers used in this study.
Table 4. Allele diversity revealed by the 66 polymorphic SSR markers used in this study.
No.SSR MarkerNaNeHoHeFSTPIC
EST-SSR ES69911853.5140.5930.7150.1710.6603
ES69918784.6080.8020.783−0.0250.7528
ES69949673.7680.4350.7350.4070.6969
ES69958251.1520.0000.1321.0000.1288
ES69958394.0150.8330.751−0.1100.7090
ES699684125.9230.4700.8310.4350.8105
ES69968964.1540.5830.7590.2320.7205
ES69954382.4340.2560.5890.5650.5090
Dg_Contig304651.7740.1030.4360.7630.3870
Dg_Contig1013563.1870.0930.6860.8640.6326
Dg_Contig6373124.3370.3920.7690.4910.7371
Dg_Contig492153.1640.3140.6840.5410.6402
Dg_Contig326461.4030.0430.2870.8520.2786
Dg_Contig667103.1470.5130.6820.2480.6518
H041.042113.3760.2260.7040.6790.6767
H049.05072.1500.4220.5350.2120.4729
NFFA017133.8280.3610.7390.5110.7197
NFFA01982.4510.0230.5920.9610.5146
NFFA024102.6770.3840.6260.3880.5754
NFFA029103.9990.6180.7500.1750.7140
NFFA04162.0010.3490.5000.3030.4652
NFFA047105.1590.4710.8060.4160.7807
NFFA061102.8000.0470.6430.9270.6176
NFFA066124.2000.4190.7620.4510.7250
NFFA10021.0970.0000.0891.0000.0848
NFFA13293.1660.5170.6840.2440.6400
NFFA14221.0840.0110.0770.8510.0742
NFFA14272.2140.4170.5480.2400.5158
NFFA14771.8120.1690.4480.6220.4287
NFFA15051.3300.2530.248−0.0180.2356
NFFA151113.3890.3450.7050.5100.6691
CNL 39102.9030.1950.6560.7020.6179
CNL 5552.3740.8240.579−0.4240.4892
CNL 85112.6480.6390.622−0.0260.5812
CNL174113.6420.5060.7250.3030.6982
Genomic SSR LMgSSR02-11G166.2940.7230.8410.1410.8228
LMgSSR10-10C228.4450.3930.8820.5540.8721
LMgSSR11-06E157.3450.6400.8640.2600.8493
LMgSSR18-02G188.2510.4780.8790.4560.8680
LMgSSR17-12E216.3510.2690.8430.6800.8279
LMgSSR18-08C166.6600.2180.8500.7430.8373
LMgSSR16-04F146.0460.7590.8350.0910.8185
LMgSSR03-04F145.6360.6780.8230.1760.8046
DT669537124.6810.6900.7860.1220.7560
DT669999216.7120.6350.8510.2530.8383
DT670318165.3440.5410.8130.3340.7907
DT670547115.0940.3720.8040.5370.7797
DT67083562.0750.2070.5180.6010.4782
DT67122393.9900.4300.7490.4260.7100
DT671606113.0420.4770.6710.2900.6362
DT671666105.8290.7590.8280.0840.8081
DT671892104.3400.4140.7700.4620.7392
DT67199942.2820.3910.5620.3040.4890
DT67239292.7630.0920.6380.8560.5924
DT672576122.9510.4480.6610.3220.6325
DT67267893.6070.2630.7230.6370.6839
DT672678116.4690.3980.8450.5300.8271
DT672713113.6420.3790.7250.4770.6851
DT67275252.1600.2990.5370.4440.4803
DT67293282.4960.1950.5990.6740.5485
DT672934104.1670.6090.7600.1980.7281
DT673496106.0260.4250.8340.4900.8133
DT673743104.7840.2530.7910.6800.7623
LTPK-008155.7690.2330.8270.7190.8107
LM15(DQ399083)94.6450.5980.7850.2380.7613
LM16(DQ399083)94.7450.4480.7890.4320.7602
Average9.93.9020.3990.6750.4340.4344
Na (number of alleles); Ne (effective number of alleles); Ho (observed heterozygosity); He (expected heterozygosity); FST (fixation index).
Table 5. Allele diversity revealed by the 11 varieties included in this study.
Table 5. Allele diversity revealed by the 11 varieties included in this study.
Na Ne HoHePA
HS3.92.80.5200.57123
KW4.12.80.3840.56716
KM3.92.70.3730.54411
GF3.82.50.3280.51814
GC14.02.80.3580.55816
GC23.82.70.3460.5498
IR14.22.90.3570.58117
IR24.73.30.5190.63226
PD4.63.10.4100.59326
KT4.23.10.4370.58020
AP3.62.80.3300.53028
Overall4.12.90.3970.566205
Na (number of alleles); Ne (effective number of alleles); Ho (observed heterozygosity); He (expected heterozygosity); PA (private alleles).
Table 6. Average genetic distance within and among the varieties for 87 individuals of IRG.
Table 6. Average genetic distance within and among the varieties for 87 individuals of IRG.
APGC1GC2GFHSIR1IR2KMKTKWPD
AP0.5140.6840.6580.6770.6310.6540.6360.6920.5650.6790.670
(0.369–0.697)(0.597–0.780)(0.548–0.789)(0.576–0.757)(0.545–0.766)(0.541–0.785)(0.553–0.771)(0.609–0.783)(0.47–0.718)(0.577–0.790)(0.574–0.769)
GC1 0.5420.5420.5710.6350.5980.6150.5770.6630.5870.588
(0.436–0.650)(0.393–0.703)(0.432–0.699)(0.529–0.760)(0.464–0.745)(0.470–0.743)(0.448–0.741)(0.573–0.770)(0.459–0.742)(0.461–0.738)
GC2 0.5270.5470.6090.5720.5760.5690.6320.5870.561
(0.451–0.592)(0.437–0.663)(0.498–0.714)(0.438–0.691)(0.478–0.687)(0.376–0.734)(0.558–0.741)(0.457–0.778)(0.422–0.700)
GF 0.5010.5950.5950.5940.5470.6630.5930.589
(0.405–0.594)(0.519–0.702)(0.474–0.728)(0.453–0.730)(0.420–0.672)(0.556–0.756)(0.501–0.707)(0.494–0.708)
HS 0.4690.6190.5870.5940.6080.6010.614
(0.387–0.599)(0.513–0.786)(0.468–0.722)(0.502–0.684)(0.493–0.711)(0.483–0.697)(0.530–0.759)
IR1 0.5650.5980.6080.6470.6050.589
(0.451–0.722)(0.473–0.725)(0.447–0.770)(0.537–0.793)(0.495–0.778)(0.447–0.728)
IR2 0.5710.6070.6250.6120.600
(0.455–0.666)(0.512–0.739)(0.505–0.730)(0.483–0.751)(0.488–0.725)
KM 0.5160.6630.5540.588
(0.417–0.631)(0.589–0.762)(0.454–0.678)(0.482–0.709)
KT 0.5340.6650.644
(0.443–0.662)(0.583–0.794)(0.522–0.729)
KW 0.5400.606
(0.434–0.642)(0.516–0.753)
PD 0.561
(0.477–0.635)
Average0.603
AP (Aspire); GC1 (Greencall); GC2 (Greencall 2ho); GF (Greenfarm); HS (Hwasan 104ho); IR1 (IR605); IR2 (IR901); KM (Kowinmaster); KT (Centaur); KW (Kowinearly); PD (Florida 80).
Table 7. Hierarchical partitioning of genetic variance using AMOVA.
Table 7. Hierarchical partitioning of genetic variance using AMOVA.
Sourcedf (Degrees of Freedom)SS (Sum of Squares)MS%FST (Value)FST {P(rand ≥ Data)}
Among Pops1146.398146.39810%--
Within Pops1723837.26322.31090%--
Total1733983.661 100%0.0920.001
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Nam, D.-G.; Baek, E.-S.; Hwang, E.-B.; Gwak, S.-C.; Lee, Y.-H.; Cho, S.-W.; Yu, J.-K.; Hwang, T.-Y. Genetic Diversity Patterns Within and Among Varieties of Korean Italian Ryegrass (Lolium multiflorum) and Perennial Ryegrass (Lolium perenne) Based on Simple Sequence Repetition. Agriculture 2025, 15, 244. https://doi.org/10.3390/agriculture15030244

AMA Style

Nam D-G, Baek E-S, Hwang E-B, Gwak S-C, Lee Y-H, Cho S-W, Yu J-K, Hwang T-Y. Genetic Diversity Patterns Within and Among Varieties of Korean Italian Ryegrass (Lolium multiflorum) and Perennial Ryegrass (Lolium perenne) Based on Simple Sequence Repetition. Agriculture. 2025; 15(3):244. https://doi.org/10.3390/agriculture15030244

Chicago/Turabian Style

Nam, Dong-Geon, Eun-Seong Baek, Eun-Bin Hwang, Sang-Cheol Gwak, Yun-Ho Lee, Seong-Woo Cho, Ju-Kyung Yu, and Tae-Young Hwang. 2025. "Genetic Diversity Patterns Within and Among Varieties of Korean Italian Ryegrass (Lolium multiflorum) and Perennial Ryegrass (Lolium perenne) Based on Simple Sequence Repetition" Agriculture 15, no. 3: 244. https://doi.org/10.3390/agriculture15030244

APA Style

Nam, D.-G., Baek, E.-S., Hwang, E.-B., Gwak, S.-C., Lee, Y.-H., Cho, S.-W., Yu, J.-K., & Hwang, T.-Y. (2025). Genetic Diversity Patterns Within and Among Varieties of Korean Italian Ryegrass (Lolium multiflorum) and Perennial Ryegrass (Lolium perenne) Based on Simple Sequence Repetition. Agriculture, 15(3), 244. https://doi.org/10.3390/agriculture15030244

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