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Article

Precise Identification of Vitis vinifera L. Varieties Using Cost-Effective NGS-Based SNP Genotyping

by
Konstantinos Tegopoulos
,
Sonia-Vasiliki Polychronidou
,
Anastasia Voumvouraki
,
Petros Kolovos
,
George Skavdis
and
Maria Ε. Grigoriou
*
Department of Molecular Biology & Genetics, Democritus University of Thrace, 68100 Alexandroupolis, Greece
*
Author to whom correspondence should be addressed.
Horticulturae 2025, 11(4), 375; https://doi.org/10.3390/horticulturae11040375
Submission received: 15 January 2025 / Revised: 26 March 2025 / Accepted: 27 March 2025 / Published: 31 March 2025
(This article belongs to the Section Viticulture)
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Versions Notes

Abstract

:
In this study, we developed, validated and applied an NGS-based SNP genotyping protocol for the molecular identification of Vitis vinifera varieties, demonstrating a reliable and efficient approach for distinguishing grapevine cultivars. By utilizing a small but highly informative set of SNP loci, this method provides effective molecular genotyping while capturing the genetic diversity needed for accurate identification. This straightforward and accessible approach allows for the rapid generation of genetic profiles, which can be compared with the profiles in existing databases to precisely identify grapevine varieties, even in cases where traditional morphological methods fall short due to environmental variability or developmental differences. The process is designed to be both time-efficient and cost-effective, making it a practical tool for routine use in vineyard management, breeding programs, and conservation efforts. Furthermore, the workflow minimizes the need for whole-genome sequencing or other resource-intensive techniques, making molecular profiling accessible to a wider range of researchers, growers, and industry professionals. Analysis of the molecular profiles of known varieties validated the accuracy of the protocol. Moreover, 14 autochthonous Greek grapevine varieties that have not been previously identified were also genotyped and the data were compared with those of all Greek varieties in the Vitis International Variety Catalogue, revealing no matching multilocus genotypes across Greece.

1. Introduction

Grapevine (Vitis vinifera L.) is a dicotyledonous, perennial plant species with a diploid genome (2n = 38), recognized as one of the most ancient and economically valuable domesticated fruit crops in the world [1,2]. The species has played a pivotal role in human agriculture for thousands of years with the archaeological records suggesting that grapevine domestication began around 6000 to 7000 years ago [3], either solely in the Western Asia region [4] or, according to a more recent hypothesis, it may have taken place in both Western Asia and the area of the Caucasus concurrently [5].
Over millennia, grapevine cultivation spread across Europe, the Mediterranean basin, and beyond, contributing to extensive genetic diversity [6,7] through sexual reproduction [8,9], spontaneous mutations [10] and selective breeding [3,11]. Historically, thousands of grape cultivars have been developed; recent estimates suggest that the species includes more than 10,000 documented cultivars worldwide, with only a small fraction being economically important [1,12,13,14]. Among the regions that have contributed to grapevine diversity, Greece holds a particularly rich history, as the country’s National Catalogue of Grapevine Varieties includes 210 wine, 36 table and three raisin indigenous grape varieties [15].
Today, Vitis vinifera remains economically vital, particularly in wine production, as its modern cultivars possess extensive genetic diversity and can adapt to a plethora of climatic conditions [12,16,17,18]. In addition to wine, Vitis vinifera is also used for producing table grapes, raisins, and grape-derived products, all of which contribute to its global economic significance [19]. The species’s role is especially pronounced in traditional wine-producing countries like Greece, where indigenous grape varieties have a key role in both the local economy and cultural heritage [20]. Therefore, the precise identification and preservation of grapevine varieties is essential not only for the wine industry but also for protecting the genetic diversity of this crucial species.
However, the species’ high level of genetic variation and its complex domestication history have long posed challenges to the identification and classification of grapevine varieties [9]. Throughout centuries of trade, migration, and cultivation, many grapevine varieties have become difficult to identify due to widespread synonymy (different names for the same variety) and homonymy (same names for different varieties) [21]. Misidentification of grapevine cultivars hinders breeding programs, complicates legal protections for breeders, and affects quality control in the wine industry [17,22]. For example, incorrect labeling of varieties can lead to legal disputes over intellectual property, mismanagement of breeding resources, and economic losses for farmers and industries. Despite the existence of germplasm collections for the characterization and conservation of the grapevine genetic resources in countries with a long viticultural history, such as France, Spain, and Italy, these countries have also faced difficulties in maintaining clear varietal distinctions [17]. Furthermore, a number of studies claim that this issue has also affected Greece, a country with long grapevine history and hundreds of indigenous varieties, some of which are reported to be synonyms of older indigenous ones [20,23]. Especially in Greece, where the wine industry focuses on traditional autochthonous cultivars [24], accurate identification is essential for protecting breeders’ rights and enhancing the efficiency of breeding programs.
Historically, grapevine identification began with phenotypic and morphological methods, which relied on physical traits such as leaf shape, berry size, and growth patterns [25]. However, these methods are highly affected by environmental factors and developmental stage, leading to inconsistent results [14,26,27]. Despite the disadvantages of using morphological traits, Negrul A.M. published the first comprehensive classification of grape cultivars in 1946 [11,28]. In the late 20th century, the introduction of biochemical markers, such as flavonoid analysis [29] and the use of isoenzymes [30], provided a new approach for identification; however, these methods also had limitations, including low resolution and labor-intensive processing [1,31]. The development of molecular markers revolutionized grapevine genotyping because they are highly polymorphic, abundant in the genome, and offer reproducibility as they are independent of the environmental conditions and developmental stage [1,32]. Therefore, DNA markers allowed a more precise characterization of the genetic diversity and identification of V. vinifera cultivars [11]. Early molecular identification methods such as RAPDs (Random Amplified Polymorphic DNA) and AFLPs (Amplified Fragment Length Polymorphisms) allowed for more detailed genetic analyses, but suffered from issues related to reproducibility and data interpretation [31,32]. In contrast, the development of SSR (Simple Sequence Repeats) and SNP (Single Nucleotide Polymorphisms) approaches marked a turning point, providing reliable and high-throughput tools for identifying grapevine varieties [11,33]. SSRs, in particular, have been widely used in grapevine identification due to their high polymorphic and codominant nature [18,34]. However, Deschamps et al. (2012) [35] and Jia G. et al. (2024) [36] report that the use of SSRs for large scale genotyping is time-consuming and low-throughput. In contrast, SNPs have emerged as the preferred marker for large-scale genotyping, as they are widely distributed within the genome and offer higher resolution and discriminatory power [37]. SNP genotyping offers accurate and cost-effective solutions through several methods, such as High-Resolution Melting (HRM) analysis, Sanger-based approaches and SNP arrays. However, HRM and Sanger-based genotyping often lack the scalability and resolution needed for large-scale genotyping studies limiting their application in plant genotyping studies [35,38,39]. As for SNP arrays, their utility has been enhanced by the development of Next Generation Sequencing (NGS) platforms which enable the automation of the detection and study of thousands of SNP loci simultaneously [40]. SNP arrays and high-throughput technologies have significantly advanced the ability to analyze grapevine genetic diversity on a previously unattainable scale [41]. For instance, next-generation sequencing has generated hundreds of thousands of SNP markers across the Vitis genome. In 2010, the first Vitis SNP chip array, Vitis9kSNP, was developed, detecting 9000 SNPs [42]; three years later the GrapeReSeq Consortium introduced the Vitis18kSNP array, which identified approximately 18,000 SNPs in the genomes of several Vitis varieties [43]. The latter has already successfully been used to study the genetic diversity of cultivars in Georgia [44] and Italy [45]. Moreover, Laucou et al. (2018) [11] reevaluated the Vitis18kSNP array and used 10,207 of these SNPs to study genetic diversity and structure among Vitis vinifera accessions. Although SNP arrays provide a useful tool for studying Vitis vinifera cultivars, one of the key challenges in current molecular studies is identifying the minimal number of SNPs needed to accurately discriminate between grapevine cultivars. The optimal number of molecular markers depends on factors such as the genetic diversity and genomic similarity of the cultivars being analyzed [11]. To this end, Cabezas et al. (2011) [46] showed that 200 grapevine cultivars can be efficiently identified with the use of 48 SNPs while 12 SNPs were able to distinguish approximately 100 Italian cultivars with a lesser degree of genetic relatedness [45]. Finally, Laucou et al. (2018) [11] reported that 14 SNPs distributed across eleven chromosomes within the V. vinifera reference genome (Accession number: PN40024) are sufficient to identify 783 varieties from different countries, including 45 from Greece. Viticulture has been a cornerstone of Greek agriculture for millennia, with over 100,000 hectares currently dedicated to grape cultivation, contributing significantly to the country’s economy [47,48]. Greek wine’s indigenous varieties, like Xinomavro and Assyrtiko, constitute an important cultural and economic asset [24]. The Greek wine sector not only supports thousands of local jobs but also plays a vital role in rural development and tourism. However, issues such as homonymy, synonymy, and incorrect labeling pose significant challenges to the industry’s future [20,47]. For example, it has been shown that varieties such as Sideritis and Mavro, once believed to be genetically identical across different regions, are actually distinct [22]; furthermore, Hvarleva et al. (2005) [49] used molecular markers to uncover cases of homonymy and synonymy between Greek and Cypriot cultivars, including the case of Sideritis, confirming earlier work by Stavrakakis et al. (1997) [22]. These cases highlight the importance of precise molecular identification in resolving synonymy and homonymy issues among indigenous Greek varieties.
In this study we have developed a reliable and cost-effective SNP-based approach for precise identification of grapevine varieties. The selected markers as described by Laucou et al. form an informative set that has allowed for precise identification of more than 700 grapevine varieties [11]; therefore, no further validation of their discriminatory power was needed. Our objective was to develop, based on the aforementioned set of SNPs, a novel genotyping method for Vitis vinifera that relies on NGS, rather than the previously described SNP-array based approach. To this end, appropriate primers were designed to amplify short SNP-containing amplicons. To reduce both time and cost, amplification of 14 SNP markers was carried out through three multiplex PCR reactions with amplicon lengths optimized for sequencing on NGS platforms. Additionally, we developed a comprehensive bioinformatic workflow that ensures rapid and accurate identification of the genotypes for each variety. Unlike labor-intensive methods such as HRM, Sanger sequencing, and SNP array-based approaches, which are limited in throughput, our NGS-based method enables the rapid and cost-effective genotyping of multiple SNPs in a single run, providing a more scalable and efficient solution for variety identification.
By following the method described in this study, researchers or growers with unknown Vitis vinifera samples can easily replicate the genotyping process, compare the genetic profiles of their samples against genotyping data from the Vitis International Variety Catalogue, and identify the variety in a quick and cost-effective manner. Using this method, we analyzed 14 previously uncharacterized Greek varieties and compared the genotypes produced with the profiles of the Greek varieties that are included in the Vitis International Variety Catalogue (VIVC) [50]. By expanding the list of molecularly characterized Greek varieties, homonymy and synonymy, which have long posed significant challenges for both breeders and the wine industry alike, are addressed [23,47]; accurate genotyping not only resolves these issues but also plays a vital role in the effective management of grapevine germplasm collections by supporting the viticultural legacy and regulatory measures for traditional cultivars, preventing cases of mislabeling and preserving the integrity of the wine industry’s genetic resources.

2. Materials and Methods

2.1. Plant Material and DNA Extraction

For this study, young leaves from 19 grapevine varieties were collected from vineyards across Greece in 2023. The varieties, along with their region of origin and code number in the Vitis International Variety Catalogue (VIVC), are presented in Table 1.
For each variety, 25 mg (dry weight) of young leaves were homogenized with a Precellys Evolution Homogenizer (Bertin Instruments, Montigny-le-Bretonneux, France) to facilitate cell lysis. DNA was extracted using the NucleoSpin Plant II Mini Kit (Macherey Nagel, Düren, Germany), following the manufacturer’s protocol. To eliminate enzyme inhibitors, the DNA was further processed with the Nucleospin Inhibitor Removal Kit (Macherey Nagel, Germany) according to the manufacturer’s protocol. DNA concentration and purity were assessed using a NanoDrop Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), while genomic DNA integrity was assessed by electrophoresis in a 0.8% w/v agarose (UltraPure Agarose, Invitrogen, Carlsbad, CA, USA) gel prestained with GelRed Nucleic Acid Gel Stain (Biotium, Fremont, CA, USA).

2.2. PCR Amplification

Based on the positions of polymorphic sites within the reference Vitis vinifera genome reported by Shi et al. (2023) [51], as described by Laucou et al. (2018) [11], PrimerBlast was employed to design 14 sets of primers (Table 2). These primers amplify 14 amplicons distributed across 11 of the 19 chromosomes in the Vitis vinifera genome, each containing a single SNP.
The 14 amplicons were amplified using three multiplex PCR reactions (R1, R2, and R3) (Table 2), with a VeritiPro Thermal Cycler (Thermo Fisher Scientific, USA). Each 30 µL reaction consisted of 10 µL of KAPA SYBR FAST qPCR Master Mix (2X) (Sigma-Aldrich, St. Louis, MO, USA), 0.1 µM of each primer, and 100 ng of genomic DNA. The thermocycling conditions for all three reactions included initial denaturation at 95 °C for 5 min, followed by 30 cycles of denaturation at 95 °C for 30 s, annealing at 58.5 °C for 30 s, and extension at 72 °C for 30 s, with a final extension at 72 °C for 5 min. Successful amplification was confirmed by electrophoresis on 4% w/v agarose gels prestained with GelRed Nucleic Acid Gel Stain (Biotium, USA).

2.3. Next-Generation Sequencing (NGS)

The products from each multiplex PCR reaction were purified, quantified, and used as templates for library preparation as described by Tegopoulos et al. (2023) [52]. DNA concentration for each library was determined by Real-Time PCR using the Ion Universal Library Quantitation Kit (Thermo Fisher Scientific, USA). Template preparation was carried out with 65 pM of library DNA using the Ion Chef System (Thermo Fisher Scientific, USA), followed by sequencing on the Ion Torrent GeneStudio S5 platform (Thermo Fisher Scientific, USA), according to the manufacturer’s instructions.

2.4. Data Analysis

For genotype analysis, Samtools (v1.17) [53] was employed to convert the raw UBAM files into FASTA format. Quality filtering and the removal of undesired fragment lengths were performed using Mothur (v1.48.0) [54]. The processed reads were then aligned to the Vitis vinifera (NCBI taxID: 29760) reference genome (Pinot Noir 40024, GCF_030704535.1) using Bowtie2 (v2.4.5) [55] to ensure accurate mapping. The alignment was visualized with Integrative Genomics Viewer (IGV, v2.11.2) [56] and SNP alleles within each amplicon were identified. Genotyping data were subsequently formatted for input into GenAlEx v6.50 [57] (Peakall and Smouse, 2012) to assess the discriminatory power of the used SNPs, to detect putative identical DNA fingerprint clones among the studied varieties, and to calculate the pairwise genetic distances, based on the genotypes of the 14 polymorphisms. Moreover, phylogenetic trees based on the pairwise genetic distances of the studied varieties were constructed using iTOL [58] while Heatmapper [59] was employed for the construction of heatmaps.

3. Results

This study focused on developing, testing, validating and applying an alternative, cost-effective NGS-based protocol for accurate genotyping of Vitis vinifera. To this end, 14 polymorphic SNPs with previously described high discriminatory power [11] were analyzed across 14 amplicons to molecularly characterize 15 economically significant indigenous Greek Vitis vinifera varieties registered in the National Catalogue of Grapevine Varieties [15] (Table S2), as well as four varieties from the USA, France, Romania and Spain. The genomic coordinates of the 14 SNP-containing amplicons along with the positions of the SNPs within the Vitis vinifera genome and their corresponding alleles are presented in Table 3. The SNP positions are referenced to the Vitis vinifera reference genome (PN40024 v.08/2023, GCF_030704535.1), ensuring precise alignment for comparative analysis.

3.1. Grapevine Varieties

The proposed protocol was tested through the molecular identification of 19 Vitis vinifera varieties. Among them, 14 were previously uncharacterized indigenous Greek grapevine varieties, all registered in the National Catalogue of Grapevine Varieties [15]. While most of these varieties are currently cultivated in different regions across Greece, they originated in seven main geographical areas: three from the Ionian Islands, four from the Peloponnese, three from the Aegean Islands, one from Macedonia, one from Crete and one from Thrace. Notably, the precise origin of one variety, Razaki, within Greece remains unknown.
To validate the protocol, five varieties with known genotyping patterns [11]—four from foreign countries and the variety Plyto from Greece—were also analyzed. The studied varieties, along with their corresponding variety numbers in the Vitis International Variety Catalogue (VIVC) [50] and their regions of origin, are presented in Table 1.

3.2. Genotyping Analysis

3.2.1. Protocol Validation

The raw sequencing data of the five varieties used as positive controls were analyzed as described in Section 2.4 to determine their genotypes. The identified genotypes were then compared to the corresponding genotyping patterns available in the Vitis International Variety Catalogue (VIVC). Despite the different genotyping methods, the comparison revealed matching multilocus genotypes across all 14 polymorphic sites for all five varieties. These results, presented in Table 4, demonstrate the high accuracy of the proposed protocol, providing robust support for its validation.

3.2.2. Variety Identification

As a secondary goal, this study aimed to investigate putative issues of homonymy or/and synonymy across Greece as well as to expand the list of Greek varieties that have been molecularly characterized. To this end, after validation as described in Section 3.2.1, the protocol was applied to 14 previously uncharacterized indigenous Greek varieties to determine their genotypes at the polymorphic sites. The raw sequencing data were analyzed as described in Section 2.4, and a comparison of the molecular profiles using GenAlEx (v.6.50) revealed no identical genotyping patterns among the studied varieties. As illustrated in Figure 1, the number of differing loci among the studied varieties ranged from 1 (e.g., Fraoula and Mavrotragano) to 13 (e.g., Gaidouria and Mavro Thrakis). The complete 14-SNP genotyping patterns for all 14 varieties are provided in Table S1.
To further analyze the 14 polymorphic loci, we combined the genotyping profiles of the 14 varieties studied here with the 45 Greek varieties present in the VIVC (Table S3) and calculated indices of genetic variability (Table 5). The results of the analysis revealed that all loci were polymorphic with a consistent number of alleles (Na = 2), which is expected given the SNP nature of the markers. The effective number of alleles (Ne) varied slightly, with Locus 7 exhibiting the highest diversity (Ne = 2.000), indicating equal allele frequencies, while Locus 1 and Locus 3 had lower values (Ne = 1.921), reflecting lower allele distribution. Shannon’s Information Index (I) ranged from 0.672 at Locus 1, 3, and 11 to a maximum of 0.693 at Locus 7, 8, and 10, confirming that these loci captured the highest genetic variability.
Observed heterozygosity (Ho) values revealed variation in heterozygote presence, with Locus 5 having the lowest value (Ho = 0.373), suggesting a predominance of homozygous individuals, while Locus 14 exhibited the highest heterozygosity (Ho = 0.576), reflecting more balanced heterozygote frequencies. Expected heterozygosity (He) remained consistent across loci, with values ranging from 0.479 at Locus1 and 3 to 0.500 at Locus 7 and 8.
The inbreeding coefficient (F) ranged from −0.161 at Locus 14, indicating an excess of heterozygotes, to 0.252 at Locus 5, reflecting a deficit of heterozygotes. Notably, loci such as Locus 7 (F = −0.051) and Locus 14 (F = −0.161) stood out for their negative F values, highlighting loci where heterozygotes were more frequent than expected. This analysis underscores that while all loci contribute to the overall genetic diversity, certain loci (e.g., Locus 7 and Locus 14) stand out as the most informative in terms of allele balance and heterozygosity.

3.2.3. Comparison with Other Greek Varieties

As noted in the introduction, Laucou et al. (2018) [11] used the same 14 SNPs to distinguish 45 Greek varieties. Building on this, our study aimed to compare these varieties with the 14 analyzed here to evaluate whether the varieties presented in this work had unique molecular profiles or if potential synonyms and/or homonyms exist across Greece. To this end, we calculated the genetic distances among the 59 Greek varieties, comprising the 45 varieties from the previous study and the 14 varieties analyzed in this work. The 14-SNP genotyping data of the previously studied varieties are available at the official site of the VIVC (https://www.vivc.de/index.php, accessed on 10 January 2025) under the section “Single Nucleotide Polymorphisms (SNPs) by varieties”. A dendrogram constructed using the unweighted pair group method with arithmetic mean (UPGMA) was developed from the pairwise genetic distances to visually present the genetic relationships among all varieties (Figure 2).
The results showed that none of the varieties shared identical allele combinations across all polymorphic loci, indicating that no synonyms were present (Figure 2). Interestingly, in three pairwise comparisons—Mavrodafni and Zante Blanc, Mavrodafni and Aetonychi Mavro, and Agiorgitiko and Akiki—the genotyping patterns differed at all 14 loci, highlighting clear genetic distinctions (Table S2).

4. Discussion

In Greece, despite their relatively high number, the cultivation of indigenous varieties is declining, posing an increasing threat to the preservation of the country’s grapevine germplasm [48]; this highlights the urgent need to identify, preserve, and protect Greece’s endangered and rich viticultural heritage. Utilizing a small but highly informative set of SNP markers [11], we developed a reliable and efficient genotyping workflow, successfully validating its application in indigenous Greek grapevine cultivars. Our genotyping protocol offers growers and researchers a reliable tool for identifying grapevine samples by comparing their genotypes with a reliable genetic database. This not only ensures the preservation and recognition of Greece’s emblematic and rare cultivars, but also supports the continued use of these varieties in wine production and other grape-based industries. Moreover, by expanding the pool of characterized indigenous varieties, the establishment of a reliable SNP-based genotyping workflow creates a solid foundation for breeding programs aimed at developing new local cultivars with specific desirable traits. Maintaining a diverse and authentic set of Greek grapevine varieties strengthens Greece’s reputation in global wine markets, where regional authenticity and heritage are increasingly valued by international consumers [60].
Furthermore, as climate change increasingly threatens viticulture, breeding programs must prioritize varieties that demonstrate adaptability to challenging conditions such as drought and high temperatures [61,62]. Molecular profiling has a critical role in these efforts by enabling breeders to identify and select specific candidates for these programs, thus safeguarding the genetic diversity and distinctiveness of Greek wines in a changing climate.
The Greek National Catalogue of Grapevine Varieties currently includes over 250 grapevine varieties [15]; before this study, 45 had been molecularly identified [11]. By providing genotypic profiles for 14 previously uncharacterized indigenous Greek varieties (Table 1), our work significantly expands this catalogue. In addition, our results reveal significant diversity among varieties from relatively confined geographic regions within Greece, with some varieties differing across all 14 studied loci (Table S2). This finding aligns with previous studies that have also reported high genetic diversity among Vitis vinifera cultivars within Greece [48,63,64], emphasizing Greece’s potential for climate-adaptive breeding initiatives. However, when compared to the larger dataset of 783 varieties from Laucou et al. [11], the Greek dataset demonstrates a mixed pattern of observed heterozygosity (Ho). Specifically, the Greek varieties exhibited lower Ho values at certain loci (e.g., loci 1, 3, 4, and 5) but higher Ho values at others (e.g., loci 7, 13, and 14). Additionally, the Greek dataset showed more extreme values of fixation index (F) values, suggesting a more localized or inbred genetic structure (Table 5 and Table S4). These findings highlight how Greece’s grapevine germplasm reflects both substantial diversity within the country and a more localized genetic structure when placed in a broader, international context.
The rich genetic diversity within Greece’s viticultural landscape provides an invaluable resource for breeding resilient grape varieties, contributing to agricultural sustainability and ensuring the continued production of high-quality, distinctive Greek wines. This diversity is also particularly significant in the face of climate change and the increasing need for drought-tolerant crops. Autochthonous Greek grape varieties offer traits like resistance to extreme conditions and adaptability to diverse soils, which are critical for developing more robust vines worldwide. By serving as a genetic reservoir, Greek vineyards not only safeguard the country’s winemaking heritage but also contribute to the global efforts in preserving biodiversity and, thus, securing the future of viticulture. Additionally, this approach opens pathways for studying historical migration and/or domestication processes within grapevine populations, and therefore contributes to theories on grapevine domestication’s complex and unresolved history [4,5,18]. Such insights enrich our understanding of grapevine evolution and its domestication, and support conservation strategies for this culturally as well as economically significant species.

5. Conclusions

The selected SNP loci and the cost-effective NGS protocol presented in this study provide a powerful, practical, and scalable approach for the molecular identification of Vitis vinifera varieties. This methodology enables rapid and precise characterization of grapevine varieties, overcoming limitations associated with traditional morphological identification methods.
Beyond its immediate applications in variety identification, this approach holds significant potential for vineyard management, breeding programs, and conservation efforts. Its efficiency and affordability make it an accessible tool for researchers, breeders, and industry stakeholders. Furthermore, by supporting the preservation of Greece’s rich viticultural heritage, this work contributes to the economic value and global competitiveness of its wine industry. Importantly, the established protocol lays the groundwork for climate-adaptive breeding programs, ensuring the long-term sustainability of grapevine cultivation in Greece and other regions facing similar environmental challenges.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae11040375/s1, Table S1: Genotyping patterns of the studied varieties for the 14 polymorphic loci. Table S2: Number of differing alleles between the 14 Greek Vitis vinifera varieties analyzed in this study. Table S3: 45 Greek Vitis vinifera varieties with known genotyping profiles for the same 14 SNPs in the Vitis International Variety Catalogue (VIVC). Table S4: Comparison of the variability indices of the 14 polymorphic loci between Greek and International Vitis vinifera varieties. The data for the international varieties were obtained from Laucou et al. [11].

Author Contributions

Conceptualization, P.K., G.S. and M.Ε.G.; methodology, K.T., P.K., G.S. and M.Ε.G.; formal analysis, K.T., S.-V.P., A.V., P.K., G.S. and M.Ε.G.; investigation, K.T. and M.Ε.G.; resources, P.K., G.S. and M.Ε.G.; data curation, K.T.; writing—original draft preparation, K.T.; writing—review and editing, K.T., S.-V.P., A.V., P.K., G.S. and M.Ε.G.; project administration, P.K., G.S. and M.Ε.G.; funding acquisition, P.K., G.S. and M.Ε.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the project “AGRO4 + - Holistic approach to Agriculture 4.0 for new farmers” (MIS 5046239) implemented under the action “Regional Excellence R&D Infrastructures”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund.

Data Availability Statement

The data presented in this study are available in Table S1. The raw data (UBAM files) are available at https://doi.org/10.5281/zenodo.14844725.

Conflicts of Interest

The authors declare no conflicts of interest.

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Horticulturae 11 00375 g001
Figure 1. The number of differing alleles between the 14 Greek Vitis vinifera varieties analyzed in this study. Each pairwise comparison represents the number of loci where the alleles differ, providing a measure of genetic variation between the varieties. The color gradient used represents the extreme values within each row, with darker shades (red) indicating higher values and lighter shades (green) indicating the lower values in that row.
Figure 1. The number of differing alleles between the 14 Greek Vitis vinifera varieties analyzed in this study. Each pairwise comparison represents the number of loci where the alleles differ, providing a measure of genetic variation between the varieties. The color gradient used represents the extreme values within each row, with darker shades (red) indicating higher values and lighter shades (green) indicating the lower values in that row.
Horticulturae 11 00375 g001
Horticulturae 11 00375 g002
Figure 2. UPGMA dendrogram illustrating the pairwise genetic distances between the 14 varieties analyzed in this study (green) and the 45 previously characterized (red) Greek Vitis vinifera varieties. The dendrogram highlights the genetic relationships among the varieties and indicates that no varieties share identical multilocus genotypes.
Figure 2. UPGMA dendrogram illustrating the pairwise genetic distances between the 14 varieties analyzed in this study (green) and the 45 previously characterized (red) Greek Vitis vinifera varieties. The dendrogram highlights the genetic relationships among the varieties and indicates that no varieties share identical multilocus genotypes.
Horticulturae 11 00375 g002
Table 1. The region of origin and its corresponding VIVC (Vitis International Variety Catalogue) number of the 19 Vitis vinifera varieties analyzed in this study.
Table 1. The region of origin and its corresponding VIVC (Vitis International Variety Catalogue) number of the 19 Vitis vinifera varieties analyzed in this study.
S/NVarietyOriginVIVCS/NVarietyOriginVIVC
1MalagouziaCentral Greece715811MavrotraganoAegean Islands40,210
2XinomavroMacedonia13,28412Asprouda ZakinthouIonian Islands708
3Mavro ThrakisThrace753913MoschofileroPeloponnese8068
4MonemvasiaPeloponnese792514FraoulaPeloponnese9226
5MavrodafniIonian Islands725815PlytoCrete9563
6MakripodiaIonian Islands40,19816CardinalUSA2091
7RazakiUnknown879017VictoriaRomania13,031
8MoscatoAegean Islands819318MonastrellSpain7915
9GaidouriaAegean Islands499819MerlotFrance7657
10AgiorgitikoPeloponnese102
Table 2. Primers used for the amplification of 14 SNP-containing amplicons across three multiplex PCR reactions, with the corresponding product lengths indicated in base pairs (bp).
Table 2. Primers used for the amplification of 14 SNP-containing amplicons across three multiplex PCR reactions, with the corresponding product lengths indicated in base pairs (bp).
PCR ReactionForward (5′-3′)Reverse (5′-3′)Product Length
R1
(Amplicons 1–5)		CAGCGAATCCCTACACGTCCTCCAATTTCGTGCCCTCTGA72 bp
GAACATAAGGCCTCGAGTCTCAGTGACCGAGGATAACACGG150 bp
AAGTTGAGCTGGGAGATGGAGTTCCAATAGGAGGGAATAGCGA226 bp
GGGATACCCGATCAGCATGAAATCCAATGGGTGGACTTCAACA352 bp
ACGGATCAAAATGAATGGCTTTGCCCACAAGATTCTAAGTTCGCC236 bp
R2
(Amplicons 6–10)		TCAAGTGAGCAAGGTGCACTAACTCTTTTGAACATTCTTGTGAGCC189 bp
ATAGGAAGCTGTGCTGAGTTGCAGCATGGTTTCCAAAAACAGGG368 bp
AAGTGCTTACACTGTGGCCCATTCATCGCCCCATACACGC83 bp
GTTGGGGCTTAATGTACCCACTTGCAACACATGGGAAAGGTGTG207 bp
CACAAGCTTTTCCAGAGACACCATTGTTGGGCACAAATACGCT115 bp
R3
(Amplicons 11–14)		CACAGCGAATGGAAACCGTGAAATCTCTTCCGACGCCGTT242 bp
GCAACAGAGAACCAGATTACTATGCAGATTCATCCCACTTGACCCAAA319 bp
CCCACCCTCGACAATCTTTGAAGAAGTGTATGGACCTGTTGGAT158 bp
AGCATTATGTAGCATCATTCTTCCCGTGGGGTTAACAGTTACACTAGA73 bp
Table 3. Details of the 14 SNP-containing amplicons analyzed in this study, including the RefSeq accession numbers, chromosome locations, amplicon coordinates, SNP positions, and observed alleles.
Table 3. Details of the 14 SNP-containing amplicons analyzed in this study, including the RefSeq accession numbers, chromosome locations, amplicon coordinates, SNP positions, and observed alleles.
SNPRefSeq AccessionChromosomeAmplicon CoordinatesSNP PositionAlleles
1NC_081805.1112,492,482 to 12,492,55312,492,522T/C
2NC_081807.133,694,974 to 3,695,0843,695,042T/C
3NC_081812.1814,069,394 to 1,4069,61914,069,512T/C
4NC_081813.195,730,489 to 5,730,8405,730,661T/C
5NC_081815.1115,380,240 to 5,380,4755,380,410A/C
6NC_081817.11316,648,039 to 16,648,22716,648,069T/C
7NC_081818.1144,382,939 to 4,383,3064,383,127A/G
8NC_081817.1144,964,985 to 4,965,0674,965,016T/G
9NC_081819.11521,381,119 to 21,381,32521,381,247T/G
10NC_081819.11521,816,380 to 21,816,49421,816,458T/G
11NC_081820.11620,981,581 to 20,981,82220,981,646A/G
12NC_081820.11622,887,648 to 22,887,96622,887,819A/G
13NC_081821.117594,944 to 595,101595,010A/G
14NC_081822.11811,492,825 to 11,492,89711,492,868T/C
Table 4. Comparison of the genotyping patterns identified in this study and the corresponding patterns recorded in the Vitis International Variety Catalogue (VIVC) for the five Vitis vinifera varieties used to validate the protocol.
Table 4. Comparison of the genotyping patterns identified in this study and the corresponding patterns recorded in the Vitis International Variety Catalogue (VIVC) for the five Vitis vinifera varieties used to validate the protocol.
LocusVariety
PlytoCardinalVictoriaMonastrellMerlot
VIVCThis StudyVIVCThis StudyVIVCThis StudyVIVCThis StudyVIVCThis Study
1CCCCCCCCCCCCTCTCCCCC
2TTTTCCCCCCCCTCTCCCCC
3TCTCTCTCTTTTCCCCCCCC
4CCCCCCCCCCCCCCCCTTTT
5CCCCCCCCCCCCACACACAC
6TCTCTCTCTCTCTCTCTTTT
7AGAGAGAGAGAGAGAGAAAA
8TGTGTGTGTGTGTGTGTGTG
9TTTTTGTGTGTGTGTGGGGG
10TGTGTGTGTTTTGGGGGGGG
11AGAGAAAAAAAAGGGGAAAA
12GGGGAGAGAAAAAAAAAGAG
13AGAGGGGGAGAGGGGGAAAA
14TCTCTTTTTCTCTCTCCCCC
Table 5. A summary of the genetic diversity metrics for the 14 SNP loci analyzed in this study. The table includes the number of samples (N), number of alleles (Na), effective number of alleles (Ne), Shannon’s Information Index (I), observed heterozygosity (Ho), expected heterozygosity (He), and fixation index (F) for each locus.
Table 5. A summary of the genetic diversity metrics for the 14 SNP loci analyzed in this study. The table includes the number of samples (N), number of alleles (Na), effective number of alleles (Ne), Shannon’s Information Index (I), observed heterozygosity (Ho), expected heterozygosity (He), and fixation index (F) for each locus.
LocusNNaNeIHoHeF
1592.0001.9210.6720.3900.4790.187
2592.0001.9990.6930.5420.500−0.085
3592.0001.9210.6720.4580.4790.045
4592.0001.9950.6920.4410.4990.116
5592.0001.9950.6920.3730.4990.252
6592.0001.9950.6920.4750.4990.048
7592.0002.0000.6930.5250.500−0.051
8592.0001.9990.6930.4750.5000.051
9592.0001.9860.6900.5080.496−0.024
10592.0001.9980.6930.4920.4990.016
11592.0001.9330.6760.5420.483−0.124
12592.0001.9910.6910.4920.4980.012
13592.0001.9330.6760.5420.483−0.124
14592.0001.9860.6900.5760.496−0.161
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Tegopoulos, K.; Polychronidou, S.-V.; Voumvouraki, A.; Kolovos, P.; Skavdis, G.; Grigoriou, M.Ε. Precise Identification of Vitis vinifera L. Varieties Using Cost-Effective NGS-Based SNP Genotyping. Horticulturae 2025, 11, 375. https://doi.org/10.3390/horticulturae11040375

AMA Style

Tegopoulos K, Polychronidou S-V, Voumvouraki A, Kolovos P, Skavdis G, Grigoriou MΕ. Precise Identification of Vitis vinifera L. Varieties Using Cost-Effective NGS-Based SNP Genotyping. Horticulturae. 2025; 11(4):375. https://doi.org/10.3390/horticulturae11040375

Chicago/Turabian Style

Tegopoulos, Konstantinos, Sonia-Vasiliki Polychronidou, Anastasia Voumvouraki, Petros Kolovos, George Skavdis, and Maria Ε. Grigoriou. 2025. "Precise Identification of Vitis vinifera L. Varieties Using Cost-Effective NGS-Based SNP Genotyping" Horticulturae 11, no. 4: 375. https://doi.org/10.3390/horticulturae11040375

APA Style

Tegopoulos, K., Polychronidou, S.-V., Voumvouraki, A., Kolovos, P., Skavdis, G., & Grigoriou, M. Ε. (2025). Precise Identification of Vitis vinifera L. Varieties Using Cost-Effective NGS-Based SNP Genotyping. Horticulturae, 11(4), 375. https://doi.org/10.3390/horticulturae11040375

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