IntroductionTop
Eggplant (Solanum melongena L., Solanaceae) is an important vegetable crop cultivated in the tropical and temperate regions of the world. Domesticated long ago in the Indo-Burma region where the greatest diversity is found, it has been a common crop in the Middle East and around the Mediterranean basin where it was introduced by the Arabs (Daunay et al., 2001; Daunay & Janick, 2007). Eggplant is a fairly good source of phenolics, vitamins and minerals with antioxidant, hepatoprotective, anti-microbial, and cardio-protective properties (Kalloo, 1993; Stommel & Whitaker, 2003; Das et al., 2011).
Intensive selection and breeding have resulted in the development of a large number of varieties with variable morphological, physiological and qualitative characteristics (Daunay et al., 2001; Kashyap et al., 2003; Frary et al., 2007). Among them, fruit color, size and shape are the most distinctive and therefore used to establish the different commercial groups, e.g. black, green, purple, striped and white, long, cylindrical, egg shaped etc. existing today (Kumar et al., 2008; Muñoz-Falcón et al., 2009).
Today global trade is concentrated on a limited number of eggplant elite varieties and F1 hybrids with increased yield and stability. The progressive dominance of those genotypes results in a considerable reduction in genetic diversity posing a threat of genetic erosion and narrowing the source of useful genes available for breeding (Muñoz-Falcón et al., 2011; Cericola et al., 2013). Local cultivars on the other hand with specific and high quality characteristics represent an elite germplasm which can be used for breeding as well as in scion-rootstock combinations for increased production and tolerance to various pathogens (Bletsos et al., 2004; Arvanitoyannis et al., 2005; Rodríguez-Burruezo et al., 2008).
In Greece, besides the commercial F1 hybrids and imported varieties, there are several traditional cultivars, e.g. Tsakoniki (short cylindrical fruits with purple stripes), Lagada (long cylindrical purple fruits), Skoutari (medium cylindrical purple fruits), EMI (large spherical purple fruits) and Santorini (large spherical white fruits), which are adapted to the local environment having an excellent texture and cooking quality. These cultivars have not been subjected to intensive breeding so far, and are available through local markets, where the fruits of the heirloom local varieties are highly appreciated (Bletsos et al., 1998a, 1998b).
During the last years, heirloom vegetables produced in their regions of origin, may acquire an added value in the market if protected by geographical indications (PGI) or designations of origin (PDO) (Hurtado et al., 2014). This approach could increase the prospects of benefits for the farmers, therefore certification of the authenticity is essential for the protection and enhancement of these cultivars (Muñoz-Falcón et al., 2011).
Morphological descriptors and molecular markers have been widely utilized for the characterization of eggplant material from different regions of origin, the study of diversity among different varieties and accessions and also the determination of the relationships among the cultivated eggplant with its weedy and wild forms and close relatives (Prohens et al., 2005; Isshiki et al., 2008; Demir et al., 2010; Ge et al., 2011, 2013; Muñoz-Falcón et al., 2011; Tümbilen et al., 2011a, 2011b; Hurtado et al., 2012; Meyer et al., 2012; Vilanova et al., 2012; Cericola et al., 2013; Davidar et al., 2015; Mutegi et al., 2015). Those studies have provided invaluable information regarding the management of germplasm collections, the conservation of eggplant genetic resources and the application of breeding programs. In addition, the combination of morphological descriptors with molecular markers provides a more comprehensive view about the degree of variation among different groups of cultivars since they sample different levels of diversity and thus have a complementary effect (Prohens et al., 2005; Muñoz-Falcón et al., 2009; Tümbilen et al., 2011b; Hurtado et al., 2012; Cericola et al., 2013).
The purpose of this study was to assess the levels of genetic and phenotypic diversity existing within and among six traditional eggplant cultivars and a F1 hybrid from Greece using both morphological and molecular markers. We also included a commercial variety and three more cultivars from Africa (Togo) and Japan, in order to estimate the degree of variation among cultivars originating from different diversity centers and regions. Genetic variation was studied using nuclear microsatellites, as well as the retrotransposon microsatellite amplified polymorphism (REMAP) markers (Kalendar et al., 1999). REMAP has been successfully applied for the determination of the genetic relationships, the germplasm identification and the assessment of genetic variation of several plant species and at various taxonomic levels (Kalia et al., 2010; Castro et al., 2012). Finally, a comparison between the genetic data and the data obtained from the morphological analysis has also been made.
Material and methodsTop
Plant material
The eggplant cultivars studied were: Tsakoniki (TSAK), Lagada (LAG), Skoutari (SKO), Santorini (SANT), EMI, Aspri Leros (LERWHIT) and Meliton F1 (MELF1) from Greece; RNL566 from Togo, Money Maker F1 hybrid (MOMA) and Wasesinkuro (WAS) from Japan and finally a commercial variety named Black Beauty (BB) (Table 1). Twelve plants per cultivar were grown in a field plot at the Technological Educational Institute (TEI) of Western Greece in Messolonghi, Greece (40.6572° N, 22.8041° E) following a completely randomized block design experiment and standard horticultural practices (pest control, fertilization and pruning) for eggplant production in this area.
Table 1. Cultivars studied, traits analyzed to generate the phenotypic data set, and mean for each trait and cultivar studied.
Morphological characterization and statistical analysis
Twelve plants per accession were characterized using 22 primary descriptors (Table 1) for plant, leaf, flower and fruit characteristics available from EGGNET (Prohens et al., 2005; Van der Weerden & Barendse, 2007) and the International Board for Plant Genetic Resources (IBPGR, 1990).
Multivariate analysis (cluster, discriminant and principal components analysis) was applied using IBM SPSS vers. 22 software. The interpretation of the results was made using both parametric (ANOVA, t-test) and nonparametric (Kruskall-Wallis, Wilcoxon-Mann-Whitney) methods. All continuous traits were transformed in ordinal, according to Terzopoulos & Bebeli (2010). Total phenotypic diversity (HT), phenotypic diversity within each cultivar (HS) and its average across all of them (Hs) were estimated applying Nei’s genetic diversity index (Nei, 1973). The proportion of the total phenotypic diversity among cultivars (GST) was estimated by applying Eq. [2] of Terzopoulos & Bebeli (2008). The comparison of Hs of all the traits were carried out using the Tukey’s mean comparison method.
DNA extraction
Total genomic DNA for 10 individual plants of each accession was extracted from 20 mg of fresh leaves using the hexadecyltrimethylammonium bromide (CTAB) method (Doyle & Doyle, 1990) with minor modifications. Quantification and quality assessment of the purified DNA was done spectrophotometrically and by gel electrophoresis. DNA samples were stored at -20 ºC until used.
DNA markers’ genotyping and data analysis
REMAP markers: For the amplification of REMAP (retrotransposon microsatellite amplified polymorphism) markers, several LTR (long terminal repeat) primers (Baumel et al., 2002) in combination with ISSR (inter-simple sequence repeat) primers obtained from the University of British Columbia (Vancouver, Canada) primer set were initially tested. PCR reactions were performed as described in Augustinos et al. (2014). Based on amplification quality and reproducibility of bands, we chose the five REMAP markers presented in Table S1 [suppl.]. Fragments were separated by agarose gel electrophoresis and visualized using a GelDocEZ imaging system (BIORAD, Germany). Fingerprints were scored to prepare binary matrices.
Microsatellite markers: Different genomic and EST-derived microsatellite markers developed for eggplant (Nunome et al., 2003; Stàgel et al., 2008; Nunome et al., 2009) were evaluated and those finally selected are presented in Table S2 [suppl.]. PCR reactions were performed as described in the respective references. Genotyping was carried out by radio-labeling and the PCR products were electrophoresed on 5% denaturing polyacrylamide gels and visualized by autoradiography. Scoring was performed by hand.
Data analysis: Given the low level of polymorphism detected for REMAP markers, only Nei’s genetic distance after Lynch & Milligan (1994) was calculated using AFLPsurv 1.0 (Vekemans et al., 2002). On the contrary, several diversity indices (mean number of alleles per locus, effective number of alleles, observed and expected heterozygosity and the allelic diversity after correction for sample size) were measured using POPGENE version 1.31 (Yeh et al., 1999), and FSTAT (Goudet, 2001) for the SSR markers. Deviations from Hardy-Weinberg Equilibrium (HWE) were tested with the G2 likelihood ratio test in POPGENE (Yeh et al., 1999). Genetic distances were measured according to Nei (1972) using POPGENE. Different packages implemented in PHYLIP 3.2 (Felsenstein, 1989) were used to construct an UPGMA dendrogram. The robustness of each node was assessed by the bootstrap method using 1000 pseudoreplicates. Visualization of the tree was made using TreeView (Page, 1996). GENALEX 6.5 software (Peakall & Smouse, 2006) was used for the analysis of molecular variance (AMOVA). To infer population structure and assign individuals to populations, we used STRUCTURE 2.3.4 software (Pritchard et al., 2000; Falush et al., 2003) with a burn-in period of 100,000 and 500,000 MCMC (Markov chain Monte Carlo) repetitions after the initial burn-in and 5 independent runs. Admixture with correlated frequencies was selected as a model, assuming K=1 to K=11 (where K stands for the assumed number of populations). The modification described by Evanno et al. (2005) was used to identify the ‘true’ number of K groups, using STRUCTURE HARVESTER software (Earl & von Holdt, 2012). A Mantel test to estimate a) the correlation between the distance matrices generated by REMAP and SSR markers respectively and b) the correlation between the SSR derived genetic data and those derived from the morphological characters was employed using PopTools version 3.2.5 (Hood, 2010) with 1000 iterations.
ResultsTop
Morphological data
The total phenotypic diversity (HT) for each trait ranged from 0.19 (fruit curvature) to 0.64 (plant height, leaf surface shape), with an average of 0.51 (Table 2). The proportion of the total phenotypic diversity (GST) among all cultivars ranged from 0.14 (days since fruit set) to 1 (leaf blade lobing and leaf surface shape). Mean phenotypic diversity within populations (Hs) ranged from 0 (leaf blade lobing and leaf surface shape) to 0.51 with an average value of 0.24 (Table 2). Using Tukey’s mean comparison method, we detected a significant difference in the within populations diversity for the characters studied. The trait which had the highest Hs values is plant growth habit (in 6 cultivars) and the traits with the lowest value of phenotypic diversity within individual landraces were leaf blade lobbing and leaf surface shape.
Table 2. Values of HT, GST, Hs for each morphological trait across all cultivars and also the number of occurrences of the traits with the highest and lowest HS in the cultivars studied.
Cluster analysis was made with hierarchical methods (Ward’s method). The dendrogram obtained (Fig. 1), clearly separated in two main clusters, but if we want to dissociate the cultivars from the fruit color we could use three, as we did. In the first cluster (BB, EMI, RNL566) the cultivars had less plant height, but similar plant growth habit with more prickliness, small fruit length, with dark fruit predominant and additional color, dark fruit flesh color, less fruit curvature and fruit length to breadth ratio. In the second cluster (SKO, MELF1, MOMA, WAS, LAG, TSAK) the cultivars had high plant height, but a slight plant growth habit with more prickliness, large fruit length with less dark fruit predominant and additional color, dark fruit flesh color, large fruit curvature and fruit length to breadth ratio. In the third cluster (LERWHIT, SANT) the cultivars had shorter plant height, but a more prostate plant growth habit with less prickliness, small fruit length with subtle fruit predominant and additional color, subtle fruit flesh color, less fruit curvature and fruit length to breadth ratio.
|
|
To determine the diversity of multiple descriptors for the morphological traits, a principal components analysis was carried out based on the correlation matrix, and varimax rotation method with Kaiser normalization was employed. The first three principal components cumulatively explained 66.8% of the total variance (Table 3, Fig. 2). The first component (30.4% of the total variance) was highly positive correlated with the fruit breadth, the fruit weight, the leaf prickles and the fruit calyx prickles, and highly negative with the fruit curvature, the fruit length and the fruit length to breadth ratio. The second component (19.2% of the total variance) was highly positively correlated with the leaf blade width, the number of flowers per inflorescence and the leaf blade length and moderate negatively with the leaf prickles and the fruit additional color. The third component (17.2% of the total variance) was highly positive correlated with the fruit predominant color and the fruit flesh color and negatively correlated with the number of leaves to first flower and the plant growth habit.
Table 3. Coefficients for morphological traits contributing to the three leading principal components and proportion of total variance explained by the principal components analysis.
|
|
Genetic variability of S. melongena cultivars
REMAP markers: The five primer combinations selected revealed a total of 87 bands, most of which were polymorphic as shown in Table S1 [suppl.]. Nevertheless, in most cases polymorphism was due to the presence or absence of specific bands in very few samples. Therefore, no further genetic variability analysis was performed, with the exception of Nei’s genetic distance values among the cultivars studied. Still, the combined use of these markers could provide an easily applied diagnostic tool for some of these cultivars (data not shown).
Microsatellite markers: For the genotyping of 10 individuals from each eggplant cultivar, nine S. melongena specific microsatellite markers were chosen (Table S2 [suppl.]). The overall variability detected was very low since only 2-6 alleles (3.67 per locus) were revealed for each locus. This low variability is also evident by the genetic diversity measures calculated (Table 4). As expected, the two EST-derived markers were the less polymorphic, presenting only two alleles each and null heterozygosity.
Table 4. Genetic variability measures for the SSR markers used.
The eggplant cultivars studied presented a low degree of polymorphism, as evident by all measures (Table 5). Three of them were monomorphic for all microsatellite markers tested. Among the most polymorphic were the two cultivars from Japan (WAS and MOMA) and one from Greece (LAG). As showed by AMOVA the large proportion of the variability is due to among populations differentiation (84%) and only a small fraction is due to within population differentiation (16%).
Table 5. Genetic variability measures and markers that are out of equilibrium (HWE) for the eggplant cultivars studied.
Genetic distances (Nei, 1972) ranged from 0.0679 (WAS-MOMA) to 1.7628 (LAG-BB). Except the two cultivars from Japan, all samples were significantly differentiated from each other, as shown using FSTAT (α=0.05) (Table 6). In the UPGMA dendrogram (Fig. 3), LERWHIT, SANT and BB were clustering apart from the other cultivars. A second clustering was formed by the non-Greek, non-European cultivars (RNL566, WAS and MOMA), although the two varieties from Japan seemed to be more closely related to the remaining Greek varieties. The STRUCTURE analysis supported the presence of three main clusters. Graphic representation of structuring at K=3 (Fig. 4) is well in accordance with the UPGMA dendrogram. The LERWHIT, SANT and BB cultivars are forming the first cluster, the non-European are forming a second, while the remaining five Greek cultivars are forming the third.
Table 6. Genetic distances (below diagonal) and significance of population differentiation (above diagonal).
|
|
|
|
A stepwise discriminant analysis was performed using the clusters of the UPGMA dendrogram as a grouping variable and the morphometrical descriptors as independent variables in order to compare the morphometrical descriptors with the molecular genetic data.
As it seems, from our analysis, discrimination between the clusters of the UPGMA dendrogram could be made if we use the morphometrical descriptors: plant height (PH), number of leaves to first flower (NOLFF), leaf blade length (LBLe), leaf blade lobing (LBLo), leaf surface shape (LSS) and fruit shape (FS).
The standardized canonical discriminant functions were:
Dis_1 = -2.99 * PH – 3.03 * NoLFF + 5.27 * LBLe + 1.67 * LBLo + 2.43 * LSS + 2.44 * FS, and
Dis_2 = -2.14 * PH + 0.99 * NoLFF + 1.22 * LBLe + 2.31 * LBLo + 0.51 * LSS + 0.05 * FS
In Fig. S1 [suppl.], an ASCII territorial map plot shows the relative location of the boundaries for the different clusters of the UPGMA dendrogram. Finally, the Mantel test showed no correlation between the genetic and the morphological data (r=0.044, p=0.61), as well as between the SSR and REMAP data (r=0.291, p=0.933).
DiscussionTop
In this study the morphological and genetic diversity of the most promising Greek eggplant traditional cultivars with high quality and reputation at least at the local markets were examined. Some of those cultivars could achieve an advanced market status as candidates for a Protected Designation of Origin (PDO) or a Protected Geographical Indication (PGI) label (EC, 2006). Usually, products with a PDO or PGI label are perceived as unique having an added value in the market and getting a higher price (Gracia & Albisu, 2001; McLaughlin, 2004). Therefore it is necessary to devise tools for the certification of the uniqueness and authenticity of those products in order to protect and enhance them (Muñoz-Falcón et al., 2011; Hurtado et al., 2014).
Morphological diversity: The eggplant cultivars displayed a relatively high level of diversity for most of the morphological traits studied (total phenotypic diversity (HT = 0.51). In addition, the mean phenotypic diversity among cultivars was relatively high (GST = 0.52), while the mean within population diversity was low (Hs = 0.24), as expected for cultivars.
The morphological differentiation and the proportion of phenotypic variation found among cultivars as indicated by the high GST values is more likely due to the artificial selection performed by the farmers, the mating system, the management of the genetic material, and also to the environmental conditions (Prohens et al., 2005; Terzopoulos & Bebeli, 2010; Hurtado et al., 2012). Some of the traits for which significant differences exist e.g. fruit size, shape and color, growth habit, prickliness among others, are controlled by a few genes which are presenting a high degree of penetration and expression. As a consequence the effect on phenotype is rapid and probably linked with response to selection and the different origin of those cultivars (Daunay, 2008; Hurtado et al., 2012).
Understanding which traits contribute the most to the phenotypic variation observed among and within cultivars is very important for breeding purposes as well as for conservation procedures (Terzopoulos & Bebeli, 2008). In the present study traits such as plant height, fruit calyx prickles, fruit length, fruit breadth, fruit length to breadth ratio, fruit predominant, additional and flesh color tend to be highly differentiated among cultivars while most of them exhibited low diversity within the cultivars. Analysis of morphological diversity showed that clustering of cultivars was strongly associated with plant height and fruit appearance such as fruit length, shape and color. Clustering of different eggplant cultivars according to fruit appearance has also been reported by other researchers (Choudhury, 1976; Tümbilen et al., 2011b; Cericola et al., 2013). This was also evident in the PCA analysis where in the first component (30.4% of the total variance) the most important parameters for separating the cultivars were those related to fruit appearance and shape such as fruit breadth, fruit weight fruit calyx prickles and fruit length. Moreover, as shown in the Ward’s dendrogram two major clusters were formed based primarily on those characters. In the first cluster there were the SKO, MELF1, MOMA, WAS, LAG, TSAK cultivars which are predominantly taller with longer fruits, while in the second cluster were those with smaller fruit length and plant height. Within this cluster another sub-cluster was formed by LERWHIT and SANT based exclusively on fruit color.
Genetic diversity: Both REMAP and SSR markers showed that the overall polymorphism revealed is low. This is in agreement with the current literature, where larger collections and more markers have been independently applied (Stàgel et al., 2008; Muñoz-Falcón et al., 2009; Ge et al., 2011, 2013; Muñoz-Falcón et al., 2011; Hurtado et al., 2012; Cericola et al., 2013). Because S. melongena is a largely autogamous species, most heritage and commercial varieties are expected to be highly homozygous (Cericola et al., 2013). In addition extensive phenotypic-based selection and the use of a limited number of individuals as starting material in breeding programs have deprived this species of the bigger part of its original genetic pool, reducing thus the amount of genetic diversity available today (Muñoz-Falcón et al., 2009).
In our study, no significant associations were observed between REMAP and SSR markers, according to the Mantel test employed. This might be due to the type of genetic polymorphism detected and the different type of information provided by each molecular marker system used (Ghislain et al., 2006). Also, poor correlation between the used genetic markers most likely indicates that they refer to different subsets of loci in the genome as for example the SSR and REMAP markers in our case (Biswas et al., 2010; Mandoulakami et al., 2015). It would be surprising to obtain a better insight of the diversity existing in the S. melongena populations with REMAP markers than with microsatellite markers. However, the combination of the REMAP markers used here produced specific patterns of bands that can distinguish among the different cultivars.
Nuclear SSRs have been widely used to study the genetic diversity and the relationships of different eggplant groups and cultivars of so far. SSRs have the ability to detect genetic relationships based on specific traits, probably caused by its sensitivity to neutrality and/or linkage disequilibrium, and therefore are more appropriate in studying specific sets of genetically related materials (Muñoz-Falcón et al., 2009).
The overall variability detected in our study was low since the SSR markers produced an average of 3.67 alleles per locus. Muñoz-Falcón et al. (2011) using also genomic and EST-SSRs revealed low levels of polymorphism. This could be due to the intensive breeding efforts and the narrow genetic background (Ge et al., 2011). On the other hand Behera et al. (2006) found broader diversity in 92 South Asian eggplant accessions but such greater variation was expected since South Asia is a primary center of eggplant diversity. According to Tümbilen et al. (2011a) genetic diversity based on molecular data is highly dependent on the number and type of marker chosen and the accessions tested and this could be the case in our study.
The genetic differentiation as indicated by the genetic distance values estimated among the cultivars studied was high. Also all of them were significantly differentiated from each other, except the two cultivars from Japan. As shown by the UPGMA dendrogram and also by the STRUCTURE analysis our data support the existence of three main clusters, the first with the foreign cultivars, the second with the LERWHIT, SANT and BB, while the third with the remaining Greek cultivars. Interestingly the two Japanese cultivars seem to be more closely related to some of the Greek ones. Hurtado et al. (2012) and Cericola et al. (2013) failed to sufficiently discriminate eggplants from different centers of origin, although this was achieved completely by Prohens et al. (2005) and Muñoz-Falcón et al. (2009) or partly by Sunseri et al. (2010). A possible explanation is that the limited genetic variability of the populations of this species (shown in other studies as well) and also the extensive selection performed on a few elite cultivars for specific traits have probably lead to extensive inbreeding, hitch-hiking effects of microsatellite markers along with traits of choice and severe bottlenecks. All factors have probably acted together, resulting to the ‘masking’ of the signal of the genetic origin of eggplant varieties.
The differentiation of the LERWHIT and SANT cultivars from the remaining Greek ones may be due to the SSR markers tested which could reflect certain phenotypic differences given that these cultivars differ in fruit shape and color from the rest. Also because these two cultivars originate from small islands it is also well supported that microevolutionary forces, including natural selection under local conditions, artificial selection by farmers, genetic drift, and recombination may have favored the isolation of local variants (Portis et al., 2006; Muñoz-Falcón et al., 2011).
In a recent study by Ganopoulos et al. (2015), thirty-six Greek eggplant accessions, were discriminated based on High Resolution Melting (HRM) barcoding profile. In our case, although we studied fewer cultivars but with more SSR markers, we did not succeed in discriminating them using nuclear microsatellites only. This could be due to a) the utilization of different SSR markers, b) the utilization of a different resolution technology, since HRM could theoretically reveal more diversity, as discussed in Ganopoulos et al. (2015) and references therein. However, the interpretation of the results can be more difficult, since melting curves are not exactly ‘descriptive’ of the events generating these differences, c) the methodology used regarding sampling: in the present study, DNA was isolated from 10 individual plants per variety, while in Ganopoulos et al. (2015) DNA per variety were isolated as a bulk.
The Mantel test showed no correlation between the genetic and the morphological data. On the other hand, the stepwise discriminant analysis performed in order to compare the morphometrical descriptors with the molecular genetic data, showed that discrimination between the clusters of the UPGMA dendrogram could be made if we use the morphometrical descriptors related to plant height (PH), number of leaves to first flower (NOLFF), leaf blade length (LBLe), leaf blade lobing (LBLo), leaf surface shape (LSS) and fruit shape (FS). These descriptors and especially those related to FS have also been found to be strongly associated with the clustering of S. melongena cultivars in several other cases (Muñoz-Falcón et al., 2008; Tümbilen et al., 2011b; Hurtado et al., 2012 etc.).
The lack of correlation between morphological and molecular data has been a matter of debate since there are many studies where a similar situation has also been confirmed (Tümbilen et al., 2011b; Hurtado et al., 2012; Cericola et al., 2013), whereas others showed a reasonable level of phenotype/genotype correlation (Prohens et al., 2005; Muñoz-Falcón et al., 2008; Caguiat & Hautea, 2014). The correlations between morphological and molecular data usually vary depending on the plant material, the morphological descriptors and the molecular markers used (Hurtado et al., 2012). Morphological markers in eggplant sample traits for which variation is usually controlled by a few genes or QTLs (Doganlar et al., 2002; Daunay, 2008) therefore a tiny portion of the genome is responsible for the phenotypic changes observed (Cericola et al., 2013). On the other hand molecular markers sample variation from a larger portion of the genome, both coding and/or non-coding (Varshney et al., 2005; Kalia et al., 2010) and thus these two types of markers may follow different evolutionary paths which explains the lack of correlation observed (Hurtado et al., 2012). Morphological descriptors and molecular markers provide complementary information and thus characterization of germplasm with both of them allows a more accurate assessment of variation (Caguiat & Hautea, 2014).
In summary, this study has successfully used morphological descriptors and SSR markers to determine the genetic diversity levels and to characterize the most important Greek traditional cultivars together with others from different centers of origin. Although low variability was detected within all cultivars, at least for the molecular markers used, a considerable amount of variation has been revealed among them with both the morphological descriptors and the molecular markers. Our results show that the plant material studied can be efficiently characterized for breeding and conservation purposes and also that we are able to devise tools for the effective screening of the whole Greek eggplant germplasm collection in the future. Moreover, these local varieties can be distinguished from the commercial ones and thus gain a higher market value, while the high quality standards of the final product are maintained.