Fruit Quality Traits and Genotypic Characterization in a Pomegranate Ex Situ ( Punica granatum L.) Collection in Greece

: Pomegranate is one of the oldest known fruit crops, well adapted to hot and dry areas and successfully grown in Mediterranean countries. During the last two decades, numerous pub-lications have revealed the traditionally known associations between pomegranate consumption and health beneﬁts, which led to increased demand by consumers and expansion in cultivation areas. Pomegranate is well adapted to areas with diverse pedoclimatic conditions and local cultivars could provide an essential source of genes for breeding. In this study, fruit phenotypic and genetic variability, and relationships, were studied in 26 Greek and foreign pomegranate cultivars/accessions grown in an ex situ collection located in Naoussa Greece, using Inter Simple Sequence Repeat (ISSR) and Start Codon Targeted (SCoT) molecular markers. Results from the principal component analysis made on fruit phenotypic characters revealed ﬁve components that accounted for 74.8% of the total variance, the ﬁrst being related to skin color parameters and the second to juice antioxidant contents and aril color. Clustering from phenotypic data allocated individuals into four clusters. A total of 184 bands were generated for all markers applied across the 26 pomegranate cultivars/accessions, with an average of 77 bands per ISSR markers and 82 bands for SCoT markers. Low variability in the phenotypic and genotypic level was indicated; nevertheless, results from the association study between phenotypic traits and molecular markers that were obtained using Multiple Regression Analysis (MRA) could prove valuable for marker-assisted breeding programs, especially from SCoT markers that were found to be strongly or averagely associated with the morphological traits and chemical components.


Introduction
Pomegranate (Punica granatum L.) is said to be originated from central Asia [1]. However, due to its high level of adaptability, pomegranate trees can be cultivated in a wide range of soil and climates in many different geographical places including the Mediterranean basin, California, and Asia. Pomegranate, as mentioned by Dioscorides and Hippocrates, had a very important place in the ancient cultures of the Mediterranean countries, while it was among the first crops, together with figs, dates, olives, and grapes, that was cultivated [1][2][3]. Pomegranate's domestication is reported to have begun around 3000-4000 BC in Iran and in India [4]. Today, more than 500 varieties of pomegranates are cultivated The accessions/cultivars were established in an ex situ collection together with commercial cultivars originating from Israel (cv. Acco) and Turkey (cv. Wonderful originated from four different sources in Greece and cv. Hicaznar). The collection was planted at the Department of Deciduous Fruit Trees in Naoussa (40 • 37 13.40 N; 22 • 06 59.80 E, at 119 masl). The annual mean temperature is 15.7 • C and precipitation is 717 mm, with lower values in the summer and greater in the winter season period. The soil pH is neutral (pH 7.4) with medium-heavy mechanical composition. The trees were planted in a randomized block design of six trees per genotype in two replicate trees per block. Fruit phenotypic characterization was performed on a six-fruit sample harvested only from the pomegranate accessions/cultivars presented in Tables 2 and 3.

Morphological Traits
Fruit was harvested when commercially ripe and according to their size and color, transferred to the laboratory and sorted for size and uniformity of shape. Fruit was hand cracked, the arils were separated, the skin and aril weight were measured, and the % edible portion was calculated. Juice was hand extracted from a known aril portion weight, weighted and the % juice per fruit and edible portion were calculated. The CIE color parameters L* (brightness or lightness; 0 = black, 100 = white), a* (−a* = greenness, +a* = redness) and b* (−b* = blueness, +b* = yellowness) were measured using a Minolta chromatometer (Minolta CR-400, Tokyo, Japan) and the hue (H*) (0 • = red-purple, 90 • = yellow, 180 • = bluish-green, 270 • = blue) and Chroma (C*), degree of departure from grey to pure chromatic color) parameters were calculated according to McGuire [29]. Readings were taken close to the stalk of each fruit exocarp. Aril color (white, rose-white, rose, darkrose, rose-red, red, very red) and tegmen hardness (soft, medium, hard) were characterized by a panel of 5 people, in accordance with the "Descriptor's list for pomegranate" set up in the framework of EC Project GENRES 29 "Conservation, Evaluation, Exploitation and Collection of Minor Fruit Tree Species" [30]. Table 2. Means (±sterror) for fruit fresh weight (g), % edible portion, % juice/fruit, % juice/edible portion, 100 aril weight (g), total soluble solid content (TSS) ( • Brix), total acidity (TA) (g 100 mL −1 ), maturity index (MI) (TSS/TA) and seed hardness (1 soft; 2 medium; 3 hard) in 26 pomegranate accessions/cultivars. Maximum, minimum, covariate coefficient (CV%) and least significant difference (LSD) are presented.

Chemical Analyses
At harvest, total soluble solid (TSS) and total acidity (TA) contents were determined in pomegranate juice from a six-fruit sample per cultivar/accession. TSS was measured using a digital refractometer (model PR-1, Atago, Japan), and TA by titration to pH 8.2 with 0.1 N NaOH and expressed as citric acid content (g 100 mL −1 ). The maturity index (MI) was calculated as the ratio TSS/TA.
The Total phenolics (TPs) content was measured using a modified Folin-Ciocalteu colorimetric method [31]. The reaction mixture consisted of 0.1 mL of 1:8 diluted juice and 0.4 mL water were mixed with 2.5 mL of 1:10 diluted Folin-Ciocalteu's phenol reagent, followed by 2 mL of 7.5% (w/v) sodium carbonate. After 5 min at 50 • C, absorbance was measured at 760 nm. Phenol content was estimated from a standard curve of gallic acid and results expressed as mg gallic acid equivalents (GAE) 100 L −1 juice.
Total antioxidant capacity (TAC) was evaluated using the 2,2-diphenyl-1-picrylhydrazyl (DPPH). Antioxidant activity was measured using the stable 1,1-diphenyl-2-picryl hydrazyl (DPPH) free radical [32], which has an intense violet color, but turns colorless as unpaired electrons are sequestered by antioxidants. Reaction mixtures containing 20 µL sample and 3 mL of 106.5 µM DPPH in MeOH, were vortexed, and then held at room temperature for 2 h. The absorbances of the reaction mixtures were measured at 517 nm and total antioxidant capacity TAC was estimated from a standard curve of ascorbic acid and results expressed as mg ascorbic acid equivalents (AAE) L −1 juice.

Molecular Analysis
Total DNA was extracted from young fresh leaves using the CTAB (cetyl trimethylammonium bromide) protocol as described by Doyle and Doyle [33]. DNA quality and concentration were measured by Quawell UV-Vis Spectrophotometer (Q5000).
Twenty molecular markers (nine ISSR markers and 11 SCoT markers were selected after a preliminary test for the genotypic characterization of the pomegranate cultivars. PCR amplifications for the ISSR markers were performed in 20 µL reaction volume containing 20 ng of genomic DNA, 1× PCR buffer, 0.4 µM of primer, 0.2 mM of DNTPs, 1 U kapa Taq polymerase. ISSR-PCR reactions were performed using a SureCycler 8800 thermocycler (Agilent Technologies, California, USA) under the cycling profile: first step at 94 • C for 4 min, followed by 35 cycles segmented in 30 s at 94 • C, 30 s at varied temperature based on primers and 40 s at 72 • C, and a final extension at 72 • C for 7 min. PCR amplifications for the SCoT markers were performed in 20 µL reaction volume containing 20 ng of genomic DNA, 1× PCR buffer, 1µM of primer, 0.25 mM of DNTPs, 1 U kapa Taq polymerase. SCoT-PCR reactions were performed using a SureCycler 8800 thermocycler (Agilent Technologies, California, USA) under the cycling profile: first step at 94 • C for 5 min, followed by 40 cycles segmented in 30 s at 94 • C, 1 min and 30 s at 50 • C and 1 min and 30 s at 72 • C, and a final extension at 72 • C for 5 min. PCR products were visualized by ethidium bromide stained 1.5% agarose gels in 1× TAE buffer for ISSR and SCoT markers. A 1000 bp DNA ladder was used as a molecular-weight size marker in side lanes of each gel.

Phenotypic Traits
Mean values, mean standard error (SE), minimum and maximum values and covariate coefficient (CV) were calculated for all the phenotypic traits. Principal Component Analysis with Varimax rotation was applied with the mean values of the phenotypic traits [34]. To determine the number of statistically significant components the latent root criterion (eigenvalue > 1) and parallel analysis was used [35]. Furthermore, the Bartlett's test of sphericity and the Kaiser-Meyer-Olkin measure of sampling adequacy were used to test the factorability of the correlation matrix.
Hierarchical Cluster Analysis was performed with the mean values of the phenotypic traits, standardized as z-scores, to construct a dendrogram using Squared Euclidean distance and Ward's joining method. All the statistical analysis was performed using SPSS version 20.0.0.

Molecular Analysis
Scoring of the DNA profiles was performed and the bands of DNA fragments for ISSR and SCoT markers were classified as present (1) or absent (0). Data were analyzed using GenAlEx 6.5 [36,37], an Excel add-in for the genetic analysis, separately for each molecular marker. Band frequencies were also calculated for each group of pomegranates depending on their origin. Genetic distance matrices were calculated, and further genetic analyses were performed, including: Analysis of Molecular Variance (AMOVA) and Principal Coordinates Analysis (PCoA). The genetic distance matrix of each molecular marker was used for the construction of the UPGMA tree with MEGA X [38]. All the above statistical analyses were repeated also for the distance matrix that resulted from the combination of the ISSR and SCoT data.
ISSR and SCoT markers' information on number of different alleles (Na), number of effective alleles (Ne), Shannon's Information Index (I), gene diversity (GD) was obtained through analysis with GenAlEx 6.5 [36,37], while polymorphism information content (PIC) and resolving power (Rp) were obtained through analysis with the online program Online Marker Efficiency Calculator (iMEC) [39].
To estimate the association between the molecular markers and the phenotypic traits, stepwise Multiple Regression Analysis (MRA) was performed [40], where every phenotypic trait was treated as a dependent variable and DNA fragments produced by the molecular The % juice/edible portion parameter gives a good idea of the palatability of arils, since it refers to the quantity of juice compared with the total weight of the arils and is related to the crude fiber content and seed size and results are adding information to the seed hardness, being also a parameter related to the palatability of arils. A relatively low variability was found in the % juice/edible portion ranging between 55.8% and 69.3% (CV% 6.2) and values were higher in 11012, Wonderful, Ermioni, 11016 and 11014, whereas lowest values were found in 11002 and Acco.
Values of the 100-aril weight ranged between 21.5 and 52.9 g (CV% 21.5) and in cvs Wonderful and Acco it was 35. Seed hardness is an important characteristic for sensory attribute of pomegranate fruit grown for fresh consumption; cultivars with soft seeds achieved higher sensory assessment scores than those with harder seeds. However, cultivars for the juice industry are required to have hard seeds. In the present study soft seeded accessions/cultivars were Acco, Ermioni, Persephoni, and Spanish and the rest were medium or hard seeded. All results are presented in Tables 2 and 3. PCA results indicated five components that account for 74.8% of the total variance (Table 4). First component (PC1) accounted for 25.3% of the total variation and contained the fruit skin color parameters (Hue angle-skin, a*-skin, L*-skin, b*-skin), except for Chromaskin and aril color. The second component (PC2) explained the 17.4% of the total variance and contained aril color and hardness, AEAC and TPs. Furthermore, the third component (PC3) explained the 12.9% and the fourth component (PC4) the 12.8% of the total variance, respectively. PC3 is related to % juice/edible portion, % juice/fruit and 100-aril weight (g) and PC4 is related to MI, TA, FFW, and Chroma-skin. Finally, the fifth component (PC5) explained 6.4% and is related to % edible portion.   According to the Hierarchical Cluster Analysis, the 26 genotypes studied were placed in 4 clusters (Figure 2). The first cluster (I) contained genotypes maintained in the ex situ collection at the Department of Deciduous Fruit Trees in Naoussa (11012, 11019, 11014, According to the Hierarchical Cluster Analysis, the 26 genotypes studied were placed in 4 clusters (Figure 2). The first cluster (I) contained genotypes maintained in the ex situ collection at the Department of Deciduous Fruit Trees in Naoussa (11012,11019,11014,11005,11016,11021,11020,11018,11026), along with Ermioni and Spanish cultivars forming a subcluster. Genotypes 11002, 11029, 11022, 11025, 11003, 11006, 11011 formed a second cluster (II), while 11002 and 11029 formed a subcluster. The third cluster (III)is also composed of ex situ genotypes (11009, 11015, 11010) along with cvs Acco and Persephone. Finally, the fourth cluster (IV) contained cvs Hicaznar, Wonderful 1 and Georgias.

Molecular Analysis
All 20 primers (Table 5) used in this study produced clear and reproducible bands that ranged from 190 bp to 2200 bp for ISSR markers and from 250 bp to 3800 bp for SCoT markers. A total of 184 bands were generated for all markers that were applied across the 26 pomegranate cultivars, with an average of 77 bands per ISSR markers and 82 bands for SCoT markers. A total of 32.34% of the ISSR fragments were polymorphic and only one private band was detected. A total of 26.67% of the SCoT fragments were polymorphic and six private bands were detected.

Molecular Analysis
All 20 primers (Table 5) used in this study produced clear and reproducible bands that ranged from 190 bp to 2200 bp for ISSR markers and from 250 bp to 3800 bp for SCoT markers. A total of 184 bands were generated for all markers that were applied across the 26 pomegranate cultivars, with an average of 77 bands per ISSR markers and 82 bands for SCoT markers. A total of 32.34% of the ISSR fragments were polymorphic and only one private band was detected. A total of 26.67% of the SCoT fragments were polymorphic and six private bands were detected. Results of Analysis of Molecular Variance (AMOVA) for ISSR markers revealed percentages of 5% among populations and 95% within populations, while for SCoT markers revealed percentages of 6% among populations and 94% within populations. Principal Coordinates Analysis (PCoA) resulted in clustering the pomegranate accessions as shown in Figure 3, which agree with the obtained clusters from the dendrograms in Figures 4 and 5. Results of Analysis of Molecular Variance (AMOVA) for ISSR markers revealed percentages of 5% among populations and 95% within populations, while for SCoT markers revealed percentages of 6% among populations and 94% within populations. Principal Coordinates Analysis (PCoA) resulted in clustering the pomegranate accessions as shown in Figure 3, which agree with the obtained clusters from the dendrograms in The dendrogram obtained from the ISSR markers by UPGMA method grouped the pomegranate cultivars into three wider clusters (Figure 4), while the UPGMA dendrogram resulted from the SCoT markers grouped the cultivars into eight smaller clusters ( Figure 5). Similarities and dissimilarities between the two dendrograms can be easily detected. Hicaznar, Georgias, 11019 and 11018 belong to the same cluster in both dendrograms. Cvs Ermioni, 11009, 11026 and 11022 may belong to the same cluster according to  The dendrogram obtained from the ISSR markers by UPGMA method grouped the pomegranate cultivars into three wider clusters (Figure 4), while the UPGMA dendrogram resulted from the SCoT markers grouped the cultivars into eight smaller clusters ( Figure 5). Similarities and dissimilarities between the two dendrograms can be easily detected. Hicaznar, Georgias, 11019 and 11018 belong to the same cluster in both dendrograms. Cvs Ermioni, 11009, 11026 and 11022 may belong to the same cluster according to the ISSR dendrogram but according to the SCoT markers two different clusters are formed that are very close to each other. The dendrogram obtained from the combined distance matrix of ISSR and SCoT markers, grouped the pomegranate genotypes into three clusters ( Figure 6

ISSR and SCoT Markers' Association Study with Phenotypic Traits Using MRA
The analysis with ISSR markers revealed many markers associated with phenotypic traits that are statistically significant (R 2 > 0.5). Three markers (UBC825820, UBC8271420 and UBC891450) were associated with % juice/edible portion.
A total of seven markers were associated with weight of 100 arils and one of them, (UBC820920) showed strong positive correlation, and it was statistically significant (beta coefficient = 0.744, p < 0.001) while UBC8911100 showed a strongly negative correlation (beta coefficient = −0.655, p < 0.001). A total of eight markers were identified for GAE mg L −1 juice, showing average correlation. For AEAC (mM), a total of six markers were identified and UBC880600 showed a strongly positive and statistically significant correlation (beta coefficient = 0.598, p < 0.001).

ISSR and SCoT Markers' Association Study with Phenotypic Traits Using MRA
The analysis with ISSR markers revealed many markers associated with phenotypic traits that are statistically significant (R 2 > 0.5). Three markers (UBC825 820 , UBC827 1420 and UBC891 450 ) were associated with % juice/edible portion.
A total of seven markers were associated with weight of 100 arils and one of them, (UBC820 920 ) showed strong positive correlation, and it was statistically significant (beta coefficient = 0.744, p < 0.001) while UBC891 1100 showed a strongly negative correlation (beta coefficient = −0.655, p < 0.001). A total of eight markers were identified for GAE mg L −1 juice, showing average correlation. For AEAC (mM), a total of six markers were identified and UBC880 600 showed a strongly positive and statistically significant correlation (beta coefficient = 0.598, p < 0.001).
MRA analysis with SCoT fragments identified eight markers associated with % edible portion and SCoT1326 830 showed a strongly positive and statistically significant correlation (beta coefficient = 0.607, p < 0.001). A total of six markers were identified for % juice/fruit and SCoT1337 650 showed a strongly negative correlation (beta coefficient = −0.713, p < 0.001). Five markers were identified for TA (%w/v) and TSS/TA, respectively. Finally, SCoT1330 1100 showed a strongly positive correlation (beta coefficient = 0.749, p < 0.001) with TSS (Brix). All MRA results are shown in Table S1 followed by a list of all alleles (Supplementary Materials).
Identified alleles per cultivar with high and moderate positive statistically significant correlation are presented in Figure 7, while alleles with high and moderate negative statistically significant correlation are presented in Figure 8. Additional information on alleles per phenotypic trait can be found on Supplementary Materials. MRA analysis with SCoT fragments identified eight markers associated with % edible portion and SCoT1326830 showed a strongly positive and statistically significant correlation (beta coefficient = 0.607, p < 0.001). A total of six markers were identified for % juice/fruit and SCoT1337650 showed a strongly negative correlation (beta coefficient = −0.713, p < 0.001). Five markers were identified for TA (%w/v) and TSS/TA, respectively. Finally, SCoT13301100 showed a strongly positive correlation (beta coefficient = 0.749, p < 0.001) with TSS (Brix). All MRA results are shown in Table S1 followed by a list of all alleles (Supplementary Materials).
Identified alleles per cultivar with high and moderate positive statistically significant correlation are presented in Figure 7, while alleles with high and moderate negative statistically significant correlation are presented in Figure 8. Additional information on alleles per phenotypic trait can be found on Supplementary Materials. III II I Figure 6. UPGMA dendrogram of the studied 26 pomegranate genotypes based on the combined genetic matrix of ISSR and SCoT markers.    Most of the markers identified alleles with both positive and negative correlation. Interestingly, most alleles identified by SCoT markers were correlated moderately or highly negative with the phenotypic traits. According to the MRA analysis the alleles per phenotypic trait with only the positive strong or moderate correlation are: (i) for % juice/edible part: UBC825_820, (ii) for Weight of 100 arils (g): UBC820_920, (iii) for GAE mgr/1L juice: UBC820_1180, UBC880_1200, UBC891_1100, UBC811_1120, and (iv) for % Most of the markers identified alleles with both positive and negative correlation. Interestingly, most alleles identified by SCoT markers were correlated moderately or highly negative with the phenotypic traits. According to the MRA analysis the alleles per phenotypic trait with only the positive strong or moderate correlation are: (i) for % juice/edible part: UBC825_820, (ii) for Weight of 100 arils (g): UBC820_920, (iii) for GAE mgr/1L juice: UBC820_1180, UBC880_1200, UBC891_1100, UBC811_1120, and (iv) for % juice/fruit: SCOT1339_500, SCOT1333_910. Pomegranate accessions that combine these alleles are 11012, 11014 (7 out of

Discussion
Fruit quality characteristics for the studied local accessions were evaluated and described in our previous study [41]. Results from both studies, for cultivars No 1-19 in Tables 2 and 3, correlation was studied for the same fruit characteristics. Results showed that values were generally well correlated (r 2 = 0.550-0.878), but there were differences in harvest maturity stage as depicted by changes in TSS (r 2 = 0.394). Variation may be attributed to different meteorological conditions [23].
Cv Wonderful was similarly reported to exhibit relatively high FFW in various other studies [23], although it is generally known that variation may exist among different orchards in the USA which was shown to be attributed to different pollination rate affecting the aril number [42].
The observed values for the % juice/edible portion parameter are in the range to those reported in other Spanish cultivars (50.3-64.2; 45.0-68.6) [45], although higher values (60-84%) have been reported in Indian germplasm [46].
The values of Greek germplasm for the 100-aril weight were greater to that reported in our previous study [41], probably due to different fruit load and age of trees. Values of 100 aril weight was similar to that reported for other Cypriot [23], Iranian [47] germplasm and UMH Germplasm Bank and commercial cultivars from Spain [48].
Different harvest maturity standards also affect the TSS and TA values [52]. Variations are expected to occur not only due to genotypic differences but also due to environmental factors; higher temperatures are expected to result in higher TSS, lower TA and lower red coloration [53]. Moreover, in Northern Greece due to adverse weather condition during the fruit maturation period in October, such as temperature drop and rain, causing cracking and russeting damages, harvest is often forced to avoid damages [54]. In California, the harvest of Wonderful is recommended to start at TSS 17.0 • Brix and TA 1.9% [55] whereas in the present study it had relatively lower TSS and greater TA (16.3 • Brix and 2.2%, respectively). In other studies, Wonderful was also harvested at a more mature stage (Spanish collection, 7.5 • Brix and 0.8%, respectively; Cypriot, 18.4 and 1.0, respectively), resulting from the warmer climate conditions.
For the MI, many authors also mentioned wide ranges for this parameter, as seen in the present study. According to Chace et al. [56], pomegranate was appropriate for fresh market when its acidity content was lower than 1.8% and its MI between 7 and 12; when MI ranged from 11 to 16, pomegranates were quite tasty. However, the MI can be influenced by environmental conditions [57], moreover it is variety dependent and, optimally, must be defined for specific varieties [49].
According to our PCA results (Table 4 & Figure 1), traits related to fruit, aril and seed had the most discriminating power (PC1 and PC2). These results agree with previous studies that reported the importance of color, juice characteristics and fruit size [27,41,58,59]. According to PCA, correlation was detected between fruit traits and chemical properties and more specifically, PC2 related antioxidant content to aril color.
Hierarchical Cluster Analysis showed that most of the genotypes contained in the first cluster, are characterized by high values of % juice/edible portion, while the second cluster is composed of genotypes maintained in the ex situ collection. Cvs 11002 and 11029 formed a subcluster that may be due to their lowest values of % juice/edible portion. Hicaznar, Wonderful 1, and Georgias showed low values of TSS and MI and formed a fourth cluster. Cluster analysis revealed low variability among the cultivars studied, which may be due to the close relatedness of many of the genotypes. More morphological data on leaf, flower, and fruit along with other characteristics, such as harvest time and flowering time would allow an even higher characterization of pomegranate cultivars based on the phenotypic traits.
Results in our study from molecular analysis with ISSR and SCoT markers, indicated low percentage of polymorphism, which could be a result of the low diversity among the material used. Low genetic variation has also been reported in other studies [11,[60][61][62]. On the contrary, higher percentage of polymorphism is reported in studies from Narzary et al. [63] and Zamani et al. [58], where large numbers of genotypes from different regions and wild genotypes were studied.
In both dendrograms resulted from ISSR ( Figure 4) and SCoT markers, ( Figure 5) cultivars 11016 and 11029 act as outliers forming their own clusters, since the genetic distance between them has the highest value among all the rest cultivars. Cultivar 11016 was differentiated because of the presence of numerous bands in comparison with the other cultivars. On the other hand, cv 11029 presented the least bands in comparison with the other cultivars. The clusters formed with the combined file of both distance matrices ( Figure 6) are similar to the ones obtained from the SCoT markers, a fact that indicates that SCoT markers provided a higher rate of polymorphism compared to the ISSR markers. The low genetic diversity among the plant material studied which wad originated from different areas of Greece suggest that these plants either have common ancestors and/or are propagated by cuttings and not seeds. Two samples 11016 from Lefkada island and 11029 from Imvros island showed very distinctive genetic pattern, different form all the other samples always being outliers suggesting unique genetic background. Furthermore, most of the varieties were different than the American bred "wonderful", yet the Spanish sample and a famous Greek variety "Ermioni" can be found in the same cluster.
A disagreement between the dendrograms obtained from phenotypic traits ( Figure 1) and molecular markers ( Figure 6) was observed, a fact that can be explained by the consequences of the environmental conditions on the cultivars' characteristics [64,65].
The MRA method is an easy and quick approach for associating traits with markers [66,67]. The MRA tool is suitable for tree crops and MAS breeding programs [40]. MRA analysis with ISSR and SCoT markers revealed many markers associated with phenotypic traits, which are strongly positive and negative statistically significant (R 2 > 0.5). Most alleles identified by SCoT markers were correlated moderately or highly negative with the phenotypic traits. These markers could prove very valuable in screening pomegranate cultivars for the desired quality traits, avoiding those that carry alleles that are negative correlated. According to the MRA analysis the alleles per phenotypic trait with only the positive strong or moderate correlation are: UBC825_820, UBC820_920, UBC820_1180, UBC880_1200, UBC891_1100, UBC811_1120, SCOT1339_500, and SCOT1333_910. Pomegranate accessions that combine many of these alleles and can be used in pyramiding for the phenotypic traits that were studied are 11012, 11014, 11016, 11020, 11026, Hicaznar, Persephone, 11009, 11015, 11022, Acco, Ermioni, and Spanish. However, accessions 11011, 11029, and 11021 did not contain any of the alleles and should be avoided in breeding programs.

Conclusions
This study is the first attempt to associate phenotypic traits and molecular markers in Greek pomegranate cvs. Although results from the present study indicated low variability in the phenotypic and genotypic level, many markers, especially SCoT markers, were found to be strongly or averagely associated with the morphological traits and chemical components, which were recorded for the pomegranate cultivars under study. All the information acquired in this study can be very valuable for future breeding programs as well as for conservation of genetic resources.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/agriculture11060482/s1, Table S1: Markers (ISSR/SCoT) associated with different phenotypic traits in pomegranate cultivars as revealed by MRA and the coefficients.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.