Evaluation of Genetic Diversity of Iranian Pomegranate Cultivars Using Fruit Morphological Characteristics and AFLP Markers

The present research evaluated the diversity of a number of Iranian pomegranate cultivars using fruit morphological characteristics and AFLP markers. Thirty-one pomegranate cultivars were collected from Yazd Pomegranate Collection in Iran to study their diversity. Seven AFLP primer combinations were used to amplify a total of 112 polymorphic fragments (47.26%). By use of AFLPs, a low genetic diversity level was detected among cultivars. The relationship between fruit characteristics was analyzed using the principal component analysis (PCA). The cluster analysis based on both fruit characteristics and AFLP data indicated that cultivars were not grouped according to their geographic origins. Moreover, the correlation between the diversity matrix based on fruit characteristics and Dice’s genetic similarity coefficient was insignificant (r=0.06). The results obtained from this study can improve the conservation and management of pomegranate germplasm resources and could be helpful in optimizing breeding programs.


Introduction
The pomegranate (Punica granatum L.), an ornamental plant which has been popular among Mediterranean peoples for centuries (Vazifeshenas et al., 2009) which is native to Iran and the Himalayas, produces delicious and edible fruits, and belongs to the Punicaceae family.There exists a local collection of pomegranate cultivars consisting of approximately 760 cultivars in the Yazd province of Iran (Behzadi Shahrbabaki, 1997).Mars and Marrakchi (1999) reported that the fruit morphological characteristics are useful for pomegranate identification; however, these morphological traits are intensely dependent on the environmental conditions.There are some reports using RAPDs markers (Talebi Bodaff et al., 2003;Sarkhosh et al., 2006), SSR markers (Currò et al., 2010;Soriano et al., 2011), RAPDs and morphological markers (Sarkhosh et al., 2009;Zamani et al., 2007), as well as AFLPs markers ( Jabir et al., 2008;Yuan et al., 2007) to analyze the genotypic characteristics and genetic relationships of pomegranate cultivars.
Due to the long history of Iranian pomegranate cultivation and the related vegetative propagation, several cases of homonymy and synonymy can be observed among this germplasm.Thus, it is essential to create a reliable classification system for Iranian pomegranates.Moreover, it is very important for using a sensitive and credible molecular technique to detect the DNA variation and identify the pomegranate germplasm, by helping breeders and nurserymen with the selection and propagation of a cultivar.
Having many advantages, such as reproducibility, high levels of polymorphism detection, genome-wide distribution of markers and no requirement for the previous knowledge of the studied genome, have caused AFLPs to be an appropriate technique for genetic diversity among the various molecular markers (Bruna et al., 2007;Polanco and Ruiz, 2002).In addition, Vos et al. (1995) reported that AFLP has been known as a more reliable technique than RFLP, RAPD.
AFLP markers have successfully been used to study the genetic diversity at the varietal level in many fruit trees, including apricot (Hurtado et al., 2002), olive (Rotondi et al., 2003) and pear (Bao et al., 2008).Despite, the various studies based on molecular markers in Iran, there is still ambiguities and debates about genetic diversity of pomegranate germplasm in Iran mainly due to different efficiency of different methods.Therefore, the aims of this project were to produce suitable markers for the characterization of pomegranate cultivars and to evaluate the diversity of Iranian pomegranate cultivars using fruit morphological characteristics and AFLPs markers.

Plant materials
Fruit and leaf samples of thirty one pomegranate genotypes were collected from adult trees from the pomegranate collection at Agricultural Research Center of Yazd, Iran (Tab.1).fragments were pre-amplified using EcoRI+1 (5´-GACT-GCGTACCAATTCA-3´) and Mse1+1 (5´-GAT-GAGTCCTGAGTAAC-3´) primers under the following conditions: 20 cycles of 94°C for 30 s, 56°C for 60 s and 72°C for 60 s and then were used as templates (without dilution).Initially, a total of 35 primer combinations, from which seven primer combinations with the strongest and greatest number of bands were selected for AFLP reaction, were tested.Selective amplification was performed using a pair of EcoRI+3 and MseI +3 primers.The amplifications consisted of the following steps: one cycle of 94°C for 30 sec, 65°C for 30 sec, and 72°C for 60 sec, followed by 13 cycles at decreasing annealing temperature in decrements of 0.7°C per cycle, then 23 cycles of 94°C for 30 sec, 56°C for 30 sec, and final extension 72°C for 60 sec.The amplification products were resolved by 6% denaturing polyacrylamid gels at 1200 volt for 120 min in 1X TBE (Tris-Borate Ethylenediaminetetraacetic acid).The AFLP markers were visualized by silver nitrate staining according to Sanguinetti et al. (1994).

Morphological and chemical fruit characteristics
Quantitative and qualitative fruit characteristics were evaluated based on morphological and chemical analysis (Mars and Marakchi, 1999;Sarkhosh et al., 2009;Tehranifar et al., 2010), using 20 mature fruit samples per genotype (Tab.2).

DNA extraction and AFLP analysis
Fresh and young fully expanded leaves from each cultivar were collected and ground in liquid nitrogen.Genomic DNA was extracted using DNeasy plant mini kits (Qiagen, Inc., CA, USA).The quantity and quality of isolated genomic DNA was determined using agarose gel [1% (w/v)] electrophoresis and a nano drop spectrophotometer (ND 1000, USA).
AFLP analysis was conducted using the minor modified standard procedure by Vos et al. (1995).Approximately 250 ng of genomic DNA was digested by restriction enzymes EcoRI and MseI and then double standard adaptors were ligated to the obtained fragments to generate templates for amplification.The digest-Ligated DNA

Data analysis
After normalizing quantitative morphological data, the mean values of each parameter were estimated for statistical analysis.The average values were utilized to calculate the principal component analysis (PCA) and cluster analysis based on the Euclidean distance between the different genotypes.The principal component analysis was used to compare the influence of each characteristic on the clustering of cultivars.Simply factors loading values equal or greater than 0.5 were considered significant.The dendrogram was conducted using Ward's methods via SPSS for the windows computer software (version, 16).
Manually, AFLP fragments were scored according to their presence (1) or absence (0) to form a raw data matrix.The statistical analysis was constructed using the NTSYS software version 2.02 (Roholf, 1998).The genetic similarities between all cultivars were estimated based on the Dice's coefficient (Nei and Li, 1979).In order to construct a dendrogram, the similarity matrix was calculated by the unweighted pair-group method of the arithmetic average (UPGMA) using the SAHN clustering model.The cophenetic coefficient was computed in order to test the goodness of fit between the cluster in the dendrogram and the similarity coefficient matrix.The Mantel test was applied to calculate the correlation between the two dendrograms produced by morphological and AFLP data (Mantel, 1967).

Fruit characteristics
Thirty-one cultivars were characterized by a large variability in quantitative morphological traits including fruit shape, color and juice (data not shown).The range of the mean values of each studied cultivar exhibited a significant diversity in the fruit characteristics.The mean, maximum, minimum and coefficient of the variation values for each characteristic among all genotypes were illustrated in Tab. 2. Among all quantitative characteristics titratable acidity (TA), Juice total phenol ( JTP) and Anthocyanin absorbance (ANA) showed higher CV values indicating a high level of variation.PCA results indicated that the first component related to fruit weight (FW), fruit length (FL), fruit volume (FV), peel thickness (PT), peel weight/ fruit (PW/F ratio), aril weight (AW), aril length (AL), aril diameter (AD), aril length/aril diameter (AL/AD ratio), aril/fruit (A/F ratio), seed weight (SW), juice volume ( JV), juice density ( JD), juice/Fruit ( J/F ratio), total sugar (TS) and total soluble solids (TSS) accounted for 31.84% of the total variation and grouped cultivars based on most of the studied physical characteristics.The second component which explained 19.24% of the total variation is dominated by five other physical characteristics explaining 95.01 of the total variance (Tab.3).According to the aforementioned seven factors, thirty-one cultivars fall into the main five clusters at a distance of 10 (Fig. 1).
The range of the similarity matrix obtained by the Dice coefficient varied between 0.793 and 0.997 with an average of 0.944 (Tab.5).The genetic relationship between 31 cultivars based on the Dice's similarity coefficient is shown in a dendrogram (Fig. 3).The cophenetic correlation coefficient calculated between the similarity matrix and cophenetic matrix, which were obtained from dendrogram data, was very high (r=0.99).According to Dice's similarity matrix and the UPGMA clustering method, the dendrogram exhibited two main groups (A-B) that were identified at the 0.81 similarity level (Fig. 3).Group A consisted of two subgroups, one containing the cultivar No. 7), which were separated by a similarity coefficient of 0.81, most of them had a similarity coefficient up to 0.95.No obvious relationships were detected between the morphology, the origins and the estimated genetic traits.According to the genetic analysis conducted by AFLP, most of the cultivars were composed of simply one group in spite of their distinct origin and morphology.The three remaining cultivars, which did not fall into this group, were morphologically distinct.According to the dendrogram (Fig. 3) and similarity matrix (Tab.5), a relatively low genetic diversity was observed among the studied cultivars.
In this study, both dendrograms obtained from the morphological and AFLP markers were not consistent with the local name and geographical origin.Furthermore, the results of this study were in agreement with the others ( Jabir et al., 2008;Narzary et al., 2009;Yuan et al., 2007), showing that the clustering of the cultivars is not related to the geographical distance.
The level of the genetic diversity highly correlated with the sample size; therefore, it would be worth mentioning that the used sample size was small in the present study.Also, another reason for the low genetic diversity could be due to the vegetative propagation.Over a period of 2500 years (Behzadi Shahrbabaki, 1997), there has been more genotypes (Currò et al., 2010), these Authors reported that higher levels of polymorphism could be detected by analyzing larger collections or natural populations in the origin areas.
A very poor correlation was obtained between the morphological distance matrix and AFLP similarities matrix (r=0.06) (Fig. 4).This result was in agreement with the results obtained by Talebi Boddaf et al. (2003), Zamani et al. (2007) and Sarkhosh et al. (2009) and confirmed the insignificant correlation between the morphology and the RAPD markers.In order to provide a better matching of the relationship between the morphological traits and the molecular markers, more morphological characteristics such as phenological traits of leaves, flowers and fruits are required to be estimated.
The first possible explanation for the lack of correspondence between morphological traits and molecular markers are that these morphological differences such as the fruit color, fruit shape, height, form of trees and branching habit are probably the result of alleles that were not detected by the present molecular markers.Wen et al. (2004) and Zahuang et al. (2004) proposed that posttranscriptional effects, translation, environmental changes and non-nuclear inheritance can lead to the lack of the correspondence between morphological traits and molecular marker.In some studies the low correlation between these markers are observed (Heidary et al., 2009;Martinez, 2003;Rotondi et al., 2003) and the others (Cavagnaro et al., 2006) it has been showed that there is a significant correlation between these markers.Another explanation would be the relatively low number of markers used in this study resulted in inadequate genome coverage (De Langhe et al., 2005).
To prepare a better matching of the relationship between morphological and molecular markers, more primers or an extended set of primer combinations must be utilized.More studies with both morphological and other markers such as Co-dominate markers, might solve this issue.

Fig. 1 .
Fig. 1.The principle component analysis dendrogram of 31 pomegranate cultivars obtained from fruit characteristics data

Fig. 3 .
Fig. 3. UPGMA dendrogram constructed using the Dice's similarity (Nei and Li, 1979) coefficient analysis based on molecular profiles revealed by AFLP marker Tab. 3. Eigen values, cumulative variance and factor loadings for each variable of the components of PCA analysis for 31 pomegranate cultivars