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Scientia Horticulturae 160 (2013) 29–36

Contents lists available at SciVerse ScienceDirect

Scientia Horticulturae

journa l h om epage: www.elsev ier .com/ locate /sc ihor t i

enetic structure and diversity analysis in Vitis vinifera L. cultivarsrom Iran using SSR markers

. Doulati-Baneha, S.A. Mohammadib,c,∗, M. Labrad

Agricultural Research Center of West Azerbyjan, P.O. Box 365, Ourmia, IranDepartment of Plant Breeding & Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz 51664, IranLaboratory of Genomics and Molecular Breeding, Center of Excellence in Cereal Molecular Breeding, University of Tabriz, Tabriz 51666, IranDepartment of Biosciences and Biotechnology, University of Milano-Bicocca, Piazza dellaScienza 2, I-20126 Milan, Italy

r t i c l e i n f o

rticle history:eceived 26 December 2012eceived in revised form 19 May 2013ccepted 21 May 2013

eywords:enotypinginger printingolymorphismrapevine

a b s t r a c t

A broad germplasm collection of 1.5 ha containing most of the Iranian grape cultivars including 31table grape, 22 juice grape, 6 table grape, raisin, 2 table grape, raisin, wine and one table grape, winewere analyzed using 23 simple sequence repeat (SSR) loci. The number of alleles obtained ranged from2(VVS3) to 15 (VMCNG2G7) with an average of 8.65 alleles/locus, and the effective number of allelesdiffered from 1.82 (VrZAG83) to 9.73 (VMCNG2G7) with a mean value of 4.41. Considering the highdegree of polymorphism, five SSR markers including VMCNG2G7, VVMD5, VVMD8, VVS2 and VrZAG64with PIC values greater than 0.80 were selected for rapid fingerprinting of many grape genotypes. Theobserved heterozygosity varied between 0.49 (VVMD17) and 0.97 (VrZAG64), without significant differ-ences from the expected values considering all the loci analyzed. Based on likelihood ratios two possibleparent–offspring relations were determined and pedigrees illustrating the relationship between these

varieties were reconstructed. The integration of the obtained results with ampelographic data allow tounivocally identify the Iranian cultivars and could become a significant tool for the certification of qualitygrapes produced in specific regions. The phenetic relationships depicted by Neighbor-Joining analyses ofSSR data were congruent and, to a large extent, in agreement with the known pedigree or history of eachcultivar. Usefulness of this SSR set for characterization of an Iranian grapevine collection is emphasized,as well as the elaboration of databases with these molecular markers.

. Introduction

Characterized with a rich, complex and diverse viticulture her-tage, grapevine (Vitis vinifera L.) is successfully grown in Iran overn area of more than 300,000 ha with a production of approxi-ately 3 million tons (Tafazzoli et al., 1993; DoulatyBaneh et al.,

007). It is estimated that out of 800–1000 grape genotypes inran, about 250 cultivars are grown and mostly used as table andried fruit (Najafi et al., 2006). However, synonyms (many namesor the same genotype) and homonyms (same name for differentenotypes) occur. One of the major concerns of modern viticul-ure (and agriculture) is the conservation and utilization of valuable

enetic resources. This requires the correct identification of cul-ivars and accessions. True-to-type is necessary when plantingineyards, making wine, managing germplasm collection, choosing

∗ Corresponding author at: Department of Plant Breeding & Biotechnology, Facultyf Agriculture, University of Tabriz, Tabriz 51664, Iran. Tel.: +98 411 3392070;ax: +98 411 3356003.

E-mail address: mohammadi@tabrizu.ac.ir (S.A. Mohammadi).

304-4238/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.scienta.2013.05.029

© 2013 Elsevier B.V. All rights reserved.

parents for controlled crosses and legally protecting new culti-vars.

Besides the study of morphological features, tools developedfor the characterization of genotypes may allow clarification ofsynonyms/homonyms and detection of the origin of species andcultivars (Laimer et al., 2005). The development of DNA-basedmarkers has provided more objective and reliable alternatives forcultivar identification. Owing to their high degree of polymor-phism, reproducibility and co-dominant nature, microsatellites orSSR (Simple Sequence Repeats) have been widely employed andfavored among various PCR-based markers. To date several VitisSSR series are publicly available at NCBI databases dbSTS andUniSTS (Thomas and Scott, 1993; Bowers et al., 1996; Sefc et al.,1999; Doligez et al., 2006; Di Gaspero et al., 2007; Welter et al.,2007; Huang et al., 2011). Several studies on grape cultivar char-acterization based on SSRs have been performed (e.g. Labra et al.,2001; De Mattia et al., 2008; Boz et al., 2011). This et al. (2004) used

six highly polymorphic SSR loci to investigate the comparability ofSSR profiles obtained in different laboratories. Baleiras-Couto andEiras-Dias (2006) used nuclear and chloroplast SSR markers to iden-tify grape varieties in must and wine. Cipriani et al. (2010) analyzed

30 H. Doulati-Baneh et al. / Scientia Horticulturae 160 (2013) 29–36

Table 1The list of grape (Vitis vinifera) genotypes used for SSR analysis and area of cultivation and site of germplasm collection.

Genotype Use/aptitude Area ofcultivation

Genotype Use/aptitude Area ofcultivation

Genotype Use/aptitude Area ofcultivation

AbiBalo Juice, Wine WA KeshmeshiQermez Table grape I SaghalSolian1 Juice WAAghMelhi Juice WA Keshmeshi Sefid Table grape,

RaisinI SaghalSolian2 Juice WA, EA

AghShani Juice WA Khalili Sefid Table grape I Sahebi Table grape IAkuzGuzi Juice WA Khalili Qermez Table grape I Sarghola Table grape KAlhaghi Table grape WA, EA Khoshnav Table grape,

Raisin, WineK Saiani Table grape K

Angotka Juice WA, K KlkaRevi Juice K SefideShakhShakh Juice WA, EAAskari Table grape I Labrusca Table grape I Shahroudi Table grape IAtouzum Juice WA, EA LalSefid Table grape WA, EA Shirazi Table grape WABarbera Wine E LalQermez Table grape WA, EA SeyahMamoli Table grape, Wine KBidaneTabriz Table grape, Raisin I LalSeyah Table grape WA, EA SeyahSardasht Table grape, Raisin KBolMazu Juic WA, K MaieMo Juice WA Surav Juice KCabernet Franc Wine E Makaii Juice WA TabarzeQermez Table grape WA, EACabernet-Sauvignon Wine E MamBraima Table grape,

RaisinWA, K TabarzeSefid Table grape WA, EA

ChavaGa Juice WA, EA, K Moseli Juice WA Taifi Table grape KChardonnay Wine E Muscat Table grape,

WineE Yaghoti Table grape I

Dastarchin Juice WA Ormia63 Juice WA Zardka Table grape KDizmari Table grape, Raisin WA, EA QaraGandoma Juice, Wine WA, EAEri Qara Juice WA QaraMelhi Juice, Wine WA, EAFakhri Table grape, Raisin WA, EA QaraShira Juice, Wine WA, KGalinBarmaghi Table grape WA, EA QaraShani Juice, Wine WA, EAGarmian Table grape WA, EA, K QzlOuzum Table grape WA, EAGoiMelki Table grape WA, EA Rasha Table grape,

Raisin, WineK

Gazandaii Table grape WA, EA Rejin Juice WA, EAHossaini Table grape I Rezghi Table grape WAInahAmjai Table grape WA, EA RishbababQermez Table grape WAJig Jiga Juice WA, EA RishbababSefid1 Table grape WAKalati Table grape WA, EA RishbababSefid2 Table grape WAKazhav Juice K Sachakh Juice WA, EA

E istan

agkefSoStodatD(eg

glI

2

2

ipfg

: Europe; EA: East Azarbyjan province, Iran; I: Iran, most of the provinces; K: Kurd

sample of 48 accessions belonging to important autochthonousrapevine varieties from north-eastern Italy as well as eight well-nown international grape cultivars using 39 SSR markers. Bozt al. (2011) genetically analyzed 55 grape cultivars originatingrom six different provinces of Southeast Anatolia, Turkey usingSR markers and identified one case of synonymous and four casesf homonymous in the studied germplasm. In literature, the use ofSR is also reported for grape parentage analysis and reconstruc-ion of pedigree (Vouillamoz et al., 2003). Despite the importancef grape in Iranian agriculture economy, and the long-standing tra-ition of grape cultivation, many of the commonly grown cultivarsre thought to be synonyms or homonyms, and no clear charac-erization has been undertaken until recently (Najafi et al., 2006;oulatyBaneh et al., 2007). In Iran, DNA based markers such as SSRs

Fatahi et al., 2003; Najafi et al., 2006) and AFLPs (DoulatyBaneht al., 2007) have already been applied to characterize some Iranianrape cultivars.

In this study, SSR markers were employed for characterizingenetic variation and cultivar relatedness in Kahriz grape collectionocated in agricultural research center of West Azarbijan province,ran including the most important grape varieties cultivated in Iran.

. Materials and methods

.1. Plant material

Sixty seven Iranian cultivars and landraces were analyzed

n this study (Table 1). For a correct allele sizing and theroduction of transferable information among labs that apply dif-erent SSR genotyping techniques (e.g. polyacrylamide gel againstenetic analyzer), five international grape varieties namely Barbera,

province, Iran; WA: West Azarbyjan province, Iran.

Cabernet Franc, Cabernet-Sauvignon, Chardonnay and Muscat werealso included.

2.2. DNA extraction and SSR analysis

For each genotype, 2–5 cm of ten young leaves were used forgenomic DNA extraction according to the procedure describedby Lodhi et al. (1994). DNA was quantified by visual comparisonwith lambda DNA molecular marker on ethidium bromide stainedagarose gels and spectrophotometer. PCR was carried out in a finalvolume of 10 �l, consisting of 1.0 �mol of each primer, 1 unit TaqDNA polymerase (SinaGene, Iran), 100 �MdNTPs, 1X PCR buffer(10 mM Tris–HCl, 50 mM KCl, 1.5 mM MgCl2), 2.5 mM MgCl2, 50 ngtemplate DNA and distilled, deionized H2O. Amplification reactionswere carried out using the following cycling profile: initial dena-turation at 94 ◦C for 4 min followed by 35 cycles of 94 ◦C for 1 min,58–68 ◦C for the different primer pairs for 1 min, and 72 ◦C for 2 minand a final extension at 72 ◦C for 7 min. Thirty grapevine SSRs wereused and a set of 23 highly polymorphic markers were consideredas the most suitable ones for assessing variation among the studiedgenotypes (Table 2). The PCR products were separated on 6% (w/v)polyacrylamide gel and visualized by silver staining.

2.3. Genetic diversity analysis

SSR allele per locus, major allele frequencies, gene diversity(Di), observed heterozygosity (HO), and polymorphic information

content (PIC) were calculated as genetic parameters of polymor-phism. The average number of alleles per locus was determinedfrom single-locus values. Gene diversity, often referred to asexpected heterozygosity, is the probability that two randomly

H. Doulati-Baneh et al. / Scientia Ho

Table 2Genetic parameters of 23 SSR loci used in the grape genotypes.

Marker Chr. NG NA Ne GD Ho PIC PIa

VMC6D12 9 22 12 4.25 0.76 0.89 0.73 0.15VMC6e1 14 21 11 4.83 0.79 0.75 0.77 0.10VMC6g1 2 9 5 2.92 0.66 0.83 0.59 0.32VMC6f1 11 15 6 4.32 0.77 0.68 0.73 0.16VMC6G10 4 12 9 2.68 0.63 0.50 0.56 0.35VMC9f2 1 25 11 5.09 0.80 0.75 0.78 0.10VMCNG2G7 1 33 15 9.73 0.90 0.89 0.89 0.04VrZAG47 – 16 8 4.45 0.77 0.89 0.74 0.15VrZAG62 7 21 11 4.61 0.78 0.75 0.76 0.12VrZAG64 10 23 11 6.17 0.84 0.97 0.82 0.08VrZAG83 4 6 3 1.82 0.45 0.26 0.37 0.54VVMD17 18 10 5 2.13 0.53 0.49 0.47 0.41VVMD21 6 8 5 2.75 0.64 0.76 0.58 0.30VVMD25 11 16 8 4.56 0.78 0.68 0.74 0.15VVMD26 1 7 5 2.08 0.50 0.55 0.42 0.49VVMD27 5 16 8 4.03 0.75 0.79 0.71 0.17VVMD32 4 18 9 3.96 0.75 0.69 0.71 0.17VVMD5 16 30 12 6.84 0.85 0.75 0.84 0.07VVMD7 7 23 9 5.51 0.82 0.78 0.79 0.11VVMD8 16 28 14 8.02 0.84 0.89 0.83 0.07VVS2 11 31 14 8.21 0.88 0.97 0.86 0.05VVS3 2 3 2 1.97 0.49 0.65 0.37 0.62VVS4 8 8 6 2.15 0.53 0.60 0.49 0.34

Mean – 17.43 8.65 4.41 0.72 0.73 0.68 0.22

SD – 1.65 0.86 2.07 0.027 0.03 0.03 0.16

Number of genotypes (NG), number of alleles (NA), effective number of allele (Ne),gene diversity (GD) or expected hetrozygosity, observed hetrozygosity (Ho), poly-ma

cetsiiism∑

(pmoledeoTaat

te2bJuewm(

loci, the observed heterozygosity was higher than expected values.

orphic information content (PIC).Cumulative probability of identity across all loci is 1.77 × 10−17.

hosen alleles at a locus within a set of genotypes will be differ-nt, and was calculated as Dl = 2n(1 −

∑p2

i/(2n − 1), where pi is

he frequency of the ith allele for each microsatellite locus in theample of n genotypes studied (Nei, 1978). Observed heterozygos-ty was determined as the proportion of heterozygous individualsn the set of genotypes with formula Ho = 1 −

∑Pind, where, Pind

s the frequency of homozygous individuals in the sample for thepecific locus. Polymorphism information content (PIC) was esti-ated as described by Botstein et al. (1980): PIC = 1 −

∑ni=1p2

i−

n−1i=1

∑nj=j+12p2

ip2

j, and probability of identity (PI) following

Paetkau et al., 1995): PI = 1 −∑n

i=1p4i

∑n−1i=1

∑nj=j+1(2pipj)

2, where

i and pj are the frequencies of the ith and jth allele of a givenarker, respectively. For all the above genetic parameters, the

verall estimates were calculated as the averages across all theoci, whereas standard deviations and confidence intervals werestimated by 1000 nonparametric bootstrapping samples acrossifferent loci. The genetic parameters were estimated using Pow-rMarker Ver. 3.20 (Liu and Muse, 2005). The effective numberf alleles (ne) was obtained according to Morgante et al. (1994).he probability of null alleles (r) was calculated for each locusccording to Brookfield (1996), r = (He − Ho)/(1 + He), where Hend Ho are expected and observed heterozygosity values, respec-ively.

The binary matrix and allele frequencies were used to analyzehe genetic relationships among the genotypes by number of differ-nt similarity and distance coefficients (Mohammadi and Prasanna,003; Kumar et al., 2004). Cluster analysis was performed usingoth distance and similarity matrices on the basis of the Neighbor-oining and UPGMA algorithms. Dendrograms were constructedsing MEGA-4 software based on 1000 bootstrap samples (Kumart al., 2004). A hierarchical analysis of molecular variance (AMOVA)

as performed on different dendrogram cutting points to deter-ine the optimum number of clusters using PowerMarker Ver. 3.20

Liu and Muse, 2005).

rticulturae 160 (2013) 29–36 31

2.4. Pedigree analysis

Parentage analysis was done on the basis of genotype’s SSR pro-files using Identity program version 1.0 (Wagner and Sefc, 1999).The cumulative likelihood ratios for the proposed parentage, likeli-hood ratios and their 95% upper confidence limits were calculatedas described by Bowers and Meredith (1997) with the relative allelicfrequencies at 23 SSR markers.

3. Results

3.1. Diversity study

Of the 30 primer pairs selected for fingerprinting seven were dis-carded because of unreadable patterns. The remaining 23 screenedin the panel of genotypes amplified a total of 199 alleles rangingfrom 2 (VVS3) to 15 (VMCNG2G7) with an average of 8.65 alle-les/locus. In addition, the effective number of alleles (Ne), which isthe number of equally frequent alleles that would be required toproduce the same homozygosity, differed from 1.82 (VrZAG83) to9.73 (VMCNG2G7) with a mean value of 4.41. A total of 401 geno-types were identified for 72 genotypes at 23 SSR loci. Genotypefrequency ranged from 0.014 to 0.065 (Table 2).

The genotype level of polymorphism was assessed by calculat-ing PIC vales for each of the 23 SSR loci. In our study, mean PICvalue was0.68 ± 0.03 and VMCNG2G7 locus with value of 0.89 andVVS3 and VrZAG83 markers with value of 0.37 showed the maxi-mum and minimum PIC, respectively. Considering the high degreeof polymorphism, five SSR markers (VMCNG2G7, VVMD5, VVMD8,VVS2 and VrZAG64) with PIC values greater than 0.80 were selectedfor rapid fingerprinting of many grape genotypes. Gene diversity orexpected heterozygosity as another parameter indicating degree ofpolymorphism ranged from 0.45 (VrZAG83) to 0.90 (VMCNG2G7),with an average of 0.72 ± 0.03 (Table 2).

Probability of identity ranged from 0.04 to 0.62. PI is defined asthe probability with which two randomly taken genotypes displaythe same SSR profile. Using the 23 SSR markers in combination, thecumulative probability of identity, which is a measure of the proba-bility of obtaining an identical genotype, was very low with a valueof 1.77 × 10−17 (Table 2). This number corresponds to a statisticalpotential of distinguishing a large number of unrelated grapevinesvarieties.

In total, 28 private alleles (allele specific to a genotype) wereidentified. Labrusca with eight alleles and BolMazu, Cabernet Franc,Chardonnay, Kazav, KhaliliSefied, MamBraima, Muscat, Ormia63,OzlOzum, QaraGandoma and SeyahSardasht only with one allelehad maximum and minimum unique allele, respectively. Barberaand Cabernet-Sauvignon with three and AghShani with two alleleswere the other genotypes having unique alleles (Table 3).

The lowest observed heterozygosity was detected at VVMD17locus with 0.49 and the highest one at VrZAG64 and VVS2 lociwith 0.97 (Table 2). Significant difference was obtained betweenobserved and maximum theoretical values of gene diversity in theSSR loci as analyzed by a paired t-test (t = −7.71, P < 0.00). Differenceamong loci with various number of alleles with respect to deviationfrom maximum theoretical values of gene diversity was not signif-icant as revealed by one way analysis of variance (F = 1.57, P = 0.22).However, in all the loci theoretical value was higher than observedvalue (Fig. 1) indicating high frequency of allelic imbalance at SSRloci analyzed. Deficiency or excess of heterozygote as measured byWright’s (1978) fixation index (FIS) indicated that at 11 of the 23

In contrast, it was lower than expected values at 12 loci. However,deviation from expected heterozygosity was not significant consid-ering all the analyzed loci as revealed by a paired t-test (t = 0.51,

32 H. Doulati-Baneh et al. / Scientia Horticulturae 160 (2013) 29–36

Table 3Specific SSR alleles (those which occurred in no more than one genotype) as identified in the 72 grape genotypes measured in this study.

Locus Allele size (bp)a Specific to Locus Allele size (bp)a Specific to

VMC6G10 184 AghShani VVMD8 170 LabruscaVVMD8 172 AghShani VVMD32 249 LabruscaVMC9f2 309 Barbera VVS2 123 LabruscaVMC9f2 213 Barbera VrZAG64 135 LabruscaVVS4 180 Barbera VMC6D12 182 MamBraimaVVMD32 245 BolMazu VVMD17 216 MuscatVVMD7 264 Cabernet Franc VrZAG62 186 MuscatVMC6G10 130 Cabernet-Sauvignon VVMD27 194 Ormia63VVMD27 175 Cabernet-Sauvignon VMC6e1 159 QaraGandomaVrZAG47 153 Cabernet-Sauvignon VVMD25 267 QzlOuzumVVMD26 255 Chardonney VrZAG62 192 SeyahSardashtVrZAG62 206 Kazav VMC6e1 167 LabruscaVVMD7 236 KhaliliSefied VVMD7 238 LabruscaVMC6D12 186 Labrusca VVMD8 170 LabruscaVMC6D12 132 Labrusca VVMD32 249 LabruscaVMC6e1 167 Labrusca VVS2 123 Labrusca

(0.019

PsV

3

mSFSaacbovvcv

3

m

Flh

VVMD7 238 Labrusca

a The size of each allele was determined based on Molecular Weight Marker VIII

= 0.61). Tests for Hardy-Weinberg equilibrium (HWE) revealedignificant deviations from HWE at the loci except VVMD7, VMC6f1,VS4, VVMD17 and VVMD32.

.2. Synonymy and homonymy

We used SSR markers to resolve synonymy, homonymy andisnaming. Synonymy was detected in the cases of ‘Moseli and

aghalSsolian’, ‘TabarzeSefid and TabarzeQermez’, ‘Dizamari andakhri’, ‘Khoshnav andRasha’and ‘KeshmeshiSefid,Rejinand Sefid-hakhshakh. The homonymous grape cultivars, ‘RishbabaSefid’nd ‘RishbabaQermez’ were also identified based on genomicnd chloroplast microsatellite data. Misnaming was found in thease of SaghalSsolian, a cultivar with relatively small seedlesslack berries. Whereas the cultivar named as SaghalSsolian inur collection had yellow, large and seeded berries. Two culti-ars with names of SaghalSsolian1 and SaghalSsolian2 showed alsoery different DNA profiles. Misnaming was also observed in thease of Yaghoti, Alhaghi, Khalili Qermez and SiyahSardasht culti-ars.

.3. Parent–offspring relations

In order to ascertain possible parent–offspring relations, SSRarkers were used. Based on likelihood ratios two possible

ig. 1. Scatter plot of gene diversity values as a function of allele number for 23 SSRoci analyzed across 73 grape genotypes. Maximum theoretical and mean valuesave also been plotted.

-1.11kbp), Roche Company.

parent–offspring relations were detected and two pedigreesillustrating the relationship between these varieties werereconstructed. One of the ascertained parent–offspring rela-tions was detected forthe ‘RishbabaSefid’ as offspring and‘Rezghi’ and ‘RishbabaQermez’ as parents. The likelihood ratiosof the probability of the proposed parentage ‘RishbabaSe-fid’ = ‘Rezghi’ × ‘RishbabaQermez’ versus two random cultivars wasextremely high >1014 (>109 with 95% upper confidence limits ofallele frequencies).

‘Dastarchin’ and ‘Askari’ were identified as parents of‘KeshmeshiSefid’, ‘BidaneQermez’, ‘Rejin’, ‘SefideShakhShakh’,‘TabarzeSefid’ and ‘TabarzeQermez’. The high likelihood ratio ofthe probability of the ‘Dastarchin’ and ‘Askari’ being the parents ofthe above mentioned cultivars versus other possibilities was high(1.09 × 109) with 95% upper confidence limits of allele frequenciesof 9.54 × 106. The second possible pedigree could be KeshmeshiSe-fid being the progeny of a cross ‘Datarchin’ and ‘Askari’ and othercultivars were developed from KeshmeshiSefid as result of muta-tions (Fig. 2).

3.4. Genetic structure inference

Analysis of genetic distance and population structure providedevidence for significant population structure in this set of geno-types. In all the resulting dendrograms, some conserved geneticrelationships were observed. The dendrograms constructed usingNJ based on shared allele and Kimura 2- parameters distance matri-ces revealed almost similar patterns. However, the dendrogramobtained using NJ algorithm and Kimura 2- parameters coefficient

provided better grouping for 73 grapevine cultivars (Fig. 3). AMOVAon different dendrogram cutting points revealed maximum differ-entiation when the genotypes assigned into five groups. The mostgenetic variation was observes among groups (∼74%) as compared

Dastarchin Askari

Mutation(a) (b)

BidaneQermez,KeshmeshiSefid, Rejin,

SefideShakhShakh, TabarzeSefid,

TabarzeQermez

KeshmeshiSefid

BidaneQermez, Rejin, SefideShakhShakh,

TabarzeSefid,TabarzeQermez

Fig. 2. Pedigree reconstruction of ‘KeshmeshiSefid’, ‘BidaneQermez’, ‘Rejin’,‘SefideShakhShakh’, ‘TabarzeSefid’ and ‘TabarzeQermez’. Two putative pedigreesare possible. (a) All the above mentioned cultivars are offspring of ‘Datarchin’ and‘Askari’. (b) KeshmeshiSefid being the progeny of a cross ‘Datarchin’ and ‘Askari’ andother cultivars were developed from KeshmeshiSefid as result of mutations.

H. Doulati-Baneh et al. / Scientia Horticulturae 160 (2013) 29–36 33

ased

w1mT

Fig. 3. Dendrogram depicting genetic relations of 73 grape cultivars b

ith within group diversity (∼26%). Groups 1–5 included 22, 8, 22,0 and 10 varieties, respectively. Cluster I consisted of 22 cultivarsostly with white berry color which are mainly used as table grape.

hese cultivars were grouped together with high bootstrap values

on Kimura-2-parameters distance and Neighbor-Joining alghorithm.

except for Atuaum and Makaii. In this group, Fakhri is a commercialcultivar which is mostly used as dry fruit in Iran in addition to freshconsumption. Early to medium maturity grape genotypes includ-ing some of the Iranian commercial cultivars such as Askari, Bidane

3 tia Ho

TFaSwba

tMtSR

4

rmmmoasloamS(1Caf

laSdge1iarM3L1larrt2saAbp

aAow

4 H. Doulati-Baneh et al. / Scien

abriz and Keshmeshi Sefid were grouped together in this cluster.our European grapes namely Barbera, Chardonnay, Cabernet Francnd Cabernet-Sauvignon along with Iranian local varieties fromardasht and Baneh regions (two origins of wild grape in the North-est of Iran) constructed another group. Genotypes with colored

erry and Muskat and AghMelhi with white berry were assigned to group.

Most of the putative synonymous varieties were groupedogether. High similarity was observed in cases of Rasha/Khoshnav,

oseli/Saglsolian-2 and KhaliliQermez/KhaliliSefid. In addition,he degree of genetic similarity was also high, for example, betweenhahroudi/Alhaghi, QaraMelhi/QaraShira, BolMazu/Angotka andasha/Khoshnav.

. Discussion

In Iran, a renewed interest in grapevine (V. vinifera) germplasmesources and analysis of genetic diversity have increased enor-ously during the past few years. We used a set of 23 SSRarkers distributed on 13 grape chromosomes to characterizeostly important grape genotypes in Iran. This et al. (2004) devel-

ped a coding strategy common based on a set of standard varietiesnd standardized method of scoring SSR markers for the analy-is of grapevine genetic resources to compare the data betweenaboratories easily. Reference data for six SSR markers and a setf internationally recognized cultivars and rootstocks are nowvailable at http://www.montpellier.inra.fr/vassal. The used set ofarkers in our study included five out of the six fully characterized

TMS loci: VVS2 (Thomas and Scott, 1993) VVMD5 and VVMD7Bowers et al., 1996) andssrVrZAG47 and ssrVrZAG62 (Sefc et al.,999). Besides five international grape varieties namely Barbera,abernet Franc, Cabernet-Sauvignon, Chardonnay and Muscat werelso included for a correct allele sizing and the production of trans-erable information among labs.

The level of polymorphism found for these materials at 23 SSRoci is comparable to that reported for other V. vinifera germplasmsssessed with SSR markers. However, factors such as number ofSR loci and repeat types, the methodologies employed for theetection of polymorphism as well as the number and nature ofenotypes analyzed have been reported to influence allelic differ-nces. Fatahi et al. (2003) resolved 4–16 alleles with an average of1.4 alleles per locus, in an Iranian grapevine collection compris-

ng 62 varieties. Najafi et al. (2006) profiled 136 Iranian genotypeslong with a set of 36 European cultivars at nine SSR loci andeported 6–12 alleles with an average of 9.33 alleles per locus.artín et al. (2003) detected 9–13 alleles per locus in analyses of

18 accessions of Spanish Vitis germplasm using six SSR markers.opes et al. (2006) with genotyping of 46 grapevine accessions at1 SSR loci identified 5–12 alleles, with a mean of 8.55 alleles per

ocus. In the other studies, Thomas and Scott (1993) reported 4–13nd Bowers et al. (1996) 6–11 alleles per locus. Jahnke et al. (2011)eported 12–25 allele at 19 SSR profile of 96 Vitis accessions (mainlyootstocks) from Hungary. Emanuelli et al. (2013) investigated pat-erns of molecular diversity at 22 common microsatellite loci in273 accessions of domesticated grapevine, its wild relative, inter-pecific hybrid cultivars and rootstocks and identified in total 499lleles ranged from 9 to 42 per locus, with an average of 22.68.

high level of gene diversity detected in grape genotypes coulde inherited from their wild ancestors and preserved due to theractice of vegetative propagation.

According to Martín et al. (2003), the PIC and effective number of

lleles are estimators of usefulness of SSRs for cultivar distinction.s assessed by number of allele, effective number of allele, numberf genotype, expected heterozygosity and PIC values, VMCNG2G7as detected as the most informative marker and VVS3 as the least

rticulturae 160 (2013) 29–36

informative one (Table 2). The cumulative probability of identityreported in this study is very smaller than those reported in theprevious studies (Lopes et al., 1999; Sefc et al., 2000) indicatingthe efficiency of the used SSR set in differentiation of Iranian grapegenotypes.

High level of genetic polymorphism was detected using 23microsatellite markers. High polymorphism allowed unique geno-typing of all the analyzed genotypes and only five markers withhigh PIC were sufficient for unambiguous identification of the Ira-nian grape genotypes. Amplification of 199 alleles in a set of 72accessions resulting in a large number of observed genotypes atthe most of microsatellite loci provided high discrimination valuefor varietal identification. Identification of 29 unique alleles indi-cates a high informative value of the SSR markers in our study. Mostof the unique alleles were found in the European genotypes indi-cating different genetic background of the Iranian and Europeangermplasm.

Under equal allele frequencies at a locus, the maximum the-oretical gene diversity will be obtained. Comparison of observedgene diversity at each locus with its corresponding maximum the-oretical value allowed assessment of the level of allelic imbalancepresent at each SSR locus (Maccaferri et al., 2003). Deficiency orexcess of heterozygote, measured by Wright’s (1978) fixation index(FIS), indicated deficiency or excess of heterozygosity in some ofthe SSR loci. An excess of heterozygous individuals may be causedby selection for yield and quality (Lopes et al., 2006) or natu-ral selection and also human selection against homozygosity ingrape plants during the course of domestication and cultivationof grapevines (Sefc et al., 2000). In contrast, a possible explanationfor deficiency of heterozygosity observed at some of the SSR locicould be due to the presence of null alleles at the locus (Lopes et al.,2006). Therefore, caution should be exercised when scoring theseloci, since heterozygosity is underestimated and segregation dis-torted. These alleles may be overcome by re-designing primers atdifferent locations when possible (Rallo et al., 2000). The proba-bility of null allele in the analyzed loci was very low or negative(Table 2). Hence, the assumption of homozygosity instead of het-erozygosity with a null allele could be considered adequate in thepresent study. In general, the very high level of heterozygosityobserved in this set of Iranian grape collection is similar to thatobserved for other sets of grape cultivars studied using SSR markers(Thomas and Scott, 1993; Bowers et al., 1996; Martín et al., 2003).This is consistent with the natural breeding system of the species(Bowers et al., 1996), and could be a consequence of both naturaland human selection against homozygosity in these plants (Sefcet al., 2000).

The genotypes analyzed in this study included commerciallygrown Iranian grapvines, and five reference cultivars from Europeincluding two of the world’s major wine cultivars (e.g., ‘Cabernet-Sauvignon’ and ‘Chardonnay’). Thus, our results are likely to beuseful in understanding the genetic evolution of grape and be ofvalue in some of the grape-growing regions of the world. Using thisset of markers, we could identify cases of synonymy, homonymyas well as misnaming. The identification of these ‘onyms’ not onlywill help to determine the true extent of genetic diversity in Iraniangermplasm, but could also be useful in a more accurate estimate ofvariety numbers as well as help the grape characterization projectin the country.

Based on our data, two putative pedigrees were identified forthese cultivars. The detected parentages were strongly supportedby high likelihood ratio values. For example, it is over 1014 timesmore likely that ‘RishbabaSefied’ is the progeny of ‘Rezghi’ and

‘RishbabQermez’ than any two random cultivars. Likelihood ratioscompare the probability of the observed genotype if the allelescame from proposed parents with the probability of the geno-type if the alleles came from two random parents or from close

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elatives of the proposed parents. Bowers and Meredith (1997)nd Vouillamoz et al. (2003) suggested calculation of cumula-ive likelihood ratios with the 95% upper confidence limits forhe allele frequencies to compensate sampling errors for lociith a smaller number of cultivars. In our study, the cumulative

ikelihood ratios were also calculated and highly supported thedentified parentages versus any two random cultivars. The val-es become lower when one of the suggested parents is assumednd the other parent is a close relative to the second suggestedarent because close relatives share many alleles with the puta-ive parents. Morphological evidences such as fruit characteristicsell supported the offspring–parents relationships identified by

SR data. However, further efforts should be made in order todentify the exact parents of other commercially important culti-ars.

Genetic diversity has been previously analyzed in Iranianrapevine germplasm using DNA markers (Fatahi et al., 2003; Najafit al., 2006; DoulatyBaneh et al., 2007), but no genetic struc-ure has been previously documented. The grapevine genotypesampled showed significant differentiation into five groups. Theeep genetic structure is, in part, a legacy of structure in ancestralrapevine populations. Both breeding system and domesticationistory have had large effects on the structuring of diversity in theenotypes analyzed. The domestication of grape seems linked tohe discovery of wine, even if it is unclear which process predatedhe other (This et al., 2006). The earliest evidence of wine produc-ion was found in Iran at the Hajji Firuz Tepe site in the northernagros mountains circa 7400–7000 BP (before present) (McGovern,004). During domestication, the biology of grapes underwent sev-ral dramatic changes to ensure greater sugar content for betterermentation, greater yield and more regular production (This et al.,006) which resulted in genetic structural changes of differentermplasms.

. Conclusion

In the present study, we used SSR markers to investigate theenetic relationships and origin of some Iranian grape cultivars.ranian grape germplasm consisted of a very large number of acces-ions and in most cases their ancestors remain largely unknown.dentification of grape varieties by the use of classical ampelo-raphic data is sometimes afflicted by misinterpretations due tonvironmental influence. Therefore, the combination of our molec-lar data with ampelographic data would provide unambiguous

nformation for identification of the existing Iranian germplasmnd could also be used for legal protection of cultivars. This worklso shows that the characterization of germplasm collectionsy means of SSRs is very useful in their management and the

dentification of parent/progeny relationships. It is expected thatranian grapevine germplasm analysis using molecular markers

ill increasingly depend on well-characterized unrelated geneticesources through the definition of core-collections. Using thisramework of genetically defined populations, it may be possibleo exploit the grapevine gene pools more effectively with popula-ion genetics-based approaches using the extensive collections ofrape genetic resources. In particular, different subpopulations areikely to provide differing levels of resolution for association map-ing studies as well as different allele frequencies associated withesirable traits for plant improvement.

cknowledgements

This work was supported by the Laboratory of Genomics andlant Molecular Breeding, Center of Excellence for Cereal Molecularreeding, University of Tabriz, Tabriz, Iran.

rticulturae 160 (2013) 29–36 35

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