Genetic diversity in Indian cucumber based on microsatellite and morphological markers

Biochemical Systematics and Ecology 51 (2013) 19–27

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Genetic diversity in Indian cucumber based on microsatellite and morphological markers Sudhakar Pandey a, *, Waquar Akhter Ansari b, Vinay Kumar Mishra a, Asheesh Kumar Singh a, Major Singh a a b

Indian Institute of Vegetable Research, Post Box-01, P.O.-Jakhini (Shahnshahpur), Varanasi 221305, Uttar Pradesh, India Department of Botany, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India

a r t i c l e i n f o

a b s t r a c t

Article history: Received 9 January 2013 Accepted 4 August 2013 Available online

Genetic variation among 44 cucumber accessions was assessed using morphological and SSR markers. High genetic variability was observed for days to 50% female flowering (37– 46 days from sowing), number of fruits per plant (1.4–6.0), individual fruit weight (0.04– 0.552 kg) and root length (14.25–32.8 cm). The pair-wise Jaccard similarity coefficient ranged between 0.25 and 0.85 indicating that the accessions represent genetically diverse populations. The allelic diversity of polymorphic markers ranged from 0.001 to 0.9396 with an average of 0.31 based on polymorphic information content. The clustering pattern of SSR markers was not in consonance with the groupings based on quantitative traits. The accession of Indian state i.e.; Madhya Pradesh and Uttar Pradesh were diverged from the accessions of other parts of India. The study provides information for future exploration and collection of cucumber germplasm in India and utilization of diverse germplasm for developing cultivars/hybrids for specific traits. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Cucumber Dendrogram SSR Genetic relationship Morphological markers

1. Introduction Cucumber (Cucumis sativus L. 2n ¼ 2x ¼ 14) is one of the most important vegetable crops and belongs to family Cucurbitaceae (Jeffery, 1980). Global production of cucumber was 57.55 million tons in 2010, of which w26% (15.17 million tons) was produced in India (FAOstat, 2012 at http://faostat.fao.org/). The productivity of cucumber in India is 6.34 mt/ha as compared to the average world productivity of 30.23 mt/ha. Cucumber is indigenous to India (Sebastian et al., 2010) and varies in terms of morphological characters such as growth habit, fruit size and colour (Staub et al., 1997). As a result of continued selection, a large number of landraces and forms with restricted local distribution have been accumulated in different growing areas. Although there is substantial variation in the morphology of cucumbers, little is known about the genetic diversity of this species in India. Assessment of genetic diversity based on phenotypes has limitations, since most of the morphological characters are greatly influenced by environmental factors and developmental stage of the plant. Among the PCR based molecular markers, SSR markers are neutral and in general have high level of transferability, therefore, such markers are significantly valuable. Different types of molecular markers, viz.; random amplified polymorphic DNAs (Horejsi and Staub, 1999), amplified fragment length polymorphisms (Li et al., 2004), inter-simple sequence repeats (Wang et al., 2007), simple sequence repeats (Danin Poleg et al., 2001) and expressed sequence tagderived SSRs (Hu et al., 2010) have been used for the assessment of genetic diversity in cucumber. There are a few

* Corresponding author. Tel.: þ91 (0)542 2635247; fax: þ91 (0)5443 229007. E-mail address: [email protected] (S. Pandey). 0305-1978/$ – see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.bse.2013.08.002

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reports of assessment of genetic diversity of cucumber in India using ISSR (Parvathaneni et al., 2011) and RAPD and ISSR markers (Manohar et al., 2012). In this study, 70 SSR markers evenly distributed across the cucumber genetic map (Ren et al., 2009) coupled with 10 morphological traits were used to analyze genetic relationships among 44 cucumber germplasm/cultivars collected from different regions of India. 2. Materials and methods 2.1. Plant materials and DNA extraction In this study, a total of 44 cucumber accessions (33 from India and 11 exotic collections) were used (Table 1). These genotypes are maintained at Division of Crop Improvement, Indian Institute of Vegetable Research (I.I.V.R.), Varanasi, India. The genomic DNA of all accessions was extracted from unexpanded young leaves by CTAB method (Doyle and Doyle, 1987) with minor modifications. Table 1 Details of cucumber accessions used in the present study. S. No

Germplasm name

Country

Source (place of collection)

Agro-climatic zonea,b in India

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

BAM-HR-115 BAM-HR-135 BAM-HR-134 BAM-HR-136 BAM-HR-114 BAM-HR-129 BAM-HR-127 BAM-HR-120 BAM-HR-117 BAM-HR-116 BAM-HR-132 BAM-HR-133 BAM-HR-119 BAM-HR-501 BAM-HR-122 BAM-HR-137 BAM-HR-103 BAM-HR-121 BAM-HR-128 BAM-HR-101 BAM-HR-504 BAM-HR-130 BAM-HR-131 BAM-HR-123 BAM-HR-108 BAM-HR-105 BAM-HR-118 BAM-HR-125 BAM-HR-139 BAM-HR-104 BAM-HR-106 BAM-HR-502 BAM-HR-112 BAM-HR-102 BAM-HR-111 BAM-HR-126 BAM-HR-107 BAM-HR-110 BAM-HR-109 BAM-HR-113 BAM-HR-124 BAM-HR-503 BAM-HR-140 BAM-HR-138

India India Holland India India India India India India India India India Bulgaria India USA India India India India India India India India India USA India Japan Srilanka India India Canada India Bulgaria USA India Bulgaria India India India India India India India Srilanka

Maharashtra Karnataka Holland Jharkhand Tamilnadu Tamilnadu Kerala Maharashtra Bihar Chhattisgarh Uttar Pradesh Odisha Bulgaria Karnataka USA Karnataka Uttar Pradesh Jharkhand Maharashtra Jharkhand Madhya Pradesh Madhya Pradesh Jharkhand Himachal Pradesh New York Kerala Japan Srilanka Rajasthan Karnataka Canada Tamilnadu Bulgaria USA Jharkhand Bulgaria Jharkhand Maharashtra Jharkhand Jharkhand Odisha Andra Pradesh Karnataka Srilanka

VII VIII – IV VIII VIII VIII VII IV V IV V – VIII – VIII IV IV VII IV VII VII IV I – VIII – – VI VIII – VIII – – IV – IV VII IV IV V V VIII –

a

Singh et al. (2009). States under each zone and geographical region mentioned in parenthesis: Zone I – Jammu & Kashmir (J & K), Himachal Pradesh and Uttaranchal (Humid Western Himalayan Region), Zone IV – Punjab, U.P., Jharkhand and Bihar (Sub-Humid Sutlej Ganga Alluvial Plain), Zone V – Chattisgarh, Orissa and Andhra Pradesh (Sub-Humid to Humid Eastern and South Eastern Uplands), Zone VI – Rajasthan, Gujarat, Haryana and Delhi (Arid Western Plain), Zone VII – Madhya Pradesh and Maharashtra (Semi-Arid Lava Plateau and Central High Lands), Zone VIII – Karnataka, Tamil Nadu and Kerala (Humid to Semi-Arid Western Ghats and Karnataka Plateau). b

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2.2. Field evaluation and data collection Field evaluation of 44 cucumber accessions was conducted at Research Farm, I.I.V.R., Varanasi, India. The experiment was laid out in a complete randomized block design (CRBD) having three replications with a row-to-row spacing of 100 cm and plant-to-plant spacing of 60 cm; each replication had 10 plants. Recommended fertilizer dose and standard cultural practices along with plant protection measures were followed (De et al., 2003). Five plants were randomly chosen and tagged to record data on 10 quantitative traits, viz., days to 50% male flowering, days to 50% female flowering, number of primary branches per plant, vine length (cm), number of fruits per plant, average fruit weight (kg), polar circumference of fruits (cm), equatorial circumference of fruits (cm), root length (cm) and yield per plant (kg). The number of primary branches was recorded before last harvesting of the marketable fruit. Fruits were harvested at edible maturity for recording the data. The 2-year pooled data for quantitative traits were analyzed (Gomez and Gomez, 1984). The effects of different scales of measurement for different quantitative traits were minimized by standardizing the data for each trait prior to cluster analysis; the STAND module of NTSYSpc (Rohlf, 1998) software was used to achieve the same. Pair-wise distance matrix was used as an input for analysis of clusters. UPGMA-based clustering was done using SAHN module of NTSYSpc. 2.3. PCR amplification A total of 70 microsatellite markers were selected across the cucumber genetic map synthesized and used for DNA amplification from 44 genotypes. The PCR reaction mixture (15 ml) consisted of 1.5 ml 10 buffer, 0.6 ml MgCl2 (25 mM), 0.18 ml dNTP (25 mM), Taq DNA polymerase 0.3 ml (5 U/ml), 9.42 ml ddH2O, 1 ml, forward primers (10 pm), 1 ml, reverse primer (10 pm) and 1 ml (40 ng) genomic DNA. All the reagents were procured from MBI Fermentas, USA. The amplification was carried out in a thermal cycler (Dyad, Bio-rad, USA) at initial denaturing step at 94  C for 4 min followed by 36 cycles of 94  C for 1 min, 50– 58  C for 1 min and 72  C for 1 min. In the last cycle, primer extension was performed at 72  C for 10 min and storage at 4  C till electrophoresis. The amplification products were separated in 2.5% agarose gel, and after electrophoresis the products were visualized in a gel documentation system (Alfa Imager 2200, Alfa Innotech Corporation, California). The 100-bp DNA ladder (Fermentas) was used as molecular size marker. 2.4. SSR data analysis The genomic DNA fragments from SSRs generated clear and unambiguous bands of various molecular weight sizes were scored for the presence (1) and absence (0) of the corresponding band among the accessions in the form of binary matrix and the data matrix was subject to further analysis using NTSYSpc version 2.11W (Rohlf, 1998). The SIMQUAL program was used to calculate the Jaccard’s coefficient (Jaccard, 1908). The similarity matrix was computed using SSR markers based on Jaccard’s coefficient following the UPGMA methods using SHAN programme of NYSYSpc to estimate similarity indices and genetic relatedness among the 44 cucumber accessions. The similarity index (SI) values were computed as a ratio of number of similar bands to the total number of bands in pair wise comparison of the accessions. A dendrogram was constructed using the unweighted pair group method with arithmetic mean (UPGMA) cluster algorithm. Polymorphic information content (PIC) of each SSR markers was calculated as per the formula: PICi ¼ 1  SPij2. Where, PICi is the polymorphic information content of a marker i; Pij is the frequency of the jth pattern for marker i and the summation extends over n patterns. PCA was also done to check the results of UPGMA based clustering using EIGEN module of NTSYSpc. 3. Results 3.1. Performance of different accessions based on morphological parameters The mean value of selected vegetative and fruit characteristics of the accessions is given in Supplementary Table S1. The days to 50% female flowering indicated the earliness of the accessions. The accession BAM-HR-138 took minimum (31) days and BAM-HR-140 took maximum (44) days for 50% male flowering. BAM-HR-105 was very early (37 days after seed sowing) in 50% female flowering, whereas, BAM-HR-132 was late (47 days after seed sowing). The number of primary branches was the maximum (5) in the accession BAM-HR-125. The accession BAM-HR-133 exhibited maximum average fruit weight (0.552 kg), while maximum number (6) of fruits per plant was recorded in the accession BAM-HR-503. The equatorial (56.6 cm) and polar (26.4 cm) circumference of fruit was maximum in accessions BAM-HR-503 and BAM-HR-133, respectively. Root length was found highest in the accession BAM-HR-502 (32.80 cm), while the vine length (220.0 cm) was found maximum in BAMHR-124. The maximum yield per plant was recorded in BAM-HR-124 (1.927 kg) followed by BAM-HR-503 (1.879 kg). 3.2. Genotype cluster analysis based on quantitative traits The taxonomic distance matrix of 10 quantitative traits was employed for cluster analysis of the 44 accessions (Fig. 1). All the cucumber accessions grouped into four major clusters, the longest branch separated BAM-HR-504 from the rest of the accessions at a taxonomic distance of 0.04–0.27 and this accession was the lone member of the cluster IIB. The first cluster (cluster I) was consisted of two accessions, viz.; BAM-HR-115 and BAM-HR-124. The second major cluster (cluster II) was

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S. Pandey et al. / Biochemical Systematics and Ecology 51 (2013) 19–27

Fig. 1. Genetic relationship among the 44 cucumber accessions based on 10 quantitative traits using UPGMA cluster analysis of the distance matrix.

consisted of 24 accessions. This cluster could be further divided into two sub-clusters, sub-cluster IIA (13 accessions) and subcluster IIB (11 accession). The third cluster (cluster III) had 12 accessions, whereas the fourth (cluster IV) had only 6 accessions. 3.3. Characterization of SSR loci Out of the 70 SSR markers, 53 produced polymorphic patterns in at least two accessions rest of the 17 markers produced monomorphic products (Table 2). A total of 163 amplification products were obtained. The mean number of alleles per locus was 3.05, and the size of amplified products ranged from 126 bp (SSR02123) to 498 bp (SSR13189). The average PIC value was estimated to be 0.31, and in terms of PIC values, SSR05737 (0.9396), SSR12810 (0.8957) and SSR15477 (0.8750) were recorded to be most informative. Marker SSR06670 recorded the least PIC value of 0.001 (Table 2). Pair-wise comparison was performed among all the accessions. Jaccord’s similarity coefficients calculated from SSR data varied from 0.16 to 0.85 with a mean value of 0.56. The highest similarity coefficient (0.85) was observed between accessions BAM-HR-107 and BAM-HR-126, whereas, it was the lowest between BAM-HR-128 and BAM-HR-115. 3.4. Cluster analysis based on SSR markers The dendrogram based on Jaccord’s similarity coefficient revealed that 44 accessions were grouped into five major clusters consisting of 37, 1, 3, 1 and 2 genotypes, respectively (Fig. 2). Cluster I could be further sub-divided into three sub-clusters; IA (6 accessions), IB (12 accessions) and IC (19 accessions). The accessions BAM-HR-503 and BAM-HR-119 were not grouped to any cluster and remain separated at similarity coefficient 0.45 and 0.32, respectively. The dendrogram showed a close similarity among BAM-HR-126 and BAM-HR-107, and BAM-HR-102 and BAM-HR-111. The accessions BAM-HR-128, BAM-HR-137, BAM-HR-119 and BAM-HR-504 were separated from BAM-HR-115, BAM-HR-135 as they fall either side of the dendrogram, indicating a high genetic diversity among these six accessions. The plant materials of this study represented 13 major states of

S. Pandey et al. / Biochemical Systematics and Ecology 51 (2013) 19–27

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Table 2 Primers, chromosome location, sequence information, fragment size and repeat motiff. Number of alleles and PIC of 53 SSRs used in this study are presented. Sl. No.

Oligo no.

Chromosome no.

Primer sequence 50 -30

Number of alleles

PIC

Fragment size

SSR motiff

1.

SSR01115

1

5

0.1968

215

(TACA)15

2.

SSR04805

1

4

0.0610

139

(GAC)9

3.

SSR03593

2

4

0.0274

210

(ATT)8

4.

SSR05492

2

4

0.1627

132

(AG)15

5.

SSR 18362

2

3

0.5026

203

(AAT)7(A)27

6.

SSR16028

2

3

0.3011

203

(CT)14(TA)14

7.

SSR12810

2

2

0.8957

177

(ATT)13

8.

SSR04035

2

3

0.1307

162

(TC)14

9.

SSR11909

2

2

0.2138

209

(ATT)18

10.

SSR13131

2

4

0.1271

209

(GAA)13

11.

SSR16183

2

3

0.3135

153

(AT)21

12.

SSR12227

2

4

0.7273

176

(TGTTTT)7

13.

SSR01253

2

3

0.1142

219

(ATC)8

14.

SSR12730

2

2

0.5434

136

(TC)29

15.

SSR30665

2

2

0.3667

165

(TCT)16

16.

SSR12682

2

3

0.1472

192

(AT)18

17.

SSR05830

3

3

0.2350

163

(GGAACG)6

18.

SSR22273

3

2

0.1317

164

(TA)19

19.

SSR05757

3

3

0.1049

218

(TTC)8

20.

SSR22071

3

3

0.3910

204

(AAG)8

21.

SSR10783

3

3

0.0517

174

(GAA)12

22.

SSR16301

3

4

0.0393

207

(AAAAG)5(AAG)8

23.

SSR00670

3

3

0.0010

156

(GAA)8

24.

SSR05737

3

2

0.9396

185

(CT)17

25.

SSR07782

3

3

0.3977

203

(GAA)8

26.

SSR 19511

3

5

0.0289

133

(GAAA)8

27.

SSR15419

3

3

0.1643

213

(CTTCCA)8

28.

SSR 19493

3

2

0.2924

219

(GAAGG)7

29.

SSR19430

3

3

0.4117

190

(AAG)11

30.

SSR16936

4

4

0.0274

216

(GGGCAT)6

31.

SSR13159

4

5

0.2051

203

(AG)18

32.

SSR05125

4

2

0.7262

220

(GAAA)7

33.

SSR22172

5

2

0.3285

200

(ATA)14

34.

SSR13340

5

F: ATTCCCAATCCCAAAAAGGT R: CTCCTCCTCCAATGAGCAAG F: TCATGTCAAGCGAAGGAAGA R: TACTGTCCGAACGTGTTCCA F: GATGATCAAATTTACAATCTTGCC R: GGCAGCCTAATTAGAATAACTCAGA F: GCAACCATTCTTTACTGTGCC R: AGGGCATTTCAAGACAAACC F: CAAGTGGACAAATATGAGCCAA R: TTCGTTTCGCAGAGTGATTG F: TTACCTTCCCCACCCTAACC R: TGACTTTTTGGGGAAACCCT F: TTCCCACAAAACAAATCTTGG R: TTTTGGAGAGAAAAGGTTGGA F: TCCGCTTCGAGTACGCAT R: ACAAGAATGCTGGAGATGGC F: AATAATACCAGTGGCCCCATC R: AAAGCTCCCTCCTCCCCTAC F: AAAGCAGAGTATGGCATGGG R: AAAAGCCAAAGAACCCAACA F: GGAGAAATTTGATGGTGTAGCC R: TTGCAAATCTCTAATACTTTGCCTT F: GGCATCGGTGAGTACCAACT R: TTTCTCCTCCTTGGCCATAA F: CGCTGGATTTGTTTGTGAAAT R: AATGTCGGGGAGTGTCACAT F: CGGTTTTTGAATTGTCTTCCC R: ATCCCGACAGTCTCTGAAGG F: AATTCCTGCTAAACCACCCC R: GGTTTGTTGAAGCCGAAAAA F: ATCCAACCCAATCCAAATCA R: GTTGTGAGCTTAAGGGCGAG F: TTTCGTTGTGCTCAGTGGAG R: ACACCTTTCTTTCAC CCCCT F: AGAACAAGAACCAGCACCGT R: ACCAACCAATCAACCAGAGC F: GGGGGAAAGAACCTGAAAGA R: ACCCACATTTCCCCATTTCT F: GCTGCTTGAATCGGTTCTGT R: GAGGAGGTAAATCATGCTCCA F: TGGGAAAATGGGAGTTTCAA R: CGAACCACCAGTATTGGACC F: TTCTCCCTAAAAAGAACCCAA R: TTCATGGTGGGAGAGATTGA F: TCTCAAACCAAGAATTGGGG R: TCCATGGAAGCAGATCTTAAAAA F: TTGCCTTCGTAAGCAAAAA R: GAAGTAAATGGGTTGGACGC F: GGGTTCGAGAAGTTGGTTGA R: AAAGCTCTGCATCCACCATT F: TGGCGTTGCTAATTGATTGA R: ACCCGATTCGTAAGATCGTC F: ATGGCAAAGCCAAAAAGATG R: TGTTGCAAATATTGCACCTTC F: AAGAGGCCAGAGATGGATGA R: GCCAAAAATAGGCCCAAAGT F: TATGGCGAAGAAGCTTTGCT R: AGGGGGATCTTGCCTCTAAA F: TAGGCCGAGTTGGTGCTAGT R: GCTGCATTTGGAATCCTTGT F: TCAACCATTGTTTGGGTCAA R: TGGGTGCTTGCTAATGTGAA F: TGGCTCCTTCACATTGTTGT R: TGGGAAAAAGGGTATGGAAA F: CAACAATGAGTGGAATGACATTTT R: GACTTGGCTTTGGGATTCAA F: GCTTTACATGGCTTCTCCCA R: TTTTCCTTGGCGACGATTAG

3

0.1880

139

(AT)21 (continued on next page)

24

S. Pandey et al. / Biochemical Systematics and Ecology 51 (2013) 19–27

Table 2 (continued ) Sl. No.

Oligo no.

Chromosome no.

Primer sequence 50 -30

Number of alleles

PIC

Fragment size

SSR motiff

35.

SSR06660

5

2

0.8652

197

(AG)14

36.

SSR21918

5

3

0.1312

168

(TC)19

37.

SSR05819

5

2

0.2014

214

(ATT)8

38.

SSR03514

5

3

0.2955

154

(TC)14

39.

SSR 18377

5

5

0.1183

220

(AAG)12

40.

SSR16842

5

2

0.6710

144

(AGA)11

41.

SSR 18949

6

2

0.1689

199

(ATGGGC)6

42.

SSR15238

6

3

0.1668

196

(TC)19

43.

SSR21186

6

3

0.5398

196

(ATT)12

44.

SSR00973

6

2

0.5594

133

(TA)14

45.

SSR04910

6

3

0.1384

149

(CTT)11

46.

SSR02123

6

3

0.0124

132

(AAG)10

47.

SSR13251

6

4

0.4912

162

(AAG)14

48.

SSR06632

6

2

0.1312

206

(TC)14

49.

SSR00083

6

5

0.4587

130

(GAA)8

50.

SSR13189

7

6

0.4685

220

(TA)17

51.

SSR33278

7

3

0.3357

211

(TTTA)7

52.

SSR15477

7

2

0.8750

213

(AAAG)12

53.

SSR11742

7

F: GATCGTTGCAAAACTCACGA R: CGATTGACAGTTCGCTGAAA F: TGCCACATTTGTATCCTCCA R: GAGAGAAATGGGGAAAAGGC F: AGATGCCAACTGATCTGCCT R: CCAACCACGGTAGTTTCGAG F: TAGGGTCCCCTTCCCTCATA R: GGGTACCCAAAAGCAAGTGA F: GCCATGGATGATGGAGTTTT R: TCCCTTTCTCTCTGTTTTCCC F: TGGGGTTAGGGTTTCAAGTG R: CAAGCTCTTCCTCAACGGAC F: CTATGGGATCTGGCTCTGGA R: GGGTTCACGCCAGTATTGTT F: TGGGAGACAATTTATCAGTCCA R: TGGTCTTCCTTATGCAAGCTCT F: TTTGAGCAACACTTCGCAAC R: GCATGTTTTCATGTCATTGGA F: TTGGGGCTGTTCTAATTTCG R: TCGTTGTTGAAGCCAAAGAA F: CAACACCCATTCATTGACAAA R: TCTGCAAAGCTCAGAAGCAA F: TGGAAAATGACAGCAACCAA R: CCATTCTTCCTTTCCACGAA F: GGTCAATCCAAAAGAGAAAGCA R: ATCAACACCATTGACGACCA F: TCAGATGTTGATTGGCTCTCA R: AGGGCCAACATTAAAGGGTC F: GGCTGTGGAGTTCAAAGAGG R: GGGAAGTCAAAATGTTTGCG F: GCAGTCATTTTGGCGATACA R: GCTGTGTCAGAAGCTCACAACT F: GCAAACGCAATTAAAACACG R: GTTGGAATGAGGGAGTGAGC F: CTGCCATTTCTGGGTTTGAT R: AATTCTTCTGGGAATGGCCT F: GCTATCCCCAAGGATGATGA R: AGCTTGGCTTCGTCTTTTGA

2

0.3156

217

(AG)30

Primer sequence information, chromosome location information, fragment size and repeat numbers of motifs shown were in the Chinese cucumber inbred line 9930 (Ren et al., 2009). PIC ¼ Polymorphic Information Content; PCR ¼ polymerase chain reaction; SSR ¼ Simple sequence repeat.

India covering 6 agro-ecological zones based on the All India Coordinated Research Project on Vegetable Crops (AICRP-VC) classification (Table 1; Singh et al., 2009). The detail of geographical area and states under each zone is appended below Table 1. The random grouping of genotypes was observed in the dendrogram, while genotypes of Madhya Pradesh (MP), Andhra Pradesh and Uttar Pradesh (UP) were separated on the basis of their on the geographical origin. Almost, all the accessions from Jharkhand (except BAM-HR-101), TamilNadu, UP, Odisha, Rajasthan, Maharashtra (except BAM-HR-128), Karnataka (except BAM-HR-137), Kerala, Himachal Pradesh, Chhattisgarh, and Bihar were grouped into cluster I. The clustering pattern of cucumber genotypes based on SSR markers was random but not in consonance with the groupings based on quantitative traits. PCA revealed that PC 1, PC 2 and PC 3 were accounted for 28, 22 and 15% of the total variation, respectively. Together, the first three PCs accounted for 66% of the total variation. Two dimensional (2-D) plot was prepared using the first two PCs. This analysis supported the grouping of the accessions in 5 clusters. Most of the accessions from sub-cluster IA, IB and IC were grouped into two major clusters in 2-D plot. The accessions BAM-HR-128 and BAM-HR-137 from cluster V were distantly separated in PCA (Fig. 3). 4. Discussion In this study, 44 cucumber accessions representing 13 major states of India and covering 6 agro-climatic zones were studied. The morphological traits revealed a considerable variability in flowering behavior, growth habit, and fruit characters. The appearance of male flowers was early compared to the female flowers except in the accession BAM-HR-113. The BAM-HR113 bore predominantly female flowers, and BAM-HR-105 was the earliest female flower bearing line. Both the lines grouped together in the clusturing based on morphological traits. Variation in growth habit based on vine length was of three types, viz.; short, medium and tall. The maximum yield was recorded in the tallest accession BAM-HR-124, which may be due to more number of internodes bearing female flowers. The variation in fruit traits was significant but comparatively low.

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Fig. 2. UPGMA dendrogram of 44 genotypes of cucumber based on the 53 SSR primers. Groups II and IV each contains a single genotype.

The allelic diversity of the polymorphic markers ranged from 0.001 (SSR6670) to 0.939 (SSR12810) with an average of 0.31. The level of polymorphism observed in this study is similar to the report of Watcharawongpaiboon and Chunwongse (2008). Overall, this study revealed low genetic diversity (3.05 alleles per polymorphic locus) in 44 cucumber germplasm. Possibly this is due to lack of polymorphism. Similar low levels of polymorphism were reported by Staub et al. (1997) in collection of Indian cucumber. The coefficient of Jaccord’s similarity ranged from 0.25 to 0.85 with a mean of 0.55. The genetic distance matrix between BAM-HR-134 and BAM-HR-114 agreed well with consensus dendrogram. Escribano et al. (2008) used SSR markers with 31 Spanish melons and reported genetic diversity between 0.14 and 0.83. Weng (2010) used SSR markers and reported genetic distance between melon and cucumber (0.933), C. metuliferus and melon (0.897), and between C. metuliferus and cucumber (0.954). BAM-HR-115 and BAM-HR-503 were found to be the most diverse genotypes based on quantitative traits and SSR markers. But non-significant correlation was observed between the Jaccord’s similarity based on SSR and average taxonomic distance based on quantitative characters. This is in agreement with the reports that morphological character based dissimilarity and Jaccord’s similarity based on molecular markers usually showed non-significant correlation (Riday et al., 2003; Pandey et al., 2008). In this study, no correspondence was observed between the clustering based on morphological traits and molecular markers. Such mismatch may be attributed to long-term differential selection for variety improvement. Ortiz (1997) and Pandey et al. (2008) also reported poor relation in the diversity based on morphological characters and molecular markers. The UPGMA-based clustering of molecular data revealed 5 clusters and in general, the results of PCA were in agreement to the UPGMA analysis, with an exception that one accession BAM-HR-503 was very close to cluster I in PCA. Messmer et al. (1993) suggested that in order to extract maximum information, coordination methods (PCA & PCoA) could be used in combination with cluster analysis, particularly when the first two or three PCs explain more than 25% of the variations. In the present study, first three PCs accounted for 66% of the total variation. Similar results on agreements between PCA and UPGMA-based clustering has previously been reported in Turkish melons (Sensoy et al., 2007) and cucumber (Hu et al., 2010). The 11 exotic collections (except for one collection from Bulgaria) were grouped within cluster I, which was separated alone in cluster IV based on molecular data. The clustering patterns of introduced accessions indicated that the germplasm collected from UP and Rajasthan states during Indo-US joint expedition have been re-introduced to India from USA. The accessions from MP and UP were grouped, separately. A similar finding was reported by Staub et al. (1997) that landraces from Rajasthan, MP and UP were genetically variable. The grouping of accessions from 11 Indian states into one cluster indicate that cucumber has relatively narrow genetic base, therefore efforts are needed to conserve highly variable accessions in order to

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Fig. 3. Depiction of genetic relationships among cucumber accessions of diverse origin using principle component analysis (PCA), as estimated by 53 SSR loci. The number of accessions corresponds to the order of the genotype given in Table 1.

minimize the genetic erosion. Long back such genetic erosion of cucumber germplasm in India was documented during the 1992 Indo-US expedition (Staub et al., 1997). Identification of genetically distinct group of accessions of MP and UP indicated that variation exists in cucumber germplasm. Lv et al. (2012) reported that the genetic back ground of Indian germplasm is heterogenous and maintained a large proportion of genetic diversity. In general the study revealed a low molecular diversity among the Indian cucumber accessions. Although, genetic erosion to biodiversity mainly due to cultivation of improved varieties has resulted in better productivity and quality, but it has resulted to alarming state of narrow genetic base. A diverse collection of cucumber accessions may provide an opportunity to broaden the genetic base and a boost to current breeding program.

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