7.2
CiteScore
3.7
Impact Factor
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
ABUNDANCE ESTIMATION IN AN ARID ENVIRONMENT
Case Study
Correspondence
Corrigendum
Editorial
Full Length Article
Invited review
Letter to the Editor
Original Article
Retraction notice
REVIEW
Review Article
SHORT COMMUNICATION
Short review
7.2
CiteScore
3.7
Impact Factor
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
ABUNDANCE ESTIMATION IN AN ARID ENVIRONMENT
Case Study
Correspondence
Corrigendum
Editorial
Full Length Article
Invited review
Letter to the Editor
Original Article
Retraction notice
REVIEW
Review Article
SHORT COMMUNICATION
Short review
View/Download PDF

Translate this page into:

Original article
31 (
4
); 780-788
doi:
10.1016/j.jksus.2018.04.022

Assessment of genetic diversity in 29 rose germplasms using SCoT marker

, , , , ,
a Bioinformatics Infrastructure Facility, University of Rajasthan, Jaipur 302004, India
b Department of Botany, University of Rajasthan, Jaipur 302004, India
c Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur 302006, India

⁎Corresponding author at: Department of Botany, University of Rajasthan, Jaipur, Rajasthan, India. kachhwahasumita@rediffmail.com (Sumita Kachhwaha)

Received: , Accepted: ,
© The Author(s) 2018
Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Peer review under responsibility of King Saud University.

Abstract

Roses are well known for commercial floriculture and greatly used in the field of perfumery, soap, cosmetics, jams & jelly and essential oil production. Due to interspecific hybridization, a large number of hybrids and cultivated varieties of rose are recognized which reveals distinguishable features in flowers such as size, shape, and color. Apart from this, the geographical distribution and polyploidy also make Rose genus more complex. Therefore, the present study was undertaken for the identification and characterization of genetic variation within 29 rose accessions through Start codon targeted polymorphism (SCoT) markers. Out of 36 primers, 32 revealed polymorphic amplification profile in 29 rose accessions with amplification ranging from 150 bp to 1.2 kb. A total of 299 polymorphic amplicons were obtained, ranging from 4 to 19 amplicons with an average of 9.34 amplicons per primer. The polymorphic information content (PIC) ranged from 0.42 to 0.92 with an average of 0.78. The dendrogram was constructed to establish genetic relationship among 29 different accessions using Neighbor-joining and Nei-Li matching coefficient. The distinguishable genetic background and a high degree of variation in the rose genotypes successfully exhibited by the SCoT markers may serve as a valuable aid in Rose improvement strategy.

Keywords

Rose
Morphology
Genetic diversity
SCoT
Polymorphism
1

1 Introduction

Rose is considered as “Queen of the Flowers”, belonging to the Rosaceae family. It is woody perennial flowering plant grown all over the globe, especially sub-tropical and temperate regions of the northern hemisphere (Werlemark and Nybom, 2010). Rose flowers are large & showy, vary in size & shape and are being utilized for commercial perfumery, essential oils production, commercial cut flower, as landscape plant, for hedging and other utilitarian purposes (Akond et al., 2012). Generally, the rose petals are used for rose oil production. About 3000 kg of rose petals can produce one kg of rose oil (Baser, 1992; Baydar and Baydar, 2005). In addition to rose oil, some important base materials for the cosmetic industry such as rose concrete, absolute and rose water are also obtained from Rosa chinensis and Rosa canina (Baydar et al., 2004). Rose hip seed oil is employed in various skin care and cosmetic products. Apart from its beautification application, there are several uses of rose viz. Rose hips of Rosa canina is used in making soup, jam, and jelly because of its high vitamin-C content. Rosa chinensis is used for stomach problems as well as in controlling cancer growth (www.Pfaf.org).

Through interspecific hybridization, a large number of hybrids and cultivated varieties have been developed which differ in color ranging from yellow and white to many shades of red and pink with single or double blooms. Due to the allopolyploidization and hybridizations, the number of rose varieties has reached to approximately 25,000 which make it difficult to classify ‘Rosa’ genus and wild type of some modern roses (Azeem et al., 2012; Zhang and Gandelin, 2003). Although, Rosa genus has a broad and overlying territory of morphological deviations that are influenced by the environmental circumstances, so the classification based on morphological data only, is not sufficient. Many researchers classify rose varieties on the basis of their morphological characteristics like flower weight, flower diameter, peduncle length, number of petals, number of stamens and oil content (Kaul et al., 2009; Panwar et al., 2010; Riaz et al., 2011; Tabaei-Aghdaei et al., 2007; Zeinali et al., 2010).

Moreover, biochemical markers as chemotaxonomic analyses of roses based on a vast range of polyphenolic compounds have also been reported in relation to identify rose taxonomy amongst the different rose species (Mikanagi et al., 1993; Okuda et al., 1992; Raymond et al., 1995; Sarangowa et al., 2014). While, Isozyme based marker studies have also been used by some research groups for the identification and classification of Rosa genus (Kim and Byrne, 1996; Walker and Werner, 1997). However, the usage of isozyme markers is limited because of a small number of constantly resolvable loci (Kim and Byrne, 1994). For assessing genetic diversity which is vital for species survival, molecular markers with the aid of modern computing facilities are best suited as they offer fast, cheap and highly discriminating properties between species and within species or varieties (Azeem et al., 2012). Information to enrich our knowledge on genetic diversity is obtained from factors such as morphological, biochemical and molecular markers which lead us to a more meaning taxonomical classification (Gonçalves et al., 2009; Mohammadi and Prasanna, 2003; Sudré et al., 2007).

DNA based markers have been used very commonly in ecological, taxonomical, comparative biology, diversity, conservation, phylogenic and genetic studies amongst plant species (Haq et al., 2014). After the advent of PCR several advancement and introduction of new concepts were employed in the improvement of various types of molecular marker technologies like, amplified fragment length polymorphism (AFLP), inter simple sequence repeats (ISSRs), simple Sequence Repeats (SSR), single sequence polymorphism (SNP). For distinct genetic applications amongst diverse plant species different markers have been used by various researchers in rice (Huang et al. 1997), bread wheat (Gupta et al., 2003), barley (Varshney et al., 2007), Jatropha curcas (Khurana-Kaul et al., 2012), Tomato Cultivars (Nawaz et al. 2015), Poaceae plants (Haq et al., 2016), Citrullus colocynthis (Verma, 2017) etc.

A novel molecular marker known as Start Codon Targeted (SCoT) polymorphism targets on short ATG start codon in plant genes has been reported (Collard and Mackill, 2009). It has several advantages over RAPD, ISSR and AFLP, as it is more stable, produce more reproducible and reliable bands and can be used effectively for population studies, genetic mapping in different plants and in the marker assisted selection programs. Similar to RAPD and ISSR markers, SCoTs are important markers which could be used for different genetic application such as, to assess genetic diversity and structure, in bulk segregation analyses, quantitative trait loci (QTL) mapping and DNA fingerprinting. These markers are directly involved in relation of gene function and can be utilized in genotyping and to explore polymorphism (Gorji et al., 2011; Poczai et al. 2013). SCoT markers have been successfully practiced for diversity analysis and diagnostic finger-printing in mango (Luo et al., 2010), peanut (Xiong et al., 2011), grape (Guo et al., 2012), Jatropha (Mulpuri et al., 2013), orchard grass (Zeng et al., 2014), Dendrobium species (Feng et al., 2015), kalmegh (Tiwari et al., 2016), cowpea (Igwe et al. 2017), plantago (Rahimi et al., 2018) and taxus (Hao et al., 2018). The aim of present study was to evaluate the effectiveness of SCoT markers to determine genetic polymorphism and diversity amongst 29 rose germplasms.

2

2 Materials and methods

2.1

2.1 Plant material

A set of 29 different cultivars of genus Rosa were collected from different location of Jaipur District, Rajasthan, India and were used for genetic polymorphism, diversity and phylogenetic relationships amongst them using SCoT markers. All the cultivars represented distinguishable morphological characteristics (Fig. 1) and differed in flower color, stem height, bloom shape and plant habit (Table 1).

Pictorial views of twenty-nine rose accessions.
Fig. 1
Pictorial views of twenty-nine rose accessions.
Table 1 List of the Rosa accessions evaluated in this study.
S.no. Cultivar Name Class Bloom Color Bloom Shape Stem Height Collection Site
1 Lovers Meeting Hybrid tea Orange blend Double tea shaped 60–90 cm Durgapura nursery, Jaipur
2 Careless Love Hybrid tea Pink blend, stripes Double cupped 50–90 cm Durgapura nursery, Jaipur
3 Avalanche Floribunda White color with hint of green around petals Double bloom 90–120 cm Janta store circle rajendra marg Jaipur
4 Black Lady Hybrid tea Deep red Double bloom 30–50 cm Durgapura nursery, Jaipur
5 Eiffel Tower Hybrid tea Medium pink Double tea shaped 120–180 cm Durgapura nursery, Jaipur
6 Sunset Blend – Lavish orange center warm coral pink – 25–70 cm Ram newas bagh nursery jaipur
7 Gold Strike Floribunda Lemon yellow Star-shaped bloom 50–70 cm Ram newas bagh nursery Jaipur
8 Candy Stripe Hybrid tea Pink blend – 90–120 cm
121–180 cm		 Ram newas bagh nursery Jaipur
9 Tajmahal Hybrid tea Red – 90–150 cm Janta store circle Jaipur
10 Claude Monet Hybrid tea White medium yellow red blend Semi double 90–120 cm Ram newas bagh nursery Jaipur
11 Paradise Hybrid tea Mauve and mauve blend Double bloom 90–120 cm Ram newas bagh nursery Jaipur
12 Double Delight Hybrid tea Red blend Double tea shaped 90–120 cm
90–180 cm		 Ram newas bagh nursery Jaipur
13 Kaiser Wilhelm I Hybrid perpetual Purple red Violet shading Double bloom 90–180 cm Ram newas bagh nursery Jaipur
14 Yellow Patio – Rich yellow Double bloom 50 cm Ram newas bagh nursery Jaipur
15 Strawberry Romance Hybrid tea Pink blend Bi color High centered 90–120 cm Ram newas bagh nursery jaipur
16 All Gold Floribunda Golden or Vibrant yellow Semi double cupped 60 cm Ram newas bagh nursery Jaipur
17 Pleasure Cluster flowered (incl. Floribunda & Grandiflora) Medium pink Double bloom 90–120 cm Ram newas bagh nursery Jaipur
18 Love Grandiflora Red reversed silvery white Fully Double 90–120 cm Ram newas bagh nursery Jaipur
19 Tangerine Jewel Hybrid Bracteata Orange blend Single to Semi double 60–90 cm Ram newas bagh nursery Jaipur
20 William Shakespeare English rose Velvety crimson or medium red Double Cupped 90–120 cm Ram newas bagh nursery Jaipur
21 Henry Hundson Hybrid rugosa White blend Double flat bloom 60–120 cm Ram newas bagh nursery Jaipur
22 Black Baccara – Black tinged burgundy red/Dark red Double tea shaped 120–150 cm Ram newas bagh nursery Jaipur
23 Pope john paul II Hybrid tea White – 120–150 cm Durgapura nursery, Jaipur
24 Radnectar Grandiflora Apricot – 120–150 cm Durgapura nursery, Jaipur
25 Baimir/Kashmir Shrub Dark Red Double tea shaped 90–120 cm Durgapura nursery, Jaipur
26 Apricot nectar Floribunda Apricot or apricot blend Cupped bloom 60–120 cm Durgapura nursery, Jaipur
27 First red Hybrid tea Classic Red – 90–120 cm Durgapura nursery, Jaipur
28 Mundi Old garden (Gallica) Crimson Semi-double bloom 75–120 cm Ram newas bagh nursery Jaipur
29 Mozart Hybrid Musk Deep Pink, white center Continuous 80–150 cm Ram newas bagh nursery Jaipur

2.2

2.2 DNA extraction and purification

Total genomic DNA was extracted from young leaves according to the CTAB method (Doyle and Doyle, 1990). Leaf samples were crushed using 5 ml of pre-heated extraction buffer [1% PVP, 1 M TrisHCl pH 8.0, 5 M NaCl, 2% CTAB, 0.5 mM EDTA, 200 μl βME] and incubated for 1 h at 65 °C. Then it was treated with equal volume of Chloroform: Isoamylalcohol mixture (24:1; v/v). DNA was pelleted with double volume of ice cold Isopropanol and washed twice with 70% ethanol. The isolated DNA was air dried and stored at −20 °C in TE buffer. The dissolved nucleic acid was treated with RNase solution and incubated at 37 °C for 1 h. Followed by, purification was carried out using Phenol: Chloroform: Isoamylalcohol (25:24:1v/v) solution and upper aqueous phase was collected after centrifugation. After that 3 M Sodium acetate (0.1v) and 500 µl absolute alcohol were added into mixture, which was followed by pellet washing with 70% ethanol then air dried and dissolved in TE buffer. DNA was stored at −20 °C and concentration was adjusted via spectrophotometric method.

2.3

2.3 PCR amplification for SCoT markers

Total 36 sets of primers were custom synthesized by Operon Technologies (Almeda, USA) according to Collard and Mackill (Collard and Mackill, 2009) (Table 2). These were 18-mer primers having GC content between 50% and 72%. All PCR reactions were carried out within a total volume of 10 µl in 96 well plate’s thermal cycler (Bio-Rad.UK) for SCoT primers. Each reaction contains 25 ng template DNA (1 µl), 1.0 µl of primer (10 pmole/µl), 0.3 µl of 100 mM of dNTPs, 0.5 unit of Taq DNA polymerase, 1.2 µl of 10× PCR buffer (Bangalore Genei, India). Amplification was performed in the following conditions – an initial denaturation at 94 °C for 3 min, followed by 35 cycles at 94 °C for 1 min, annealing for 1 min and extension at 72 °C for 2 min with a final extension at 72 °C for 5 min. The PCR conditions mainly for annealing temperatures (varying from 50 °C to 58 °C) were standardized for each primer and amplified products were stored at 4 °C. All amplified products were resolved on 1.5% high resolution agarose gel made in 0.5× TBE buffer then preformed electrophoresis for 3.5 h at 70 V and visualized with ethidium bromide (10 mg/mL). The image of banding patterns was captured under UV light using gel documentation system (Bio-Rad).

Table 2 Sequence of SCoT primers and GC content (%).
Primer Sequences (5′–3′) %G/C Primer Sequences (5′–3′) %G/C
SCoT1 CAACAATGGCTACCACCA 50 SCoT19 ACCATGGCTACCACCGGC 67
SCoT2 CAACAATGGCTACCACCC 56 SCoT20 ACCATGGCTACCACCGCG 67
SCoT3 CAACAATGGCTACCACCG 56 SCoT21 ACGACATGGCGACCCACA 61
SCoT4 CAACAATGGCTACCACCT 50 SCoT22 AACCATGGCTACCACCAC 56
SCoT5 CAACAATGGCTACCACGA 50 SCoT23 CACCATGGCTACCACCAG 61
SCoT6 CAACAATGGCTACCACGC 56 SCoT24 CACCATGGCTACCACCAT 61
SCoT7 CAACAATGGCTACCACGG 56 SCoT25 ACCATGGCTACCACCGGG 67
SCoT8 CAACAATGGCTACCACGT 50 SCoT26 ACCATGGCTACCACCGTC 61
SCoT9 CAACAATGGCTACCAGCA 50 SCoT27 ACCATGGCTACCACCGTG 61
SCoT10 CAACAATGGCTACCAGCC 56 SCoT28 CCATGGCTACCACCGCCA 67
SCoT11 AAGCAATGGCTACCACCA 50 SCoT29 CCATGGCTACCACCGGCC 72
SCoT12 ACGACATGGCGACCAACG 61 SCoT30 CCATGGCTACCACCGGCG 72
SCoT13 ACGACATGGCGACCATCG 61 SCoT31 CCATGGCTACCACCGCCT 67
SCoT14 ACGACATGGCGACCACGC 56 SCoT32 CCATGGCTACCACCGCAC 67
SCoT15 ACGACATGGCGACCGCGA 67 SCoT33 CCATGGCTACCACCGCAG 67
SCoT16 ACCATGGCTACCACCGAC 56 SCoT34 ACCATGGCTACCACCGCA 61
SCoT17 ACCATGGCTACCACCGAG 67 SCoT35 CATGGCTACCACCGGCCC 72
SCoT18 ACCATGGCTACCACCGCC 67 SCoT36 GCAACAATGGCTACCACC 56

2.4

2.4 Genetic diversity analysis

All PCR amplified SCoT fragments were detected on gels and scored as binary data, for their presence (1) or absence (0) by visual observation. In order to ensure credibility only reproducible and well defined bands were scored. Smeared and weak bands were excluded. Polymorphic and monomorphic bands were determined for each SCoT primer. The dendrogram was constructed based on neighbor-joining and Nei and Li similarity matrix through Free tree/Tree view software’s (Pavlicek et al., 1999). The genetic diversity displayed among different genotypes were based on their similarity matrix which were obtained from binary data existence. Bayesian clustering was conducted to infer population structure and assign individuals to populations based on SCoT genotypes using STRUCTURE 2.3.4 software (Pritchard et al., 2000; Falush et al., 2003) with different values of the number of clusters (K). To obtain the optimum K value, the length of the burning period was 100,000 iterations followed by 200,000 Monte Carlo Markov Chain replicates. Each K value was run 10 times with values ranging from K 1 to K 10, it was plotted against the mean estimate of log probability of the data L(K). The real number of sub-population was identified using the maximum L(K) value. Population structure was calculated with ΔK using STRUCTURE Harvester, based on the second-order rate of change of likelihood distribution mean L (“K”) and with respect to K estimated (Evanno et al., 2005).

3

3 Results

The genetic polymorphism, diversity and phylogenetic relationships were established amongst 29 distinct cultivars of Rosa genus through SCoT markers or polymorphism in short conserved region flanking the ATG start codon. The cultivars were collected from different location of Jaipur, Rajasthan, India which displayed certain dissimilarity in their morphological traits. Each rose cultivar had distinct characteristic differences amongst bloom color (deep red, lavish orange, white blend etc.), bloom shape (double cupped, double flat bloom or double tea shaped) and stem height which range from 25–190 cm.

A sum of 36 SCoT primers was examined for PCR optimization, characterization, and their amplification within 29 different Rosa germplasms. Most of the primers revealed polymorphic and reproducible amplification profiles and yielded 299 unblurred and bright bands ranging from 150 bp to 1.2 kb in size. While, the numbers of bands ranged from four to nineteen, with an average of 9.34 bands per primer and SCoT21 displayed maximum number (19) of banding patterns and primers while SCoT17 and SCoT22 showed least number (4) of banding profiles (Table 3). However, primers SCoT7, SCoT9, SCoT27, and SCoT32 were unable to produce any amplification with Rose genomic DNA. These results indicated that a high polymorphism could be disclosed by SCoT markers in Rosa accessions. Furthermore, the amplification profile generated by SCoT1, SCoT2, SCoT21 and SCoT28 primers has been presented in Fig. 2.

Table 3 Percent amplification (PA), Total number of bands (TNB), Polymorphism information content (Pic) obtained by SCoT primers in Rosa accessions.
Primer No. PA TNB Pic Value
1 86.66 10 0.8344
2 86.66 14 0.894706
3 80 8 0.5275
4 43.33 7 0.8206
5 83.33 5 0.4289
6 86.66 10 0.7715
8 23.33 8 0.779
10 40 5 0.6113
11 73.33 15 0.89792
12 43.33 9 0.8568
13 63.33 9 0.7953
14 80 11 0.868508
15 90 14 0.912109
16 86.66 7 0.6821
17 66.66 4 0.5899
18 83.33 9 0.8402
19 76.66 13 0.89504
20 63.33 10 0.8686
21 86.66 19 0.929811
22 93.33 4 0.6549
23 83.33 6 0.77
24 40 7 0.7343
25 53.33 7 0.7678
26 80 8 0.8192
28 90 14 0.875743
29 73.33 7 0.7043
30 86.66 10 0.7954
31 66.66 10 0.8339
33 43.33 11 0.901844
34 83.33 9 0.8056
35 93.33 11 0.876509
36 90 8 0.8237
Total average 72.49 299 (9.34) 0.7864
SCoT Amplification Profile of Primer (A) SCoT21 (B) ScoT2 (C) SCoT28 And (D) SCoT1 amongst the 29 Rosa accessions. Lanes marked as 1 to 29 which represent the accessions according to serial numbers in Table A.1 and M represents 100 bp molecular weight ladder.
Fig. 2
SCoT Amplification Profile of Primer (A) SCoT21 (B) ScoT2 (C) SCoT28 And (D) SCoT1 amongst the 29 Rosa accessions. Lanes marked as 1 to 29 which represent the accessions according to serial numbers in Table A.1 and M represents 100 bp molecular weight ladder.

The binary matrix was constructed from the amplified bands obtained from SCoT primers extension. This formulated matrix was used to unveil genetic affinity and genetic diversity among 29 Rosa germplasms. In the present study, the highest similarity coefficient was observed between Rosa Black Baccara & Rosa Lovers Meeting followed by Rosa Careless Love & Rosa Black Baccara and Rosa Pope John Paul II and Rosa Black Baccara also showed close occurrence. The lowest similarity was observed between Rosa Baimir and Rosa Apricot Nectar with the value of 0.24 coefficients (Supplementary Table 1). Thus these results unveiled the closeness of Rosa Black Baccara to Rosa Lovers Meeting, Rosa Careless Love and Rosa Pope John Paul II, while Rosa Baimir and Rosa Apricot Nectar showed dissimilarity between them.

A dendrogram was constructed amongst 29 distinct rose genotypes using binary data that was based on Nei-Li similarity coefficient/Neighbor-joining through free-tree/tree view software. It was observed that the dendrogram grouped all the cultivars into two major cluster I and II. While, cluster I included only Rosa Strawberry Romance and cluster II was subdivided into two groups A and B. Group A was further divided into two subgroups, IIa and IIb. The subgroup IIa revealed 14 different rose cultivars in relation to their closeness and subgroup IIb was with two cultivars i.e. Rosa Paradise and Rosa Tajmahal which were distinct from subgroup IIa and cluster I cultivar. As well, group B was with 12 different rose cultivars and divided into two subgroups namely; IIc and IId. While, the IIc was with only one cultivar (Rosa Tangerine jewel) and IId consisted 11 distinct Rose cultivars (Fig. 3). Bayseian clustering conducted was used to assign the 29 Rosa germplasms with reference to their population structure. The estimated membership fraction ranged from K 1 to K 10 and the most reliable probabilities obtained at K = 3 (Fig. 4). The highest ad hoc measure of delta K was observed at K = 3 (Fig. 5) which indicates the 29 germplasms are divided into three subgroups. The membership probability threshold score was observed 0.78 (Fig. 6). The 29 germplasms were separately assigned to each subgroup and fractions less than 0.22 score were considered to be admixed.

A dendrogram revealed genetic relationships among 29 different Rosa accessions using SCoT marker.
Fig. 3
A dendrogram revealed genetic relationships among 29 different Rosa accessions using SCoT marker.
Pattern of variation of 29 accessions with 32 SCoT markers, bar length representing the membership probability of accessions belonging to different subgroups at K = 3.
Fig. 4
Pattern of variation of 29 accessions with 32 SCoT markers, bar length representing the membership probability of accessions belonging to different subgroups at K = 3.
Graph of estimated membership fraction for K = 3.
Fig. 5
Graph of estimated membership fraction for K = 3.
Population structure of 29 accessions arranged based on inferred ancestry.
Fig. 6
Population structure of 29 accessions arranged based on inferred ancestry.

4

4 Discussion

Molecular markers are considered to be the best for characterization of genetic polymorphism at DNA level, germplasm characterization, genetic diversity analysis, parentage determination, genetic distance estimation, gene mapping, and marker-assisted selection (Gupta and Rustgi, 2004; Varshney et al., 2005; Agarwal et al., 2008; Madhumati, 2014). Till date, a variety of molecular markers technologies have been developed through innovations of different new principle and approaches in molecular markers system. On account of these, different marker technologies have been investigated in Rosa genus for their genetic characterizations using distinct types of molecular markers such as simple sequence repeats (SSR), random amplified polymorphic DNA (RAPD) and inter-simple sequence repeats (ISSRs) (Akond et al., 2012; Azeem et al., 2012; Panwar et al., 2015).

The present study, reports the use of utilitarian marker such as SCoT primers for comparing genetic diversity and for establishing genetic relationships among 29 Rosa germplasms. SCoT marker technique used in the current study is simple, low cost, fast, effective and highly reproducible with requirement of small amount of DNA in addition to no prior information of DNA sequence. These markers are very easy to design based on ATG context that is conserved region surrounding the translation initiation codon, thus the SCoT marker technique correspond to functional genes and their correlating characters (Xiong et al., 2011). Disparate from RAPD, AFLP and ISSR marker system, SCoT is gene targeted marker with multilocous nature and it can generate more information correlated with biological traits and helpful in high genetic polymorphism. Evaluation of SCoT markers in diversity analysis and diagnostic fingerprinting has already been established in Mango (Luo et al., 2012), Orchid (Bhattacharyya et al., 2013), Date palm (Al-Qurainy et al., 2015), Diospyros (Deng et al., 2015), Elymus sibiricus (Zhang et al., 2015), Vigna unguiculata (Igwe et al., 2017) and taxus (Hao et al., 2018).

In the present investigation, a set of 36 SCoT primers were used to examine genetic polymorphism, out of total, 32 SCoT primers produced unambiguous and reproducible banding profile with 150–1200 bp product size but 4 SCoT primers failed to amplify the rose genomic DNA. High polymorphism (100%) as reported in the present study is in compliance with earlier investigations in rose (Henuka et al., 2015) who reported (98.54% polymorphism) with RAPD markers. Panwar et al. (2015) who observed 94% of genetic polymorphism with ISSR markers then Prasad et al. (2006) who reported 87.5% of polymorphism in fragrant rose cultivars with RAPD markers. Our study is comparable to high SCoT based polymorphism obtained in several other plant species also viz. 97.10% in Atriplex halimus (Elframawy et al., 2016), 96.68% in Diospyros (Deng et al., 2015), 95.71% in Quercus brantii (Alikhani et al., 2014), and 93% in Grape (Guo et al., 2012).

The present study is first report to have shown significant genetic polymorphism amongst various Rosa germplasm using SCoT markers. A total of 299 scorable bands were identified through the amplification of 32 SCoT primers in 29 diverse rose germplasms. The amplification ranged from 4 bands to 19 bands with an average of 9.42 bands per primer which is nearly comparable with earlier study by Panwar et al. (2015) who reported an average of 11 bands per primer in 32 rose cultivars with ISSR primers while polymorphism generated by RAPD marker is not comparable to our work as it gave only 6.5 bands per primer (Henuka et al., 2015). Moreover, an average PCR amplification found to be 72.49% and polymorphic information content (PIC) ranged from 0.42 (SCoT-5) to 0.92 (SCoT-21) with an average of 0.78 which exhibit similarity to PIC value obtained in previous studies such as, 0.72 by SSR marker (Ghose et al., 2013), 0.88 by STMS marker system (Fernández-Romero et al., 2009), while in RAPD a value (0.38) was obtained by Henuka et al. (2015). Thus, the parameters such as primer polymorphism and polymorphic information content (PIC) used in the present study are found to be very supportive to examine markers for their usefulness in the fingerprinting process.

The binary matrix from the PCR amplified product was used for similarity index/coefficient calculation that was based on Nei-li matching coefficient. The maximum similarity was identified between Rosa Careless Love & Rosa Black Baccara, and between Rosa Pope John Paul II & Rosa Black Baccara. Lowest similarity was seen between Rosa Baimir and Rosa Apricot Nectar. Furthermore, a genetic relationship amongst 29 distinct rose accessions was evaluated by the construction of dendrogram using data of 32 SCoT markers amplification. Several distinct clusters of rose accessions were recognized which fell down into various edges of dendrogram that might be possible of their differences in genetic constitutions which represent distinct morphological characters and variations amongst them. Further, this grouping is also supported by Bayseian clustering algorithm using STRUCTURE software which was conducted amongst different 29 Rosa germplasm (Evanno et al. 2005). The Inference of best K is explained by using the delta K method which was found to be the best at K = 3 that clearly separated the rosa germplasm into major three groups. This method recognize the true number of clusters (K) in the given individual collections using an ad hoc statistic ΔK based on the rate of change in the log probability of data between successive K values (Evanno et al., 2005). Moreover, Bayesian clustering approach has the ability to use genetic information to determine the population membership of individuals without assuming predefined populations. They allocate either members or portion of their genome to a number of clusters based on multilocus genotypes (Chen et al., 2007). Vukosavljev et al. (2013) also tried to classify garden rose in three groups- wild, old garden and modern garden rose on the basis of several morphological parameters. Several earlier studies have established the genetic relationship amongst rose accessions based on several morphological traits such as number of petals, stem width, number of side shoots, or rose hips to diversify rose genotypes (Gitonga et al., 2014; Riaz et al., 2007). Diverse studies have been conducted to analyze genetic relationship based on different marker technologies in rose, like STMS (Fernández-Romero et al., 2009), AFLP (Pirseyedi et al., 2005), SSR (Akond et al., 2012; Ghose et al., 2013; Nadeem et al., 2014; Samiei et al., 2010; Vukosavljev et al., 2013), ISSR (Mirali et al., 2012; Panwar et al., 2015; Zhou et al., 2009) and RAPD (Agaoglu et al., 2000; Henuka et al., 2015; Panwar et al., 2015).

5

5 Conclusion

This is first report of genetic polymorphism on 29 different Rosa germplasm using SCoT markers technique. It is single primer PCR based amplification methods which depend on gene-targeted formulation and as a better alternative to RAPD, ISSR, AFLP, SRAP, and TRAP techniques. Some attributes like higher polymorphism, high reproducibility, low cost, easy to access, and time saver nature made SCoT method more reliable and suitable to study genetic relationship amongst various Rosa germplasm. Our study identified the polymorphic nature of SCoT marker without facilitating functional validation of genes and established genetic diversity among different rosa germplasms. Therefore, this practice will be useful for planning conservation strategies and also helpful in rose improvement programs such as linkage mapping, QTL mapping, genotype identification, gene pyramiding and marker assisted selection.

Acknowledgments

The authors express sincere thanks to Bioinformatics Infrastructure Facilities (DBT-BIF), Council of Scientific and Industrial Research (CSIR) and Indian Council of Medical Research (ICMR) for financial support. We are also grateful to DRS-Phase-II, Department of Botany, University of Rajasthan, DST-PURSE and UGC-UPE for providing necessary research facilities.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

  1. , , , . Molecular analysis of genetic diversity oil rose (Rosa damascena Mill.) grown Isparta (Turkey) region. Biotechnol. Biotechnol. Equip.. 2000;14:16-18.
    [Google Scholar]
  2. , , , . Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Rep.. 2008;27:617-631.
    [Google Scholar]
  3. , , , . Molecular characterization of selected wild species and miniature roses based on SSR markers. Sci. Hortic. (Amsterdam). 2012;147:89-97.
    [Google Scholar]
  4. , , , , . SCoT marker for the assessment of genetic diversity in Saudi Arabian date palm cultivars. Pakistan J. Bot.. 2015;47:637-643.
    [Google Scholar]
  5. , , , , , . Genetic variability and structure of Quercus brantii assessed by ISSR, IRAP and SCoT markers. Gene. 2014;552:176-183.
    [Google Scholar]
  6. , , , , , . Genetic diversity of rose germplasm in Pakistan characterized by random amplified polymorphic DNA (RAPD) markers. Afr. J. Biotechnol.. 2012;11(47):10650-10654.
    [Google Scholar]
  7. , . Turkish rose oil. Perfumer Flavorist. 1992;17:45-46.
    [Google Scholar]
  8. , , . The effects of harvest date, fermentation duration and Tween 20 treatment on essential oil content and composition of industrial oil rose (Rosa damascena Mill.) Ind. Crops Prod.. 2005;21(2):251-255.
    [Google Scholar]
  9. , , , . Analysis of genetic relationships among Rosa damascena plants grown in Turkey by using AFLP and microsatellite markers. J. Biotechnol.. 2004;111:263-267.
    [Google Scholar]
  10. , , , , . Start Codon Targeted (SCoT) marker reveals genetic diversity of Dendrobium nobile Lindl., an endangered medicinal orchid species. Gene. 2013;529:21-26.
    [Google Scholar]
  11. , , , , . Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study. Mol. Ecol. Resour.. 2007;7(5):747-756.
    [Google Scholar]
  12. , , . Start codon targeted (SCoT) polymorphism: a simple, novel DNA marker technique for generating gene-targeted markers in plants. Plant Mol. Biol. Report.. 2009;27:86-93.
    [Google Scholar]
  13. , , , , , , . Investigation and analysis of genetic diversity of diospyros germplasms using SCoT molecular markers in Guangxi. PLoS One. 2015;10(8):e0136510.
    [Google Scholar]
  14. , , . Isolation of plant DNA from fresh tissue. Focus. 1990;12:13-15.
    [Google Scholar]
  15. , , , . Genetic variation among fragmented populations of Atriplex halimus L. Using Start Codon Targeted (SCoT) and ITS1-5.8 S-ITS2 Region Markers. Am. J. Mol. Biol.. 2016;6:101.
    [Google Scholar]
  16. , , , . Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol.. 2005;14:2611-2620.
    [Google Scholar]
  17. , , , . Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 2003;164:1567-1587.
    [Google Scholar]
  18. , , , , , , , . Start codon targeted (SCoT) and target region amplification polymorphism (TRAP) for evaluating the genetic relationship of Dendrobium species. Gene. 2015;567:182-188.
    [Google Scholar]
  19. , , , , , , . Cytological and molecular characterisation of a collection of wild and cultivated roses. Floriculture Ornamental Biotechnol.. 2009;3(1):28-39.
    [Google Scholar]
  20. , , , , , , , , . Structuration of the genetic and metabolite diversity among Prince Edward Island cultivated wild rose ecotypes. Sci. Hortic. (Amsterdam). 2013;160:251-263.
    [Google Scholar]
  21. , , , , , , , . Genetic variation, heritability and genotype by environment interaction of morphological traits in a tetraploid rose population. BMC Genet.. 2014;15:146.
    [Google Scholar]
  22. , , , , , . Heirloom tomato gene bank: assessing genetic divergence based on morphological, agronomic and molecular data using a Ward-modified location model. Genet. Mol. Res.. 2009;8:364-374.
    [Google Scholar]
  23. , , , , . Efficiency of arbitrarily amplified dominant markers (SCoT, ISSR and RAPD) for diagnostic fingerprinting in tetraploid potato. Am. Potato J.. 2011;88(3):226-237.
    [Google Scholar]
  24. , , , . Genetic diversity in some grape varieties revealed by SCoT analyses. Mol. Biol. Rep.. 2012;39:5307-5313.
    [Google Scholar]
  25. , , . Molecular markers from the transcribed/expressed region of the genome in higher plants. Funct. Integr. Genom.. 2004;4:139-162.
    [Google Scholar]
  26. , , , , , , . Transferable EST-SSR markers for the study of polymorphism and genetic diversity in bread wheat. Mol Genet Genom.. 2003;270(4):315-323.
    [Google Scholar]
  27. , , , , , , , , , , , , , . Development of SCoT-based SCAR marker for rapid authentication of Taxus media. Biochem. Genet. 2018:1-12.
    [Google Scholar]
  28. , , , , , . Identification and characterization of micRosatellites in expressed sequence tags and their cross transferability in different plants. Int. J. Genom. 2014:12.
    [Google Scholar]
  29. , , , , , , , , . Assessment of functional EST-SSR markers (Sugarcane) in cross-species transferability, genetic diversity among poaceae plants, and bulk segregation analysis. Genetics Res. Int. 2016:16.
    [Google Scholar]
  30. , , , . Characterization and analysis of genetic diversity among different species of rose (Rosa species) using morphological and molecular markers. Indian J. Agr. Sci.. 2015;85(2):240-245.
    [Google Scholar]
  31. , , , , , , , , , , . RFLP mapping of isozymes, RAPD and QTLs for grain shape, brown planthopper resistance in a doubled haploid rice population. Mol. Breed.. 1997;3(2):105-113.
    [Google Scholar]
  32. , , , , , , . Assessment of genetic diversity in Vigna unguiculata L. (Walp) accessions using inter-simple sequence repeat (ISSR) and start codon targeted (SCoT) polymorphic markers. BMC Genetics. 2017;18(1):98.
    [Google Scholar]
  33. , , , , , , . Morphological and molecular analyses of Rosa damascene x R. bourboniana interspecific hybrids. Sci. Hortic. (Amsterdam). 2009;122:258-263.
    [Google Scholar]
  34. , , , . Characterization of genetic diversity in Jatropha curcas L. germplasm using RAPD and ISSR markers. Ind. J. Biotech.. 2012;11:54-61.
    [Google Scholar]
  35. , , . 365 Biosystematical classification of Genus Rosa using isozyme polymorphisms. HortScience. 1994;29(5):483.
    [Google Scholar]
  36. , , . Interspecific hybrid verification of Rosa with isozymes. HortScience. 1996;31:1207-1209.
    [Google Scholar]
  37. , , , , , . Genetic relationship and diversity of Mangifera indica L.: revealed through SCoT analysis. Genet. Resour. Crop Evol.. 2012;59:1505-1515.
    [Google Scholar]
  38. , , , , , . Analysis of diversity and relationships among mango cultivars using Start Codon Targeted (SCoT) markers. Biochem. Syst. Ecol.. 2010;38(6):1176-1184.
    [Google Scholar]
  39. , . Potential and application of molecular markers techniques for plant genome analysis. Int. J. Pure App. Biosci.. 2014;2(1):169-188.
    [Google Scholar]
  40. , , , , . Flower flavonol and anthocyanin distribution in subg. Rosa. Biochem. Syst. Ecol.. 1993;23:183-200.
    [Google Scholar]
  41. , , , . Genetic characterization of Rosa damascena species growing in different regions of Syria and its relationship to the quality of the essential oils. Int. J. Med. Arom. Plants. 2012;2:41-52.
    [Google Scholar]
  42. , , . Review & Interpretation analysis of genetic diversity in crop plants – Salient statistical tools. Crop Sci.. 2003;43:1235-1248.
    [Google Scholar]
  43. , , , . Start codon targeted (SCoT) polymorphism in toxic and non-toxic accessions of Jatropha curcas L. and development of a codominant SCAR marker. Plant Sci.. 2013;207:117-127.
    [Google Scholar]
  44. , , , , , , , . Hybrid identification, morphological evaluation and genetic diversity analysis of Rosa x hybrida by SSR markers. Aust. J. Crop Sci.. 2014;8:183.
    [Google Scholar]
  45. , , , , , . Agro-morphological and molecular characterization of local tomato cultivars grown in pakhal region of pakistan using RAPD markers. Middle-East J. Sci.. 2015;23(5):856-860.
    [Google Scholar]
  46. , , , , , , , , . Hydrolysable tannins as chemotaxonomic markers in the Rosaceae. Phytochemistry. 1992;31:3091-3096.
    [Google Scholar]
  47. , , , . Genetic divergence analysis in rose (Rosa x hybrida) using morphological markers. J. Ornam. Hortic.. 2010;13(2):122-126.
    [Google Scholar]
  48. , , , , . Molecular fingerprinting and assessment of genetic diversity in rose (Rosa× hybrida) Indian J. Biotechnol.. 2015;14:518-524.
    [Google Scholar]
  49. , , , . Short communication freetree-freeware program for construction of phylogenetic trees on the basis of distance data and bootstrap/jackknife analysis of the tree robustness. Application in the RAPD Analysis of Genus Frenkelia. Folia Biol.. 1999;45:97-99.
    [Google Scholar]
  50. , , , , , . Analysis of the genetic diversity 12 Iranian Damask rose (Rosa damascena Mill.) genotypes using amplified fragment length polymorphism markers. Iran. J. Biotechnol.. 2005;3:225-230.
    [Google Scholar]
  51. , , , , , , , . Advances in plant gene-targeted and functional markers: a review. Plant Methods. 2013;9(1):6.
    [Google Scholar]
  52. , , , . Molecular characterization of fragrant rose cultivars. Indian J. Hortic.. 2006;63:229-234.
    [Google Scholar]
  53. , , , . Inference of population structure using multilocus genotype data. Genetics. 2000;155:945-959.
    [Google Scholar]
  54. , , , , . SCoT marker diversity among Iranian Plantago ecotypes and their possible association with agronomic traits. Sci. Hort.. 2018;233:302-309.
    [Google Scholar]
  55. , , , . Fingerprinting the selection process of ancient roses by means of floral phenolic metabolism. Biochem. Syst. Ecol.. 1995;23:555-565.
    [Google Scholar]
  56. , , , , , . Assessment of biodiversity based on morphological characteristics and RAPD markers among genotypes of wild rose species. Afr. J. Biotechnol.. 2011;10:12520-12526.
    [CrossRef] [Google Scholar]
  57. , , , , , , . Assessment of biodiversity based on morphological characteristics among wild rose genotypes. Pak. J. Agri. Sci. 2007;44:2.
    [Google Scholar]
  58. , , , , , , , , . Genetic diversity and genetic similarities between Iranian rose species. J. Hortic. Sci. Biotechnol.. 2010;85:231-237.
    [Google Scholar]
  59. , , , , , , . Flavonol glycosides in the petal of Rosa species as chemotaxonomic markers. Phytochemistry. 2014;107:61-68.
    [Google Scholar]
  60. , , , , , , , . Genetic resources of vegetable crops: a survey in the Brazilian germplasm collections pictured through papers published in the journals of the Brazilian Society for Horticultural Science. Hortic. Bras.. 2007;25:496-503.
    [Google Scholar]
  61. , , , , , , , . Morphological and oil content variations amongst Damask rose (Rosa damascena Mill.) landraces from different regions of Iran. Sci. Hortic. (Amsterdam). 2007;113:44-48.
    [Google Scholar]
  62. , , , , , , , . Study of arbitrarily amplified (RAPD and ISSR) and gene targeted (SCoT and CBDP) markers for genetic diversity and population structure in Kalmegh [Andrographis paniculata (Burm. f.) Nees] Ind. Crops Prod.. 2016;86:1-11.
    [Google Scholar]
  63. , , , . Genic micRosatellite markers in plants: features and applications. Trends Biotechnol.. 2005;23:48-55.
    [Google Scholar]
  64. , , , , , . Comparative assessment of EST-SSR, EST-SNP and AFLP markers for evaluation of genetic diversity and conservation of genetic resources using wild, cultivated and elite barleys. Plant Sci.. 2007;173(6):638-649.
    [Google Scholar]
  65. , , , , , , , , . Genetic diversity and differentiation in roses: a garden rose perspective. Sci. Hortic. (Amsterdam). 2013;162:320-332.
    [Google Scholar]
  66. , , , , . RAPD and ISSR marker assessment of genetic diversity in Citrullus colocynthis (L.) Schrad: a unique source of germplasm highly adapted to drought and high-temperature stress. 3 Biotech. 2017;7(5):288.
    [Google Scholar]
  67. , , . Isozyme and randomly amplified polymorphic DNA (RAPD) analyses of cherokee rose and its putative hybrids Silver Moon’ and Anemone’. J. Am. Soc. Hortic. Sci.. 1997;122:659-664.
    [Google Scholar]
  68. , , . 4 Dogroses: botany, horticulture, genetics, and breeding. Hortic. Rev. (Am. Soc. Hortic. Sci). 2010;36:199.
    [Google Scholar]
  69. , , , , , , , . Start codon targeted polymorphism for evaluation of functional genetic variation and relationships in cultivated peanut (Arachis hypogaea L.) genotypes. Mol. Biol. Rep.. 2011;38:3487-3494.
    [Google Scholar]
  70. , , , . A study of morphological variations and their relationship with flower yield and yield components in Rosa damascena. J. Agric. Sci. Technol.. 2010;11:439-448.
    [Google Scholar]
  71. , , , , , , . Genetic diversity of orchardgrass (Dactylis glomerata L.) germplasms with resistance to rust diseases revealed by Start Codon Targeted (SCoT) markers. Biochem. Syst. Ecol.. 2014;54:96-102.
    [Google Scholar]
  72. , , . Cultivar identification by image analysis. Encycl. Rose Sci.. 2003;1:124-135.
    [Google Scholar]
  73. , , , , . Potential of start codon targeted (SCoT) markers to estimate genetic diversity and relationships among chinese Elymus sibiricus accessions. Molecules. 2015;20:5987-6001.
    [Google Scholar]
  74. , , , , . Genetic diversity of different roses revealed by ISSR. Genomics Appl. Biol.. 2009;28(2):311-315.
    [Google Scholar]

Appendix A

Supplementary data

Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.jksus.2018.04.022.

Appendix A

Supplementary data

Supplementary data


Fulltext Views
18

PDF downloads
5
View/Download PDF
Download Citations
BibTeX
RIS
Show Sections

Suggested read for related articles:

© Copyright 2025 – Journal of King Saud University – Science.

Published by Scientific Scholar on behalf of King Saud University.

ISSN (Print): 1018-3647
ISSN (Online): 2213-686X

Scientific Scholar CrossRef Creative Commons Open Acess Portico