Detailed information of 21 accessions of
Abstract
Chlorophytum borivilianum is a traditional medicinal plant distributed throughout the tropics and subtropics. In the present investigation, RAPD and ISSR analyses were used to assess the genetic diversity among 21 accessions collected from different geographical regions of India using 20 RAPD primers and 6 ISSR primers. RAPD and ISSR primers revealed 92.26% and 82.76% polymorphism, respectively. Similarity in coefficient values ranged from 0.321 to 0.707 for RAPD and 0.363 to 0.846 for ISSR markers. The dendrogram developed by RAPD and ISSR marker‐based analysis grouped the 21 accessions into different clusters. Mantel test employed for detection of goodness of fit established the cophenetic correlation value for both the primer systems and it was observed to be significant. Clustering of accessions within groups was also similar based on RAPD‐ and ISSR‐derived dendrograms. In our study, both marker systems were similar except for the percentage polymorphism which was found to be greater using RAPD, thus indicating the greater effectiveness of RAPD primers for estimating genetic variation of C. borivilianum.
Keywords
- Chlorophytum borivilianum
- ISSR
- RAPD
- Diversity
- medicinal plant
1. Introduction
All the plants considerably possess some medicinal or perfumery or mixed properties. In the course of time, the human beings were able to distinguish between the harmful and useful plants. The world Health Organization (WHO) has listed over 21,000 plant species that have been reported for medicinal uses around the world. Among these, over 100 botanicals are reported to have consistent large demand and are traded in major drug markets in the world. In the developing countries, about 80% of the people depend upon the traditional system of the medicine, as it shows no or less side effects [1]. India is considered as a veritable emporium of medicinal and aromatic plants. In India, about 2500 plant species belonging to more than 1000 genera are used by traditional healers and about 500 plant species are utilized by 159 different pharmaceutical companies [2].
Many of the medicinal plant species are facing threats of extinction due to over and improper exploitation, habitat loss, degradation of land, urbanization, etc. On the other hand, the increasing global demand for the medicinal plants necessitates an accelerated cultivation and conservation of them. However, before the widespread domestication of such plant species is implemented, it would be important to determine their genetic diversity so that the useful genotypes could be effectively used as cultivars by farmers or breeders and it would, in turn, facilitate the efficient conservation, management and utilization of the species. For the purpose of conservation and to carry out successful breeding programmes, proper identification of the plant is of prime importance, for which an accurate, reliable and more authentic system of classification is required. Conventionally, identification and classification of plant groups are solely based on similarities and dissimilarities in morphological features, particularly, the floral character which are considered to be consistent. As already established, expression of morphological characters is the outcome of interaction between the environment and the genotype and is highly influenced by climatic and edaphic factors. Certain biochemical markers such as isozymes and storage proteins are used for identification of cultivars as well as characterization of somaclonal variation. However, the number of genetic loci generated with chemical/ biochemical (isozymes) were quite lower than detected with DNA markers [3]. Molecular techniques are very much useful not only to identify the genotypes for authentication but also to assess and exploit the genetic variability [4]. DNA fingerprinting of all the genetic resources of the medicinal plants is necessary to generate a molecular database as well as to utilize the information in a systematic manner.
During last 20 years, the advent of the PCR and the DNA sequencing techniques has allowed a very significant development of this approach, which leads to great change in the traditional vision of the classification of the organisms. The DNA marker systems are considered to be the best tools for determining the genetic diversity, as they are unlimited in number, show high polymorphism and are independent of environmental interaction, i.e. they are highly heritable. The application of the DNA marker systems in agricultural research has progressed rapidly over the past few years, especially, in the area of cultivar identification and characterization [5] as well as determination of population diversity in many plant species [6]. Among these, random amplified polymorphic DNA (RAPD) and inter‐simple sequence repeats (ISSR) markers provide a larger number of potential markers that are useful for the analysis of genetic diversity, often using fast, simple and reliable protocols that minimize the amount and quality of DNA required. Genetic diversity in a population is considered to represent its evolutionary potential. Genetic variation has implications for conservation at the species level. Evidently, molecular markers could be used to derive genetic relationship with an increased level of accuracy and also can provide valuable data on diversity through their ability to detect variation at the DNA level.
2. Materials and methods
2.1. Plant materials
A total of 21 accessions of
Sr. no. | Germplasm (name) | Place of collection |
---|---|---|
1 | Ch1 | Madhya Pradesh |
2 | Ch2 | Madhya Pradesh |
3 | Ch3 | Madhya Pradesh |
4 | Ch4 | Madhya Pradesh |
5 | Ch5 | Madhya Pradesh |
6 | Ch6 | Madhya Pradesh |
7 | Ch7 | Madhya Pradesh |
8 | Ch8 | Madhya Pradesh |
9 | Ch9 | Madhya Pradesh |
10 | Ch10 | Rajasthan |
11 | Ch11 | Rajasthan |
12 | Ch12 | Rajasthan |
13 | Ch13 | Rajasthan |
14 | Ch14 | Rajasthan |
15 | Ch15 | Maharashtra |
16 | Ch16 | Maharashtra |
17 | Ch17 | Maharashtra |
18 | Ch18 | Gujarat |
19 | Ch19 | Gujarat |
20 | Ch20 | Gujarat |
21 | Ch21 | Gujarat |
2.2. Molecular characterization
2.2.1. Isolation, purification and quantification of genomic DNA
Since isolation of DNA from
2.2.2. RAPD and ISSR analysis
RAPD analysis was performed using randomly and arbitrarily 10‐base primers (Operon Technologies Inc., Alameda, California). A preliminary screening was carried out using 100 RAPD primers following the protocol of Williams et al. [14] with minor modifications. ISSR analysis was carried out using ISSR primers (Banglore Genei, India) based on the protocol of Zietkiewicz et al. [15] with some modifications. The amplified products were separated in 1.5% agarose gel for both the markers used. After electrophoresis, the gel was visualized under the UV light and photographed in a gel documentation system (Syngene, United Kingdom). The sizes of the amplicons were determined by comparing them with that of the ladder.
2.2.3. Scoring of the data for RAPD and ISSR
The data were scored as 1 for the presence and 0 for the absence of the band for each primer‐accession combination for RAPD and ISSR analysis.
2.2.4. Data analysis
2.2.4.1. Resolving power (Rp )
Resolving power of the primer/primer combination was calculated as per Prevost and Wilkinson [16] as:
2.2.4.2. PIC and primer index
The primer index (PI) was calculated from the polymorphic index (PIC). The polymorphic index was calculated as PIC = Σ
2.2.4.3. Jaccard's similarity
Jaccard's coefficient of similarity [19] was measured and a dendrogram based on similarity coefficients generated by the unweighted pair group method using arithmetic averages (UPGMA) [20], and the sequential agglomerative hierarchial and nested (SHAN) clustering was obtained. The entire analysis was performed using the statistical package NTSYS‐pc 2.02e [21].
2.2.4.4. Cophenetic correlation
The correlation among different dendrograms generated from the data obtained from RAPD and ISSR was calculated separately and the cophenetic correlation for both the markers was also calculated from the total data.
2.2.4.5. Principal co‐ordinate analysis
To visualize the genetic variation among 21 accessions of
To study the efficiency of each marker technique, the Mantel ‘Z’ test was performed for the comparison of each marker system with the combined data and between the marker systems [23].
3. Results
The present work pertains to the study of genetic diversity among 21 accessions of
3.1. RAPD analysis
A total of 100 random decamer oligonucleotide primers from 20 numbers from each series (OPA, OPC, OPD, OPP and OPT) were screened using an accession (Ch12; Raj 4) of
The Jaccard's coefficient showed that MH2 and MH3 were most closely related with a similarity value of 0.707 followed by MH3 and Akola with similarity value of 0.697. The matrix value ranged from 0.321 to 0.707. It was observed that MP5 and Guj2 were most remotely placed with the similarity coefficient of 0.321. The average similarity coefficient was 0.639 between any two accessions taken into account.
The dendrogram constructed by the UPGMA method of pooled RAPD data led to the segregation of the 21 accessions into two distinct groups. All accessions were distributed between 39 units. Surprisingly, the first contained only one accession, Ch8 collected from Madhya Pradesh and second contained remaining 20 accessions collected from different diversified regions (Madhya Pradesh, Rajasthan, Maharashtra and Gujarat) though they belong to same species. The second major cluster divided into two sub‐major clusters out of which one sub‐major cluster IIA contained four accessions (Ch1, Ch4, Ch5 and Ch7) collected from Madhya Pradesh with ~47% similarity and the another one (Group IIB) clutched Ch2 and Ch3 collected from Madhya Pradesh depicting 47% genetic variability. The third sub‐major cluster (Group IIC) comprised four accessions Ch6 (Madhya Pradesh collection), Ch15, Ch16 and Ch17 collected from Maharashtra showing genetic similarity at the level of 68%. Another group, IID, consisted of two accessions (Ch12 and Ch13) collected from Rajasthan and other two Ch19 and Ch21 of Gujarat collections. Another group (IIE) holds two accessions (Ch18 and Ch20) collected from Gujarat showed 51% (approx.) genetic diversity. Accession no. Ch10 and Ch11 of Rajasthan collections fall in group IIF depicting 52% genetic similarity whereas cluster (IIG) containing two accessions (Ch9 and Ch14) showed genetic variability of about 51% collected from Madhya Pradesh and Rajasthan, respectively.
The cophenetic correlation showed maximum (0.7065) correlation between Ch15 (MH2) and Ch16 (MH3); both collected from Maharashtra. The average cophenetic correlation between two accessions was found to be 0.584.
3.2. ISSR analysis
A total of 10 ISSR primers were used out of which 06 primers resulted in amplification of 29 loci. Out of 29 loci, 24 loci were polymorphic and 05 loci were monomorphic in nature. Among the polymorphic loci, only one (01) locus was found to be unique in nature. The amplicons were observed in the range of 180 to >3000 base pair (Figure 3). The resolving power of the primers varied from 1.905 to 8.667 while the primer index varied from 0.091 to 0.499. Best resolving power (8.667) was observed in the primer (CA)8AT and the minimum (1.905) with primer (CA)6GG. The maximum ISSR primer index (0.499) was observed with the primer (CT)9G and the minimum (0.091) with primer (CA)6GG.
The Jaccard's coefficient showed that Ch13 (Raj 5 was most closely related) to Ch 14 (Raj 11) with a similarity value of 0.846 followed by Ch7 (MP7) and Ch10 (Raj1) with a similarity value of 0.833. The matrix value ranged from 0.363 to 0.846. The Rajasthan collected accession (Ch11) and Ch18 (Dangs) of Gujarat collections were most remotely placed with the similarity coefficient of 0.363. Between any two accessions, the average similarity coefficient was observed as 0.627.
Six (06) ISSR primers were used to construct a dendrogram by using the UPGMA method which led to the segregation of the accessions into two distinct groups. All accessions were distributed between 36 units. The resultant dendrogram formed a cluster with the 18 accessions in one group and the other 3 accessions into a separate group. Again 18 accessions discriminated into several groups. Group I consisted of only three accessions, Ch9, Ch11 and Ch19 collected from Madhya Pradesh, Rajasthan and Gujarat, respectively that showed about 35% genetic variability. Group II comprises five groups such as IIA, IIB, IIC, IID and IIE. Group IIC is the largest, which comprises six accessions, Ch3, Ch6 and Ch7 Collected from Madhya Pradesh, Ch 10 and Ch12 from Rajasthan and Ch18 collected from Gujarat showed 61% genetic similarity. The second largest Group IIB holds five accessions from which Ch2 and Ch8 collected from Madhya Pradesh, Ch13 and Ch14 from Rajasthan and Ch20 collected from Gujarat showed about genetic variation of about 40%. IID formed a group with three accessions, Ch 15, Ch16 and Ch17 of Maharashtra collections showing about 70% genetic similarity among them. The smallest Group IIE consisted accessions Ch4 and Ch5 of Madhya Pradesh showed 30% genetic variability between them.
The cophenetic correlation showed the maximum cophenetic correlation value (0.846) between Ch13 (Raj5) and Ch16 (Raj11); both are collected from Rajasthan. The average cophenetic correlation between two accessions was found to be 0.605.
3.3. Combined RAPD and ISSR data analysis
Both RAPD and ISSR produced a total of 197 loci of which 179 loci were polymorphic and 18 loci were monomorphic. A dendrogram was constructed by pooled RAPD and ISSR data using Jaccard's similarity coefficient and SHAN clustering.
The Jaccard's similarity coefficient showed a wide range of correlation among all the accessions. The average similarity coefficient between any two accessions was approximately 0.565. Maximum similarity (0.709) was observed between Ch15 and Ch16. However, Ch 8 and Ch17 were found distantly related with a similarity coefficient value of 0.423.
3.4. RAPD‐ and ISSR‐derived dendrogram analysis from Jaccard's coefficient
The dendrogram constructed from combined RAPD and ISSR data segregates a single accession (Ch 8) of Madhya Pradesh Collection from the rest of the accessions sharing a node at 48% level of genetic variation (Figure 4). The subsequent sub‐cluster contained five groups (IIA, IIB, IIC, IID and IIE). The accessions (Ch1, Ch4 and Ch5) formed a group (IIA) which were collected from Madhya Pradesh. Then group IIB consisted of Ch2 and Ch3 collected from Madhya Pradesh showed 47% genetic variability. The IID holds five accessions out of which Ch12 and Ch13 of Rajasthan collections and Ch19, Ch20 and Ch21 of Gujarat collections showed 52% genetic similarity. Thereafter Group IIC consisted of four accessions among which Ch6 collected from Madhya Pradesh and three accessions such as Ch15, Ch16 and Ch17 collected from Maharashtra shared 68% genetic similarity among them. About 47% variability was observed between Ch10 and Ch11 of Rajasthan collections which fall in Group IIE. In this study, it was observed that genetic relation/ variability among the accessions were found more or less similar to RAPD analysis. The maximum cophenetic correlation value (0.707) existed between Ch15 (MH2) and Ch16 (MH3); both were collected from Maharashtra. The average cophenetic correlation between two accessions was found to be 0.590.
As can be seen in Figure 5, the accessions of
Matrix comparison showed the
The programme takes two symmetric similarity or dissimilarity matrices and plots, one matrix against the other element by element (but with the diagonal values ignored). It also computes the product‐moment correlation,
4. Discussion
Characterization of genetic resource collection has been greatly facilitated by the availability of a number of molecular marker systems. Different types of molecular markers have been used to assess the genetic diversity in crop species, but no single technique is universally ideal. Therefore, the choice of the technique depends on the objective of the study, sensitivity level of the marker system, financial constraints, skill and facilities available [25].
The molecular markers best studied for detecting genetic diversity should be relatively easy and inexpensive to use and should rapidly evolve enough variable within populations [26]. There are numerous DNA‐based molecular marker systems suitable for genetic diversity assessment. Random amplified polymorphic DNA (RAPD) and inter‐simple sequence repeat (ISSR) marker, in addition to its suitability of genetic diversity study, are highly polymorphic, reproducible, cost effective and require no prior information of sequence [27]. These facts suggest RAPD and ISSR could be unbiased tools to evaluate the changes of diversity in agronomically important crops [28].
RAPD generated a total of 168 bands ranging from 300 to >3000 bp of which 7.73% were monomorphic common to all accessions, 2.97% were unique present in four accessions and 92.26% were polymorphic in many of the accessions. This high degree of polymorphism detected with 20 RAPD primers indicates a high marker index. This is consistent with the results obtained by Shin et al. [29] in watermelon and Galderisi et al. [30] in fig. Some RAPD primers (OPA16, OPA18) produced more markers probably because genomic DNA sequences possess high frequency of annealing sites [31]. ISSR markers have been used to evaluate the genetic diversity in several medicinal plants [32–35]. The larger number of polymorphic markers (as compared to number of primers used) generated by ISSR‐PCR can be attributed to the fact that the centromeric region contains a large amount of repeated sequences [36]. A higher level of polymorphism was detected by both RAPD and ISSR in our study corroborated to other reports [37, 38].
The present results showed that molecular markers, ISSR and RAPD, efficiently identify
Though RAPD and ISSR markers demarcated 21 accessions into several groups, clustering of accessions within groups was not similar. A possible explanation for the difference in resolution of RAPD and ISSR is that the two marker techniques targeted different portions of the genome. The inter‐simple sequence repeats target regions lying within the micro‐satellite, and the amplification loci of RAPD are mainly in the gene expression region [14, 15]. These differences may also be attributed to marker sampling errors and/or the level of polymorphism detected reinforcing the importance of the number of loci and their coverage of the overall genome in obtaining reliable estimates of genetic relationships among cultivars [42, 43]. The putatively similar bands originating for RAPDs in different individuals are not necessarily homologous, although they may share the same size in base pairs. This result sometimes may lead to wrong results when calculating genetic relationships [44].
The dendrogram did not indicate any clear pattern of clustering according to the locations from where germplasm were collected, indicating little and no location specificity among
Matching the clustering result with their collection sites revealed that the geographical distribution of most accessions was not found to be defined. The majority of accessions in sub‐group IIC were from different places of Madhya Pradesh and Rajasthan. Similarly, three of five accessions in the sub‐group IID were collected from Maharashtra whereas two were from Madhya Pradesh. Similar conclusions were made by Padmesh et al. [47] in
On the basis of percent of polymorphism (RAPD = 92.26 and ISSR = 82.76), RAPD markers were marginally more informative than ISSR in the assessment of genetic diversity in
The Mantel test on the similarity matrices produced by RAPD and ISSR markers showed significant correlation (
In this study, superiority of RAPD markers over ISSR were observed with regards to polymorphism detection, as RAPD detected 92.26% as compared to 82.76% for ISSR markers. This is in contrast to the results as obtained for other several plant species where ISSR was proved to be superior as compared to RAPD [44, 56–58]. More polymorphism, in the case of RAPD than ISSR markers might be due to the fact that out of 10 primers used in the study, only 6 primers amplified 609 numbers of fragments for ISSR. While in the case of RAPD, all the 20 primers which were used in the investigation amplified 3528 number of fragments. The same polymorphism pattern was observed in jatropha, podophyllum and apricot [59–61]. RAPD also provides marker even for cultivars identification [62] determining hybridity among the sexual cross made intentionally to exploit the genetic variability [63, 64] and germplasm evaluation [31].
To conserve the diversity of genetic resources, a large number of accessions are to be conserved for which problems are encountered in documentation, conservation, multiplication and evaluation. Therefore, to minimize the cost of genetic conservation ensuring representation of maximum genetic variation, a set of accessions (core collection) should be selected to represent the genetic diversity of a base collection with minimum redundancy [12]. From the PCA of the 38 accessions, it was evident that some of the accessions were found overlapping each other depicting redundancy which should be eliminated. Since there are different strategies for developing a core collection using morphological or marker data, however molecular markers are more stable and efficient in estimating the genetic relatedness among the individuals [65, 66].
The molecular analyses of RAPD and ISSR markers were extremely useful for studying the genetic relationship/variability between and among
5. Conclusion
The present findings demonstrate the use of RAPD and ISSR markers in estimating genetic diversity and identifying a core collection in
Acknowledgments
The authors wish to acknowledge the support from the Directorate of Medicinal and Aromatic Plants Research (DMAPR) Boriavi, Anand for providing necessary facilities. The authors are also thankful to K.A. Geetha, Principal Scientist (Plant Breeding) for providing the material for the study.
References
- 1.
Sharma R. (2003). Medicinal Plants of India—An Encyclopedia. Daya Publishing House; New Delhi: 333 p. - 2.
Chopra AK, Khanna DR, Prasad G, Malik DS, Bhutiani R. (eds.). Medicinal Plants: Conservation, Cultivation and Utilization. 2007. Daya Publishing House; New Delhi. - 3.
Kongkiatngam P, Waterway MJ, Fortin MG, Coulman BE. Genetic variation within and among cultivars of red clover ( Trifolium pratense ): comparison of morphological, isozyme and RAPD markers. Euphytica. 1995;84 : 237–246. - 4.
Whitkus R, Doebley J, Wendel JF. Nuclear DNA markers in systematics and evolution. In: Phillips L. and Vasil I. K. (eds), DNA–Based Markers in Plants. Kulwer Academic Publishers; Dordrecht, The Netherlands: 1994. pp. 116–141. - 5.
Nybom H. DNA fingerprinting‐A useful tool in fruit breeding. Euphytica . 1994;77: 59–64. - 6.
Ipek M, Simon P. Genetic Diversity in Garlic ( Allium sativum L.) as Assessed by AFLPs and Isozymes.American Society for Horticultural Science 98th Annual Conference & Exhibition .J uly 2001; 22 – 25. - 7.
Singh A, Chauhan HS. Safed musli (Chlorophytum borivilianum): distribution, biodiversity and cultivation. Journal of Medicinal and Aromatic Plant Science. 2003; 25 : 712–719, 2003. - 8.
Oudhia P.: My experiences with wonder crop Safed Moosli. In: Souvenir. International Seminar on Medicinal Plants and Quality Standardization, VHERDS, Chennai, India, 9–10 June, 2001a. - 9.
Oudhia P, Tripathi RS. Scope of cultivation of important medicinal plants in Chhattisgarh plains. In: Proc. National conference on Health care and Development of Herbal Medicines, IGAU, Raipur (India), 29–30 Aug. 1997, 1999: pp.71–78. - 10.
Oudhia P, Joshi BS.. The decreasing availability of natural medicinal plants in Chhattisgarh: A study. In: Abstr. VI National Science Conference, Mahatma Gandhi Gramoday Chitrakut Vishwavidyalaya, Chitrakut, India: 2000;18. - 11.
Bordia PC, Joshi A, Simlot MM. Safed Musli. In: K.L. Chadha and Rajendra Gupta (eds.), Advances in Horticulture Vol. II. Medicinal and Aromatic Plants. Malhotra Publishing House, New Delhi. 1995; pp. 429–451. - 12.
Franklin OH, Brown AHD. Plant Genetic Resources Today‐ A Critical Appraisal. In: Crop Genetic Resources: Conservation and Evaluation. Holden, JHW and Williams JT (eds) Allen and Unwin; Winchester, Massachusetts, USA: 1984; pp. 187–196. - 13.
Doyle JJ, Doyle JL. Isolation of plant DNA from fresh tissue. Focus. 1990; 12 : 13–15. - 14.
Williams JGK, Kubelic AR, Livak KJ, Rafalsky JA, Tingey SV. DNA polymorphisms amplified by arbitrary primer are useful as genetic markers. Nucleic Acid Research. 1990; 18 : 6531–6535. - 15.
Zietkiewicz E, Rafalski A, Labuda D. Genome fingerprinting by simple sequence repeats (SSR)‐anchored PCR amplification. Genomics. 1994; 20 : 176–183. - 16.
Prevost A, Wilkinson MJ. A new system of comparing PCR primers applied to ISSR fingerprinting of potato cultivars. Theoretical andApplied Genetics. 1999;98: 107–112. - 17.
Smith JSC, Chin ECL, Shu H, Smith OS, Wall SJ, Senior ML, Mitchell SE, Kresovich S, Ziegle J. An evaluation of the utility of SSR loci as molecular markers in maize (Zea mays L.): comparison of data from RFLPs and pedigree. Theoretical and Applied Genetics. 1997; 95 : 163–173. - 18.
Ghilslain M, Zhang D, Fazardo D, Huamann Z, Hismans RH. Marker‐assisted sampling of the cultivated Andean Potato Solanum fureja collection using RAPD markers. Genetic Resources and Crop Evolution. 1999; 46 : 547–555. - 19.
Jaccard P. Nouvelles researches sur la distribution florale. Bull. Soc. Vaud. Sci. Nat. 1908; 44 : 223–270. - 20.
Sneath PHA, and Sokal R. Numerical Taxonomy. Freeman, San Francisco, CA, USA. 1973. - 21.
Rohlf, FJ. NTSYS‐PC: Numerical taxonomy and Multivariate Analysis System. Exeter Software, New York, USA. 1997. - 22.
Ludwig AJ, Reynolds FJ. Statistical Ecology—A Primer on Methods and Computing. John Wiley and Sons, New York, USA. 1988. - 23.
Mantel NA. The detection of disease clustering and a generalized regression approach. Cancer Research. 1967; 27 : 209–220. - 24.
Gepts P. The Use of Molecular and Biochemical Markers in Crop Evolution Studies. In: Hecht MK (eds) Evolutionary Biology, 27. Plenum Press, New York, 1993; pp. 51–94. - 25.
Yoseph B, Boltha AM, Myburg AA. A comparative study of molecular and morphological methods of describing genetic relationship in traditional Ethiopian highland maize. African Biotechnology. 2005; 4 (7): 586–595. - 26.
Esselman EJ, Jiangqiang J, Croford DJ, Windus JL, Wolf AD. Clonal diversity in the rare Calamagrostis porteri ssp. Insperata (Poaceae): Comparative results for allozymes and Random amplified polymorphic DNA (RAPD) and Inter simple sequence repeat (ISSR) markers. Molecular Ecology. 1999;8 (3): 443–451. - 27.
Bornet BC, Muller FP, Branched M. Highly informative nature of inter simple sequence repeats (ISSR) sequences amplified using tri and tetra nucleotide primers from DNAs of cauliflower (Brassica oleracea var. Botrytus L). Genome. 2002; 45 : 890–896. - 28.
Brantestam AK, Bothmer RV, Dayteg C, Rashal I, Tuvesson S, Weibull J. Inter simple sequence repeat analysis of genetic diversity and relationships in cultivated barley of Nordic and Baltic origin. Hereditas. 2004; 141 (2): 186–187. - 29.
Shin JS, Lee SJ, Park KW. Genetic diversity in Citrullus vulgaris L. germplasm through RAPD analysis. Korean Journal of Breeding. 1995;27 (1): 94–107. - 30.
Galderisi U, Cipollaro M, Di Bernardo G, De Masi L. Identification of the edible fig ‘Bianco del Cilento’ by random amplified polymorphic DNA analysis. Horticultural Science. 1999;34: 1263–1265. - 31.
Virk PS, Ford‐Lloyd BV, Jackson MT, Newbury HJ. Use of RAPD for the study of diversity within plant germplasm collections. Heredity. 1995; 74 : 170–179. - 32.
Zhou Y, Zhou C, Yao H, Liu Y, Tu R. Application of ISSR markers in detection of genetic variation among Chinese yam ( Dioscorea opposita Thunb) cultivars. Life Science Journal. 2008;5(4) : 6–12. - 33.
Wang Z, Feng SG, Lu JJ, Shi NN, Liu JJ. Phylogenetic study and molecular identification of 31 Dendrobium species using inter‐simple sequence repeat (ISSR) markers. Scientia Horticulturae. 2009;122 :440–447. - 34.
Tamhankar S, Ghate V, Raut A, Rajput B. Molecular profiling of “Chirayat” complex using inter simple sequence repeat (ISSR) markers. Planta Medica. 2009; 75 (11): 1266–1270. - 35.
Ding G, Li X, Ding X, Qian L. Genetic diversity across natural populations of Dendrobium officinale , the endangered medicinal herb endemic to China, revealed by ISSR and RAPD markers. Russian Journal of Genetics. 2009;45(3 ): 327–334. - 36.
Bhuyan N, Basanta KB, Sharma RN. Genetic diversity analysis in traditional lowland rice (Oryza sitiva) of Assam using RAPD and ISSR markers. Current Science. 2007; 93 (7): 976–979. - 37.
Parsons BJ, Newbury HJ, Jackson MT, Ford‐Lloyd BV. Contrasting genetic diversity relationships are revealed in rice ( Oryza sativa L.) using different marker types. Molecular Breeding. 1997;3 : 115–125 - 38.
Raghunathchari P, Khanna VK. RAPD analysis of genetic variability in Indian scented rice (Oryza sativa L.) germplasm. Current Science. 2000; 79 : 994–998. - 39.
Fracaro F, Zacaria J, Echeverrigaray S. RAPD based genetic relationships between populations of three chemotypes of Cunila galioides Benth. Biochemical Systematics and Ecology, 2005;33(4 ):409–417. - 40.
Yuan XF, Dai ZH, Wang XD, Zhao B. Assessment of genetic stability in tissue‐cultured products and seedlings of Saussurea involucrata by RAPD and ISSR markers. Biotechnology Letters. 2009;31(8) : 1279–1287. - 41.
Manica‐Cattani MF, Zacaria J, Pauletti G, Atti‐Serafini L, Echeverrigaray S. Genetic variation among South Brazilian accessions of Lippia alba Mill. (Verbenaceae) detected by ISSR and RAPD markers. Brazilian Journal of Biology. 2009; 69 : 2. - 42.
Loarce Y, Gallego R, Ferrer E. A comparative analysis of genetic relationships between rye cultivars using RFLP and RAPD markers. Euphytica. 1996; 88 : 107–115. - 43.
Souframanien J and Gopalakrishna T. A comparative analysis of genetic diversity in blackgram genotypes using RAPD and ISSR markers. Theoretical and Applied Genetics. 2004; 109 : 1687–1693. - 44.
Fernandez M, Figueiras A, Benito C. The use of RAPD and ISSR markers for detecting DNA polymorphism, genotyping identification and genetic diversity among barley cultivars with known origin. Theoretical and Applied Genetics. 2002; 104 : 845–851. - 45.
Arif M, Zaidi NW, Singh YP, Haq QMR. A comparative analysis of ISSR and RAPD markers for study of genetic diversity in shisham ( Dalbergia sissoo ). Plant Molecular Biology Reports. 2009;27 : 488–495. - 46.
Sehgal D, Raina SN. Genotyping safflower ( Carthamus tinctorius ) cultivars by DNA fingerprints. Euphytica. 2005;146 : 67–76. - 47.
Padmesh P, Sadu KK, Seeni S, Pushpangadan P. The use of RAPD in assessing genetics variability in Andrographis paniculata Nees, a hepatoprotective drug. Current Science. 1999;76 : 833–835. - 48.
Chen JM, Gituru WR, Wang YH, Wang QF. The extent of clonality and genetic diversity in the rare Caldesia grandis (Alismataceae): comparative results for RAPD and ISSR markers. Quaternary Botany. 2006;84 : 301–307. - 49.
Kumar P, Singh K, Vikal Y, Randhawa LS, Chahal GS. Genetic diversity studies of elite cotton germplasm lines Using RAPD markers and morphological characters. Indian Journal of Genetics. 2003; 63 (1): 5–10. - 50.
Bachmann K. Nuclear DNA markers in plant biosystematics research. Opera Botany. 1997; 32 : 137–148. - 51.
Landergott U, Holderegger R, Kozlowski G and Schneller JJ. Historical bottlenecks decrease genetic diversity in natural populations of Dryopteris cristata . Heredity 2001;87 : 344–355. - 52.
Penner GA. RAPD Analysis of Plant Genomes. In: Methods of Genome Analysis in Plants. Jauhar PP (ed.). CRC, Boca Raton, 1996; 251–268. - 53.
Penner GA, Bush A, Wise R, Kim W, Domier L, Kasha K, Laroch A, Scoles GS, Gedak G. Reproducibility of random amplified polymorphic DNA (RAPD) analysis among laboratories. PCR Methods and Applications. 1993; 2 :341–345. - 54.
Mattioni C, Casasoli M, Gonzalez M, Ipinza R, Villani F. Comparison of ISSR and RAPD markers to characterize three Chilean Nothofagus species. Theoretical and Applied Genetics. 2002; 43 : 224–231. - 55.
Dai A, Guo, Zhang L, Chang. Application of random amplified polymorphic DNA and inter‐simple sequence repeat markers in the genus Crataegus . Annals of Applied Biology. 2009;154 : 175–181. - 56.
Mattioni C, Casasoli M, Gonzalez M Ipinza R, Villani F. Comparison of ISSR and RAPD markers to characterize three Chilean Nothofagus species. Theoretical and Applied Genetics 2002; 104 : 1064–1070. - 57.
Zhao WG, Zhang JQ, Wangi YH, Chen TT. Analysis of genetic diversity in wild populations of mulberry from Western Part of Northeast China determined by ISSR markers. Genetics Molecular Biology. 2007;7: 196–203. - 58.
Huang J & Sun SM. Genetic diversity and relationships of sweet potato and its wild relatives in Ipomoea series Batatas (Convolvulaceae) as revealed by inter‐simple sequence repeat (ISSR) and restriction analysis of chloroplast DNA. Theoretical and Applied Genetics 2009; 100 : 1050–1060. - 59.
Gupta M, Chyi YS, Romero‐Severson J, Owen JL. Amplification of DNA markers from evolutionarily diverse genomes using single primers of simple‐sequence repeats. Theoretical and Applied Genetics. 1994; 89 : 998–1006. - 60.
Alam MA, Naik K, Mishra GP. Congruence of RAPD and ISSR markers for evaluation of genomic relationship among 28 populations of Podophyllum hexandrum Royle from Himachal Pradesh, India. Turkish Journal of Botany. 2009;33: 1–12 - 61.
Kumar M, Mishra GP, Singh R, Kumar J, Naik PK, SB. Singh SB. Correspondence of ISSR and RAPD markers for comparative analysis of genetic diversity among different apricot genotypes from cold arid deserts of trans‐Himalayas. Physiology and Molecular Biology of Plants. 2009; 15 :225–236. - 62.
Torres, E., Iriondo, J. M. and Perez, C. (2003). Genetic structure of an endangered plant, Antirrhinum microphyllum (Scrophulariaceae): allozyme and RAPD analysis. American Journal of Botany. 203; 90 (1): 85–92. - 63.
Patra NK, Tanveer H, Khanuja SPS, Shasany AK, Singh HP, Singh VR, Kumar S. A unique interspecific hybrid spearmint clone with growth properties of Mentha arvensis L. and oil qualities ofMentha spicata L. Theoretical and Applied Genetics. 2001;102 : 471–476. - 64.
Collins D, Mill RR, Moller M. Species separation of Taxus baccata, T. cannadensis and T. cuspidate (Taxaceae) and origins of their reputed hybrids inferred from RAPD and cpDNA data. American Journal of Botany. 2003;90 : 175–182. - 65.
Dhanaraj AL, Rao EVB, Swamy KRM, Bhat MG, Prasad DT, Sounder SN. Using RAPDs to assess the diversity in Indian cashew ( Anarcardium occidentale L.) germplasm. Journal of Horticultural Science and Biotechnology. 2002;77: 41–47. - 66.
Shashidhara G, Hema MV, Binu Koshy Farooqi AA.. Assessment of genetic diversity and identification of core collection in sandalwood germplasm using RAPDs. Journal of Horticultural Science & Biotechnology. 2003; 78 (4): 528–536.