Characterization of genetic diversity in Jatropha curcas L. germplasm using RAPD and ISSR markers
Varsha Khurana-Kaul1, Sumita Kachhwaha1 and S L Kothari1,2*
1Department of Botany and 2Centre for Converging Technologies (CCT), University of Rajasthan, Jaipur 302 004, India
Received 26 July 2010; revised 22 May 2011; accepted 18 July 2011
Jatropha curcas L. is a rapidly emerging biofuel crop attracting a lot of interest, triggering large investments and rapid expansion of cultivation areas. In the present investigation, the genetic relationships of 29 J. curcas accessions were assessed based on randomly amplified polymorphic DNA (RAPD) and inter simple sequence repeat (ISSR) analyses. A total of 72 polymorphic primers (47 RAPD and 25 ISSR) were used. Amplification of genomic DNA of the 29 genotypes, using RAPD analysis, yielded 552 fragments that could be scored, of which 334 were polymorphic with an average of 7.1 polymorphic fragments per primer. Number of amplified fragments varied from 2 to 23 and ranged in size from 100-3,500 bp. The 25 polymorphic ISSR primers used in the study produced 336 bands across 29 genotypes, of which 201 were polymorphic. The number of amplified bands varied from 7 to 20 with a size range of 100-3,500 bp. Molecular polymorphism was 60.5 and 59.8% with RAPD and ISSR markers, respectively. Mantel test between the two Jaccard’s similarity matrices gave r=0.8623, showing good fit correlation between RAPD and ISSR based similarities. Clustering of genotypes within groups remained more or less similar in ISSR and combined data of RAPD and ISSR.
Keywords: Genetic diversity, ISSR, Jatropha curcas, microsatellites, polymorphism, RAPD
Introduction
Bio-diesel is becoming popular as an alternative to diesel on account of high demand, necessary policy support and technological feasibility. India consumes approximately 40 million tonnes of diesel annually and has to import most of it1. Therefore, any alternative to diesel becomes national priority.
Recently, government of India launched “National Mission on Bio-diesel” with a view to find a cheap and renewable liquid fuel based crop. Among the potential non-edible oil yielding crops, Jatropha curcas (physic nut) (Family: Euphorbiaceae) has received the greatest attention lately as a promising biofuel plant in tropical and sub-tropical countries2. Jatropha can grow in poor soils with low rainfall and its oil can be substituted with diesel without any alteration in the existing automobile engines3. These properties of the plant have fuelled intense research on this crop in recent years.
In spite of numerous favourable attributes, the full potential of the crop has not been realized due to lack of planned breeding programmes for the creation of new and improved varieties4. Once genetically distinct varieties have been identified, these will serve
as a useful resource for cultivation under different climates and development of new varieties through breeding. It has been reported that variability of J. curcas in central India is mainly limited to seed source variation in morphology, germination and seedling growth5. Divergence in seed oil traits of Jatropha has been reported from a limited number of locally collected accessions6.
Molecular markers have been used to monitor DNA sequence variation in and among the species and create new sources of genetic variation by introducing new and favourable traits from landraces and related species. They are reliable indicators of genetic diversity because they are neutral to environmental influence and reveal differences at the whole genome level10. Among the various molecular markers employed to assess diversity, PCR based markers, such as, RAPD11 and ISSR12, are being popular as their application does not need any prior sequence information. On the other hand, microsatellites or simple sequence repeat (SSR) are the markers of choice for breeding applications13. Each of these methods has been widely used to identify and determine relationship at species and cultivar levels14-17. In Jatropha sp., analyses of genetic diversity have been carried out using RAPDs and ISSRs alone or in combination18-20.
__________
*Author for correspondence:
Tel & Fax: +91-141-2703439 E-mail: slkothari@lycos.com
The objective of the present study was to investigate genetic variation among different genotypes of J.
curcas using RAPD and ISSR markers. This would act as initial step towards selection and breeding of superior genotypes including those having high oil content and resistance to pests.
Materials and Methods
Plant Material and DNA Extraction
A representative set of 29 accessions of J. curcas were collected from different regions of Rajasthan in
the year 2008 (Table 1). Total genomic DNA was extracted from young leaves following the standard CTAB method21 with minor modifications. Leaves (5 g) were ground in liquid nitrogen, then homogenized in 25 mL of extraction buffer (2% CTAB, 20 mM EDTA, 2% PVP, 1.4 M NaCl, 100 mM Tris-HCl pH 8.0 and 1% β-mercaptoethanol) and incubated at 65°C for 1 h. The supernatant was treated with RNAase A, incubated at 37°C for 30 min and extracted twice with chloroform:isoamylalcohol (24:1 v/v). The DNA was pelleted with chilled
Table 1—Details of J. curcas germplasm collected from different locations in Rajasthan, India
No. Acc. code Location Latitude and longitude Collection site
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
U1 U2 U3 U4 U5 U6 U7 U8 U10 U11 B1 B2 B3 B4 B5 S1 A1 A2 C1 C2 C3 C4 D1 D2 D3 R1 R2 R3 U9
Bhuwana, Udaipur Sisarma, Udaipur Choti Undri, Udaipur Daken Kota, Udaipur Jhadol, Udaipur Jhadol, Udaipur Gogunda, Udaipur Saira, Udaipur Salumbar, Udaipur Bhamrasiya, Udaipur Bhilwara
Bhilwara Bhilwara Bhilwara Bhilwara Pindwara, Sirohi Lasara, Banswara Lasara, Banswara ATC Farm, Chittor Jalampur, Chittor Pratapgarh Pratapgarh Dungarpur Dungarpur Aaspur, Dungarpur Kumbhalgarh, Rajsamand Gundi ka Bhilwara, Rajsamand Majera, Rajsamand
Jaisamand, Udaipur
24° 34' 16 N 73° 41' 29 E 24° 34' 07 N 73° 39' 13 E 24° 34' 15 N 73° 40' 52 E 24° 34' 06 N 73° 41' 21 E 24° 21' 39 N 73° 32' 18 E 24° 21' 39 N 73° 32' 18 E 24° 56' 15 N 73° 49' 11 E 24° 34' 16 N 73° 41' 29 E 24° 08' 12 N 74° 03' 12 E 24° 37' 28 N 73° 53' 36 E 25° 21' 14 N 74° 34' 45 E 25° 21' 14 N 74° 34' 45 E 25° 21' 14 N 74° 34' 45 E 23° 21' 09 N 74° 38' 28 E 23° 21' 09 N 74° 38' 28 E 24° 48' 01 N 73° 26' 31 E 23° 32' 42 N 74° 26' 31 E 23° 32' 42 N 74° 26' 31 E 24° 53' 47 N 74° 37' 58 E 24° 53' 02 N 74° 38' 06 E 24° 02' 11 N 74° 46' 49 E 24° 02' 11 N 74° 46' 49 E 23° 51' 06 N 74° 10' 51 E 23° 51' 06 N 74° 10' 51 E 23° 56' 54 N 74° 05' 30 E 25° 08' 51 N 73° 35' 00 E 25° 00' 04 N 73° 50' 52 E 24° 46' 49 N 73° 44' 49 E 24° 14' 40 N 73° 57' 11 E
Wild Wild Farm land Farm land Wild Wild Wild Wild Farm land Farm land Farm land Farm land Farm land Wild Wild Wild Farm land Farmland Farm land Wild Farm land Farm land Wild Wild Farm land Wild Farm land Farm land Wild
isopropanol and washed twice with 70% ethanol. The pelleted DNA was air dried and stored at –20°C.
DNA concentration was determined using known amount of λ DNA as standard.
RAPD Amplification
PCR amplification was performed with 52 random decamer primers obtained from Operon Technologies (Almeda, USA). Amplification was performed in 10 µL reaction volume containing 2.5 ng DNA, 1× PCR buffer (10 mM Tris pH 9.0, 50 mM KCl, 1.5 mM MgCl2 ), 100 µM each dNTP, 0.4 µM of RAPD primer and 0.3 U of Taq DNA polymerase (Bangalore Genei, India). PCR reactions were performed in a Gene Amp 9700 Thermal Cycler (Perkin Elmer Applied Biosystems) with an initial denaturation at 94°C for 3 min, followed by 45 cycles at 94°C for 45 sec, 36°C for 45 sec and 72°C for 2 min with a final extension at 72°C for 7 min.
Amplified products were separated on 1.5% agarose gel in 1× TAE buffer by electrophoresis at 100 V and visualized with ethidium bromide staining. The size of the amplification products was determined by comparison to λ DNA digested with EcoRI and HindIII.
ISSR Amplification
PCR amplification was performed with 26 ISSR primers (University of British Columbia, Vancouver, Canada). The PCR reaction mixture (10 µL) consisted of 2.5 ng DNA, 1× PCR buffer (10 mM Tris pH 9.0, 50 mM KCl, 1.5 mM MgCl2 ), 0.2 µL of 25 mM MgCl2, 200 µM of each dNTP, 0.4 µM of ISSR primer and 0.6 U of Taq DNA polymerase (Bangalore Genei, India). PCR amplifications were performed with initial denaturation at 94°C for 4 min, followed by 35 cycles of 30 sec at 92°C, 1 min at annealing temperature (depending upon the primer), 2 min elongation at 72°C and final extension at 72°C for 7 min. The amplified products were electrophoresed in 1× TAE buffer at 100 V on a 1.8% agarose gel using EcoRI and HindIII double digest as mol wt standard.
Statistical Analysis
The DNA fingerprint patterns obtained were converted into binary data matrices containing arrays of 0s and 1s. The RAPD and ISSR bands were scored visually for the presence (1) or absence (0) of bands of various mol wt sizes. Only polymorphic and reproducible bands were considered for the analysis.
Data were analysed using SIMQUAL route to generate Jaccard’s similarity coefficient using NTSYS-pc version 2.02e22 (Numerical Taxonomy System). Similarity matrices were utilized to construct dendrograms independently for both the marker systems and also on pooled marker data using UPGMA (Unweighted Pair Group Method with Arithmetic Average) algorithm and SAHN clustering.
Finally, a principal coordinate analysis was performed in order to highlight the resolving power of the ordination. A two (2-D) and three dimensional (3-D) principal component analysis was constructed to provide another means of testing the relationships among accessions using EIGEN program (NTSYS-PC).
The robustness of each phenogram was evaluated by a bootstrap analysis23 of each data set using the computer program WINBOOT24. Each phenogram was reconstructed 1000 times by repeated sampling with replacement. The frequency with which a particular grouping was identified was taken to reflect the strength of the grouping.
Results
PCR fingerprinting of J. curcas DNA produced clear, reproducible and polymorphic banding patterns that allowed characterization of accessions used in the present study. In case of RAPD analysis, 52 RAPD primers were used for initial screening of J. curcas genotypes, of which 47 primers revealed polymorphic banding patterns. The 47-decamer primers amplified DNA fragments across the 29 genotypes with the number of amplified fragments varying from 2 (OPJ-06) to 24 (OPC-04) (Fig. 1a) in the molecular size range of 100-3,500 bp. A total of 552 bands were produced that could be scored, out of which 334 bands were polymorphic with an average of 7.1 polymorphic bands per primer. Percent polymorphism ranged from 22.2 (OPE-01) to 100%
(OPH-02, OPU-09 and OPE-16) with an average of 60.5% polymorphism (Table 2). Similarity matrix values using Jaccard’s coefficient ranged from 0.31, between U3 and U10, to 0.85 between DI and D2, and R1 and R2. At 60% similarity, the accessions separated into six clusters (Fig. 2a). Cluster I comprised of most of the genotypes. Accessions U9 and U10 remained as outliers and showed maximum variation (65%) from other genotypes. The result of principal coordinate analysis was comparable to the cluster analysis (Fig. 3a).
Of the 26 ISSR primers screened, 25 produced 336 amplification products, out of which 201 were polymorphic with an average of 8.0 polymorphic bands per primer. The number of bands amplified per primer varied between 7 (UBC-817, -823) and 20 (UBC-835) with average band size between
100-3,500 bp (Fig. 1b). Percentage polymorphism ranged from 18.2 (UBC-855) to 100% (UBC-828) with an average of 59.8% polymorphism (Table 2). A dendrogram based on UPGMA analysis with ISSR data is shown in Fig. 2b. Jaccard’s similarity coefficient ranged from 0.24 to 0.90. Dendrogram
Fig. 1 (a & b)—a. RAPD profile of J. curcas genotypes produced with primer OPC-04 (M, λ DNA double digest with EcoRI and HindIII REs; Nc, Negative control (no DNA); & lanes 1 to 29, Samples used in the study as listed in Table 1); b. ISSR profile of different J.
curcas genotypes produced with primer UBC-880 [M, λ DNA double digest with EcoRI and HindIII Res; Nc, Negative control (no DNA); & lanes 1 to 29, Samples used in the study as listed in Table 1]
Table 2—Comparison of DNA marker systems in J. curcas L.
Marker system No. of primers used Total bands scored Total no. of polymorphic bands
% polymorphism Av. polymorphism [bands primer-1]
RAPD ISSR RAPD+ ISSR
47 25 72
552 336 888
334 201 535
60.5 59.8 60.2
7.1 8.0 7.4
Fig. 2 (a-c)—Dendrograms (UPGMA) representing genetic relationships among 29 accessions of J. curcas using Jaccard’s similarity coefficients: a. RAPD databased dendrogram; b. ISSR databased dendrogram; & c. Combined (RAPD+ISSR) databased dendrogram.
(Numbers on the nodes of the cluster indicate the bootstrap values generated by 1000 replications using the program WINBOOT. Only bootstrap values higher than 30% are shown. The labels represent the accession codes as given in Table 1.)
analysis separated the accessions into five clusters at 63% similarity. Cluster I comprised of 24 operational taxonomic units (OTUs). The highest value of similarity coefficient (0.90) was detected between accessions A1 and A2. Accession U10 had distinct OTU as in case of RAPD analysis. The result of PCA was comparable to cluster analysis (Fig. 3b).
The RAPD and ISSR data were combined for UPGMA cluster analysis. Dendrogram constructed on the basis of RAPD+ISSR polymorphism separated the accessions into six distinct clusters at 37% variation (Fig. 2c) with Jaccard’s similarity coefficient ranging from 0.30 to 0.85. Group I was the largest cluster and consisted of 24 accessions. Group II, III, IV, V and VI consisted of one accession each. The PCA analysis based on RAPD+ISSR polymorphism grouped the accessions into seven clusters (Fig. 3c). A few differences in clustering were observed with UPGMA clustering and principal coordinate analysis.
Accessions U11, B1, B2, B3 and S1 were grouped separately from cluster I in PCA. The matrices for two markers, RAPD and ISSR were also compared by using Mantel’s test25 and the correlation value between the matrices were found high (r=0.8623), indicating good correlation between the two molecular marker systems.
Discussion
Knowledge about degree of genetic diversity among and within natural population in and outside centre of origin is required to gain the first idea about where to find potentially valuable genetic material.
RAPD and ISSR studies have been widely used for population genetic studies in both wild26-28 and cultivated29-30 plants. During the present study, 47 RAPD and 25 ISSR primers produced 535 polymorphic bands that discriminated 29 J. curcas genotypes into six clusters. Both RAPD and ISSR markers exhibited >50% polymorphism, which is in contrast to the earlier reports18-19 where the polymorphism detected with these markers was low.
This low level of variation of J. curcas in India has been attributed to the small number of introductions and their vegetative propagation. It is interesting to note that irrespective of the type of marker used the accession U10 showed genetic dissimilarity in both phenogram as well as PCA.
Both UPGMA-phenogram as well as PCA (based on combined RAPD+ISSR data) displayed similar grouping of accessions with some minor deviations
Fig. 3 (a-c)—3-D plot of 29 accessions of J. curcas by principal coordinate analysis using Jaccard’s similarity coefficients: a.
RAPD markers; b. ISSR markers; & c. Combined markers from RAPD and ISSR analysis. (The labels represent the accession codes as given in Table 1)
but failed to show any pattern of variation that can be related to geographic location of the accessions. A possible explanation for this difference is that the two marker techniques target different portions of the genome. The ability to resolve genetic variation among different genotypes may be more directly related to the number of polymorphisms detected with each marker technique rather than a function of which technique in employed31. UPGMA-phenogram classified the accessions into six clusters, while PCA grouped them into seven clusters as shown in other studies also18. The phenogram showed grouping of 24 out of 29 accessions studied (82.75%) into a single cluster. The PCA results corresponded well with the grouping of accessions based on cluster analysis with minor deviations. This association of genotypes from contiguous regions may be the result of similar agro- climatic conditions or due to seed movement and gene flow32. The phenogram showed highest genetic similarity (Jaccard’s similarity coefficient 0.85) between accessions UI and U2, and R1 and R2, that have come from Udaipur and Rajsamand locations.
The higher genetic similarity indicates the higher probability of origin of all these accessions (U1, U2, R1 and R2) from the same source and eventually distribution to different locations. To find the robustness and stability of the phenogram to group accessions in different clusters, the data were analyzed for bootstrap analysis with 1,000 replicates.
Higher bootstrap values (>30) obtained at all major nodes in phenogram indicate the stability of grouping of accessions in different clusters.
Our study indicates a modest level of genetic variation in the J. curcas accessions as revealed by RAPD and ISSR marker techniques. Similar conclusions were made by Gupta et al33 and Subramanyam et al34 while assessing genetic variation in various J. curcas accessions collected from different agro-climatic regions of India. This could be used for estimation of genetic relationships, which ultimately help in characterization of J. curcas germplasm. This would also aid in developing and planning breeding strategies for this plant by helping breeders to identify diverse genotypes and makes it possible to carry out early selections and thus reduce the time between recurrent selections and increase genetic gains per year.
Acknowledgement
V K-K thanks Council of Scientific and Industrial Research, New Delhi for the award of SRF and
Dr M Sujatha, Directorate of Oilseeds Research, Rajendranagar, Hyderabad for technical guidance.
References
1 Divakara B N, Upadhyaya H D, Wani S P & Gowda C L L, Biology and genetic improvement of Jatropha curcas L.: A review, Appl Energy, 87 (2010) 732-742.
2 Kumar A & Sharma S, An evaluation of multipurpose oil seed crop for industrial uses (Jatropha curcas L.): A review, Ind Crop Prod, 28 (2008) 1-10.
3 Gopinath M C & Sudhakaran R, Biofuels: Opportunities and challenges in India, In Vitro Cell Dev Biol-Plant, 45 (2009) 350-371.
4 Rao G R, Korwar G R, Shanker A K & Ramakrishna Y S, Genetic associations, variability and diversity in seed characters, growth, reproductive phenology and yield in Jatropha curcas (L.) accessions, Trees, 22 (2008) 697-709.
5 Ginwal H S, Rawat P S & Srivastava R L, Seed source variation in growth performance and oil yield of Jatropha curcas Linn. in central India, Silve Genet, 53 (2004) 186-192.
6 KaushikN, KumarK, KumarS, Kaushik N & Roy S, Genetic variability and divergence studies in seed traits and oil content of Jatropha (Jatropha curcas L.) accessions, Biomass Bioenerg, 31 (2007) 497-502.
7 Sujatha M & Mukta N, Morphogenesis and plant regeneration from tissue cultures of Jatropha curcas, Plant Cell Tissue Organ Cult, 44 (1996) 135-141.
8 Khurana-Kaul V, Kachhwaha S & Kothari S L, Direct shoot regeneration from leaf explants of Jatropha curcas in response to thidiazuron and high copper contents in the medium, Biol Plant, 54 (2010) 369-372.
9 Varshney A & Johnson T S, Efficient plant regeneration from immature embryo cultures of Jatropha curcas, a biodiesel plant, Plant Biotechnol Rep, 4 (2010) 139-148.
10 Caetano-Anolles G & Gresshoff P M, DNA markers:
Protocols, applications and overviews (Wiley-Liss, New York) 1997.
11 Williams J G, Kubelik A R, Livak J, Rafalski J & Tingey S V, DNA polymorphisms amplified by arbitrary primers are useful as genetic markers, Nucleic Acids Res, 18 (1990) 6531-6535.
12 Zietkiewiez E, Rafalski A & Labuda D, Genome fingerprinting by simple sequence repeat (SSR)-anchored polymerase chain reaction amplification, Genomics, 20 (1994) 176-183.
13 Gupta P K & Varshney R K, The development and use of microsatellite markers for genetic analysis and plant breeding with emphasis on bread wheat, Euphytica, 113 (2000) 163-185.
14 Kantety R V, Zeng X, Bennetzen J L & Zehr B E, Assessment of genetic diversity in dent and popcorn (Zea mays L.) inbred lines using inter-simple sequence repeat (ISSR) amplification, Mol Breed, 1 (1995) 365-373.
15 Raina S N, Rani V, Kojima T, Ogihara Y, Singh K P et al, RAPD and ISSR fingerprints as useful genetic markers for analysis of genetic diversity, varietal identification and phylogenetic relationships in peanut (Arachis hypogaea) cultivars and wild species, Genome, 44 (2001) 763-772.
16 Arif M, Zaidi N W, Singh Y P, Haq Q M R & Singh U S, A comparative analysis of ISSR and RAPD markers for study of genetic diversity in shisham (Dalbergia sissoo), Plant Mol Biol Rep, 27 (2009) 488-495.
17 Ikegami H, Nogata H, Hirashima K, Awamura M &
Nakahara T, Analysis of genetic diversity among European and Asian fig varieties (Ficus carica L.) using ISSR, RAPD and SSR markers, Genet Resour Crop Evol, 56 (2009) 201-209.
18 Basha S D & Sujatha M, Inter and intra-population variability of Jatropha curcas (L.) characterized by RAPD and ISSR markers and development of population-specific SCAR markers, Euphytica, 156 (2007) 375-386.
19 Kumar R V, Tripathi Y K, Shukla P, Ahlawat S P & Gupta V K, Genetic diversity and relationships among germplasm of Jatropha curcas L. revealed by RAPDs, Trees, 23 (2009) 1075-1079.
20 Basha S D, Francis G, Makkar H P S, Becker K & Sujatha M, A comparative study of biochemical traits and molecular markers for assessment of genetic relationships between Jatropha curcas L. germplasm from different countries, Plant Sci, 176 (2009) 812-823.
21 Doyle J J & Doyle J L, Isolation of plant DNA from fresh tissue, Focus, 12 (1990) 13-15.
22 Rohlf F J, NTSYS-pc: Numerical taxonomy and multivariate analysis system, version 2.02e (Exeter Publications, Setauket, New York) 1998.
23 Felsenstein J, Confidence limits on phylogenies: An approach using the bootstrap, Evolution, 39 (1985) 783-791.
24 Yap I V & Nelson R J, Winboot: A program for performing bootstrap analysis of binary data to determine the confidence of UPGMA-based dendrograms, in IRRI Discussion Paper Series No.14 (Manila, Philippines, International Rice Research Institute) 1990.
25 Mantel N, The detection of disease clustering and generalized regression approach, Cancer Res, 27 (1967) 209-220.
26 Das M, Bhattacharya S, Basak J & Pal A, Phylogenetic relationships among the bamboo species as revealed by morphological characters and polymorphism analyses, Biol Plant, 51 (2007) 667-672.
27 Dikshit H K, Jhang T, Singh N K, Koundal K R, Bansal K C et al, Genetic differentiation of Vigna species by RAPD, URP and SSR markers, Biol Plant, 51 (2007) 451-457.
28 Sidkar B, Bhattachatya M, Mukherjee A, Banerjee A, Ghosh E et al, Genetic diversity in important members of Cucurbitaceae using isozyme, RAPD and ISSR markers, Biol Plant, 54 (2010) 135-140.
29 Iruela M, Rubio J, Cubero J I, Gil J & Millan T, Phylogenetic analysis in the genus Cicer and cultivated chichpea using RAPD and ISSR markers, Theor Appl Genet, 104 (2002) 643-651.
30 Souframanien J & Gopalakrishna T, A comparative analysis of genetic diversity in blackgram genotypes using RAPD and ISSR markers, Theor Appl Genet, 109 (2004) 1687-1693.
31 Loarce Y, Gallego R & Ferrer E, A comparative analysis of genetic relationships between rye cultivars using RFLP and RAPD markers, Euphytica, 88 (1996) 107-115.
32 Padmesh P, Sabu K K, Seen S & Pushangadhan P, The use of RAPD in assessing genetic variability in Andrographis paniculata Nees, a hepatoprotective drug, Curr Sci, 76 (1999) 833-835.
33 Gupta S, Srivastava M, Mishra G P, Naik P K & Chauhan R S, Analogy of ISSR and RAPD markers for comparative analysis of genetic diversity among different Jatropha curcas genotypes, Afr J Biotechnol, 7 (2008) 4230-4243.
34 Subramanyam K, Rao D M, Devanna N, Arvinda A &
Pandurangada V, Evaluation of genetic diversity among Jatropha curcas (L.) by RAPD analysis, Indian J Biotechnol, 9 (2010) 283-288.
