characterization of genetic diversity in jatropha...

Indian Journal of Biotechnology

Vol 11, January 2012, pp 54-61

Characterization of genetic diversity in Jatropha curcas L. germplasm

using RAPD and ISSR markers

Varsha Khurana-Kaul1, Sumita Kachhwaha

1 and S L Kothari

1,2*

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: [email protected]

KHURANA-KAUL et al.: GENETIC DIVERSITY IN J. CURCAS L.

55

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

INDIAN J BIOTECHNOL, JANUARY 2012

56

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).


57

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


58

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.)


59

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)


60

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.

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