- Email: [email protected]
Genetic diversity within and among the wild populations of Murraya koenigii (L.) Spreng., as revealed by ISSR analysis
Biochemical Systematics and Ecology 39 (2011) 139–144
Contents lists available at ScienceDirect
Biochemical Systematics and Ecology journal homepage: www.elsevier.com/locate/biochemsyseco
Genetic diversity within and among the wild populations of Murraya koenigii (L.) Spreng., as revealed by ISSR analysis Sushma Verma, T.S. Rana* Conservation Biology and Molecular Taxonomy Laboratory, National Botanical Research Institute (Council of Scientific and Industrial Research), Rana Pratap Marg, Lucknow 226001, India
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 August 2010 Accepted 29 January 2011 Available online 22 February 2011
Murraya koenigii (L.) Spreng., commonly known as curry leaf plant, is found in the different hilly regions of India. In the present study, fifty-nine accessions representing eight wild populations of M. koenigii were analyzed using thirteen ISSR primers. A total of 152 bands were amplified, out of which,136 were polymorphic corresponding to 89.47% polymorphism across the accessions. The pairwise population genetic distances were calculated for all the populations that varied from 0.05 to 0.13 between the populations of M. koenigii. AMOVA and Nei’s genetic diversity analysis revealed higher genetic variations within populations than among the populations. The clustering of populations in the dendrogram was not in congruence with geographical affiliations. The results indicate that the ISSR method is sufficiently informative and powerful to estimate the genetic diversity in M. koenigii populations. As M. koenigii is an important wild plant genetic resource, therefore, information on genetic variability might be a potential source as breeding material for development of commercially valuable traits in M. koenigii plants. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Genetic diversity Population structure Wild Murraya koenigii ISSR
1. Introduction The genus Murraya Koenig ex L., (Rutaceae) comprises of about 11 species of shrubs and small trees distributed in tropical and sub-tropical regions of Sri Lanka, India, South China to South-East Asia, Malaysia, New Guinea, North-East Australia and New Caledonia (Swingle and Reece, 1967). In India the genus is represented by two species, Murraya koenigii (L.) Spreng., and Murraya paniculata (L.) Jack., having (2n) 18 chromosomes (Nair and Nayar, 1997; Ohri, 2002). M. koenigii is an aromatic shrub or small tree up to 6 m in height and 15–40 cm in diameter, and commonly known as curry leaf plant in India. The species occurs throughout India, both in wild and cultivated forms. Wild forms in India are found growing in the hills of Assam, Central India, Himachal Pradesh, Kerala, Punjab, Sikkim, Tamil Nadu, Uttarakhand, West Bengal and Western Ghats (Dastur, 1970), whereas in Southern parts of India it is being extensively cultivated for its aromatic leaves. M. koenigii has long been used in traditional Indian system of medicine, such as Ayurveda and Unani (Kirtikar and Basu, 1935; Anonymous, 1962; Dastur, 1970; Drury, 1978; Joseph and Peter, 1985; Chopra et al., 1958; Warman, 1999). Fresh leaves yield a highly odoriferous essential oil which is used as a fixative for soap perfume (Joseph and Peter, 1985). In the present study ISSR markers were used to unravel the genetic diversity across eight natural populations of M. koenigii occurring in different parts of Punjab, Himachal Pradesh and Uttarakhand states of India. To the best of our knowledge, this is the first attempt to estimate the genetic diversity in M. koenigii using ISSR markers. * Corresponding author. Tel.: þ91 522 2297854; fax: þ91 522 2205836. E-mail address: [email protected] (T.S. Rana). 0305-1978/$ – see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.bse.2011.01.017
140
S. Verma, T.S. Rana / Biochemical Systematics and Ecology 39 (2011) 139–144
Table 1 Sampling details of different wild Murraya koenigii populations considered in the present study. Population namea (approx. pop size)
Accession nos. (sample size)
Locality, stateb
Altitude (m)
Latitude/longitude
PTH (100) TNK (80) SRI (150) DD1 (120) DD2 (100) UNA (180) PLM (250) NNG (300)
Mk01–Mk06 Mk07–Mk12 Mk13–Mk20 Mk21–Mk26 Mk27–Mk32 Mk33–Mk41 Mk42–Mk49 Mk50–Mk59
Pithoragarh, UK Tanakpur, UK Srinagar, UK DehraDun, UK DehraDun, UK Una, HP Palampur, HP Nangal, PNJ
1333 255 973 640 682 350 1220 326
29 510 N/80 090 E 29 050 N/80 050 E 30 130 N/78 460 E 30 200 N/78 040 E 30 310 N/78 020 E 31 470 N/76 070 E 32 050 N/76 470 E 31 220 N/76 210 E
a b
(6) (6) (8) (6) (6) (9) (8) (10)
Population names in bold are abbreviated to the names of the respective locality, where the populations were collected from. UK ¼ Uttarakhand, HP ¼ Himachal Pradesh, PNJ ¼ Punjab.
2. Materials and methods 2.1. Plant material Eight wild populations of M. koenigii from Punjab, Himachal Pradesh and Uttarakhand states of India were surveyed and a total of fifty-nine accessions representing these eight populations were collected. Each population surveyed represented about 50–300 mature individuals. The population names and the details of accessions along with their geographic coordinates are given in Table 1, and the locations of the sampling sites have been shown in Fig. 1. Voucher specimens have been prepared for all the collected materials and have been deposited in the herbarium of National Botanical Research Institute (LWG), Lucknow. The leaf tissues were also collected and preserved over silica gel till the DNA isolation. 2.2. DNA extraction and ISSR amplification Total genomic DNA from silica gel dried leaf tissue was isolated using DNeasy plant mini kit (Qiagen) following the protocol of the manufacturer. The quantity and quality of isolated total genomic DNA was determined using 0.8% agarose gel electrophoresis in 0.5 TBE buffer for mobility relative to known concentrations of lambda DNA double-digested with EcoRI and HindIII. A set of 100 anchored microsatellite primers was procured from University of British Columbia, Canada. All the 100 ISSR primers were screened with two template DNAs, and based on reproducibility and discreteness of the primers, 13 ISSR
Fig. 1. Map of India highlighting the states where the wild populations of Murraya koenigii have been sampled. The three states are enlarged to show specific population sampling sites of wild Murraya koenigii.
S. Verma, T.S. Rana / Biochemical Systematics and Ecology 39 (2011) 139–144
141
primers were finally selected. PCR amplification of 50 ng DNA was performed in 10 mM Tris–HCl (pH 7.5), 50 mM KCl, 2.0 mM MgCl2, 0.2 mM each dNTPs, 0.2 mM primer and 0.9 U Taq DNA polymerase (Bangalore Genei, Bangalore, India), in a 25 ml reaction using PTC 200 thermocycler (MJ Research, Inc., USA). After initial denaturation at 94 for 4 min, each cycle consisted of 1 min denaturation at 94 C, 1 min annealing at 52 C, 2 min extension at 72 C along with 7 min extension at 72 C at the end of 35 cycles. The amplified PCR products were electrophoresed on 1.5% agorose gel using 0.5 TBE buffer at constant voltage of 5 V/cm. After electrophoresis the gel was stained in ethidium bromide and then visualized and archived using UV Tech Gel Documentation System (UK). The patterns were photographed and stored as digital pictures in gel documentation system. 2.3. Data analysis Data were scored as presence (1) or absence (0) of a band. Only distinct and well-separated bands were included in the analysis. These data were then used for the intra- and inter-population as well as group analysis in POPGENE program (Yeh et al., 1997). The Shannon information index (Lewonton, 1972), Nei’s genetic diversity (Nei, 1973) and percentage of polymorphism were estimated at intra-population level. Nei’s method of intra-population genetic diversity (Nei and Li, 1979), which is the probability that two random alleles in a population can be distinguished with the genetic marker used, is widely accepted (Muluvi et al., 1999; Gaudeul et al., 2000). The inter-population genetic diversity was assessed by calculating the total genetic diversity (HT), genetic diversity within population (HS), genetic diversity among populations (DST), coefficient of genetic differentiation (GST) and gene flow (Nm) at different groups of population to understand the genetic diversity pattern in natural populations of M. koenigii. Nei’s GST is the proportion of diversity calculated from the total genetic diversity in all populations and the GST values range from zero to one, with low values indicating little variation among populations (Culley et al., 2002). The pairwise genetic distances between the populations were used for the generation of a UPGMA tree that was viewed annotated and printed using TreeView ver. (1.6.5) (Page, 2001). The analysis of molecular variance (AMOVA) and Mantel correlation were carried out using the program GenAlEx (ver. 6.1) (Peakall and Smouse, 2006). AMOVA is used to perform a hierarchical analysis of genetic distance (Muluvi et al., 1999), whereas the Mantel test was used to investigate correlations between the genetic diversity matrix and spatial distances between the populations. 3. Results The genetic diversity within and among the eight populations of M. koenigii representing 59 accessions was analyzed using 13 ISSR primers. Of the total 152 amplified bands that varied from 180 to 3000 bp in size, 136 were polymorphic with an average of 10.46 bands per primer. The lowest number of bands (7) was obtained with Primer UBC 856, whereas primer UBC 810 resulted in the highest number of bands (17). The average percentage polymorphism across all accessions was 89.47%. The mean PIC value observed was 0.234 across all primers (Table 2). Intra-population genetic diversity revealed that the UNA population from Himachal Pradesh was most diverse with maximum values of Nei’s gene diversity (0.24), Shannon information index (0.35) and percentage polymorphism (64.44%), respectively, whereas PTH population from Uttarakhand showed least gene diversity (0.15), Shannon information index (0.22) and percentage polymorphism (41.48%) (Table 3). Average percentage polymorphism observed was 89.47% across eight populations of M. koenigii. The Nei’s genetic distances (Nei, 1973) based on the allele frequencies of the ISSR markers, were calculated for each pair of populations to estimate the extent of genetic divergence. PTH (UK) population showed maximum genetic distance (0.13) to DD1 (UK) and DD2 (UK) populations, whereas the least genetic distance (0.05) was observed between UNA (HP) and NNG (PNJ) populations (data not shown). Based on the genetic distances, a UPGMA dendrogram was
Table 2 ISSR primers used to analyze the accessions of Murraya koenigii and the extent of polymorphism determined with these primers. Primer name
Sequence 50 –30
UBC UBC UBC UBC UBC UBC UBC UBC UBC UBC UBC UBC UBC
(AG)8T (GA)8T (GA)8A (AG)8YT (AG)8YC (AG)8YA (GA)8YG (AC)8YA (AC)8YG (GAA)6 (GGAGA)3 (GGGTG)3 VDV(CT)7
807 810 812 834 835 836 842 856 857 868 880 881 886
Total
Loci amplified
Polymorphic loci
Percentage polymorphism (%)
PIC value
Approx. band range size (bp)
09 17 13 13 10 11 16 07 11 08 13 12 12
07 17 11 11 07 10 15 07 10 07 13 12 09
77.77 100 84.61 84.61 70 90.9 93.75 100 90.9 87.5 100 100 75
0.163 0.360 0.153 0.112 0.231 0.224 0.332 0.231 0.217 0.239 0.265 0.295 0.215
340–1500 300–2250 240–2500 230–3000 350–1500 300–2250 180–2250 700–2500 450–2000 500–2500 400–1400 500–2500 340–1500
152
136
89.47
0.234
180–3000
142
S. Verma, T.S. Rana / Biochemical Systematics and Ecology 39 (2011) 139–144
Table 3 Intra-population diversity measures of wild Murraya koenigii based on ISSR data. The population names are abbreviated as given in Table 1. na ¼ number of alleles; ne ¼ effective number of alleles; h ¼ Nei’s genetic diversity; I ¼ Shannon’s information index. Population
Sample size
Meana na
PTH TNK SRI DD1 DD2 UNA PLM NNG a
6 6 8 6 6 9 8 10
1.4 1.46 1.49 1.45 1.43 1.64 1.51 1.63
ne (0.49) (0.50) (0.50) (0.49) (0.49) (0.48) (0.50) (0.48)
1.26 1.31 1.31 1.30 1.31 1.42 1.34 1.32
h (0.37) (0.40) (0.38) (0.39) (0.40) (0.39) (0.41) (0.35)
0.15 0.17 0.18 0.17 0.17 0.24 0.19 0.19
Percentage polymorphism (%)
I (0.20) (0.21) (0.20) (0.20) (0.21) (0.20) (0.21) (0.18)
0.22 0.26 0.26 0.25 0.25 0.35 0.28 0.30
(0.28) (0.29) (0.29) (0.29) (0.30) (0.29) (0.30) (0.26)
41.48 46.67 49.63 45.19 43.70 64.44 51.11 63.70
Values in parenthesis are the standard deviation.
computed, which showed two major clusters, cluster I and cluster II. Cluster I consisted of PTH and TNK populations from the Uttarakhand and cluster II is further divisible into two subclusters IIa and IIb. Subcluster IIa included SRI, DD1 and DD2 populations, all from Uttarakhand state, whereas subcluster IIb consisted of populations like UNA, PLM and NNG representing Himachal Pradesh and Punjab states respectively (Fig. 2). Clustering pattern of different populations in the dendrogram revealed that the genetic diversity is not perfectly corroborating with the geographical diversity. Besides these, specific population parameters such as percentage of polymorphic loci, total genetic diversity (HT), within population diversity (HS), among population diversity (DST), coefficient of genetic differentiation (GST), gene flow (Nm) between the populations, and AMOVA analyses were also carried out at the different groups of populations. All the eight populations were grouped into three main groups Group I, II and III based on their geographical proximity and the clustering behaviour in the UPGMA dendrogram (Fig. 2). These groups were again re-grouped into three different combinations for the estimation and understanding of genetic exchange between them. Thus, total six groups were formed and the populations included in each group have been given in Table 4. Amongst the first three main groups (I, II and III), the Group III that included the three populations from HP and PNJ showed highest percentage of mean polymorphic loci (80%) followed by Group II (68.89%) and Group I (57.04%), and the mean HS values were higher than the mean DST values in each case (Table 4). This suggests that the variability within the populations of HP and PNJ is greater than the UK populations and the diversity within the populations is greater rather than the diversity between the populations. The genetic divergence at the inter-population level was assessed by the GST value which was calculated from the mean values of HT and HS over all loci. Higher value of GST (0.19) was observed in the Group II revealing low level of genetic exchange between the populations (Nm ¼ 2.10) in comparison to the other two groups. The combination of the three main groups into Group (I þ II), (I þ III) and (II þ III) was analyzed to understand the pattern of genetic exchange and the genetic divergence between the main groups. The Group (I þ II) showed comparatively low level of genetic exchange (Nm ¼ 4.59) than the Group (II þ III) (Nm ¼ 5.95). It suggests that more gene exchange is taking place in the Uttarakhand (SRI, DD1, DD2) and Himachal Pradesh; Punjab (UNA, PLM, NNG) populations in comparison to the other populations (PTH and TNK) of Uttarakhand, irrespective of their geographically affinities with other groups. Furthermore, the less gene flow (Nm ¼ 4.58) was observed in the two Groups (I þ II and I þ III) which revealed that the rate of gene flow decreases between the populations with greater spatial distances in case of M. koenigii (Table 4). Analyses of molecular variance (AMOVA) revealed maximum percentage of variation among individuals of populations (80%) followed by 11% among populations within a region, and 9% among regions (Table 5). A Mantel correlation (r) between PTH
I
TNK SRI DD1
IIa
DD2
II UNA PLM
IIb
NNG
1
Fig. 2. UPGMA dendrogram based on Nei’s genetic distances generated by ISSR data showing differentiation among eight wild populations of Murraya koenigii. Population names representing the names of sample collection localities are abbreviated as in Table 1.
S. Verma, T.S. Rana / Biochemical Systematics and Ecology 39 (2011) 139–144
143
Table 4 ISSR estimates of genetic diversity in subdivided populations of Murraya koenigii following Nei’s method. HT ¼ total genetic diversity; HS ¼ genetic diversity within population; DST ¼ genetic diversity among populations; GST ¼ coefficient of genetic differentiation; Nm ¼ gene flow. Group (populations)
Sample size
Mean HT
Mean HS
Mean DST
Mean GST
Mean Nm
Polymorphic loci (%)
Group I (2)a Group II (3)b Group III (3)c Group (I þ II) (5) Group (I þ III) (5) Group (II þ III) (6) All populations (8)d
12 20 27 32 39 47 59
0.1912 0.2179 0.2435 0.2371 0.2486 0.2610 0.2515
0.1650 0.1761 0.2104 0.2138 0.2242 0.2408 0.1862
0.026 0.041 0.033 0.023 0.024 0.020 0.065
0.1370 0.1918 0.1358 0.0981 0.0984 0.0775 0.2597
3.15 2.10 3.18 4.59 4.58 5.95 1.4256
57.04 68.89 80.00 76.30 83.70 86.67 89.47
a b c d
Group I (2) includes populations PTHþTNK from Uttarakhand. Group II (3) includes (SRIþDD1þDD2) from Uttarakhand. Group III (3) includes (UNAþPLMþNNG) from Himachal Pradesh and Punjab. All the populations from Uttarakhand, Himachal Pradesh and Punjab.
the genetic diversity matrix and geographical distances between populations was found weakly correlated (r ¼ 0.562, p ¼ 0.0004) revealing different patterns of genetic diversity among the populations of M. koenigii. These findings also support the clustering of three populations (SRI, DD1 and DD2) from Uttarakhand region to the populations from Himachal Pradesh region in the dendrogram (Fig. 2). 4. Discussion In the present study, ISSR markers were used to estimate the genetic diversity within and among the different populations of M. koenigii. The genetic polymorphism detected by these markers is congruent with the assumption, that natural populations of out-breeding species are expected to harbor higher genetic diversity than inbreeding ones. Our results are corroborating with the findings of other out-crossing species such as Gaultheria fragrantissimsa (Apte et al., 2006), Lens culinaris (Fikiru et al., 2007) and Punica (Narzary et al., 2010). Furthermore, the high genetic diversity found in M. koenigii might have been maintained from generation to generation. The maintenance of genetic polymorphism in natural populations can also reflect the process of adaptation to environmental heterogeneity (Hedrick, 1976). The levels of genetic diversity determined among eight populations of M. koenigii in the present study are unequal with respect to the various genetic diversity parameters. The genetic diversity was highest in UNA (HP) population and lowest in PTH (UK) population (Table 3). The genetic structure of plant populations reflects the interactions of various factors including the long-term evolutionary history of the species (Shifts in distribution, habitat fragmentation and population isolation), genetic drift, mating system, gene flow and selection (Schaal et al., 1998). The overall degree of genetic differentiation as estimated in M. koenigii, are congruent with those reported in other out-crossing plant species (Loveless and Hamrick, 1984; Hamrick and Godt, 1989; Heywood, 1991). As evident from the present study, populations of M. koenigii showed moderate GST value demonstrating no significant genetic differentiation across the populations. Furthermore, high within population genetic diversity as revealed by AMOVA, where within population variance was higher than among them, indicating a relatively restricted population differentiation as expected, and such a pattern of population genetic structure has been confirmed in many other out-crossing species (Hamrick and Godt, 1996; Nebauer et al., 1999; Deshpande et al., 2001). The exchange of genes among populations is known to homogenize allele frequencies among populations and determine the relative effect of selection and genetic drift. High gene flow between populations precludes local adaptations and will also impede the process of speciation (Barton and Hewitt, 1985), but Nm value greater than 1.0 is considered necessary to prevent divergence resulting from genetic drift (Wright, 1951). An estimate of gene flow in M. koenigii populations revealed that the gene flow among populations though not very high but might be significant to prevent genetic drift indicating exchange of genes among them (Table 4). The patterns of genetic variation detected in the populations of M. koenigii did not show any clear correspondence between geographical affiliations and pairwise genetic distances as revealed by Mantel test. Therefore, geographical distances do not explain inter-population genetic divergence and the isolation by distance is ruled out. The genetic differentiation and gene flow between the natural populations of M. koenigii are not homogeneous, and are independent of their spatial distances, which is also supported by the estimation of weak Mantel correlation between the genetic and the actual geographical distances between the populations in the present study. Table 5 AMOVA analysis within and between populations of wild Murraya koenigii, used in the present study. The AMOVA test was carried out using GenAlEx program for ISSR data. The population names and their locality are given in Table 1. Source of variations (degree of freedom) Among Regions (2) Among populations (5) Within population (51) Total (58)
Sum of squares 118.194 149.560 738.686 1006.441
Mean of squares
Variance component
59.097 29.912 14.484
1.596 2.078 14.484
Percentage of variations (%) 9 11 80
18.158
100
144
S. Verma, T.S. Rana / Biochemical Systematics and Ecology 39 (2011) 139–144
The results indicate that the ISSR method is sufficiently informative and powerful to estimate the genetic diversity in M. koenigii populations. Our study provides a significant insight into genetic diversity at the intra- and inter-populations levels of M. koenigii, which of course needs to be further broadened by considering other natural populations of this species from various locales. The information on genetic variability of this economically important wild plant genetic resource might be a potential source as breeding material for development of commercially valuable traits in M. koenigii plants. Acknowledgements The authors are thankful to the Director, National Botanical Research Institute (CSIR), Lucknow for facilities. Partial financial support from the Department of Science and Technology, New Delhi (to TSR) is gratefully acknowledged. References Anonymous, 1962. The Wealth of India: Raw Material, VI L–M. Publication and Information Directorate CSIR, New Delhi. Apte, G.S., Bahulikar, R.A., Kulkarni, R.S., Lagu, M.D., Kulkarni, B.G., Suresh, H.S., Rao, P.S.N., Gupta, V.S., 2006. Genetic diversity analysis in Gaultheria fragrantissima Wall. Ericaceae from the two biodiversity hotspots in India using ISSR markers. Curr. Sci. 9112, 1634–1640. Barton, N.H., Hewitt, G.M., 1985. Analysis of hybrid zones. Annu. Rev. Ecol. Syst. 16, 113–148. Chopra, R.N., Chopra, I.C., Handa, K.L., Kapur, L.D., 1958. Indigenous Drugs of India, second ed. UN Dhar and Sons Pvt Ltd, Calcutta. Culley, T.M., Wallace, L.E., Gengler-Nowak, K.M., Crawford, D.J., 2002. A comparison of two methods of calculating GST, a genetic measure of population differentiation. Am. J. Bot. 89, 460–465. Dastur, J.F., 1970. Medicinal Plants of India and Pakistan, third ed., pp. 115. Deshpande, K.U., Apte, G.S., Bahulikar, R.A., Lagu, M.D., Kulkarni, B.O., Suresh, H.S., Singh, N.P., Rao, M.K., Gupta, V.S., Pant, A., Ranjekar, P.K., 2001. Genetic diversity across the natural populations of three montane plant species from the WG, India revealed by ISSR repeats. Mol. Ecol. 10, 2397–2408. Drury, H.C., 1978. The Useful Plants of India, second ed. Allen, London, pp. 78. Fikiru, E., Kassahun, T., Endashaw, B., 2007. Genetic diversity and population structure of Ethiopian lentil Lens culinaris Medikus landraces as revealed by ISSR marker. Af. J. Biotechnol. 6 (12), 1460–1468. Gaudeul, M., Taberlet, P., Till-Bottraud, I., 2000. Genetic diversity in an endangered alpine plant. Eryngiumalpinum L. (Apiaceae) inferred from amplified fragment length polymorphism markers. Mol. Ecol. 9, 1625–1637. Hamrick, J.L., Godt, M.J.W., 1989. Allozymes diversity in plant species. In: Brown, A.H.D., et al. (Eds.), Plant Population Genetics. Breeding and Genetic Resources. Sinauer, Sunderland, Massachusetts, USA, pp. 43–63. Hamrick, J.L., Godt, M.J.W., 1996. Effects of life history traits on genetic diversity in plant species. Philos. Trans. R. Soc. London, Ser. B 351, 1291–1298. Hedrick, P.W., 1976. Genetic polymorphism in heterogenous environments: a decade later. Annu. Rev. Ecol. Syst. 17, 535–566. Heywood, J.S., 1991. Spatial analysis of genetic variation in plant populations. Annu. Rev. Ecol. Syst. 22, 335–355. Joseph, S., Peter, K.V., 1985. Curry leaf (Murraya koenigii), perennial, nutritious, leafy vegetable. Econ. Bot. 39 (1), 68–73. Kirtikar, K.R., Basu, B.D., 1935. Indian Medicinal Plants, vol. 4. Lalit Mohan Basu, Allahabad. Lewonton, R.C., 1972. The apportionment of human diversity. Evol. Biol. 6, 381–398. Loveless, M.D., Hamrick, J.L., 1984. Ecological determinants of genetic structure in plant populations. Annu. Rev. Ecol. Syst. 15, 65–95. Muluvi, G.M., Sprent, J.I., Soranzo, N., Provan, J., Odee, D., Folkard, S., McNicol, J.W., Powell, W., 1999. Amplified fragment length polymorphism (AFLP) analysis of genetic variation in Moringa oleifera Lam. Mol. Ecol. 8, 463–470. Nair, K.N., Nayar, M.P., 1997. Rutaceae. In: Hajra, P.K., Nair, V.J., Daniel, P. (Eds.), The Flora of India 4. Botanical Survey of India, Calcutta, pp. 259–408. Narzary, D., Rana, T.S., Ranade, S.A., 2010. Genetic diversity in inter-simple sequence repeat profiles across natural populations of Indian pomegranate Punica granatum L. Plant Biol. 12, 806–813. Nebauer, S.G., DelCastillo, A.L., Segura, J., 1999. RAPD variation within and among natural populations of outcrossing willow-leaved foxglove Digitalis obscura L. Theor. Appl. Genet. 98, 985–994. Nei, M., 1973. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad. Sci. U.S.A. 70, 3321–3323. Nei, M., Li, W.H., 1979. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc. Natl. Acad. Sci. U.S.A. 76, 5269–5273. Ohri, D., 2002. Genome size variation in some tropical hardwoods. Biol. Plant 45, 455–457. Page, R.D.M., 2001. TreeView (Win32), Ver. 1.6.5. Available from: http://taxonomy.zoology.gla.ac.uk/rod/treeview.html. Peakall, R., Smouse, P.E., 2006. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295. Available from: http://www.anu.edu.au/BoZo/GenAlEx. Schaal, B.A., Hayworth, D.A., Olsen, K.M., Rauscher, J.T., Smith, W.A., 1998. Phylogeographic studies in plants: problems and prospects. Mol. Ecol. 7, 465–474. Swingle, W.T., Reece, P.C., 1967. The botany of citrus and its wild relatives of the orange sufamily (Family Rutaceae, subfamily Aurantioideae). In: Reuther, W., Webber, H.J., Batcher, L.D. (Eds.), The Citrus Industry, vol. 1. University of California Press, Berkeley, CA, U.S.A, pp. 190–430. Warman, C.K., 1999. Trees of India: A Colour Altas. CBS, New Delhi. Wright, S., 1951. The genetical structure of populations. Ann. Eug. 15, 313–354. Yeh, F.C., Yang, R., Boyle, T., 1997. POPGENE – A Microsoft Windows-Based Freeware for Population Genetic Analysis, Ver. 1.32 (32 bit). Available from: http://.ualberta.ca/wfeyeh.
Contents lists available at ScienceDirect
Biochemical Systematics and Ecology journal homepage: www.elsevier.com/locate/biochemsyseco
Genetic diversity within and among the wild populations of Murraya koenigii (L.) Spreng., as revealed by ISSR analysis Sushma Verma, T.S. Rana* Conservation Biology and Molecular Taxonomy Laboratory, National Botanical Research Institute (Council of Scientific and Industrial Research), Rana Pratap Marg, Lucknow 226001, India
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 August 2010 Accepted 29 January 2011 Available online 22 February 2011
Murraya koenigii (L.) Spreng., commonly known as curry leaf plant, is found in the different hilly regions of India. In the present study, fifty-nine accessions representing eight wild populations of M. koenigii were analyzed using thirteen ISSR primers. A total of 152 bands were amplified, out of which,136 were polymorphic corresponding to 89.47% polymorphism across the accessions. The pairwise population genetic distances were calculated for all the populations that varied from 0.05 to 0.13 between the populations of M. koenigii. AMOVA and Nei’s genetic diversity analysis revealed higher genetic variations within populations than among the populations. The clustering of populations in the dendrogram was not in congruence with geographical affiliations. The results indicate that the ISSR method is sufficiently informative and powerful to estimate the genetic diversity in M. koenigii populations. As M. koenigii is an important wild plant genetic resource, therefore, information on genetic variability might be a potential source as breeding material for development of commercially valuable traits in M. koenigii plants. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Genetic diversity Population structure Wild Murraya koenigii ISSR
1. Introduction The genus Murraya Koenig ex L., (Rutaceae) comprises of about 11 species of shrubs and small trees distributed in tropical and sub-tropical regions of Sri Lanka, India, South China to South-East Asia, Malaysia, New Guinea, North-East Australia and New Caledonia (Swingle and Reece, 1967). In India the genus is represented by two species, Murraya koenigii (L.) Spreng., and Murraya paniculata (L.) Jack., having (2n) 18 chromosomes (Nair and Nayar, 1997; Ohri, 2002). M. koenigii is an aromatic shrub or small tree up to 6 m in height and 15–40 cm in diameter, and commonly known as curry leaf plant in India. The species occurs throughout India, both in wild and cultivated forms. Wild forms in India are found growing in the hills of Assam, Central India, Himachal Pradesh, Kerala, Punjab, Sikkim, Tamil Nadu, Uttarakhand, West Bengal and Western Ghats (Dastur, 1970), whereas in Southern parts of India it is being extensively cultivated for its aromatic leaves. M. koenigii has long been used in traditional Indian system of medicine, such as Ayurveda and Unani (Kirtikar and Basu, 1935; Anonymous, 1962; Dastur, 1970; Drury, 1978; Joseph and Peter, 1985; Chopra et al., 1958; Warman, 1999). Fresh leaves yield a highly odoriferous essential oil which is used as a fixative for soap perfume (Joseph and Peter, 1985). In the present study ISSR markers were used to unravel the genetic diversity across eight natural populations of M. koenigii occurring in different parts of Punjab, Himachal Pradesh and Uttarakhand states of India. To the best of our knowledge, this is the first attempt to estimate the genetic diversity in M. koenigii using ISSR markers. * Corresponding author. Tel.: þ91 522 2297854; fax: þ91 522 2205836. E-mail address: [email protected] (T.S. Rana). 0305-1978/$ – see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.bse.2011.01.017
140
S. Verma, T.S. Rana / Biochemical Systematics and Ecology 39 (2011) 139–144
Table 1 Sampling details of different wild Murraya koenigii populations considered in the present study. Population namea (approx. pop size)
Accession nos. (sample size)
Locality, stateb
Altitude (m)
Latitude/longitude
PTH (100) TNK (80) SRI (150) DD1 (120) DD2 (100) UNA (180) PLM (250) NNG (300)
Mk01–Mk06 Mk07–Mk12 Mk13–Mk20 Mk21–Mk26 Mk27–Mk32 Mk33–Mk41 Mk42–Mk49 Mk50–Mk59
Pithoragarh, UK Tanakpur, UK Srinagar, UK DehraDun, UK DehraDun, UK Una, HP Palampur, HP Nangal, PNJ
1333 255 973 640 682 350 1220 326
29 510 N/80 090 E 29 050 N/80 050 E 30 130 N/78 460 E 30 200 N/78 040 E 30 310 N/78 020 E 31 470 N/76 070 E 32 050 N/76 470 E 31 220 N/76 210 E
a b
(6) (6) (8) (6) (6) (9) (8) (10)
Population names in bold are abbreviated to the names of the respective locality, where the populations were collected from. UK ¼ Uttarakhand, HP ¼ Himachal Pradesh, PNJ ¼ Punjab.
2. Materials and methods 2.1. Plant material Eight wild populations of M. koenigii from Punjab, Himachal Pradesh and Uttarakhand states of India were surveyed and a total of fifty-nine accessions representing these eight populations were collected. Each population surveyed represented about 50–300 mature individuals. The population names and the details of accessions along with their geographic coordinates are given in Table 1, and the locations of the sampling sites have been shown in Fig. 1. Voucher specimens have been prepared for all the collected materials and have been deposited in the herbarium of National Botanical Research Institute (LWG), Lucknow. The leaf tissues were also collected and preserved over silica gel till the DNA isolation. 2.2. DNA extraction and ISSR amplification Total genomic DNA from silica gel dried leaf tissue was isolated using DNeasy plant mini kit (Qiagen) following the protocol of the manufacturer. The quantity and quality of isolated total genomic DNA was determined using 0.8% agarose gel electrophoresis in 0.5 TBE buffer for mobility relative to known concentrations of lambda DNA double-digested with EcoRI and HindIII. A set of 100 anchored microsatellite primers was procured from University of British Columbia, Canada. All the 100 ISSR primers were screened with two template DNAs, and based on reproducibility and discreteness of the primers, 13 ISSR
Fig. 1. Map of India highlighting the states where the wild populations of Murraya koenigii have been sampled. The three states are enlarged to show specific population sampling sites of wild Murraya koenigii.
S. Verma, T.S. Rana / Biochemical Systematics and Ecology 39 (2011) 139–144
141
primers were finally selected. PCR amplification of 50 ng DNA was performed in 10 mM Tris–HCl (pH 7.5), 50 mM KCl, 2.0 mM MgCl2, 0.2 mM each dNTPs, 0.2 mM primer and 0.9 U Taq DNA polymerase (Bangalore Genei, Bangalore, India), in a 25 ml reaction using PTC 200 thermocycler (MJ Research, Inc., USA). After initial denaturation at 94 for 4 min, each cycle consisted of 1 min denaturation at 94 C, 1 min annealing at 52 C, 2 min extension at 72 C along with 7 min extension at 72 C at the end of 35 cycles. The amplified PCR products were electrophoresed on 1.5% agorose gel using 0.5 TBE buffer at constant voltage of 5 V/cm. After electrophoresis the gel was stained in ethidium bromide and then visualized and archived using UV Tech Gel Documentation System (UK). The patterns were photographed and stored as digital pictures in gel documentation system. 2.3. Data analysis Data were scored as presence (1) or absence (0) of a band. Only distinct and well-separated bands were included in the analysis. These data were then used for the intra- and inter-population as well as group analysis in POPGENE program (Yeh et al., 1997). The Shannon information index (Lewonton, 1972), Nei’s genetic diversity (Nei, 1973) and percentage of polymorphism were estimated at intra-population level. Nei’s method of intra-population genetic diversity (Nei and Li, 1979), which is the probability that two random alleles in a population can be distinguished with the genetic marker used, is widely accepted (Muluvi et al., 1999; Gaudeul et al., 2000). The inter-population genetic diversity was assessed by calculating the total genetic diversity (HT), genetic diversity within population (HS), genetic diversity among populations (DST), coefficient of genetic differentiation (GST) and gene flow (Nm) at different groups of population to understand the genetic diversity pattern in natural populations of M. koenigii. Nei’s GST is the proportion of diversity calculated from the total genetic diversity in all populations and the GST values range from zero to one, with low values indicating little variation among populations (Culley et al., 2002). The pairwise genetic distances between the populations were used for the generation of a UPGMA tree that was viewed annotated and printed using TreeView ver. (1.6.5) (Page, 2001). The analysis of molecular variance (AMOVA) and Mantel correlation were carried out using the program GenAlEx (ver. 6.1) (Peakall and Smouse, 2006). AMOVA is used to perform a hierarchical analysis of genetic distance (Muluvi et al., 1999), whereas the Mantel test was used to investigate correlations between the genetic diversity matrix and spatial distances between the populations. 3. Results The genetic diversity within and among the eight populations of M. koenigii representing 59 accessions was analyzed using 13 ISSR primers. Of the total 152 amplified bands that varied from 180 to 3000 bp in size, 136 were polymorphic with an average of 10.46 bands per primer. The lowest number of bands (7) was obtained with Primer UBC 856, whereas primer UBC 810 resulted in the highest number of bands (17). The average percentage polymorphism across all accessions was 89.47%. The mean PIC value observed was 0.234 across all primers (Table 2). Intra-population genetic diversity revealed that the UNA population from Himachal Pradesh was most diverse with maximum values of Nei’s gene diversity (0.24), Shannon information index (0.35) and percentage polymorphism (64.44%), respectively, whereas PTH population from Uttarakhand showed least gene diversity (0.15), Shannon information index (0.22) and percentage polymorphism (41.48%) (Table 3). Average percentage polymorphism observed was 89.47% across eight populations of M. koenigii. The Nei’s genetic distances (Nei, 1973) based on the allele frequencies of the ISSR markers, were calculated for each pair of populations to estimate the extent of genetic divergence. PTH (UK) population showed maximum genetic distance (0.13) to DD1 (UK) and DD2 (UK) populations, whereas the least genetic distance (0.05) was observed between UNA (HP) and NNG (PNJ) populations (data not shown). Based on the genetic distances, a UPGMA dendrogram was
Table 2 ISSR primers used to analyze the accessions of Murraya koenigii and the extent of polymorphism determined with these primers. Primer name
Sequence 50 –30
UBC UBC UBC UBC UBC UBC UBC UBC UBC UBC UBC UBC UBC
(AG)8T (GA)8T (GA)8A (AG)8YT (AG)8YC (AG)8YA (GA)8YG (AC)8YA (AC)8YG (GAA)6 (GGAGA)3 (GGGTG)3 VDV(CT)7
807 810 812 834 835 836 842 856 857 868 880 881 886
Total
Loci amplified
Polymorphic loci
Percentage polymorphism (%)
PIC value
Approx. band range size (bp)
09 17 13 13 10 11 16 07 11 08 13 12 12
07 17 11 11 07 10 15 07 10 07 13 12 09
77.77 100 84.61 84.61 70 90.9 93.75 100 90.9 87.5 100 100 75
0.163 0.360 0.153 0.112 0.231 0.224 0.332 0.231 0.217 0.239 0.265 0.295 0.215
340–1500 300–2250 240–2500 230–3000 350–1500 300–2250 180–2250 700–2500 450–2000 500–2500 400–1400 500–2500 340–1500
152
136
89.47
0.234
180–3000
142
S. Verma, T.S. Rana / Biochemical Systematics and Ecology 39 (2011) 139–144
Table 3 Intra-population diversity measures of wild Murraya koenigii based on ISSR data. The population names are abbreviated as given in Table 1. na ¼ number of alleles; ne ¼ effective number of alleles; h ¼ Nei’s genetic diversity; I ¼ Shannon’s information index. Population
Sample size
Meana na
PTH TNK SRI DD1 DD2 UNA PLM NNG a
6 6 8 6 6 9 8 10
1.4 1.46 1.49 1.45 1.43 1.64 1.51 1.63
ne (0.49) (0.50) (0.50) (0.49) (0.49) (0.48) (0.50) (0.48)
1.26 1.31 1.31 1.30 1.31 1.42 1.34 1.32
h (0.37) (0.40) (0.38) (0.39) (0.40) (0.39) (0.41) (0.35)
0.15 0.17 0.18 0.17 0.17 0.24 0.19 0.19
Percentage polymorphism (%)
I (0.20) (0.21) (0.20) (0.20) (0.21) (0.20) (0.21) (0.18)
0.22 0.26 0.26 0.25 0.25 0.35 0.28 0.30
(0.28) (0.29) (0.29) (0.29) (0.30) (0.29) (0.30) (0.26)
41.48 46.67 49.63 45.19 43.70 64.44 51.11 63.70
Values in parenthesis are the standard deviation.
computed, which showed two major clusters, cluster I and cluster II. Cluster I consisted of PTH and TNK populations from the Uttarakhand and cluster II is further divisible into two subclusters IIa and IIb. Subcluster IIa included SRI, DD1 and DD2 populations, all from Uttarakhand state, whereas subcluster IIb consisted of populations like UNA, PLM and NNG representing Himachal Pradesh and Punjab states respectively (Fig. 2). Clustering pattern of different populations in the dendrogram revealed that the genetic diversity is not perfectly corroborating with the geographical diversity. Besides these, specific population parameters such as percentage of polymorphic loci, total genetic diversity (HT), within population diversity (HS), among population diversity (DST), coefficient of genetic differentiation (GST), gene flow (Nm) between the populations, and AMOVA analyses were also carried out at the different groups of populations. All the eight populations were grouped into three main groups Group I, II and III based on their geographical proximity and the clustering behaviour in the UPGMA dendrogram (Fig. 2). These groups were again re-grouped into three different combinations for the estimation and understanding of genetic exchange between them. Thus, total six groups were formed and the populations included in each group have been given in Table 4. Amongst the first three main groups (I, II and III), the Group III that included the three populations from HP and PNJ showed highest percentage of mean polymorphic loci (80%) followed by Group II (68.89%) and Group I (57.04%), and the mean HS values were higher than the mean DST values in each case (Table 4). This suggests that the variability within the populations of HP and PNJ is greater than the UK populations and the diversity within the populations is greater rather than the diversity between the populations. The genetic divergence at the inter-population level was assessed by the GST value which was calculated from the mean values of HT and HS over all loci. Higher value of GST (0.19) was observed in the Group II revealing low level of genetic exchange between the populations (Nm ¼ 2.10) in comparison to the other two groups. The combination of the three main groups into Group (I þ II), (I þ III) and (II þ III) was analyzed to understand the pattern of genetic exchange and the genetic divergence between the main groups. The Group (I þ II) showed comparatively low level of genetic exchange (Nm ¼ 4.59) than the Group (II þ III) (Nm ¼ 5.95). It suggests that more gene exchange is taking place in the Uttarakhand (SRI, DD1, DD2) and Himachal Pradesh; Punjab (UNA, PLM, NNG) populations in comparison to the other populations (PTH and TNK) of Uttarakhand, irrespective of their geographically affinities with other groups. Furthermore, the less gene flow (Nm ¼ 4.58) was observed in the two Groups (I þ II and I þ III) which revealed that the rate of gene flow decreases between the populations with greater spatial distances in case of M. koenigii (Table 4). Analyses of molecular variance (AMOVA) revealed maximum percentage of variation among individuals of populations (80%) followed by 11% among populations within a region, and 9% among regions (Table 5). A Mantel correlation (r) between PTH
I
TNK SRI DD1
IIa
DD2
II UNA PLM
IIb
NNG
1
Fig. 2. UPGMA dendrogram based on Nei’s genetic distances generated by ISSR data showing differentiation among eight wild populations of Murraya koenigii. Population names representing the names of sample collection localities are abbreviated as in Table 1.
S. Verma, T.S. Rana / Biochemical Systematics and Ecology 39 (2011) 139–144
143
Table 4 ISSR estimates of genetic diversity in subdivided populations of Murraya koenigii following Nei’s method. HT ¼ total genetic diversity; HS ¼ genetic diversity within population; DST ¼ genetic diversity among populations; GST ¼ coefficient of genetic differentiation; Nm ¼ gene flow. Group (populations)
Sample size
Mean HT
Mean HS
Mean DST
Mean GST
Mean Nm
Polymorphic loci (%)
Group I (2)a Group II (3)b Group III (3)c Group (I þ II) (5) Group (I þ III) (5) Group (II þ III) (6) All populations (8)d
12 20 27 32 39 47 59
0.1912 0.2179 0.2435 0.2371 0.2486 0.2610 0.2515
0.1650 0.1761 0.2104 0.2138 0.2242 0.2408 0.1862
0.026 0.041 0.033 0.023 0.024 0.020 0.065
0.1370 0.1918 0.1358 0.0981 0.0984 0.0775 0.2597
3.15 2.10 3.18 4.59 4.58 5.95 1.4256
57.04 68.89 80.00 76.30 83.70 86.67 89.47
a b c d
Group I (2) includes populations PTHþTNK from Uttarakhand. Group II (3) includes (SRIþDD1þDD2) from Uttarakhand. Group III (3) includes (UNAþPLMþNNG) from Himachal Pradesh and Punjab. All the populations from Uttarakhand, Himachal Pradesh and Punjab.
the genetic diversity matrix and geographical distances between populations was found weakly correlated (r ¼ 0.562, p ¼ 0.0004) revealing different patterns of genetic diversity among the populations of M. koenigii. These findings also support the clustering of three populations (SRI, DD1 and DD2) from Uttarakhand region to the populations from Himachal Pradesh region in the dendrogram (Fig. 2). 4. Discussion In the present study, ISSR markers were used to estimate the genetic diversity within and among the different populations of M. koenigii. The genetic polymorphism detected by these markers is congruent with the assumption, that natural populations of out-breeding species are expected to harbor higher genetic diversity than inbreeding ones. Our results are corroborating with the findings of other out-crossing species such as Gaultheria fragrantissimsa (Apte et al., 2006), Lens culinaris (Fikiru et al., 2007) and Punica (Narzary et al., 2010). Furthermore, the high genetic diversity found in M. koenigii might have been maintained from generation to generation. The maintenance of genetic polymorphism in natural populations can also reflect the process of adaptation to environmental heterogeneity (Hedrick, 1976). The levels of genetic diversity determined among eight populations of M. koenigii in the present study are unequal with respect to the various genetic diversity parameters. The genetic diversity was highest in UNA (HP) population and lowest in PTH (UK) population (Table 3). The genetic structure of plant populations reflects the interactions of various factors including the long-term evolutionary history of the species (Shifts in distribution, habitat fragmentation and population isolation), genetic drift, mating system, gene flow and selection (Schaal et al., 1998). The overall degree of genetic differentiation as estimated in M. koenigii, are congruent with those reported in other out-crossing plant species (Loveless and Hamrick, 1984; Hamrick and Godt, 1989; Heywood, 1991). As evident from the present study, populations of M. koenigii showed moderate GST value demonstrating no significant genetic differentiation across the populations. Furthermore, high within population genetic diversity as revealed by AMOVA, where within population variance was higher than among them, indicating a relatively restricted population differentiation as expected, and such a pattern of population genetic structure has been confirmed in many other out-crossing species (Hamrick and Godt, 1996; Nebauer et al., 1999; Deshpande et al., 2001). The exchange of genes among populations is known to homogenize allele frequencies among populations and determine the relative effect of selection and genetic drift. High gene flow between populations precludes local adaptations and will also impede the process of speciation (Barton and Hewitt, 1985), but Nm value greater than 1.0 is considered necessary to prevent divergence resulting from genetic drift (Wright, 1951). An estimate of gene flow in M. koenigii populations revealed that the gene flow among populations though not very high but might be significant to prevent genetic drift indicating exchange of genes among them (Table 4). The patterns of genetic variation detected in the populations of M. koenigii did not show any clear correspondence between geographical affiliations and pairwise genetic distances as revealed by Mantel test. Therefore, geographical distances do not explain inter-population genetic divergence and the isolation by distance is ruled out. The genetic differentiation and gene flow between the natural populations of M. koenigii are not homogeneous, and are independent of their spatial distances, which is also supported by the estimation of weak Mantel correlation between the genetic and the actual geographical distances between the populations in the present study. Table 5 AMOVA analysis within and between populations of wild Murraya koenigii, used in the present study. The AMOVA test was carried out using GenAlEx program for ISSR data. The population names and their locality are given in Table 1. Source of variations (degree of freedom) Among Regions (2) Among populations (5) Within population (51) Total (58)
Sum of squares 118.194 149.560 738.686 1006.441
Mean of squares
Variance component
59.097 29.912 14.484
1.596 2.078 14.484
Percentage of variations (%) 9 11 80
18.158
100
144
S. Verma, T.S. Rana / Biochemical Systematics and Ecology 39 (2011) 139–144
The results indicate that the ISSR method is sufficiently informative and powerful to estimate the genetic diversity in M. koenigii populations. Our study provides a significant insight into genetic diversity at the intra- and inter-populations levels of M. koenigii, which of course needs to be further broadened by considering other natural populations of this species from various locales. The information on genetic variability of this economically important wild plant genetic resource might be a potential source as breeding material for development of commercially valuable traits in M. koenigii plants. Acknowledgements The authors are thankful to the Director, National Botanical Research Institute (CSIR), Lucknow for facilities. Partial financial support from the Department of Science and Technology, New Delhi (to TSR) is gratefully acknowledged. References Anonymous, 1962. The Wealth of India: Raw Material, VI L–M. Publication and Information Directorate CSIR, New Delhi. Apte, G.S., Bahulikar, R.A., Kulkarni, R.S., Lagu, M.D., Kulkarni, B.G., Suresh, H.S., Rao, P.S.N., Gupta, V.S., 2006. Genetic diversity analysis in Gaultheria fragrantissima Wall. Ericaceae from the two biodiversity hotspots in India using ISSR markers. Curr. Sci. 9112, 1634–1640. Barton, N.H., Hewitt, G.M., 1985. Analysis of hybrid zones. Annu. Rev. Ecol. Syst. 16, 113–148. Chopra, R.N., Chopra, I.C., Handa, K.L., Kapur, L.D., 1958. Indigenous Drugs of India, second ed. UN Dhar and Sons Pvt Ltd, Calcutta. Culley, T.M., Wallace, L.E., Gengler-Nowak, K.M., Crawford, D.J., 2002. A comparison of two methods of calculating GST, a genetic measure of population differentiation. Am. J. Bot. 89, 460–465. Dastur, J.F., 1970. Medicinal Plants of India and Pakistan, third ed., pp. 115. Deshpande, K.U., Apte, G.S., Bahulikar, R.A., Lagu, M.D., Kulkarni, B.O., Suresh, H.S., Singh, N.P., Rao, M.K., Gupta, V.S., Pant, A., Ranjekar, P.K., 2001. Genetic diversity across the natural populations of three montane plant species from the WG, India revealed by ISSR repeats. Mol. Ecol. 10, 2397–2408. Drury, H.C., 1978. The Useful Plants of India, second ed. Allen, London, pp. 78. Fikiru, E., Kassahun, T., Endashaw, B., 2007. Genetic diversity and population structure of Ethiopian lentil Lens culinaris Medikus landraces as revealed by ISSR marker. Af. J. Biotechnol. 6 (12), 1460–1468. Gaudeul, M., Taberlet, P., Till-Bottraud, I., 2000. Genetic diversity in an endangered alpine plant. Eryngiumalpinum L. (Apiaceae) inferred from amplified fragment length polymorphism markers. Mol. Ecol. 9, 1625–1637. Hamrick, J.L., Godt, M.J.W., 1989. Allozymes diversity in plant species. In: Brown, A.H.D., et al. (Eds.), Plant Population Genetics. Breeding and Genetic Resources. Sinauer, Sunderland, Massachusetts, USA, pp. 43–63. Hamrick, J.L., Godt, M.J.W., 1996. Effects of life history traits on genetic diversity in plant species. Philos. Trans. R. Soc. London, Ser. B 351, 1291–1298. Hedrick, P.W., 1976. Genetic polymorphism in heterogenous environments: a decade later. Annu. Rev. Ecol. Syst. 17, 535–566. Heywood, J.S., 1991. Spatial analysis of genetic variation in plant populations. Annu. Rev. Ecol. Syst. 22, 335–355. Joseph, S., Peter, K.V., 1985. Curry leaf (Murraya koenigii), perennial, nutritious, leafy vegetable. Econ. Bot. 39 (1), 68–73. Kirtikar, K.R., Basu, B.D., 1935. Indian Medicinal Plants, vol. 4. Lalit Mohan Basu, Allahabad. Lewonton, R.C., 1972. The apportionment of human diversity. Evol. Biol. 6, 381–398. Loveless, M.D., Hamrick, J.L., 1984. Ecological determinants of genetic structure in plant populations. Annu. Rev. Ecol. Syst. 15, 65–95. Muluvi, G.M., Sprent, J.I., Soranzo, N., Provan, J., Odee, D., Folkard, S., McNicol, J.W., Powell, W., 1999. Amplified fragment length polymorphism (AFLP) analysis of genetic variation in Moringa oleifera Lam. Mol. Ecol. 8, 463–470. Nair, K.N., Nayar, M.P., 1997. Rutaceae. In: Hajra, P.K., Nair, V.J., Daniel, P. (Eds.), The Flora of India 4. Botanical Survey of India, Calcutta, pp. 259–408. Narzary, D., Rana, T.S., Ranade, S.A., 2010. Genetic diversity in inter-simple sequence repeat profiles across natural populations of Indian pomegranate Punica granatum L. Plant Biol. 12, 806–813. Nebauer, S.G., DelCastillo, A.L., Segura, J., 1999. RAPD variation within and among natural populations of outcrossing willow-leaved foxglove Digitalis obscura L. Theor. Appl. Genet. 98, 985–994. Nei, M., 1973. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad. Sci. U.S.A. 70, 3321–3323. Nei, M., Li, W.H., 1979. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc. Natl. Acad. Sci. U.S.A. 76, 5269–5273. Ohri, D., 2002. Genome size variation in some tropical hardwoods. Biol. Plant 45, 455–457. Page, R.D.M., 2001. TreeView (Win32), Ver. 1.6.5. Available from: http://taxonomy.zoology.gla.ac.uk/rod/treeview.html. Peakall, R., Smouse, P.E., 2006. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295. Available from: http://www.anu.edu.au/BoZo/GenAlEx. Schaal, B.A., Hayworth, D.A., Olsen, K.M., Rauscher, J.T., Smith, W.A., 1998. Phylogeographic studies in plants: problems and prospects. Mol. Ecol. 7, 465–474. Swingle, W.T., Reece, P.C., 1967. The botany of citrus and its wild relatives of the orange sufamily (Family Rutaceae, subfamily Aurantioideae). In: Reuther, W., Webber, H.J., Batcher, L.D. (Eds.), The Citrus Industry, vol. 1. University of California Press, Berkeley, CA, U.S.A, pp. 190–430. Warman, C.K., 1999. Trees of India: A Colour Altas. CBS, New Delhi. Wright, S., 1951. The genetical structure of populations. Ann. Eug. 15, 313–354. Yeh, F.C., Yang, R., Boyle, T., 1997. POPGENE – A Microsoft Windows-Based Freeware for Population Genetic Analysis, Ver. 1.32 (32 bit). Available from: http://.ualberta.ca/wfeyeh.