Skip to main content

Estimation of genetic diversity and population genetic structure in Gymnema sylvestre (Retz.) R. Br. ex Schult. populations using DAMD and ISSR markers



Gymnema sylvestre (Retz.) R. Br. ex Schult. is a well-known medicinal plant against diabetes in India. There is as such no organized cultivation in India, and the plant is still being collected from the wild for their therapeutic uses. It is, therefore, important to estimate the genetic diversity and population genetic structure of G. sylvestre to ascertain the genetically diverse germplasm. The present study, therefore, was undertaken to analyze the genetic variability in 118 accessions belonging to 11 wild populations of G. sylvestre using directed amplification of minisatellite-region DNA (DAMD) and inter simple sequence repeats (ISSR).


The present genetic analyses of 11 populations with 25 markers (8 DAMD and 17 ISSR) revealed significant genetic diversity (H = 0.26, I = 0.40, PPL = 80.89%) at a species level, while the average genetic diversity at the population level was low. Among the 11 populations studied, PCH and UTK populations showed maximum genetic diversity, followed by KNR and AMB, while TEL population revealed the lowest genetic diversity. AMOVA and Gst values (0.18) revealed that most of the genetic variations are found within populations and very less among populations, and higher gene flow (Nm = 2.29) was found to be responsible for the genetic homogenization of the populations. The clustering pattern resulting from the UPGMA dendrogram was in congruence with STRUCTURE and PCoA, segregating all the 11 populations into two main genetic clusters: cluster I (populations of North and Central India) and cluster II (populations of South India). The clustering patterns obtained from all three statistical methods indicate that the genetic structure in G. sylvestre populations corresponds to the geographical diversity of the populations and represents a strong genetic structure.


The genetically diverse populations identified during the present study could be a potential genetic resource for further prospecting and conserving this important plant resource.


The genus Gymnema R. Br. (Apocynaceae) comprises approximately of 37 species worldwide [1], and about 14 species are found in India [2]. Among these, Gymnema sylvestre (Retz.) R. Br. ex Schult., commonly known as “Gurmar” or “Madhunashini,” is an important medicinal plant because of its reported anti-diabetic properties [3, 4]. It has a wide distribution range from tropical parts of Africa, Saudi Arabia, India, Sri Lanka, Southern China, and Southeast Asia to tropical Australia [5]. In India, it is reported to be distributed in the entire Peninsular region and has extended to Uttar Pradesh and Bihar [5, 6], covering tropical moist and dry deciduous, tropical thorn, tropical semi-evergreen, and tropical wet and dry evergreen forests [7]. G. sylvestre is a perennial, slow-growing, woody climber, with very small pale yellow-colored flowers in lateral umbel cyme that reaches up to 8–10 m height or more on the host plant. The presence of various triterpenoids such as gymnemic acid, gymnemasaponins, gymnemosides, gymnemanol, gymnemagenin, and gymnemasin along with several other glycosides such as gymsyloside F and gymsyloside G makes it a valuable plant in pharmaceuticals [8, 9]. Apart from these, its other major phytoconstituents are alkaloid drugs, anthracene derivatives, coumarin drugs, essential oils, flavonoids, valepotriates, etc. [10]. Besides anti-diabetic properties, G. sylvestre also has hypolipidemic, hepatoprotective [11], neuroprotective [12], immunomodulatory [13], cardioprotective [14], anti-inflammatory, antioxidant [15], anti-cancerous [16], antibacterial [17], and antiviral properties [18]. G. sylvestre has been used as an important ingredient in the preparations of many poly-herbal formulations for diabetes such as D-400 or Diabecon [19], IME-9 [20], and BGR-34 [21].

G. sylvestre is one of the 242 highly traded medicinal plants in India. The increased commercial demand for this highly valuable plant species has resulted in its overexploitation by local traders, and a big threat is operating on the species in the wild [22]. In addition to this main concern, the species is under pressure from habitat loss, invasion by exotic species, deterioration of the environment, and climate change. Also, the natural regeneration process of the species is slow and difficult due to low viability and poor germination rate of seed [23]. According to the Conservation Assessment and Management Prioritization (CAMP) exercises, G. sylvestre has been declared near threatened (NT) in Maharashtra, vulnerable (VU) in West Bengal, Madhya Pradesh, Chhattisgarh, Andhra Pradesh, and endangered (EN) in Rajasthan [22]. It is therefore critical to investigate comprehensively the patterns of genetic diversity and population structure in this medicinally important plant species so that appropriate conservation strategies and management programs can be undertaken.

There are many molecular markers available these days to analyze the genetic diversity and population genetic structure in plants. The most frequently used molecular markers are directed amplification of minisatellite-region DNA (DAMD) [24] and inter simple sequence repeats (ISSR) [25]. The main advantages of using these methods are that they are quick, inexpensive, robust, reproducible, highly polymorphic, randomly distributed throughout the genomes, and widely applicable to any genome [24,25,26]. These methods have been effectively used in the genetic diversity studies of many medicinal plant species like Ephedra foliata [27] Curcuma [28], Bergenia ciliata [29], and Illicium griffithii [26]. ISSR markers have also been found useful in unraveling the genetic diversity in some members like Leptadenia pyrotechnica [30], Rauwolfia tetraphylla [31], and Decalepis salicifolia [32] of the family Apocynaceae.

There are a few reports available on the genetic diversity of G. sylvestre using molecular markers [33,34,35,36,37,38,39,40]. There is, however, no comprehensive information available on the genetic diversity and population genetic structure of the species based on DAMD and ISSR methods. To the best of our knowledge, the present study on genetic diversity and population genetic structure of G. sylvestre using a combination of DAMD and ISSR markers seems to be a maiden attempt.


Plant materials and DNA extraction

Plant samples were collected from seven Indian states (Uttar Pradesh, Madhya Pradesh, Chhattisgarh, Maharashtra, Telangana, Karnataka, and Kerala). In the present analysis, we have considered a total of 118 accessions of 11 populations of G. sylvestre. A closely related species Tylophora indica (Burm. f.) Merr. was used as the out-group (Table 1, Fig. 1). The geocoordinates such as latitude (°N), longitude (°E), and elevation (m) for each population were recorded with the help of a global positioning system receiver (Garmin, USA). Fresh leaf samples were cleaned and stored dry at room temperature using self-indicating blue silica gel. Voucher specimens of all samples were prepared and have been deposited in the herbarium of CSIR-National Botanical Research Institute (LWG), Lucknow, Uttar Pradesh, India.

Table 1 Details of wild populations of G. sylvestre collected from different locations of India
Fig. 1
figure 1

Location map prepared using QGIS v3.14 “Pi” for the collected wild populations of G. sylvestre in India. Population codes are as given in the Table 1

Total genomic DNA from silica-dried leaf tissues was isolated using the CTAB method [41]. The quality of the extracted DNA was checked on 0.8% agarose gel, stained with ethidium bromide and compared with a set of known concentrations of lambda DNA (double digested with EcoRI and HindIII) using UV light photography in UVITEC gel documentation system (MilliporeSigma, USA), and quantity was measured by UV spectroscopy using a Nanodrop UV Spectrophotometer (Thermo Scientific, USA).

PCR amplification using DAMD and ISSR markers

The 20 DAMD markers available in the public domain [24] were custom synthesized from Sigma Aldrich Chemicals Private Limited, Bangalore, Karnataka, India. A set of 100 anchored ISSR markers was procured from the University of British Columbia, Canada. PCR amplification with DAMD and ISSR markers was carried out according to Zhou et al. [42].

All 20 DAMD markers were screened with three template DNAs of G. sylvestre, of which eight markers resulted in distinct and reproducible fragments that were subsequently used for further amplification of the entire set of 11 populations. The amplification with DAMD marker was carried out in a 20 μl reaction mixture containing 4 μl of template DNA (80 ng), 2 μl of 10 mM dNTP mix (2.5 mM each dNTP), 1.2 μl of 25 mM MgCl2, 2.5 μl of suitable 10 × assay buffer (Taq Buffer A) supplied along with the enzyme, 1.5 μl of 10 μM primer, and 0.33 μl of 3.0 U Taq DNA polymerase (Genei Laboratories Private Limited, Bangalore, Karnataka, India) using a Proflex PCR System (Applied Biosystems, Life Technologies, USA). After initial denaturation at 94 °C for 4 min, each cycle consisted of 1 min of denaturation at 94 °C, 2 min of annealing at (50 °C–55 °C), and 2.5 min extension at 72 °C, along with 7 min final extension at the end of 40 cycles. The amplification pattern of 118 G. sylvestre individuals is shown by the representative gel profile of marker FvIIex8C (Fig. S1a).

Similarly, 100 ISSR markers were screened with three template DNAs of G. sylvestre, of which 17 markers that resulted in distinct and reproducible fragments were finally selected for further amplification of the entire set of 11 populations. ISSR-PCR amplification of 2 μl of template DNA (40 ng) was carried out in a 20 μl reaction volume, containing 1 μl of 10 mM dNTP mix (2.5 mM each dNTP), 1 μl of 25 mM MgCl2, 2.5 μl of suitable 10 × assay buffer (Taq Buffer A) supplied along with the enzyme, 1.5 μl of 10 μM primer, and 0.33 μl of 3.0 U Taq DNA polymerase (Genei Laboratories Private Limited, Bangalore, Karnataka, India) using a Proflex PCR System (Applied Biosystems, Life Technologies, USA). After initial denaturation at 94 °C for 4 min, each cycle consisted of 1 min of denaturation at 94 °C, 1 min of annealing at (50 °C–54 °C), and 2 min extension at 72 °C along with 7 min final extension at the end of 35 cycles. The amplification pattern of 118 G. sylvestre individuals is shown by the representative gel profile of marker UBC-809 (Fig. S1b).

A total of 25 markers (8 DAMD and 17 ISSR) were finally selected and profiles of the complete set of 118 individuals of G. sylvestre and one individual of outgroup species T. indica were carried out with these 25 markers (Table 2). The amplified PCR products were loaded on 1.5% agarose gel, and stained with ethidium bromide, using 1X TBE buffer at a constant voltage of 5 V/cm. The size of each fragment for all the markers was determined using low range (100–3000 bp) DNA ruler plus (Genei Laboratories Private Limited, Bangalore, Karnataka, India). After electrophoresis, the gel was visualized using UVITech gel documentation system (Millipore Sigma, USA), and the patterns were photographed and documented as digital images.

Table 2 Details of DAMD and ISSR markers and the polymorphism obtained with these markers

Scoring of PCR profiles and data analyses

Data analysis with the two markers (8 DAMD and 17 ISSR) was carried out for all 25 reproducible profiles showing consistent banding patterns. A binary data matrix was generated indicating the presence (1) or absence (0) of a band for each marker in the assay. The data matrices (DAMD, ISSR, and cumulative; DAMD + ISSR) were analyzed statistically to evaluate the relative informativeness of the two markers independently and cumulatively, by calculating the percentage of polymorphic loci (PPL), polymorphic information content (PIC), resolving power (Rp), diversity index (DI), effective multiplex ratio (EMR), and marker index (MI) for each marker [25, 43, 44]. Mantel Z-statistics to test the correlation (r) between the three pairs of datasets (cumulative v/s DAMD, cumulative v/s ISSR, and DAMD v/s ISSR) and to ascertain goodness of fit between these markers computed using MXCOMP module of NTSYS-pc software version 2.02e [45]. Sequential agglomerative hierarchical nested (SAHN) clustering module [46] using the unweighted pair group method with arithmetic mean (UPGMA) method for both individual marker and cumulative marker data was carried out in NTSYS-pc software version 2.02e to generate the UPGMA tree of G. sylvestre individuals with the out-group species which was visualized in MEGA11 software [47]. Genetic similarity and distances based on Jaccard’s coefficient [48] were calculated using neighbor-joining (NJ) and UPGMA in the FREETREE program version [49] both by individual marker system and cumulatively.

The 11 populations considered in the present study were assumed to be in Hardy–Weinberg equilibrium for estimating the parameters of genetic diversity at population and species levels. The various genetic diversity parameters viz., PPL, observed number of alleles (Na), effective number of alleles (Ne), Nei’s gene diversity (H) [50], Shannon information index (I) [51], total genetic diversity (Ht), genetic diversity within populations (Hs), Nei’s genetic differentiation coefficient among populations (Gst), and gene flow among populations (Nm) were calculated in POPGENE program version 1.32 [52] using cumulative marker data. Besides this, the population dendrogram based on Nei’s original measures of genetic distances [53] using the UPGMA method was also calculated in POPGENE program using cumulative marker data. The UPGMA dendrogram was visualized in FigTree version 1.4.4 software [54].

Hierarchical partitioning of genetic variability at three strata, i.e., within populations, among populations, and among regions were examined by analysis of molecular variance (AMOVA) and clustering of populations in principal coordinate analysis (PCoA) for spatial representation of the relative genetic distances among all the individuals of G. sylvestre were done in GenAlEx program version 6.5 [55] using cumulative marker data. A Mantel test [56] was also conducted in GenAlEx program at 9999 permutations to determine the correlation (r) between pair-wise geographic and genetic distances of 11 populations and to evaluate the isolation by distance (IBD) model using cumulative marker data.

A cumulative dataset consisting of a set of individuals genotyped using multilocus markers was evaluated for clustering of populations using the Bayesian approach (Markov chain Monte Carlo (MCMC)) implanted in STRUCTURE software version 2.3 [57, 58]. This quantitative clustering method stratifies the populations by computing the proportion of the genome of an individual originating from each inferred population. For analysis, the number of K with prior population information was inferred using the admixture model with correlated allele frequencies. The program was run 20 times for each “K,” ranging from 1 to 12. Each run consisted of a burn-in time of 50,000 and a MCMC replication of 150,000. The number of K was obtained in a web-based python program STRUCTURE HARVESTER version 0.6.94 [59] by implementing the method described by Evanno et al. [60]. STRUCTURE HARVESTER generated plots comprise the mean of estimated Ln probability of data, Evanno results showing the mean rate of change of the likelihood distribution, mean of the absolute value of second order rate of change of the likelihood distribution, and the number of “K” groups that best fit the data.


DAMD and ISSR polymorphism

A total of 155 loci were amplified with 8 DAMD markers in 118 individuals of 11 populations, with an average of 19.38 loci per marker. Out of these 155 loci, 128 were polymorphic corresponding to 82.58% polymorphism, with an average of 16 polymorphic loci per marker. The size of amplified products varied approximately from 170 to 1600 bp. Among the 8 DAMD markers, 33.6 showed maximum polymorphism (90.48%), whereas the FvIIex8 showed minimum (71.43%). The marker URP38F came up with the maximum Rp value (37.86), whereas HBV with minimum (21.44). The PIC value was found to be maximum (0.30) for the marker FvIIex8c, whereas minimum (0.20) for 14C2. Among the 8 DAMD markers, two DAMD markers were found to be reasonably informative with PIC values > 0.25 and < 0.50 (33.6 = 0.29, FvIIex8c = 0.30), whereas the remaining six markers were slightly informative with their PIC values < 0.25 (14C2 = 0.20, FvIIex8 = 0.23, HBV = 0.24, HVR = 0.22, URP9F = 0.23 and URP38F = 0.22; Table 2).

A total of 274 loci were obtained with 17 ISSR markers with an average of 16.12 loci per marker. Out of these 274 loci, 219 were polymorphic corresponding to 79.93% polymorphism, with an average of 12.88 polymorphic loci per marker. The size of amplified products varied from 190 to 1500 bp. Among the 17 ISSR markers, UBC-860 showed maximum polymorphism (94.74%) whereas the UBC-866 showed minimum (50.00%). The marker UBC-835 came up with the maximum Rp value (30.83), whereas UBC-840 with minimum (13.05). The PIC value was found to be maximum (0.33) for the marker UBC-842 whereas minimum (0.12) for UBC-808. Out of 17 ISSR markers, seven markers were found to be reasonably informative with PIC values > 0.25 and < 0.50 (UBC-810 = 0.29, UBC-826 = 0.26, UBC-830 = 0.32, UBC-835 = 0.27, UBC-841 = 0.26, UBC-842 = 0.33, UBC-860 = 0.28), whereas rest of the ten markers were slightly informative with their PIC values < 0.25 (UBC-808 = 0.12, UBC-809 = 0.22, UBC-812 = 0.20, UBC-823 = 0.20, UBC-825 = 0.22, UBC-836 = 0.24, UBC-840 = 0.21, UBC-866 = 0.22, UBC-868 = 0.18, UBC-887 = 0.23; Table 2).

The PIC and DI values calculated for each marker system (DAMD: PIC = 0.24, DI = 0.29; ISSR: PIC = 0.24, DI = 0.30) were found almost equal for both the marker systems. However, the Rp, EMR, and MI values of the DAMD were found to be higher (Rp = 26.38, EMR = 13.21, MI = 3.88) than the ISSR (Rp = 22.44, EMR = 10.30, MI = 3.14) indicating the higher informativeness of DAMD over ISSR markers in estimating the genetic diversity of G. sylvestre. Cumulative analysis with 25 markers (8 DAMD + 17 ISSR) revealed 80.89% polymorphism with PIC, Rp, DI, EMR, and MI values of 0.24, 24.41, 0.30, 11.76, and 3.51, respectively (Table 2). The Mantel test performed for three pairs of datasets (DAMD v/s cumulative, ISSR v/s cumulative, and DAMD v/s ISSR) revealed a perfect positive correlation (r = 1) for each pair of data sets. The Mantel test indicates the effectiveness and suitability of both marker systems in estimating the genetic diversity and population genetic structure in natural populations of G. sylvestre (Table S1).

Analysis of genetic diversity

After considering and analyzing the various genetic diversity parameters viz., Na, Ne, H, I, and PPL; the PCH (Na = 1.59, Ne = 1.35, H = 0.20, I = 0.30, PPL = 58.74%) and UTK population (Na = 1.59, Ne = 1.34, H = 0.20, I = 0.30, PPL = 58.51%) were found to be the most diverse, closely followed by KNR (Na = 1.51, Ne = 1.36, H = 0.20, I = 0.29, PPL = 51.28%) and AMB population (Na = 1.52, Ne = 1.33, H = 0.19, I = 0.28, PPL = 51.52%), whereas TEL population was found to be least diverse (Na = 1.29, Ne = 1.19, H = 0.11, I = 0.16, PPL = 29.37%) among 11 populations of G. sylvestre (Table 3).

Table 3 Intra-population diversity statistics of G. sylvestre populations computed in POPGENE software

The total genetic diversity of the G. sylvestre was found significantly high (Na = 1.81, Ne = 1.44, H = 0.26, I = 0.40, PPL = 80.89%), but the average genetic diversity of a population (Na = 1.46, Ne = 1.29, H = 0.16, I = 0.24) was found much lower than the genetic diversity at the species level. This could be due to the differences in genetic diversity of different populations, from PCH and UTK (PPL = 59%) to TEL (PPL = 29.37%) with an average of 45.12% polymorphism per population. In the total genetic diversity of G. sylvestre (Ht = 0.26), most of the part was found to be contributed by genetic diversity within populations (Hs = 0.21; Table 3).

The region-wise genetic diversity analysis revealed that the South Indian region which comprised of total five populations showed the highest genetic diversity (Na = 1.73, Ne = 1.39, H = 0.24, I = 0.36, and PPL = 72.73%) closely followed by Central-Indian region with four populations (Na = 1.69, Ne = 1.36, H = 0.22, I = 0.33, and PPL = 69.00%), while the North-Indian region with two populations showed the lowest genetic diversity (Na = 1.47, Ne = 1.26, H = 0.15, I = 0.23, and PPL = 46.85%; Table S2).

Analysis of population genetic structure

The hierarchical partitioning of the total genetic variations of the species by AMOVA affirmed most of the genetic variations within populations (62%), followed by among regions (22%), and low among populations (16%) (Table 4). This result was further supported by Gst among 11 natural populations of G. sylvestre (0.18), which indicates that of the total genetic variations of species, there are only 18% genetic variations among populations, and most of the genetic variations, i.e., 82%, are within populations. This means that these 11 populations are genetically less differentiated from each other (Table 3). This was further substantiated by the significant value of gene flow (Nm = 2.29) among 11 populations which is responsible for lowering the genetic differentiation among populations (Table 3). The population genetic structure of G. sylvestre was also revealed by the Nei’s gene or genetic diversity value, as of the total genetic diversity of the species (Ht = 0.26), most of the part (Hs = 0.21; 80.77%) was found to be occupied by genetic diversity within populations (Table 3).

Table 4 AMOVA analysis carried out using GenAlEx program for the cumulativea (DAMD and ISSR) data of 118 individuals of G. sylvestre

The inter-population genetic identities ranged from 0.8072 (BDK v/s KNR) to 0.9627 (BDK v/s LAL) and the inter-population genetic distances ranged from 0.0380 (BDK and LAL) to 0.2142 (BDK v/s KNR). From these values, the genetic identities and genetic distances are found to be congruent with the geographical distances between populations (Table 5). Mantel test carried out for calculating the correlation between pairwise geographic and genetic distances in G. sylvestre populations revealed a very strong positive correlation (r = 0.831, p ˂ 0.0001) between geographic distance and genetic distance, indicating that the current genetic structure of the G. sylvestre populations is according to the geographical distances and isolation between populations (Fig. S2).

Table 5 Inter-population Nei’s original measures of genetic identity and genetic distance calculated among populations of G. sylvestre using the POPGENE software

The UPGMA-based population dendrogram clustered all 11 assumed populations of G. sylvestre into two major clusters: cluster I contained all six populations of the North and Central Indian region while cluster II contained all five populations of the South Indian region. In cluster, I, the BDK and LAL populations of the North Indian region are clearly separated out as an independent sub-cluster Ia from the sub-cluster Ib of four Central Indian populations (MFS, PCH, CFS, and DHM). In the sub-cluster Ib, the Chhattisgarh population (DHM) clearly separated out as an independent sub-cluster Ib2 from sub-cluster Ib1 of the rest of the three populations of Madhya Pradesh (MFS, PCH, and CFS). In cluster II, MBL and TEL populations were segregated out as two different independent sub-clusters (IIa and IIb1, respectively) from the rest of the three populations (AMB, UTK, and KNR) comprising sub-cluster IIb2. Furthermore, in this sub-cluster IIb2, the KNR population got segregated as an independent sub-cluster IIb2b from the sub-cluster IIb2a of AMB and UTK populations (Fig. 2). PCoA represented the G. sylvestre individuals spatially into three axes corresponding to their genetic distances and grouped all the 118 individuals of 11 G. sylvestre populations into two clusters (I and II). Cluster I comprised all 60 individuals of six populations of North and Central India, while cluster II contained rest of the 58 individuals belonging to five populations of South India. The three axes showed 48.62%, 64.59%, and 76.64% cumulative variations, respectively (Fig. 3). Bayesian genetic clustering algorithm of STRUCTURE employed for analyzing population genetic structure revealed two genetic clusters (K = 2) of G. sylvestre from 11 assumed populations. The cluster I contained all 60 individuals from six natural populations of North and Central India (BDK, LAL, MFS, PCH, CFS, and DHM), which showed an average ancestry membership participation coefficient of 95.5% (BDK = 99.8%, LAL = 99.4%, MFS = 94.4%, PCH = 95.4%, CFS = 93.4%, and DHM = 90.7%) to the inferred cluster I. Cluster II contained all 58 individuals from five natural populations of South India (MBL, TEL, AMB, UTK, and KNR) which showed an average ancestry membership participation of 93.9% (MBL = 95.9%, TEL = 77.1%, AMB = 99.6%, UTK = 99.7%, KNR = 97.2%) to the inferred cluster II. Therefore, the Bayesian assignment of 118 G. sylvestre individuals of the 11 assumed geographic populations emerged as the existence of two genetic clusters (Table 6, Fig. S3). The fixation index (Fst) which was obtained from STRUCTURE analysis for the two genetic clusters was 0.37 and 0.26, respectively, which indicates that there is adequate genetic differentiation within each genetic cluster.

Fig. 2
figure 2

UPGMA dendrogram showing relationships among 11 populations of G. sylvestre in India and values at nodes showing branch length. Population codes are as given in the Table 1

Fig. 3
figure 3

Principal coordinate analysis (PCoA) performed in the program GenAlEx showing clustering of G. sylvestre populations in India. The population codes are as given in the Table 1

Table 6 Proportion of ancestry membership coefficient of each pre-defined populations in each of the two clusters


As the wild populations of G. sylvestre are dwindling due to its exploitation for therapeutic uses in traditional and modern medical systems, thorough information on the species genetic diversity and population genetic structure is of utmost priority for its sustainable use, conservation, and management plans. Therefore, the present study was undertaken to analyze the genetic diversity and population structure in 11 representative populations of G. sylvestre sampled from the North, Central, and Southern parts of India. The genetic diversity of a species is the product of billions of years of evolution, and it should be studied and protected for a healthy ecosystem and its functions. As the genetic variations do not recover as quickly as population size, it must be preserved appropriately. The varied gene pool of a species allows it to adapt to the changing climatic circumstances and anthropogenic stress so that it can grow and persist in nature, and it is also significant in its environment because it supports the diversity of other related species [61]. Genetic drift, natural selection, mutation, recombination, and gene flow are all important evolutionary mechanisms that influence genetic diversity within a given species and its populations and also influence the genetic structure of a species. A species’ genetic diversity is further governed by its breeding system, seed dispersal technique, life form, geographic range, actual number and sizes of populations, isolation, historical events, ecological and evolutionary history, and anthropogenic activities [61,62,63,64].

Genetics has a predominant role in natural population conservation. The increasing importance of genetics in conservation may be attributed to the availability of various kinds of molecular markers, and they also have an impact on our capacity to answer important conservation concerns [61]. Previous comparative studies among DNA marker systems have shown that combining data from all and varied kinds of markers produces the most comprehensive and best findings in a species; therefore, new DNA marker systems are constantly valuable in genetic diversity studies [65, 66]. In plants like Trichosanthes dioica and Mangifera indica, SPAR (single primer amplification reaction) techniques involving more than one DNA marker system produce more realistic genetic variation [67, 68]. All earlier genetic diversity investigations in G. sylvestre were either limited to a narrower geographical area or had fewer individuals or both [33,34,35,36,37,38,39,40]. Using 25 molecular markers (combination of two marker systems: 8 DAMD and 17 ISSR), a comprehensive and cumulative analysis of genetic diversity and population genetic structure of G. sylvestre was used in the analyses of genetic diversity in 11 natural populations of G. sylvestre which revealed high levels of polymorphism by both the markers independently (DAMD = 82.58% and ISSR = 79.93%) as well as cumulatively (80.89%), indicating that G. sylvestre has a broad genetic base. Each marker system was found to be capable of detecting considerable genetic diversity in G. sylvestre demonstrating their discriminative efficacy and application in the current investigation. ISSR polymorphism (79.93%) obtained from the present study corroborates with earlier studies carried out in G. sylvestre [33, 34, 36]. However, Rathore et al. detected a high level of genetic polymorphisms (ISSR = 97.08%) across 117 individuals of G. sylvestre [35]. Present analyses of G. sylvestre populations with minisatellite marker system (DAMD marker) showing considerable genetic polymorphisms (82.58%) which is reinforced by the studies conducted in other medicinally important plant species from tropical or sub-tropical climate of India such as Jatropha curcas (DAMD = 92.02%) [69], Musa acuminata (DAMD = 88.46%) [70], Curcuma longa (DAMD = 84.40%) [71], and Ephedra foliata (DAMD = 79.70%) [28]. In the present investigation, the DAMD marker system outperformed the ISSR marker system in terms of Rp, EMR, and MI in analyzing the genetic diversity in G. sylvestre.

The intra-population genetic diversity analysis for 11 natural populations of G. sylvestre revealed that PCH and UTK populations have maximum genetic diversity closely followed by KNR and AMB populations while TEL showed the least genetic diversity. Among the four highly diverse populations, three (UTK, KNR, and AMB) are from the South-Indian region and one (PCH) is from the Central Indian region. The high genetic diversity of these three South-Indian populations (UTK, KNR, and AMB) corroborates with the high diversity found at lower latitudes as compared to higher latitudes. These populations are having lowest three latitudes among all the 11 studied populations. Many previous studies have also come up with the idea that high genetic diversity is found in plants and vertebrates at lower latitudes [72]. Also, their respective climatic condition, i.e., monsoon type with short dry seasons might be supporting their high genetic diversity. And the comparatively big population sizes of UTK and AMB populations are also supporting their high genetic diversity. The high genetic diversity of PCH population from the Central Indian region might be the result of a combination of existing climatic conditions (monsoon type with dry winter) and a big population size. The comparatively least genetic diversity of TEL population might be because of the combination of high temperatures and little precipitation with a small population size. The Indian land has been divided into 9 climate zones viz., “monsoon type with short dry season,” “monsoon type with dry winter,” etc., according to the Köppen’s climate classification system, and these monsoon type climatic conditions accompanied by humidity might be supporting high genetic diversity in plants [73]. As the lianas are mostly found in wet tropical forests, and their abundance and distribution are influenced by rainfall, seasonality, soil fertility, and elevation, therefore, “monsoon-type climatic conditions” are prerequisites for their diversity. Also, in the region-wise genetic diversity estimation, among the three regions, the South Indian region showed maximum genetic diversity closely followed by the Central-Indian region whereas, the North-Indian region showed minimum genetic diversity. In the present investigation, cumulative (8 DAMD + 17 ISSR) marker analysis of 11 G. sylvestre populations revealed a considerably high level of genetic diversity at the species level (H = 0.26, I = 0.40, PPL = 80.89%) which is comparable to high genetic diversity indices value of other outcrossing plant species like Justicia adhatoda (H = 0.25, I = 0.39) [74], Trifolium repens (H = 0.28, I = 0.44) [75], and Asarum mandshuricum (H = 0.28, I = 0.42) [76]. In general, outcrossing plant species have significantly higher genetic diversity than selfing species [63, 77]. Nei’s gene diversity value (H = 0.26) for G. sylvestre was found to be higher than the Nei’s gene diversity calculated for dicotyledons (H = 0.17), long-lived perennials (H = 0.18), and widespread plants (H = 0.18) [64]. The Nei’s genetic diversity value for G. sylvestre (H = 0.26) is within the range of “0.17–0.33” which has been estimated earlier for outcrossing plants [78]. The value of H is also comparable to the previously estimated value of H (by ISSR; H = 0.27 and by RAPD; H = 0.26) in G. sylvestre [35]. Like many other members of the sub-family, Asclepiadoideae, G. sylvestre has prominent pollinia and entomophily for cross-pollination, and the most frequent insects for cross-pollination are from the order; Hymenoptera (bees and wasps), Lepidoptera (butterflies and moths), and Diptera (flies) [79]. Besides this, the wind-dispersed seeds (comose seeds) have a significant role in gene flow [33]. Therefore, the considerable prevailing genetic diversity in G. sylvestre could be predominantly explained by its life form (long-lived, perennial, liana), breeding system (outbreeding due to conspicuous entomophily), seed (comose seed) wind dispersal mechanism, and geographic range (widely distributed in the entire peninsular Indian region). Generally, long-lived perennials with an outbreeding system, wind dispersal mechanism, and wide geographic spread have a high genetic diversity [64]. Outcrossing species of the Apocynaceae-Asclepiadoideae group have been found to have a high genetic diversity [80]. Although the total genetic diversity for G. sylvestre (H = 0.26) was found to be remarkably high, the average genetic diversity per population of G. sylvestre (H = 0.16) was found significantly lower than this total genetic diversity for the species because the average genetic diversity per population gets reduced by differences in genetic diversity by populations. As different populations have to face different climatic conditions, as well as their respective population size also differ from one another so, each population prevails in a different amount of genetic diversity. However, when we compare our estimated genetic diversity of G. sylvestre (H = 0.26) with other species of its group Apocynaceae; Asclepiadoideae (Vincetoxicum katoi and Vincetoxicum yamanakae; H = 0.31) [81], it seems comparatively less with respect to them. Despite these significant life history traits of G. sylvestre which are responsible for its high genetic diversity, the increasing anthropogenic pressure like habitat destruction and fragmentation and global climate change might be reducing its genetic diversity and fitness. Another important feature of G. sylvestre which was observed in the wild is that it also produces new progenies by clonal propagation and, in nature, its seed germination rate is also very poor [23]. These two factors also might be responsible for reducing genetic diversity.

Genetic drift, natural selection, recombination, and mutation are the major evolutionary forces responsible for making each population of a species genetically distinct from another population of the same species; therefore, population diversification occurs and over millions of years of time evolution of new species takes place, as these evolutionary forces are helping in maintaining the required genetic differentiation between different populations of a species and also provide the basis for local adaptation to different climatic conditions for each population which is crucial for the evolutionary potential of species. On the other hand, gene flow among populations is the unifying force that binds geographically separated populations into a single evolutionary unit-the species. Without gene flow between populations, populations will become genetically distinct from one another, and if extinction happened to one population, that would result in a significant loss of a particular genetic variation [61]. The value of Gst ranges from zero to one, and the value of Nm ˃ one and ˂ four is considered moderate [26]. The value of Nei’s genetic differentiation among populations (Gst = 0.18) obtained from the present analyses showed low genetic differentiation among 11 different populations of G. sylvestre which was created by a moderate rate of migration of genetic materials (Nm = 2.29) among these 11 populations. Being an outcrossing species, the significant gene flow (in the form of pollen and comose seeds) happens to be responsible for lowering the genetic differentiation among populations of species, although it is not proven yet that either entomophily or comose seeds are contributing more towards the gene flow. AMOVA also revealed only 16% of genetic variations present among the populations and most of the genetic variations are present within populations (62%). Results obtained from Gst, AMOVA, Ht, and Hs showed that of the total genetic variations of the species, most of them are occurring within the populations. In a former study, it was found that the outcrossing plant species with wind dispersal mechanism and long-life spans like G. sylvestre has maximum genetic variations within populations while the selfing plant species have maximum genetic variations among populations [77].

The genetic identities and genetic distances between 11 populations of G. sylvestre were in congruence with geographical diversity. The IBD model also suggested that the current genetic structure of 11 different populations is in accordance with the geographical distances. Population genetic structure analyses using STRUCTURE resulted into two major genetic clusters (K = 2) from the 11 distant populations of G. sylvestre suggesting that the moderate amount of gene flow (Nm = 2.29) which has homogenized the nearby populations thus highlighting two major genetic clusters prominently, with an average 5.3% admixing of individuals in which the cluster I showed 4.5% admixing to cluster II and cluster II showed 6.1% admixing to cluster I. Most of the admixing were shown by the peripheral populations like DHM = 9.3% (from North and Central Indian region) and TEL = 22.9% (from South Indian region), respectively. The population genetic structure of G. sylvestre with two genetic clusters or two genetic groups (K = 2) obtained from the present analyses reveals an intermediate type of species sub-division which is strictly in congruence to the geography and can also be called two geographical groups: the first group of North and Central Indian individuals and the second group of South Indian Individuals. These two genetic clusters show local adaptation to two different geographical areas, respectively, which is good for the evolutionary potential of a species. Within each genetic cluster, there is significant genetic divergence among populations which means all the populations are also locally adapted. The UPGMA and PCoA analyses have supported the result of STRUCTURE as they also came up with two genetic clusters in G. sylvestre.


Genetic polymorphisms from the cumulative data were found to be more appropriate as single type of molecular marker does not provide the best estimate of genome-wide variability in an organism. Comprehensive analyses of genetic diversity and population genetic structure in G. sylvestre using cumulative marker data (ISSR + DAMD) with a larger dataset covering a large niche of species were thought to be more informative than previous studies in the species. Considering the medicinal importance of G. sylvestre, overexploitation, and depleting populations, different conservation strategies are required for the protection of diverse populations to maintain the species gene pool. Populations like PCH, UTK, KNR, and AMB with maximum genetic diversity should be prioritized for conservation, whereas populations with low genetic diversity, such as MBL and TEL, require adequate management to maximize the diversity. Besides, there is an urgent need to develop promising tissue culture protocols to increase the regeneration process to safeguard existing natural populations and to introduce maximum individuals in impoverished areas to maintain diversity. Results obtained from the present study may be helpful in the identification, collection, and prioritization of genetically varied G. sylvestre individuals for its improvement and conservation. The establishment of commercial agro-techniques and standardized harvesting protocols may reduce the pressure on populations of medicinal plants growing in the wild.

Availability of data and materials

All data generated or analyzed during this study are included in this published article [and its supplementary information files].



Directed amplification of minisatellite-region DNA


Inter simple sequence repeats


Percentage of polymorphic loci


Polymorphic information content


Resolving power


Diversity index


Effective multiplex ratio


Marker index


Unweighted pair group method with arithmetic mean

N a :

Observed number of alleles

N e :

Effective number of alleles

H :

Nei’s gene diversity

I :

Shannon information index

H t :

Total genetic diversity

H s :

Genetic diversity within populations

G st :

Nei’s genetic differentiation coefficient among populations

N m :

Gene flow among populations


Analysis of molecular variance


Principal coordinate analysis


Isolation by distance


Markov chain Monte Carlo


  1. Borsch T, Berendsohn W, Dalcin E, Delmas M, Demissew S, Elliott A, Fritsch P, Fuchs A, Geltman D, Güner A, Haevermans T (2020) World Flora online: placing taxonomists at the heart of a definitive and comprehensive global resource on the world’s plants. Taxon 69:1311–1341

    Article  Google Scholar 

  2. Karthikeyan S, Sanjappa M, Moorthy S (2009) Flowering plants of India: dicotyledons. Vol 1. Flora of India

  3. Kumari J, Kumar J, Kumari N, Kumari K, Nirala DP (2018) Traits association and path value analysis of Gymnema sylvestre cuttings for higher survival percentage under the influence of PGRs. J Pharmacogn Phytochem 7:2298–2300

  4. Adi BS, Adi GB, Jamadade AK, Reddy ESR (2020) A comparison of the efficacy of Gymnema sylvestre 6 Ch and Gymnema sylvestre mother tincture in cases of type 2 diabetes mellitus. Int J Curr Med Pharm Res 02:133–138

    Google Scholar 

  5. Jamadagni PS, Pawar SD, Jamadagni SB, Gautam M, Gaidhani SN, Prasad GP, Gurav AM (2021) Recent updates in research on Gymnema sylvestre. Pharmacogn Rev 15:128–133

    Article  Google Scholar 

  6. Ishwarya MS, Ramessh C, Singh RD, Palanikumar M, Samsai T (2021) Evaluating growing media and plant growth regulators on the establishment of rooting in Sirukurinjan (Gymnema sylvestre L.) in Southern Tamil Nadu. J Pharm Innov 10:1975–1978

    Google Scholar 

  7. Babu KN, Ayushi K, Wilson VK, Ayyappan N, Parathasarathy N (2021) The woody flora of Shettihalli Wildlife Sanctuary, Central Western Ghats of Karnataka, India - a checklist. J Threat Taxa 13:20033–20055

    Article  Google Scholar 

  8. Kalariya KA, Gajbhiye N, Minipara D, Meena RP, Kumar S, Saha A, Trivedi A, Manivel P (2019) Deep sequencing-based de novo transcriptome analysis reveals biosynthesis of gymnemic acid in Gymnema sylvestre (Retz.) Schult. Ecol Genet Genom 13:100047

    Google Scholar 

  9. Yen PH, Yen DTH, Thanh NTV, Hung NA, Bang NA, Tai BH, Nhiem NX, Kiem PV (2021) Gymsyloside F and Gymsyloside G, two new pregnane glycosides from the leaves of Gymnema sylvestre and their α-glucosidase and α-amylase inhibitory activities. Nat Prod Commun 16:1934578X211029532

    Google Scholar 

  10. Koshti L, Patil D, Patil A (2019) Chemo-profile development of Gymnema sylvestre R. Br. leaves using high performance thin layer chromatography (HPTLC) technique. J Pharmacogn phytochem 8:664–668

    Google Scholar 

  11. Kousar S, Aslam B, Muhammad F, Khan JA (2021) Hepatoprotective and hypolipidemic activities of Caesalpinia bonduc seed kernels and Gymnema sylvestre leaves extracts in alloxan-induced diabetic rats. Pak J Pharm Sci 34:307–311

    Google Scholar 

  12. Pandey A, Pathak S, Mishra A, Gupta G, Rawat S (2022) Neuroprotective effect of Gymnema sylvestre in alloxan induced diabetic neuropathic in albino rats. PBI Journal 1:1–23

    Google Scholar 

  13. Kar PP, Rath B, Ramani YR, Maharana CS (2019) Amelioration of cyclophosphamide induced immunosupression by the hydro-alcoholic extract of Gymnema sylvestre leaves in albino rats. Biomed Pharmacol J 12:251–258

    Article  Google Scholar 

  14. Pradeepkumar B, Sudheer A, Srinath Reddy T, Somasekhar Reddy K, Narayana G, Veerab-hadrappa K (2018) Cardioprotective activity of flavonoid fraction of Gymnema sylvestre leaves on doxorubicin induced cardiac damage. J Young Pharm 10:422–426

    Article  Google Scholar 

  15. Jangam A, Tirunavalli SK, Adimoolam BM, Kasireddy B, Patnaik SS, Erukkambattu J, Thota JR, Andugulapati SB, Addlagatta A (2023) Anti-inflammatory and antioxidant activities of Gymnema sylvestre extract rescue acute respiratory distress syndrome in rats via modulating the NF-κB/MAPK pathway. Inflammopharmacology 20:1–22

    Google Scholar 

  16. Packialakshmi B, Sowndriya SR (2019) Anti-cancer effect of Gymnema sylvestre leaf extract against MG63, human osteosarcoma cell line - an in vitro analysis. Int J Curr Res Rev 11:18–24

    Article  Google Scholar 

  17. Tabassum A, Raj MS (2020) Plant extracts as potential antibacterial agent against pathogens causing diabetic foot ulcers. J Sci Res 64:13–18

    Google Scholar 

  18. Yadav N, Kumar A, Bhargav N (2021) A review on: antiviral activity of medicinal plants against pandemic COVID 19 in India. Gap Interdiscip 4:88–97

    Google Scholar 

  19. Al-Hassani AN (2013) Clinical efficacy of Diabecon in treatment of type 2 diabetes mellitus, in newly diagnosed diabetic patients and in those on drug treatment (Glibenclamide and Metformin) in Erbil Governarate-Kurdistan Region/Iraq. Al Mustansiriyah J Pharmaceut Sci 3:44–51

    Google Scholar 

  20. Panda AK (2017) Efficacy of ayurveda formulation ayush-82 (IME-9) in newly diagnosed type 2 diabetics: retrospective analysis of individual data. J Tradit Med Clin Naturopat 6:2

    Google Scholar 

  21. Gupta BP, Sharma I, Kohli N, Sharma S, Rathi A, Sharma AK (2018) Preliminary clinical assessment and non-toxicity evaluation of an ayurvedic formulation BGR-34 in NIDDM. J Tradit Complement Med 8:506–514

    Article  Google Scholar 

  22. Gowthami R, Sharma N, Pandey R, Agrawal A (2021) Status and consolidated list of threatened medicinal plants of India. Genet Resour Crop Evol 68:2235–2263

    Article  Google Scholar 

  23. Tanuja M, Preeti C (2018) In vitro micropropagation of nodal explant of Gymnema sylvestre R. Br. a multidimensional plant. Int J Res Appl Sci Eng Technol 6:4195–4197

    Article  Google Scholar 

  24. Heath DD, Lwama GK, Devlin RH (1993) PCR primed with VNTR core sequences yields species specific patterns and hypervariable probes. Nucl Acids Res 21:5782–5785

    Article  Google Scholar 

  25. Prevost A, Wilkinson MJ (1999) A new system of comparing PCR primers applied to ISSR fingerprinting of potato cultivars. Theor Appl Genet 98:107–112

    Article  Google Scholar 

  26. Borah R, Bhattacharjee A, Rao SR, Kumar V, Sharma P, Upadhaya K, Choudhury H (2021) Genetic diversity and population structure assessment using molecular markers and SPAR approach in Illicium griffithii, a medicinally important endangered species of Northeast India. J Genet Eng Biotechnol 19:118

    Article  Google Scholar 

  27. Meena B, Singh N, Mahar KS, Sharma YK, Rana TS (2019) Molecular analysis of genetic diversity and population genetic structure in Ephedra foliata: an endemic and threatened plant species of arid and semi-arid regions of India. Physiol Mol Biol Plants 25:753–764

    Article  Google Scholar 

  28. Satyawada RR, Wahlang DR, Lamo JM (2020) Molecular and cytogenetical approaches for genetic diversity analysis of wild and cultivated medicinal plant species from North-East India with focus on genus Curcuma. In: Khasim SM, Long C, Thammasiri K, Lutken H (eds) Medicinal plants: biodiversity, sustainable utilization, and conservation. Springer, Singapore

    Google Scholar 

  29. Tiwari V, Meena B, Nair NK, Rana TS (2020) Molecular analyses of genetic variability in the populations of Bergenia ciliata in Indian Himalayan Region (IHR). Physiol Mol Biol Plants 26:975–984

    Article  Google Scholar 

  30. Nair S, Juleri MU, Satyawada RR, Dagla HR (2018) Genetic diversity analysis of Leptadenia pyrotechnica in Jodhpur region of India. Gene Rep 10:157–161

    Article  Google Scholar 

  31. Rohela GK, Jogam P, Bylla P, Reuben C (2019) Indirect regeneration and assessment of genetic fidelity of acclimated plantlets by SCoT, ISSR, and RAPD markers in Rauwolfia tetraphylla L.: an endangered medicinal plant. Biomed Res Int 2019:1–14

  32. Gokul S, Rodrigues V, Kumar A, Verma RS, Shukla AK, Sundaresan V (2020) Population genetics coupled chemical profiling for conservation implications of Decalepis salicifolia (Bedd. ex Hook. f.) Venter, an endemic and critically endangered species of Western Ghats. India Biochem Genet 58:452–472

    Article  Google Scholar 

  33. Mohd S, Zanan RL, Wakte KV, Mathure SV, Kad TD, Deokule SS, Nadaf AB (2012) Genetic diversity assessment of Gymnema sylvestre (Retz.) R. Br. ex Sm. populations from Western Ghats of Maharashtra. India Genet Resour Crop Evol 59:125–134

    Article  Google Scholar 

  34. Mouna HM, Reddy JM, Rajasekharan PE, Shareef I, Sreekanth B (2014) Assessment of genetic diversity in the medicinal climber Gymnema sylvestre from Karnataka, India. Int J Innov Res Sci Eng Technol 3:10497–10501

    Google Scholar 

  35. Rathore PK, Madihalli S, Hegde S, Hegde HV, Bhagwat RM, Gupta VS, Kholkute SD, Jha TB, Roy S (2016) Assessment of genetic diversity of Gymnema sylvestre (Retz.) R.Br. from Western Ghats and Eastern India. India J Biodivers Environ Sci 9:82–92

    Google Scholar 

  36. Verma A, Dhawan S, Singh S, Bharati KA, Jyotsana, (2016) Genetic and chemical profiling of Gymnema sylvestre accessions from Central India: its implication for quality control and therapeutic potential of plant. Phcog Mag 12:S407

    Article  Google Scholar 

  37. Jinu U, Thiayagarajan M, Javed S, Alatar AA, Venkatachalam P (2019) Detection of molecular and phytochemical variations in Tamilnadu landraces of Gymnema sylvestre genotype: an important anti-diabetic plant. J Agr Sci Tech 21:1521–1534

    Google Scholar 

  38. Boomibalagan P, Subramanian SR, Rajasekharan PE, Karpakal S, Veeranan U, Saminathan E, Narayanan V, Kathiresan D (2021) Genetic relationship and polymorphism of selected medicinal plants of Asclepiadaceae using RAPD molecular analysis method. Ecol Genet Genom 21:100101

    Google Scholar 

  39. Kalariya KA, Poojara L, Minipara D, Saran PL, Meena RP, Macwan S, Manivel P (2021) Development and validation of EST-SSR markers in Gymnema sylvestre R.Br. J Plant Physiol Pathol. 9:5

    Google Scholar 

  40. Polaiah AC, Damor PR, Reddy RN, Manivel P, Shivakumara KT, Suthar MK, Thondaiman V, Manjesh GN, Bindu KH, Kumar J (2023) Development of genomic SSR markers in Gymnema sylvestre (Retz.) R. Br. ex Sm. using next generation DNA sequencing and their application in genetic diversity analysis. J Appl Res Med Aromat Plants. 34:100455

    Google Scholar 

  41. Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh tissue. Focus 12:13–15

    Google Scholar 

  42. Zhou Z, Bebeli PJ, Somers DJ, Gustafson JP (1997) Direct amplification of minisatellite-region DNA with VNTR core sequences in the genus Oryza. Theor Appl Genet 95:942–949

    Article  Google Scholar 

  43. Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32:314–331

    Google Scholar 

  44. Powell W, Morgante M, Andre C, Hanafey M, Vogel J, Tingey S, Rafalski A (1996) The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol Breed 2:225–238

    Article  Google Scholar 

  45. Rohlf FJ (1998) NTSYS-pc: numerical taxonomy and multivariate analysis system. ver. 2.02e. Exeter Software, Setauket

    Google Scholar 

  46. Sneath PHA, Sokal RR (1973) Numerical taxonomy: the principles and practice of numerical classification. W. H. Freeman and Company, San Francisco

    MATH  Google Scholar 

  47. Tamura K, Stecher G, Kumar S (2021) MEGA11: molecular evolutionary genetics analysis version 11. Mol Biol Evol 38:3022–3027

    Article  Google Scholar 

  48. Jaccard P (1908) Nouvelles recherches sur la distribution florale. Bull Soc Vaud Sci Nat 44:223–270

    Google Scholar 

  49. Pavlicek A, Hrda S, Flegr J (1999) FreeTree-freeware program for construction of phylogenetic tress on the basis of distance data and bootstrap/jackknife analysis of the tree robustness. Application in the RAPD analysis of genus Frenkelia. Folia Biol (Praha) 45:97–99

    Google Scholar 

  50. Nei M, Li WH (1979) Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci USA 76:5269–5273

    Article  MATH  Google Scholar 

  51. Lewontin RC (1972) The apportionment of human diversity. Evol Biol 6:381–398

    Google Scholar 

  52. Yeh FC, Yang RC, Boyle T (1999) POPGENE ver. 1.32 (32 bit): Microsoft Windows-based freeware for population genetic analysis, quick user guide. Center for International Forestry Research, University of Alberta, Edmonton

  53. Nei M (1972) Genetic distance between populations. Am Nat 106:283–292

    Article  Google Scholar 

  54. Rambaut A (2018) FigTree.v1.4.4: molecular evolution, phylogenetics, and epidemiology. Institute of evolutionary biology, University of Edinburgh.

  55. Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in excel. Population genetic software for teaching and research-an update. Bioinformatics 28:2537–2539

    Article  Google Scholar 

  56. Mantel NA (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220

    Google Scholar 

  57. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    Article  Google Scholar 

  58. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587

    Article  Google Scholar 

  59. Earl DA, von Holdt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361

    Article  Google Scholar 

  60. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620

    Article  Google Scholar 

  61. Allendorf FW, Luikart GH (2007) Conservation and the genetics of populations. Blackwell Publishing Ltd, Oxford

    Google Scholar 

  62. Carvalho YG, Vitorino LC, Souza UJ, Bessa LA (2019) Recent trends in research on the genetic diversity of plants: implications for conservation. Diversity 11:62

    Article  Google Scholar 

  63. Gamba D, Muchhala N (2020) Global patterns of population genetic differentiation in seed plants. Mol Ecol 29:3413–3428

    Article  Google Scholar 

  64. De Kort H, Prunier JG, Ducatez S, Honnay O, Baguette M, Stevens VM, Blanchet S (2021) Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat Commun 12:516

    Article  Google Scholar 

  65. Chen MY, He XH, Zhang YL, Lu TT, He WQC, Yang JH, Huang X, Zhu JW, Yu HX, Luo C (2022) Genetic diversity and relationship analyses of mango (Mangifera indica L.) germplasm resources with ISSR, SRAP CBDP and CEAP markers. Sci Hortic. 301:111146

    Article  Google Scholar 

  66. Sharma P, Nath AK, Dhiman SR, Dogra S, Sharma V (2022) Characterization of carnation (Dianthus caryophyllus L.) genotypes and gamma irradiated mutants using RAPD, ISSR and SSR markers. S Afr J Bot 148:67–77

    Article  Google Scholar 

  67. Adhikari S, Biswas A, Saha S, Biswas A, Ghosh P (2020) SPAR methods reveal high genetic diversity within populations and moderate gene flow of pointed gourd (Trichosanthes dioica Roxb) germplasm. Biocatal Agric Biotechnol 29:101760

    Article  Google Scholar 

  68. Jena RC, Chand PK (2021) Multiple DNA marker-assisted diversity analysis of Indian mango (Mangifera indica L.) populations. Sci Rep 11:1–15

    Article  Google Scholar 

  69. Murty SG, Patel F, Punwar BS, Patel M, Singh AS, Fougat RS (2013) Comparison of RAPD, ISSR, and DAMD markers for genetic diversity assessment between accessions of Jatropha curcas L. and its related species. J Agr Sci Tech 15:1007–1022

    Google Scholar 

  70. Lamare A, Rao SR (2015) Efficacy of RAPD, ISSR and DAMD markers in assessment of genetic variability and population structure of wild Musa acuminata colla. Physiol Mol Biol Plants 21:349–358

    Article  Google Scholar 

  71. Verma S, Singh S, Sharma S, Tewari SK, Roy RK, Goel AK, Rana TS (2015) Assessment of genetic diversity in indigenous turmeric (Curcuma longa) germplasm from India using molecular markers. Physiol Mol Biol Plants 21:233–242

    Article  Google Scholar 

  72. Zhang JX, Chen MH, Gan L, Zhang CJ, Shen Y, Qian J, Han ML, Guo YX, Yan XB (2020) Diversity patterns of Bermuda grass along latitudinal gradients at different temperatures in Southeastern China. Plants 9:1–12

    Article  Google Scholar 

  73. Köppen W (1936) Das geographische System der Klimate. Gebruder Borntrager 1:1–44

    Google Scholar 

  74. Kumar A, Mishra P, Singh SC, Sundaresan V (2014) Efficiency of ISSR and RAPD markers in genetic divergence analysis and conservation management of Justicia adhatoda L., a medicinal plant. Plant Syst Evol 300:1409–1420

    Article  Google Scholar 

  75. Wu F, Ma S, Zhou J, Han C, Hu R, Yang X, Nie G, Zhang X (2021) Genetic diversity and population structure analysis in a large collection of white clover (Trifolium repens L.) germplasm worldwide. Peer J 9:1–17

    Article  Google Scholar 

  76. Jeong HJ, Kim JG (2022) Populations of Asarum mandshuricum are strongly differentiated despite a high outcrossing rate. J Plant Biol 65:241–251

    Article  Google Scholar 

  77. Huang R, Zhang ZD, Wang Y, Wang YQ (2021) Genetic variation and genetic structure within metapopulations of two closely related selfing and outcrossing Zingiber species (Zingiberaceae). AoB Plants 13:plaa065

    Article  Google Scholar 

  78. Schoen DJ, Brown AH (1991) Intraspecific variation in population gene diversity and effective population size correlates with the mating system in plants. Proc Natl Acad Sci 88:4494–4497

    Article  Google Scholar 

  79. Sharma BK (2022) Reproductive biology of Gymnema sylvestre and Tylophora indica (Asclepiadaceae) in Central India. Int J Res Trends Innov 7:1023–1029

    Google Scholar 

  80. Yamashiro T, Yamashiro A, Inoue M, Maki M (2016) Genetic diversity and divergence in populations of the threatened grassland perennial Vincetoxicum atratum (Apocynaceae-Asclepiadoideae) in Japan. J Hered 107:455–462

    Article  Google Scholar 

  81. Yamashiro T, Yamashiro A, Ogawa M, Kurosaki N, Kobayashi T, Maki M (2012) High population genetic diversity and regional differentiation in the endemic Vincetoxicum katoi-V. yamanakae complex (Apocynaceae; Asclepiadoideae) in Japan. Acta Phytotax Geobot 63:29–40

    Google Scholar 

Download references


The authors are thankful to Director CSIR-National Botanical Research Institute, Lucknow for the facilities and encouragement. Financial support received from the Council of Scientific and Industrial Research; New Delhi (CSIR-Phytopharmaceutical Mission, HCP-0010) is gratefully acknowledged. We are thankful to the forest departments of different states (Uttar Pradesh, Madhya Pradesh, Chhattisgarh, Maharashtra, Telangana, Karnataka, and Kerala) for their support in the collection of plant samples. The manuscript has the institutional communication number: CSIR-NBRI_MS/2022/10/08.


This work was financially supported by the CSIR-Phytopharmaceutical Mission, HCP-0010.

Author information

Authors and Affiliations



T. S. Rana conceived and designed the research. Kanchana Vaishnav and Baleshwar Meena collected the plant samples from different locations in India. Kanchana Vaishnav performed all the experiments. Kanchana Vaishnav and Vandana Tiwari analyzed the data. Kanchana Vaishnav prepared the first draft of the manuscript, and T. S. Rana edited and finalized the draft. All the authors read and approved the final draft.

Corresponding author

Correspondence to T. S. Rana.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Fig. S1a. A representative profile of G. sylvestre DNA (Gs001 to Gs118) amplified by DAMD primer FVIIex8c. ‘M’ represents a low range DNA ruler (100bp-3kb). Outgroup species DNA is shown at the last (OG). Fig. S1b. A representative profile of G. sylvestre DNA (Gs001 to Gs118) amplified by ISSR primer UBC-809. ‘M’ represents a low range DNA ruler (100bp-3kb). Outgroup species DNA is shown at the last (OG).

Additional file 2:

Fig. S2. Correlation between geographical and genetic distances of populations of G. sylvestre in India, showing geographical distance (in Km) in X axis and Nei’s genetic distance (cumulative marker data) in Y axis.

Additional file 3:

Fig. S3. Bayesian analysis of 118 individuals of G. sylvestre in STRUCTURE software showing, a. Bar plot representing individuals arranged according to its most likely ancestry b. Evanno table highlighting that value of K which describes the genetic groups of species c. Graph showing the peak corresponding to that value of K which tells genetic groups of species estimated by Evanno method.

Additional file 4:

Table S1. Mantel Z-statistics carried out for three pairs of data matrices (DAMD, ISSR and cumulative*) in G. sylvestre.

Additional file 5:

Table S2. Region-wise diversity statistics of G. sylvestre calculated for the three major regions using POPGENE software.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vaishnav, K., Tiwari, V., Durgapal, A. et al. Estimation of genetic diversity and population genetic structure in Gymnema sylvestre (Retz.) R. Br. ex Schult. populations using DAMD and ISSR markers. J Genet Eng Biotechnol 21, 42 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • DAMD
  • Gymnema sylvestre
  • Genetic diversity
  • ISSR
  • Population genetic structure