Assessment of the genetic diversity of licorice (Glycyrrhiza glabra L.) populations by RAPD and ISSR markers

Abstract

Glycyrrhiza glabra L. is a perennial herb of medicinal and industrial significance. To understand its genetic variability and population structure, 60 individuals representing 7populations from 4 Indian states were analyzed using RAPD and ISSR markers. DNA was extracted from young leaf tissues using a modified CTAB method. 9 RAPD and 12 ISSR primers produced 81 and 88 bands, respectively, with 97.43% and 100% polymorphism. Genetic diversity indices showed moderate variation: the observed number of alleles (Na) ranged from 1.97 to 2.00, effective alleles (Ne) from 1.07 to 1.21, Shannon’s index from 0.0710 to 0.1555, and Nei’s gene diversity from 0.1518 to 0.2747. AMOVA showed that 85% (RAPD) and 81% (ISSR) of total genetic variation resided within populations. PCoA indicated moderate differentiation, supported by Gst values of 0.1479 (RAPD) and 0.2247 (ISSR). UPGMA and neighbour-joining analyses grouped individuals into three major clusters, consistent with STRUCTURE results identifying K = 3. High polymorphism and moderate genetic differentiation indicate shared ancestry with regional divergence. These findings provide valuable insights for the conservation, germplasm management, and future genetic improvement of G. glabra.

Background

Glycyrrhiza glabra L. (family fabaceae) is a perennial herb extensively used in the pharmaceutical, food, beverage, and cosmetic industries [1]. Despite its economic and medicinal significance, G. glabra also poses ecological concerns in regions where it grows vigorously outside its native range [2, 3]. The species behaves as a persistent weed due to its exceptional resilience and difficulty of control, largely attributable to its aggressive underground structures. G. glabra reproduces vegetatively through extensive stolons that can reach lengths of up to two meters. Its deep and tenacious root system, extending over three meters into the soil, renders it highly persistent and resistant to eradication from cultivated areas, as even small detached root fragments can regenerate. Once established, this robust vegetative growth enables licorice to dominate the ground, often outcompeting other plant species and exhibiting aggressive colonization [4, 5]. Glycyrrhizin is an important bioactive compound present in stolons or rhizomes of this plant [6]. Glycyrrhizin is also a nonsugar sweet-tasting agent. The species is native to northern Asia, the Middle East, and southern Europe, and is cultivated across various regions of Europe, the United States, and Asia, including India [7, 8]. The herb is well known for its antiviral [9], antimicrobial [10], antioxidant and inflammatory [11], antitumour [12], antidiabetic [13], hematopoietic [14] and many other properties. Owing to the high demand for plants in pharmaceutical industries and overexploitation of this species, natural populations have been depleted at a high rate [8]. The characterization of plants is carried out through the assessment of phenotypic, biochemical, and genetic attributes [15, 16]. Unlike morphological and biochemical methods, DNA markers allow inference of genetic variation and relationships [17]. Previous studies provide evidence of several aspects, including allelic frequency distribution, the amount and distribution of genetic variation, and population structure. DNA-based molecular markers, being independent of environmental influences, provide accurate and reproducible information on genetic variation, making them more promising than other marker types [17]. Random amplified polymorphic DNA (RAPD) and inter simple sequence repeat (ISSR) markers selected for genetic diversity analysis because they are popular and highly efficient in evaluating the interspecific and intraspecific genetic variation of plants with medicinal value [18, 19]. RAPD markers offer several advantages, as they enable DNA amplification without prior knowledge of nucleotide sequences, require minimal sample quantities, efficiently detect inter-individual variation, and are simple, rapid, and cost-effective [20, 21]. Likewise, ISSR markers are easy to use, economical, and highly reproducible [16]. Previous studies have reported genetic divergence across populations (Gst) in Glycyrrhiza uralensis [22, 23]. Research on the genetic diversity of G. glabra has received limited attention, and the data that are available are scarce. The present study aimed to assess the interspecific and intraspecific genetic diversity, as well as the population structure, of G. glabra collected from different geographical regions of India via DNA-based molecular markers. The present study revealed genetically differentiated populations of G. glabra which likely possess unique allelic compositions. These results underscore the necessity of prioritizing populations for in situ and ex situ conservation, as well as for strategic germplasm management to maintain genetic diversity and facilitate future breeding and improvement programs.

Methods

Plant materials

Sampling was conducted on 60 accession of G. glabra L. belonging to seven populations distributed across four states in India (Fig. 1). The plant material used in this study was identified by Dr. Devendra Kumar Pandey (School of Bioengineering and Biosciences, Lovely Professional University). A voucher specimen (18082020) has been deposited in the Lovely Professional University Herbarium, Punjab, India, under the accession number. The collection sites were situated at elevations ranging from 120 to 300 m. The distances between populations varied considerably, with the closest sites (LP and L) separated by approximately 41.5 km, whereas the farthest sites (L and K) were nearly 907.5 km apart. Plant material was obtained from individuals propagated vegetatively in botanical gardens and from field collections. Approximately 5 g of young leaves from each plant were clipped and sealed in silica gel-filled zip-lock bags. After being transported to the laboratory, the samples were promptly stored at − 20 °C to preserve tissue integrity until DNA extraction.

Fig. 1.

Sampling sites of G. glabra from regions across the Northern-parts of India

DNA extraction

DNA was procured from young leaf samples using a improved CTAB protocol [24]. Approximately 200 mg of dried young leaf tissue was pre-treated with chilled absolute ethanol for 15 min and homogenized in 1 mL of CTAB buffer containing 2% PVP. The mixture was incubated at 65 °C for 1 h, centrifuged for 12 min (10,000 rpm), and the supernatant was then treated with 2 µL of RNase for 20 min at room temperature. The mixture was then extracted with phenol, chloroform, and isoamyl alcohol (25:24:1, v/v/v) and centrifuged at 8,000 rpm for 10 min. Then, the obtained aqueous phase was mixed with 3.0 M sodium acetate and chilled isopropanol and incubated overnight at − 20 °C for DNA precipitation. The DNA pellet was collected by centrifugation. After washing with 70% ethanol, the pellet was air-dried at room temperature and subsequently dissolved in 50 µL of TE buffer. The DNA content and purity were assessed by Shimadzu UV-vis spectrophotometer, and the integrity was checked on a 1.4% agarose gel using a 100-bp ladder. Out of the 60 samples, 50 good-quality DNA extracts were selected for PCR amplification (Table 1).

Table 1.

Working concentration of CTAB-DNA extraction buffer

Reagents/solvent	Amount to add (for 10 ml)	Final concentration
Tris-Cl buffer (pH 8.0) 0.1 M	2 ml	2%
EDTA 0.025 M	0.5 ml	0.5%
NaCl 1.5 M	3 ml	3%
CTAB	3ml	3%
β-mercaptoethanol	0.1 ml	0.1%
PVP	0.2 g	0.2%
PCR water	1.4 ml	1.4%

CTAB extraction buffer was freshly prepared every time for immediate use

RAPD/ISSR-PCR analysis

A total of 21 PCR primers (Table 2), including 9 RAPD primers out of 13 tested (Genei, India) and 12 ISSR primers out of 20 screened (Sigma, USA), were chosen for molecular characterization of the G. glabra populations because of their unambiguous and repeatable amplification patterns. PCRs for the RAPD and ISSR markers were performed in a 20 µL volume (see Table 3). All amplifications were performed via a 96-well thermal cycler (Applied Biosystems, USA). ISSR amplification involved an initial denaturation step at 94 °C for 5 min, succeeded by 45 cycles of denaturation (94 °C, 1 min), annealing (50.5 °C, 1.5 min), and extension (72 °C, 1 min). The RAPD protocol followed comparable cycling parameters, except for an annealing temperature of 39 °C for 1.5 min. The amplified fragments were electrophoresed on 1.4% agarose gels prepared in 1X TBE buffer at a voltage of 90–100 V. The bands were visualized via a Bio-Rad gel documentation system. Each gel contained a 100 bp DNA ladder (Hi-media) that served as a molecular size reference.

Table 2.

List of RAPD and ISSR primers used for genetic diversity assessment of G. glabra populations

Sl. No	RAPD	Primer sequence	Sl. No	ISSR	Primer sequence
1	OPJ01	CCCGGCATAA	1	UBC807	AGAGAGAGAGAGAGAGT
2	OPJ02	CCCGTTGGGA	2	UBC808	AGAGAGAGAGAGAGAGC
3	OPJ03	TCTCCGCTTG	3	UBC810	GAGAGAGAGAGAGAGAT
4	OPJ04	CCGAACACGG	4	UBC826	ACACACACACACACACC
5	OPJ05	CTCCATGGGG	5	UBC828	TGTGTGTGTGTGTGTGA
6	OPJ06	TCGTTCCGCA	6	UBC834	AGAGAGAGAGAGAGAGYT
7	OPJ07	CCTCTCGACA	7	UBC835	AGAGAGAGAGAGAGAGYC
8	OPJ08	CATACCGTGG	8	UBC841	GAGAGAGAGAGAGAGAYC
9	OPJ10	AAGCCCGAGG	9	UBC843	CTCTCTCTCTCTCTCTRA
			10	UBC845	CTCTCTCTCTCTCTCTRG
			11	UBC848	CACACACACACACACARG
			12	UBC855	ACACACACACACACACYT

Table 3.

PCR reaction mixture for RAPD and ISSR analysis

Biochemicals	Volume for 1 Reaction
10x Taq DNA polymerase Buffer (Genei)	2 µL
2.5 mM MgCl2 (Thermo Scientific)	1.5 µL
10mM dNTPs (Thermo Scientific)	1 µL
RAPD/ISSR Primers (Genei)	1.5 µL
1U/µL Taq DNA polymerase (Sigma)	1 U
DNA template	2 µL
PCR grade water	11 µL

Statistical analysis

The RAPD and ISSR amplification profiles were graded in binary format, with 1 and 0 indicating the presence or absence of clear bands, respectively. Microsoft Excel was used to calculate marker efficiency measures such as polymorphic PIC, MI, and RP (Tables 4 and 5). The PIC was computed via the formula PIC = 2f(1 − f), where f represents the frequency of amplified fragments. MI was derived as MI = PIC × EMR, with EMR = nβ, where n is the average number of amplified fragments and β = PB/(PB + MB). RP was estimated as ΣIb, where Ib = 1 − (2|0.5 − p|), and p is the proportion of genotypes showing the fragment. Binary data from both marker systems were analyzed using PopGene version 1.32 to estimate genetic diversity parameters, including the percentage of polymorphic loci (PPB), observed alleles (Na), effective alleles (Ne), Shannon’s information index (I), Nei’s gene diversity (H), total diversity (Ht), within-population diversity (Hs), and the genetic differentiation coefficient (Gst). AMOVA and principal coordinate analysis (PCoA) were performed in GenAlEx 6.5. A UPGMA dendrogram based on Nei’s (H) unbiased genetic distance was generated via PopGene, whereas a neighbor-joining tree for all 50 accessions was constructed via MEGA 6. The Mantel test was performed in GenAlEx 6.5 to evaluate the correlation between genetic and geographic distances. Population structure was examined using STRUCTURE version 2.3.4, and the optimal number of genetic clusters (K) was determined following the Evanno method implemented in STRUCTURE HARVESTER.

Table 4.

Description of RAPD marker information for G. glabra populations of India

Primers	TB	PIC	MI	RP
OPO1	8	0.10	0.21	0.88
OPO2	8	0.10	0.21	0.88
OPO3	2	0.20	0.24	0.44
OPO4	9	0.16	0.54	1.56
OPO5	12	0.10	0.16	1.24
OPO6	10	0.16	0.65	1.80
OPO7	7	0.16	0.44	1.24
OPO8	11	0.14	0.43	1.64
OPO10	14	0.14	0.61	2.20
Total	81
Average		0.14	0.39	1.32

Table 5.

Description of ISSR marker information for G. glabra populations of India

Primers	TB	PIC	MI	RP
UBC 807	7	0.16	0.43	1.12
UBC 808	4	0.09	0.05	0.40
UBC 810	10	0.15	0.57	1.72
UBC 826	9	0.36	10.71	4.64
UBC 828	11	0.27	4.45	3.80
UBC 834	10	0.30	8.69	4.84
UBC 835	7	0.12	0.20	0.96
UBC 841	6	0.34	10.04	3.92
UBC 843	6	0.09	0.08	0.48
UBC 845	5	0.30	3.43	2.36
UBC 848	7	0.22	2.41	3.40
UBC 855	6	0.26	2.03	1.92
Total	88
Average		0.22	3.59	2.46

TB total bands, PIC polymorphic information content, MI marker index and Rp resolving power of primer

Results

RAPD- and ISSR-based polymorphisms

The RAPD analysis results, summarized in Table 4, revealed that the amplified bands number/primer diverse from 2 to 14. OPO3 produced the fewest bands, whereas OPO10 generated the elevated number of 14 bands, resulting in a total of 81 amplified fragments. The fragment sizes ranged from 100 bp to 1,200 bp. The PIC for the RAPD primers ranged between 0.10 and 0.20. OPO1, OPO2, and OPO5 presented the lowest PIC (0.10), whereas OPO3 presented the highest PIC (0.20), with an average of 0.14 across all the primers. The MI, considered as the product of the PIC and effective multiplex ratio (EMR), ranged from 0.16 to 0.65, with OPO10 showing the maximum value (0.65) and OPO5 the minimum (0.16), averaging 0.39 per primer. The RPs varied between 0.44 and 2.20, with OPO10 achieving the highest RP (2.20) and OPO3 the lowest (0.44), averaging 1.32. For ISSR analysis, 13 primers were selected for further evaluation, as listed in Table 5. The number of amplified bands per primer varied from 5 for UBC 845 to eleven for UBC 828, yielding a total of 88 fragments. The amplified products ranged in size from 100 to 1,400 bp. PIC values were observed in the range of 0.09–0.36 with UBC 808 and 843 having the lowest PIC (0.09) and UBC 826 having the highest (0.36), resulting in an average PIC of 0.22 per primer. The MI values ranged widely from 0.05 to 10.71, with UBC 826 displaying the highest MI (10.71) and UBC 808 the lowest (0.05), resulting in an average of 3.59. The RPs of the ISSR primers ranged from 0.40 to 4.84, with UBC 834 showing the highest RP (4.84) and UBC 808 the lowest (0.40), with an average RP of 2.46.

Genetic diversity within populations revealed by RAPD and ISSR markers

The genetic variation in the 7 populations of G. glabra was evaluated along with the species using RAPD and ISSR markers and is summarized in Tables 6 and 7. A high degree of genetic polymorphism was observed at the species level, where ISSR detected complete polymorphism (100%), while in RAPD it was 97.43%. At the species level, Na was 1.9743 for RAPD and 2.0000 for ISSR. Ne was 1.0788 for RAPD and 1.2111 for ISSR. Shannon’s information index (I) was 0.0710 for RAPD and 0.1555 for ISSR. For Nei’s gene diversity, the estimated H was 0.1518 for RAPD and 0.2747 for ISSR. In comparison, genetic variation at the population level was lower, averaging 20.63% for the RAPD markers and reaching a moderate 41.49% with the ISSR markers.

Table 6.

Genetic diversity within populations and genetic differentiation parameters of seven populations of G. glabra by RAPD

Populations	N a	N e	I	H	PPB%		Ht	Hs	Gst	N m
LP	1.6914	1.2929	0.194	0.3080	69.14
P	1.0988	1.0275	0.0359	0.0209	9.88
L	1.0864	1.0201	0.0163	0.0291	8.64
H	1.5556	1.0894	0.0758	0.1443	55.56
K	1.0123	1.0023	0.0020	0.0037	1.23
LK	1.000	1.000	0.000	0.000	0.00
D	1.000	1.000	0.000	0.000	0.00
Average	1.2064	1.0617	0.0463	0.0723	20.63
Species level	1.9743	1.0788	0.0710	0.1518	97.43	Total	0.0518	0.0441	0.1479	2.883

Where, N_a is observed number of alleles; N_e is effective number of alleles; 𝐻 is Nei’s gene diversity; 𝐼 is Shannon’s information indices; PPB is percentage of polymorphic bands; 𝐻_t is total genetic diversity; H_s is genetic diversity within populations; G_st is the relative magnitude of genetic differentiation among populations; N_m is the estimate of gene flow among populations

Table 7.

Genetic diversity within populations and genetic differentiation parameters of seven populations of G. glabra by ISSR markers

Populations	N a	N e	I	H	PPB%		Ht	Hs	Gst	N m
LP	1.7234	1.4072	0.2479	0.3750	72.34
P	1.4468	1.2348	0.1479	0.2270	44.68
L	1.3298	1.1267	0.0852	0.1383	32.98
H	1.7447	1.1526	0.1177	0.2128	74.47
K	1.4255	1.1818	0.1171	0.1860	42.55
LK	1.1915	1.0860	0.0546	0.0861	19.15
D	1.0426	1.0246	0.0149	0.0224	4.26
Average	1.4149	1.0246	0.1122	0.1782	41.49
Species level	2.0000	1.2111	0.1555	0.2747	100	Total	0.1447	0.1122	0.2247	1.7255

Where, N_a is observed number of alleles; N_e is effective number of alleles; 𝐻 is Nei’s gene diversity; 𝐼 is Shannon’s information indices; PPB is percentage of polymorphic bands; 𝐻_t is total genetic diversity; H_s is genetic diversity within populations; G_st is the relative magnitude of genetic differentiation among populations; N_m is the estimate of gene flow among populations

Genetic variation

For RAPD, the values for genetic diversity within the population (Hs) and total genetic diversity (Ht) were 0.0441 and 0.0518, respectively, whereas the values for the ISSR markers were 0.1122 and 0.1447. The Gst for RAPD was 0.1479, showing a reasonably low genetic variation of 14.79% within populations, whereas ISSR markers had a higher Gst of 0.2247, reflecting 22.47% variance within populations. Gene flow among populations was considerably greater for RAPD, with an Nm value of 2.883, than for ISSR, with an Nm value of 1.7255. AMOVA analysis confirmed that a substantial proportion of the variation occurred within populations, accounting for 85% with RAPD and 81% with ISSR markers, whereas the variation among populations was low, at 15% and 19%, respectively (Fig. 2). RAPD (P = 0.003) and ISSR (P = 0.002) analyses revealed significant differences (Table 8). Mantel’s test was performed to examine the correlation between genetic and geographical distances, which revealed a moderate negative correlation for both markers (RAPD: r = −0.176, P = 0.752; ISSR: r = −0.247, P = 0.799) (Fig. 3). The ISSR markers presented genetic distances ranging from 0.006 (between the H and D populations) to 1.08 (between the P and D populations). The RAPD markers revealed no genetic distance among the K, LK, and D populations, with the greatest distance of 0.026 observed between the LP and LK, LP and D, and LP and K populations (Table 9).

Fig. 2.

Analysis of molecular variance (AMOVA) showing genetic variations (A) RAPD showing 15% among and 85% within; (B) ISSR showing 19% among and 81% within the populations

Table 8.

Analysis of molecular variance (AMOVA) based on RAPD and ISSR markers in G. glabra populations

Marker	Source of variations	Degree of freedom	Sum of squares	Mean square	Variance components	% of total variance	P value
RAPD	Among populations	6	60.290	10.048	0.805	15	< 0.003
	Within populations	43	189.950	4.417	4.417	85	< 0.003
ISSR	Among populations	6	147.187	24.531	2.174	19	< 0.002
	Within populations	43	401.233	9.331	9.331	81	< 0.002

Fig. 3.

Correlation between genetic and geographic distance among the populations (A) RAPD (B) ISSR

Table 9.

Nei’s unbiased measures of genetic identity (above diagonal) and genetic distance (below diagonal) of the seven populations of G. glabra obtained from RAPD and ISSR markers

RAPD
LP	P	L	H	K	LK	D	Population
******	0.975	0.977	0.975	0.974	0.974	0.974	LP
0.025	******	0.998	0.996	0.998	0.998	0.998	P
0.024	0.002	******	0.997	0.999	0.999	0.999	L
0.025	0.004	0.003	******	0.997	0.997	0.997	H
0.026	0.002	0.001	0.003	******	1.000	1.000	K
0.026	0.002	0.001	0.003	0.000	******	1.000	LK
0.026	0.002	0.001	0.003	0.000	0.000	******	D

ISSR
LP	P	L	H	K	LK	D	Population
******	0.948	0.967	0.968	0.944	0.953	0.951	LP
0.053	******	0.940	0.927	0.908	0.908	0.891	P
0.033	0.062	******	0.988	0.957	0.975	0.981	L
0.032	0.076	0.012	******	0.981	0.990	0.994	H
0.058	0.096	0.044	0.019	******	0.985	0.974	K
0.048	0.096	0.025	0.010	0.015	******	0.991	LK
0.050	1.08	0.019	0.006	0.026	0.009	******	D

Combined
LP	P	L	H	K	LK	D	Population
******	0.961	0.972	0.972	0.958	0.963	0.962	LP
0.040	******	0.968	0.960	0.953	0.952	0.946	P
0.029	0.032	******	0.993	0.977	0.987	0.990	L
0.029	0.041	0.008	******	0.089	0.993	0.995	H
0.042	0.048	0.023	0.011	******	0.992	0.986	K
0.037	0.049	0.014	0.007	0.008	******	0.995	LK
0.039	0.056	0.010	0.005	0.014	0.005	******	D

Cluster analysis and principal coordinate analysis

A combined UPGMA clustering of marker data based on Nei’s unbiased genetic distances was carried out to investigate genetic divergence among the seven populations (Fig. 4). In the resulting dendrogram, the populations LK, D, K, H, and L clustered into a major cluster, while LP and P were placed separately. Interestingly, LP was closer to population L, while P tended to deviate from the major cluster. Within the major cluster, populations LK and D shared a subcluster, indicating close genetic proximity, with D closer to H. Interestingly, P was closer to LK. To investigate genetic relationships among all fifty individuals further, a neighbor-joining analysis was conducted based on Nei’s genetic distances, which provided similar patterns to UPGMA clustering shown in Fig. 5. Most of the individuals from the same population were clustered together with strong intrapopulation genetic similarities. However, few individuals were intermingled with other populations, indicating genetic diversity within the populations. Principal coordinate analysis was conducted to visualize genetic distances in a two-dimensional scatter plot and confirm the results obtained in clustering analysis. For RAPD, the first and second coordinates explained 17.07% and 7.18% of the total genetic variation, respectively; for ISSR, these values were 12.85% and 8.99%; and for the combined dataset, the first and second coordinates represented 10.54% and 9.39% of the total genetic diversity, respectively (Fig. 6A–C). The scatter plot confirmed the grouping pattern obtained in clustering analysis, in which individuals of LP and P were dispersed separately, while individuals from the remaining populations formed intermix clusters. To clarify the population structure further, Bayesian clustering analysis was conducted by using STRUCTURE. The Delta K showed an optimal K = 3 as presented in Fig. 7. The ISSR-based structure pattern was largely congruent with the combined UPGMA dendrogram. A total of three clusters were observed including LP and L (green); P, H and K (blue); and D and LK (red). Noticeably, though the UPGMA placed P and LP away from the major cluster, the STRUCTURE analysis grouped them together, reflecting that there is variation depending on the clustering methods (Figs. 8 and 9).

Fig. 4.

Combined UPGMA dendrogram based on Nei’s (1972) unbiased measures of genetic distance

Fig. 5.

Neighbour joining dendrogram circular clustering pattern for all individuals of G. glabra based on (A) RAPD, (B) ISSR markers

Fig. 6.

Two-dimensional plot of principal coordinate analysis (PCoA) showing clustering of individuals belonging to 7 populations of G. glabra obtained from (A) RAPD; (B) ISSR markers (C) Pooled data set-scatter plot

Fig. 7.

Bayesian assignment analysis with maximum K value = 3

Fig. 8.

Population structure of all the populations of G. glabra based on ISSR data set

Fig. 9.

Bands produced with UBC 810; L = DNA ladder, lane 1–10 are LP samples, lane 11–16 are P samples, lane 17–21 are L samples, lane 22–33 are H samples, 34–39 are K samples, lane 40–44 are LK samples and lane 45–50 are D samples

Discussion and conclusion

To date, few genetic studies have focused on Glycyrrhiza species, and information on the genetic diversity of G. glabra remains scarce. For breeding programs, species introductions, or large-scale cultivation, prior knowledge of genetic variation is essential [25]. This study represents the first detailed investigation employing RAPD and ISSR markers to evaluate the genetic diversity and population structure of cultivated G. glabra. RAPD and ISSR markers have been widely used to evaluate genetic variability in medicinal plants and their combination provides a broader genomic perspective, enhancing the resolution of inferred relationships. Marker informativeness revealed that ISSR markers outperformed RAPD markers in detecting polymorphisms, as reflected by the PIC: RAPD = 0.14, ISSR = 0.22, MI: RAPD = 0.39, ISSR = 3.59, and resolving power (RP: RAPD = 1.32, ISSR = 2.46). Similar observations have been reported in Ocimum spp [26]. and Hypericum spp [27]. Within-population genetic variation was low, as indicated by Shannon’s information index (I: RAPD = 0.0463, ISSR = 0.1122) and Nei’s gene diversity (H: RAPD = 0.0772, ISSR = 0.1782). At the species level, high polymorphism was observed (PPB: RAPD = 97.43%, ISSR = 100%), whereas population-level diversity ranged from low (RAPD = 20.63%) to moderate (ISSR = 41.49%), which was consistent with reports in G. uralensis [23]. Low intrapopulation diversity is expected in self-pollinated species because of restricted gene flow [28], whereas long-lived perennial species typically retain high diversity [29].

The Gst was moderate (RAPD = 0.1479, ISSR = 0.2247), slightly below the average for long-lived plants (RAPD) but higher than that for typical dicotyledons (ISSR). Gene flow (Nm) was greater with RAPD (2.883) than with ISSR (1.7255), reflecting the low population-level differentiation typical of self-pollinated species. Comparable patterns have been reported in G. glabra populations from Iran via AFLP markers [30] and from China via SSR markers [31]. The low total (Ht) and within-population (Hs) genetic variation detected by both markers (Ht: RAPD = 0.0518, ISSR = 0.1447; Hs: RAPD = 0.0441, ISSR = 0.1122) may be due to the sparse distribution in India. In sparsely distributed species, genetic drift and limited gene flow often reduce diversity [32]. AMOVA confirmed greater genetic variation within populations than among populations, a pattern typical of perennial plants [33]. The observed admixture in the PCoA and cluster analyses further supported the limited population-level differentiation. Mantel’s test showed no significant correlation between geographic and genetic distances, indicating that spatial separation does not strongly affect genetic structuring. UPGMA clustering resulted in the formation of a major cluster including LK, D, K, H, and L, with LK and D forming a subcluster, reflecting the closeness of the genetic relationships, while P and LP deviated from this major cluster. STRUCTURE analysis showed similar results, although it grouped the individuals into three clusters, P and LP came out together and might share a common ancestor. The narrow genetic base observed might be due to the limited seed formation, long germination period, and inbreeding, which would slow down genetic diversification [34, 35]. Biological and geographic factors may also contribute to these differences, including limited cross-pollination, restricted seed dispersal, historical admixture, sparse population distribution, and habitat heterogeneity. Considering these factors in future studies would provide a deeper understanding of the observed population structure and strengthen the interpretation of genetic relationships among G. glabra populations. The rarely found perennial life history along with restricted population density of G. glabra highlights the urgent need for focused on conservation and protection strategies, particularly regarding its high medicinal value in Ayurveda. These findings confirm that RAPD and ISSR markers were useful in assessing the genetic variation of G. glabra. The information obtained will be helpful for the management of the species and help in planning a strategy for the development of a breeding program towards its improvement in India. However, future studies incorporating advanced molecular techniques, such as SSRs or SNP-based genotyping, could provide a more detailed and comprehensive understanding of the genetic diversity and population structure of G. glabra.

Abbreviations

AFLP

Amplified Fragment Length Polymorphism

AMOVA

Analysis of Molecular Variance

bp

Base Pair

CTAB

Cetyltrimethylammonium Bromide

DNA

Deoxyribonucleic Acid

EMR

Effective Multiplex Ratio

Gst

Coefficient of Genetic Differentiation

H

Nei’s Gene Diversity

Hs

Within-Population Genetic Diversity

Ht

Total Genetic Diversity

ISSR

Inter Simple Sequence Repeat

K

Number of Genetic Clusters (in STRUCTURE analysis)

LP, L, LK, D, K, H, P

Population codes for Glycyrrhiza glabra sampling sites

MI

Marker Index

Na

Observed Number of Alleles

Ne

Effective Number of Alleles

Nm

Gene Flow Estimate

PCR

Polymerase Chain Reaction

PCoA

Principal Coordinate Analysis

PIC

Polymorphic Information Content

PVP

Polyvinylpyrrolidone

RAPD

Random Amplified Polymorphic DNA

RP

Resolving Power

TE

Tris-EDTA Buffer

TBE

Tris-Borate-EDTA Buffer

UPGMA

Unweighted Pair Group Method with Arithmetic Mean

Authors’ contributions

NWA performed the experiment; conducted the literature survey; TD wrote and edited the draft, validation, figures, revision; TM: Supervision, editing, review; DKP conceived the idea and supervised the work.

Funding

None.

Data availability

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

Declarations

Ethics approval and consent to participate

NA.

Consent for publication

All the authors read and approved the final manuscript. We declare that it has not been published elsewhere and that it has not been submitted simultaneously for publication elsewhere.

Competing interests

The authors declare no competing interests.