Skip to main content
NCBI home page
BMC Plant Biology logoLink to BMC Plant Biology
. 2026 Jan 26;26:340. doi: 10.1186/s12870-025-08089-y

Start codon targeted (SCoT) based molecular diversity and population structure analysis in black turmeric (Curcuma caesia Roxb.)

Mala Ram 1, Radheshyam Sharma 2, Sudhir Kumar Upadhayay 3, Gholamreza Abdi 4,✉, Devendra Jain 1,✉
PMCID: PMC12918065  PMID: 41582155

Abstract

Black turmeric (Curcuma caesia Roxb.) is an endangered perennial herb of the Zingiberaceae family. It is widely recognized for its rich phytochemical profile and therapeutic uses. Despite its significance, this crop is underexplored due to limited genomic research. To address this, we assessed the genetic variation and population structure of 54 black turmeric accessions collected from 16 districts of central India covering six agro-climatic zones using SCoT marker system. Out of 36 markers screened, 20 markers showed polymorphic and reproducible bands. These primers amplified 179 distinct fragments (150 –1300 bp; average 8.8 bands per primer) and revealed a high polymorphism rate (avg. 91.13%). The mean Polymorphism Information Content (PIC), Marker Index (MI), and Resolving Power (RP) were 0.234, 1.74 and 6.42, respectively, indicating a high level of marker informativeness. UPGMA clustering grouped the accession into six different clusters. Analysis of molecular variance (AMOVA) revealed that 94% of the genetic variation was present within populations. Bayesian clustering identified five genetic groups (K = 5). This study is the first comprehensive use of SCoT markers in Curcuma caesia for demonstrating their robustness and reliability for evaluating genetic diversity. These findings provide insights for germplasm conservation, management strategies and future breeding programs aimed at enhancing the agronomic and medicinal value of black turmeric.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-025-08089-y.

Keywords: Black turmeric, SCoT markers, Genetic diversity, Population structure, Germplasm conservation

Introduction

Curcuma caesia Roxb., usually known as black turmeric or kali haldi, is a very rare and valuable member of the Zingiberaceae family [25]. The nature of this plant is perennial and its rhizomes generally appear as dark bluish colour. It is a native of India and Southeast Asia [54]. Traditionally, black turmeric has been used in different indigenous medicine systems for the treatment of various diseases including leucoderma, asthma, tumors, bronchitis, and skin diseases [59].

The phytochemical property of Curcuma caesia is extremely rich, containing curcumin, camphor, turmerone, ocimene, cineole, borneol, and several flavonoids [41]. Several studies have proven its pharmacological potency, such as antioxidant, antimicrobial, anti-inflammatory, antifungal, analgesic, hepatoprotective, anti-asthmatic, anti-hyperglycemic and anticonvulsant activities [22]. Moreover, the essential oils derived from it possess strong antifungal and antibacterial activity, and are thus potential candidate for natural drug and pharmaceutical use [35]. In contrast to the more widely used Curcuma longa, black turmeric has special bioactive substances, including significant levels of anthocyanins (1.3–7.14%), which give it its unusual colour and strong antioxidant qualities [37].

Compared to Curcuma longa (yellow turmeric), this is still underexplored despite its significant medicinal and industrial values. India’s central forest department classified this crop as an endangered species because of unregulated harvesting [43]. The limited geographical range and scattered populations render Curcuma caesia highly susceptible to genetic erosion and local extinctions [37, 41]. Morphological discrimination of the different genotypes remains difficult due to the lack of obvious floral characteristics, annual development patterns, and environmental impacts on phenotypic manifestation [25, 49]. In addition, the crop exhibits complex karyomorphological features such as small chromosomes and varying ploidy levels, which make the traditional taxonomic techniques ineffective in accurate genetic description [37, 41].

Molecular markers are important tools for describing genetic variability within and across species [24]. Various markers, including random amplified polymorphic DNA (RAPD) [7], inter simple sequence repeat (ISSR) [7, 20] and simple sequence repeat (SSR) [36] are successfully used for evaluation of genetic diversity. However, these studies were conducted across the Curcuma species. There is currently very few published molecular research on the Curcuma caesia, indicating that the field is still in its infancy [14]. RAPD markers are cost-effective and widely used but lacks reproducibility due to their low annealing temperatures [6]. Similarly, ISSR marker is more reproducible than RAPD, but it targets non-coding regions, which may not adequately reflect functional genetic variation [42]. Even SSR markers show high polymorphism due to co-dominance inheritance, but it requires prior sequence information [5]. Thus, a robust and reliable molecular marker system is required that can provide consistent and reproducible results for genetic diversity assessment in Curcuma caesia.

Recently, PCR based molecular markers called Start Codon Targeted (SCoT) are used and have become popular for assessing genetic variation in plants. Collard and Mackill [13] described this PCR based gene targeted marker system as a substitute for traditional methods such as RFLP, RAPD, ISSR, AFLP etc. This system could produce more genetic polymorphism patterns due to its nature of targeting the flanking sequences of the genome’s functional genes rather than random or non-coding regions, hence, it is more informative and effective marker system compared to other traditional systems [9, 12, 55]. This marker system demonstrates exceptional reproducibility because of 18-mer primers with a high GC content (about 50–72%) and high annealing temperature (about 48–54 °C) which ensure greater stability compare to traditional system like ISSR or RAPD [13, 53]. In addition, SCoT markers don’t need any previous genomic sequence information to design primers. This makes them a good choice for non-model species such as Curcuma caesia.

This marker system has been used successfully to assess genetic diversity in many important crops, including rice (Oryza sativa) [13], wheat (Triticum aestivum) [1, 2, 46], barley (Hordeum vulgare) [19] chickpea (Cicer arietinum) [57], cowpea (Vigna unguiculata) [26] mango (Mangifera indica) [39], chestnut (Castanea sativa) [16], lentil (Lens culinaris) [52], sugar beet (Beta vulgaris) [64] alfalfa (Medicago sativa) [28] and coconut (Cocos nucifera) [32]. This shows its effectiveness across different plant groups, which makes SCoT markers especially good for conservation genetics and breeding programs. Recently published study about SCoT marker shows that it is more efficient than ISSR in evaluating genetic variability in Musa species [27].

The present study is mainly focused on revealing the level of existing genetic variation among the germplasm obtained and characterizing them via PCR-based molecular methods. Addressing the reliability issues associated with traditional RAPD and ISSR markers, this study represents the first application of SCoT markers in Curcuma caesia, offering a more robust and dependable method for genetic analysis. The results of this study will substantially enhance our understanding of genetic diversity patterns in Curcuma caesia. These finding will also provide essential information for conservation strategies, help develop core collections for off-site conservation, and support breeding programs focused on creating better cultivars with improved medicinal properties and farming performance.

Materials and methods

Plant materials

Rhizomes of fifty-four local germplasm accessions of C. caesia were gathered from 16 districts covering six agro-climatic zones of central India (Supplementary Table 1) were kindly provided by the centre of excellence for medicinal and aromatic plants, Jawaharlal Nehru Krishi Vishwavidhayala, Jabalpur, India. The distance between each sample population (> 200 plants/ha) collection site was > 10 km. Collected rhizomes were planted at the experimental unit, Rajasthan College of Agriculture, MPUAT, Udaipur and fresh young leaf tissues were collected for extraction of genomic DNA.

Genomic DNA isolation

Based on the procedure described by Doyle and Doyle (1990), genomic DNA was isolated from 2 g of young surface-sterilized leaf tissues of the C. caesia cultivars in accordance with Bhadra and Bandyopadhyay [8]. The extracted DNA samples were evaluated qualitatively and quantitatively using UV spectrophotometric assays and electrophoresis on 0.8% agarose gels, respectively. Each sample was kept in aliquots at 4 °C for short-term use and at—80 °C for long-term preservation after being diluted with TE buffer (pH 7.0) to a final concentration of 50 ng µL−1.

Optimization of the PCR program

Thirty-six SCoT primers utilized in the present study were selected from a primer’s list created by Collard and Mackill [13] and synthesized by Barcode Biosciences Pvt. Ltd., Bangalore, India. The 18-mer primers that were used had GC contents ranging from 50 to 72%. Using a thermal cycler (Biorad), amplification was carried out using 5 µl of 2X PCR buffer (Jumpstart ready mix-P1107), 1 µL of primer, and 50 ng of template DNA in a reaction volume of 10 μL. The first denaturation was done at 94 °C for 3 min, followed by 40 cycles that included denaturation at 94 °C for one minute, annealing at 50 °C–57 °C for 1 min, extension at 72 °C for two minutes, and final extension for 5 min at 72 °C. The amplified PCR products were separated using horizontal gel electrophoresis on a 1.5% agarose gel for two and a half hours at 80 V. The findings were then recorded under UV light using the Gel Documentation system (Eppendorf, USA). A 1 kb DNA ladder (Thermo Scientific) was used to measure the size of the amplified products.

Primer screening

36 SCoT primers were used for the initial screening in order to identify the genetic diversity in the germplasm. Twenty primers out of thirty-six were successfully amplified. The reproducibility of the SCoT markers was confirmed by repeating PCR amplifications, producing similar banding patterns. These 20 primers were selected for the study of genetic variation because to their distinct and noticeable banding pattern (Table 1).

Table 1.

Details of SCoT primers used in the present study

Primer Sequence G + C Content (%)
SCoT 1 CAACAATGGCTACCACCA 50
SCoT 2 CAACAATGGCTACCACCC 56
SCoT 3 CAACAATGGCTACCACCG 56
SCoT 4 CAACAATGGCTACCACCT 50
SCoT 5 CAACAATGGCTACCACGA 50
SCoT 6 CAACAATGGCTACCACG 56
SCoT 7 CAACAATGGCTACCACGG 56
SCoT 8 CAACAATGGCTACCACGT 50
SCoT 9 CAACAATGGCTACCAGCA 50
SCoT 10 CAACAATGGCTACCAGCC 56
SCoT 11 AAGCAATGGCTACCACCA 50
SCoT 12 ACGACATGGCGACCAACG 61
SCoT 13 ACGACATGGCGACCATCG 61
SCoT 14 ACGACATGGCGACCACGC 67
SCoT 15 ACGACATGGCGACCGCGA 67
SCoT 16 ACCATGGCTACCACCGAC 56
SCoT 17 ACCATGGCTACCACCGAG 61
SCoT 18 ACCATGGCTACCACCGCC 67
SCoT 19 ACCATGGCTACCACCGGC 67
SCoT 20 ACCATGGCTACCACCGCG 67
SCoT 21 ACGACATGGCGACCCACA 61
SCoT 22 AACCATGGCTACCACCAC 56
SCoT 23 CACCATGGCTACCACCAG 61
SCoT 24 CACCATGGCTACCACCAT 56
SCoT 25 ACCATGGCTACCACCGGG 67
SCoT 26 ACCATGGCTACCACCGTC 61
SCoT 27 ACCATGGCTACCACCGTG 61
SCoT 28 CCATGGCTACCACCGCCA 67
SCoT 29 CCATGGCTACCACCGGCC 72
SCoT 30 CCATGGCTACCACCGGCG 72
SCoT 31 CCATGGCTACCACCGCCT 67
SCoT 32 CCATGGCTACCACCGCAC 67
SCoT 33 CCATGGCTACCACCGCAG 67
SCoT 34 ACCATGGCTACCACCGCA 61
SCoT 35 CATGGCTACCACCGGCCC 72
SCoT 36 GCAACAATGGCTACCACC 56
Open in a new tab

Data analysis

Scoring of all electrophoretic amplification banding patterns was done and designated as either present (1) or absent (0), and a binary matrix was created. A dendrogram was created using NTSYS-PC version 2.02e program [56] by the unweighted pair group method with arithmetic averages (UPGMA) with the sequential, agglomerative, hierarchical, and nested clustering method (SAHN) module in addition to principal co-ordinate analysis (PCoA). As recommended by Jain et al. [30] and Kaur et al. [31], measurements of polymorphism information content (PIC), percent polymorphism (PP), marker index (MI), resolving power (RP), effective multiple ratios (EMR) were made. Moreover, the number of different alleles (Na), number of effective alleles (Ne), shannon’s information index (I), expected heterozygosity (He) and unbiased expected heterozygosity (uHe) were estimated based on SCoT banding pattern for each population using GenAlEx software (v. 6.5). The analysis of molecular variance (AMOVA) and coefficient of genetic differentiation (PhiPT) within and among populations of black turmeric was estimated using GenAlEx software (v. 6.5). The STRUCTURE version 2.3.4 program was used to cluster the black turmeric population based on SCoT marker data using Bayesian clustering with different values of the number of clusters (K) [11, 50]. The STRUCTURE Harvester program was used to determine the population structure with respect to ΔK [3].

Result

DNA amplification and polymorphism in Curcuma caesia

In the present study, 36 SCoT markers were used to investigate genetic diversity among fifty-four Curcuma caesia germplasms. Out of the 36 SCoT markers, 20 SCoT markers were selected due to their unique and consistent DNA amplification profiles and were used for analysing genetic variability among the germplasms. Analysing the banding pattern of SCoT marker revealed the level of percentage polymorphism ranging from 66.67% (SCoT9) to 100% (SCoT1, SCoT3, SCoT11, SCoT12, SCoT18, SCoT19, SCoT21, SCoT28, SCoT30, SCoT34 and SCoT36) with an average percentage polymorphism of 91.13%. The black turmeric germplasm has shown appreciable genetic variation. A total of 179 visible DNA bands were generated by SCoT markers, with sizes ranging from 150 bp to 1.3 kb, whereas the distribution of these bands varied from 3 (SCoT9) to 12 (SCoT12) with an average of 8.8 DNA bands per primer (Figs. 1 and 2).

Fig. 1.

Fig. 1

Open in a new tab

PCR profiling of SCoT primer (SCoT 12) on 1.5% (w/v) agarose gel

Fig. 2.

Fig. 2

Open in a new tab

PCR profiling of SCoT primer (SCoT 26) on 1.5% agarose gel

Marker efficiency in Curcuma caesia

By calculating many parameters such as PIC, EMR, MI, and RP, the effectiveness of SCoT markers was evaluated across 54 Curcuma caesia germplasms (Table 2). In the current study, the PIC value ranged from 0.129 (SCoT19) to 0.380 (SCoT26) with an average PIC value of 0.234. Since PIC values indicate the capacity to discriminate of a marker, SCoT26 has the highest PIC value reflecting its superior capacity for genotype differentiation. The EMR value varied from 1.33 (SCoT9) to 15.00 (SCoT12) with an average EMR value of 7.62, and the mean MI value was 1.74 which ranged from 0.42 (SCoT9) to 3.83 (SCoT12), while the average RP value was 6.42 which varied from 3.7 (SCoT36) to 10.15 (SCoT12, SCoT29). The highest RP value recorded for SCoT12 and SCoT29 shows their strong capacity to distinguish between the genotypes. These results showed the enormous selective capacity of these markers to identify genetic variants and showed the efficiency of their potential.

Table 2.

Genetic diversity information of 54 black turmeric germplasm using 20 SCoT primer loci

Primer Tm TB MB PB PP PIC EMR MI RP
SCoT1 54 8 0 8 100.00 0.326 8.00 2.61 4.44
SCoT3 56 11 0 11 100.00 0.249 11.00 2.74 6.81
SCoT6 52 5 1 4 80.00 0.226 3.20 0.72 3.93
SCoT9 54 3 1 2 66.67 0.313 1.33 0.42 2.19
SCoT11 54 7 0 7 100.00 0.135 7.00 0.94 5.96
SCoT12 58 15 0 15 100.00 0.255 15.00 3.83 10.15
SCoT17 61 7 2 5 71.43 0.252 3.57 0.90 5.26
SCoT18 67 6 0 6 100.00 0.267 6.00 1.60 3.93
SCoT19 67 5 0 5 100.00 0.129 5.00 0.64 6.85
SCoT21 58 13 0 13 100.00 0.211 13.00 2.74 9.70
SCoT23 58 8 1 7 87.50 0.184 6.13 1.13 6.44
SCoT24 56 9 1 8 88.89 0.204 7.11 1.45 6.78
SCoT25 60 8 1 7 87.50 0.190 6.13 1.17 6.44
SCoT26 58 10 2 8 80.00 0.380 6.40 2.43 5.56
SCoT28 60 7 0 7 100.00 0.224 7.00 1.57 4.85
SCoT29 62 14 2 12 85.71 0.235 10.29 2.41 10.15
SCoT30 62 12 0 12 100.00 0.162 12.00 1.95 9.63
SCoT34 58 10 0 10 100.00 0.199 10.00 1.99 7.26
SCoT35 62 12 1 11 91.67 0.237 10.08 2.39 8.37
SCoT36 56 6 1 5 83.33 0.303 4.17 1.26 3.70
Open in a new tab

Tm Melting temperature, TB Total Band, MB monomorphic bands, PB polymorphic bands, PP percent polymorphism, PIC polymorphism information content, EMR effective multiplex rati, MI marker index, Resolving Power

Measures of genetic variation in Curcuma caesia

Using the SCoT marker systems, the genetic divergences between 54 germplasms of C. caesia were assessed in order to determine the variety and diversity at genome level distributions. A total 20 SCoT primers amplified 179 scorable bands with 95% (170 bands) showing a frequency ≥ 5%. There were no locally common bands detected at 25% or 50% frequency. The grand mean and standard error (SE) calculated over loci and population. The mean number of samples analyzed (N) was 27.00 ± 1.429. Observed numbers of different alleles (Na) showed a mean of 0.992 ± 0.053, while the mean of the effective number of alleles (Ne) was 1.235 ± 0.018. Shannon’s information index (I) showed the mean value of 0.216 ± 0.014. Similarly, the mean value of heterozygosity (He) and the mean value of unbiased heterozygosity (uHe) were 0.141 ± 0.010 and 0.142 ± 0.010, respectively (Table 3).

Table 3.

Genetic diversity indices of Curcuma caesia accessions using SCoT markers

Parameter Value ± SE
Sample Size (N) 27.0 ± 1.429
Number of Different Alleles (Na) 0.992 ± 0.053
Effective Number of Alleles (Ne) 1.235 ± 0.018
Shannon’s Information Index (I) 0.216 ± 0.014
Expected Heterozygosity (He) 0.141 ± 0.01
Unbiased Expected Heterozygosity (uHe) 0.142 ± 0.01
Number of total Bands 179
Number of Bands (Freq. ≥ 5%) 170
Number of Private Bands 179
Number of Locally Common Bands (≤ 25%) 0
Number of Locally Common Bands (≤ 50%) 0
Population Mean He 0.281 ± 0.013
Population Mean uHe 0.284 ± 0.013
Open in a new tab

Population structure in Curcuma caesia

AMOVA and PCoA based diversity assessment

Analysis of molecular variance shows significant genetic diversity within the Curcuma caesia germplasm collection. The AMOVA analysis of 54 accessions showed the estimated variance components were 19.868 for within population variation (that is, 94%) and 1.281 for among population variation (that is, 6%), with a total variance of 21.148 (Fig. 3, Table 4). The PhiPT value of 0.061 (p < 0.001) showed statistically significant but moderate genetic differentiation between populations.

Fig. 3.

Fig. 3

Open in a new tab

Chart of percentages of molecular variance

Table 4.

Summary table of AMOVAe

Source Df SS MS Est. Var %
Among Population 1 54.444 54.444 1.281 6%
Within Population 52 1033.111 19.868 19.868 94%
Total 53 1087.556 21.148 100%
Stat Value P (rand > = data)
PhiPT 0.061 0.001
Open in a new tab

The spatial arrangement of Curcuma caesia accessions was measured using Principal Coordinate Analysis (PCoA) to identify relationships between germplasm. The PCoA plot revealed the majority of accessions formed a dense cluster in the central left region of the plot. Two accessions (BTC53 and BTC54) appeared as clear outliers positioned separately in the lower right side. It’s confirmed their genetic divergence from the main population. It showed their branching in the dendrogram and also separate clusters in the STRUCTURE analysis. Several intermediate accessions (BTC22 and BTC23) also showed separation from the main cluster. Which reflect their intermediate genetic relationships. The continuous distribution of points in the PCoA space demonstrated that genetic variation within the collection follows a pattern of gradual differentiation rather than sharp discontinuities. It supports the presence of both discrete genetic lineages and continuous variation within the Curcuma caesia germplasm studied (Fig. 4).

Fig. 4.

Fig. 4

Open in a new tab

Two-dimensional Principal Coordinates Analysis (PCoA) of 54 accessions of Curcuma caesia based on NTsys software. The two axes represent two principal coordinates and the black turmeric germplasms belonging to the same clusters in the dendrogram were plotted close to each other, indicating genetic similarity, while genotypes from different clusters are spaced apart, reflecting genetic divergence

Phylogenetic analysis

The phylogenetic relationship among the population was measured by cluster analysis of SCoT marker data using NTSYS software. A similarity matrix was generated among 54 germplasms of C. caesia using Jaccard’s similarity coefficient index. Therefore, the similarity coefficient ranged from 0.29 (BTC3 and BTC54) to 0.95 (BTC1 and BTC2). The average similarity coefficient among all genotypes was 0.68, indicating moderate to high genetic diversity among the germplasms. Additionally, the phylogenetic relationship was identified through the construction of a dendrogram based on the UPGMA among the Curcuma caesia accessions (Fig. 5). The population is primarily grouped into six clusters, based on a genetic similarity threshold of 67–70% (with major cluster separation occurring around 0.67–0.70 on the similarity coefficient scale). Cluster VI is the biggest cluster containing the majority of the accessions (23), showing high genetic similarity (> 0.80). Followed by cluster V having 11, clusters IV having 9, cluster III having 8, cluster II having two and cluster I having only one accession. Cluster II indicating their genetic divergence from the main population cluster. It showed the lowest similarity coefficients (0.59–0.65) compared to other genotypes, suggesting unique genetic compositions within the studied germplasm. The majority of the accessions show high genetic similarity (> 0.80). The cophenetic correlation (r) was significant (r > 0.85, P < 0.01), showing that the hierarchical clustering effectively represents the genetic distance relationship among the accessions.

Fig. 5.

Fig. 5

Open in a new tab

Unweighted pair group method arithmetic averages (UPGMA) among 54 black turmeric germplasm based on Jaccard similarity distance

Bayesian clustering using STRUCTURE

For calculating optimal K, ten independent runs (three replicates per K) were conducted and the mean log probability [Ln P(K)] and its rate of change were used alongside the ΔK method of Evanno et al. (2005). As shown in Table 5 and the ΔK plot (Fig. 6), Ln P(K) increased from K = 1 (–3470.13) to a maximum at K = 5 (–2503.83), then declined for higher K values. The ΔK exhibited the highest peak at K = 5 (ΔK = 830.16), which far exceeds the values at other K. This indicates that K = 5 explains the best hierarchical population structure.

Table 5.

Bayesian clustering analysis of Curcuma caesia

K Replications Mean LnP(K) Stdev LnP(K) Ln'(K) |Ln''(K)| Delta K
1 3 −3470.13 1.42 NA NA NA
2 3 −2989.77 1.32 480.37 297.9 225.62
3 3 −2807.3 6.81 182.47 243.47 35.77
4 3 −2868.3 208.25 −61 425.47 2.05
5 3 −2503.83 1.05 364.47 872 830.13
6 3 −3011.37 1007.39 −507.53 384.43 0.32
7 3 −3134.47 724.76 −123.1 233.07 0.32
8 3 −3024.5 1187.33 109.97 496.27 0.42
9 3 −3410.8 406.54 −386.3 635.63 1.56
10 3 −3161.47 540.88 249.33 NA NA
Open in a new tab
Fig. 6.

Fig. 6

Open in a new tab

ΔK plot at K = 5 using Bayesian clustering considered as appropriate number of the population as depicted in graph

Bayesian clustering analysis of Curcuma caesia germplasms recognized five genetically distinct groups (K = 5) as the most likely population structure, based on the ΔK peak at K = 5 (ΔK = 830.16) and the highest mean LnP(K) value (−2503.83). On the basis of individual membership coefficients (Q-values), five clusters were formed that is Cluster 1 (red), Cluster 2 (green), Cluster 3 (blue), Cluster 4 (yellow), Cluster 5 (pink). Two highly divergent accessions (BTC22 and BTC23) formed Cluster 1 (Red), while two minor lineage accessions (BTC53 and BTC54) formed Cluster 5 (pink). Approximately 12% of individuals exhibited admixed ancestry (0.20 < Q < 0.80), indicating limited gene flow among clusters. These clusters showed strong concordance with UPGMA and PCoA results, highlighting the strength of the population structure (Fig. 7).

Fig. 7.

Fig. 7

Open in a new tab

Genetic diversity among 54 Curcuma caesia accessions by bar plot at K = 5 using STRUCTURE in Bayesian clustering. Fifty-four black turmeric germplasms were classified into five populations with distinct colours. The proportions of these coloured bars illustrate the admixture levels within the genotypes. The Y-axis displays the estimated ancestry of each genotype from the respective populations

Discussion

Black turmeric (Curcuma caesia Roxb.), is an endangered perennial herb of the Zingiberaceae family. It is native to India and Southeast Asia with cultivation primarily in central Indian states [54]. It possesses rich phytochemical properties, including curcumin, camphor, turmerone, anthocyanin, turmerone, ocimene, cineole, borneol, and several flavonoids [41], contributing to its diverse pharmacological activities, such as antioxidant, antimicrobial, anti-inflammatory, and hepatoprotective effects [22]. Molecular markers are considered the most efficient techniques for examining genetic variation at the DNA level. The characterization of germplasm, parentage identification, diversity analysis, gene mapping, genetic distance estimate, and marker-assisted selection all heavily rely on molecular markers [4, 21, 40, 62]. Different molecular markers such as RAPD, ISSR, and SSR have been employed for genetic diversity assessment in Curcuma species [7, 20, 36]. But due to some limitations like reproducibility, functional relevance, and sequence dependency necessitated the adoption of more robust marker systems. The long-term environmental adaptability and its direct influence on the metabolite profile are demonstrated by the genetic variety revealed by these markers.

Genetic diversity combined with environmental factors significantly influences secondary metabolite biosynthesis and composition across populations [44, 47]. Li et al. [38] reported the significant differences in the content and composition of secondary metabolites between wild and cultivated populations of Polygonatum odoratum, Dioscorea nipponica, and Acanthopanax sessiliflorus. In black turmeric, metabolite profile variance is directly associated with genetic diversity and geographical origin, influencing phytochemical and genetic composition [10, 25]. Studies have demonstrated that curcumin content, essential oil composition, and bioactive compounds vary substantially among Curcuma caesia accessions from different agro-climatic regions, reflecting adaptation to diverse environmental conditions [10, 34]. Benya et al. (2023) studied the industrially important traits of black turmeric and recommended elite genotypes from different agroclimatic zones for comitial cultivation having anthocyanin > 6%, leaf oil ≥ 1.2%, rhizome oil > 1.5%, eucalyptol > 20%, and camphor > 21.

In the present study, 36 SCoT markers were employed for the optimization of PCR and genetic diversity assessment within 54 Curcuma caesia accessions. 20 SCoT markers selected due to their unique and consistent DNA amplification profiles generated 179 scorable bands with an average of 8.8 amplicons per primer, with sizes ranging from 150 bp to 1.3 kb. It revealed the level of percentage polymorphism ranging from 66.67% to 100% with an average percentage polymorphism of 91.13% showing greater diversity among germplasms. The similar results were observed by Jain et al. [29] studied ISSR based molecular diversity in wild Curcuma populations and reported high level (86%−97%) polymorphism, which is essential for crop improvement towards yield, resistance to abiotic and biotic stress and improved medicinal capabilities. The observed polymorphism levels align with previous molecular diversity study by Singh et al. [60] reported 83–100% polymorphism using ISSR markers in C. longa, and Mohanty et al. [45] reported 100% polymorphic RAPD bands across ten Zingiberaceae species including C. caesia. Similarly, Verma et al. [63] documented 82% polymorphism using DAMD and ISSR markers in indigenous turmeric germplasm. Similar results were found in a number of other plant species, including 88.98% in common beans [65], 96.18% in orchids [18] and 75% in Kalmegh [61]. These high levels of amplification efficiency and polymorphism percentage obtained in this study validate SCoT markers as a robust tool for genetic diversity assessment in black turmeric, offering consistency and reproducibility superior to conventional marker systems.

Further, the efficiency of markers was assessed using key parameters such as PIC, MI, and RP. For instance, the PIC for the SCoT marker varied from 0.129 to 0.380, with an average PIC of 0.234. This PIC is an important metric of marker efficacy, which assesses a marker’s capacity to differentiate between genotypes [15]. Results of this study align with previous reports for SCoT markers in chickpea (0.232) [48], are within the range documented for dominant marker systems, while SSR markers typically showed higher PIC values (0.67–0.86 in turmeric) [59] due to their co-dominant nature and multi-allelic detection capacity. Despite relatively lower PIC values compared to SSR markers, SCoT markers offer greater reproducibility and target genomic regions that are functionally significant. These characteristics makes SCoT markers more effective for genetic diversity assessment in species with limited genomic information. RP as a measure of primer efficacy, highlighting its importance in evaluating genetic diversity [51]. The average values of RP were 6.42, which varied from 3.7 to 10.15 using SCoT markers. Our results are quite comparable with studies on the same marker system in other plant species. Similar RP value reported in other crops such as in barley [23] where 10 SCoT primers yielded RP values ranging from 4.00 to 11.40 with an average RP of 7.76 per primer, and in Aegilops triuncialis [33], which showed RP values from 1.79 to 7.96, with the mean value of 4.14 per primer. Similarly, in hexaploid wheat (Triticum aestivum) [17], SCoT primers show RP values between 3.12 and 9.45, with an average value of 6.02 per primer, underscoring the robust discriminatory power of SCoT markers across diverse crop species. Further, the mean MI value of 1.74 ranging from 0.42 to 3.83, aligns well with results from other plant species using SCoT markers. For example, in Triticum aestivum [17], MI values ranged from 0.35 to 2.95 with an average of 1.84 per primer. Similarly, in Hordeum vulgare [23], MI ranged from 0.50 to 3.20, averaging 1.63 across primers. In Aegilops triuncialis [33], MI values varied between 0.48 and 2.87, with a mean MI of 1.55, highlighting the consistent informativeness of SCoT markers for genetic diversity studies in diverse crop species.​

The assessment of genetic divergence in 54 Curcuma caesia accessions using 20 SCoT primers yielded a high level of genome‐wide variation, as evidenced by 179 scorable bands (95% at ≥ 5% frequency), a mean Na of 0.992 ± 0.053, Ne of 1.235 ± 0.018, Shannon’s I of 0.216 ± 0.014, and He/uHe of 0.141 ± 0.010/0.142 ± 0.010. These results indicate substantial allelic richness and moderate genetic diversity within populations, comparable to those reported in other crops [17, 23, 29, 33]. Within the genus Curcuma, earlier ISSR-based diversity studies published comparable genetic parameters by Jain et al. [29] reported Na (9.17 ± 0.03), Ne (1.65 ± 0.27), I (0.55 ± 0.49), He (0.37 ± 0.58) and uHe (0.38 ± 0.23) in Curcuma sp., whereas Saha et al. [58] documented Na = 1.86, Ne = 1.60, and Shannon’s index I = 0.50 in four Curcuma species, including C. caesia, while Mohanty et al. [45] found that C. caesia showed the highest amplification profile (263 bands) among ten Zingiberaceae species using RAPD and ISSR markers. These values underscore the enhanced resolution of SCoT markers in capturing functional region polymorphism compared to traditional marker systems.

The SCoT marker based genetic relationships amongst 54 black turmeric germplasms were also differentiated using UPGMA based dendrogram and six distinct clusters were obtained due to their diverse gene pool and genetic constitutions. These findings were also supported by PCoA analysis and Bayseian clustering algorithm using STRUCTURE software and the delta K value was found to be K = 5, separated the black turmeric populations into five major groups. These results represent significant genetic diversity among the studied black turmeric germplasms, which is critical for crop improvement, breeding and conservation studies. The AMOVA revealed that 94% of the variance present among the collected black turmeric genotypes, resulted from its potential in exploring its significant diversity. These results were supported by Chawla et al. [11] studied diversity among Withania genotypes and reported the high diversity within this population is desirable for robust breeding program and discovering crop improvement traits.

Conclusion

This work is the first full look at genetic diversity in Curcuma caesia utilizing the start codon targeted (SCoT) marker system. Twenty highly informative SCoT primers produced 179 scorable and reproducible bands, indicating a significant degree of polymorphism among 54 accessions sourced from six agro-climatic zones in central India. Marker efficiency measures including PIC, MI, EMR, and RP showed that SCoT primers are quite good at telling apart various genotypes of this endangered medicinal plant. Diversity indices and AMOVA findings showed that there is a lot of variances within populations (94%), which means that genetic diversity is mostly maintained within populations rather than between them. Complementary studies using UPGMA, PCoA, and STRUCTURE reliably delineated the germplasm into discrete genetic clusters, pinpointing unique and divergent accessions of value for conservation and prospective agricultural enhancement. In general, this work provides the first indication that SCoT markers are strong and useful for genetically characterizing C. caesia. The information gathered here is a useful starting point for conserving germplasm, building core collections, and planning breeding projects to improve the agricultural and therapeutic uses of black turmeric. To protect the genetic integrity of this endangered species and promote its long-term cultivation and pharmacological value, it will be important to strengthen efforts to protect it and use it in a sustainable way.

Supplementary Information

Supplementary Material 1. (820.5KB, docx)

Acknowledgments

Declaration of statements

The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

Authors’ contributions

Conceptualization, DJ; methodology, ML; validation, MR and RSS; formal analysis, MR, DJ; investigation, MR, RSS and DJ; writing, MR, G.A, SKU and DJ; writing—review and editing, SKU and DJ; All authors have read and agreed to the published version of the manuscript.

Funding

Authors are thankful to the National Medicinal Plant Board (NMPB), Ministry of AYUSH, GoI, New Delhi, for providing the research project.

Data availability

All data generated or analysed during this study are included in this published article.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

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

Contributor Information

Gholamreza Abdi, Email: abdi@pgu.ac.ir.

Devendra Jain, Email: devendrajain@mpuat.ac.in, Email: devroshan@gmail.com.

References

  • 1.Abdelhameed AA, Ali M, Darwish DBE, et al. Induced genetic diversity through mutagenesis in wheat gene pool and significant use of SCoT markers to underpin key agronomic traits. BMC Plant Biol. 2024;24:673. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Abouseada HH, Mohamed ASH, Teleb SS, et al. Genetic diversity analysis in wheat cultivars using SCoT and ISSR markers, chloroplast DNA barcoding and grain SEM. BMC Plant Biol. 2023;23:193. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Agarwal A, Gupta V, Haq SU, Jatav PK, Kothari SL, Kachhwaha S. Assessment of genetic diversity in 29 rose germplasms using SCoT marker. J King Saud Univ Sci. 2019;31(4):780–8. 10.1016/j.jksus.2018.04.022. [Google Scholar]
  • 4.Agarwal M, Shrivastava N, Padh H. Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Rep. 2008;27:617–31. [DOI] [PubMed] [Google Scholar]
  • 5.Amiteye S. Basic concepts and methodologies of DNA marker systems in plant molecular breeding. Heliyon. 2021;7:e08093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Babu KN, Sheeja TE, Minoo D, Rajesh MK, Samsudeen K, Suraby EJ, et al. Random amplified polymorphic DNA (RAPD) and derived techniques. In: Molecular plant taxonomy: methods and protocols. New York: Springer; 2020. p. 219–47. [DOI] [PubMed] [Google Scholar]
  • 7.Basyal P, Rajbahak S, Maharjhan S, Ghimire M, Thapa CB, Pant B. Micropropagation and genetic homogeneity assessment of Curcuma aeruginosa Roxb. Plant Cell Tissue Organ Cult. 2025;161:84. [Google Scholar]
  • 8.Bhadra S, Bandyopadhyay M. A fast and reliable method for DNA extraction from different plant parts of Zingiberaceae. J Bot Soc Bengal. 2015;69:91–8. [Google Scholar]
  • 9.Cabo S, Ferreira L, Carvalho A, Martins-Lopes P, Martín A, Lima-Brito JE. Potential of Start Codon Targeted (SCoT) markers for DNA fingerprinting of newly synthesized tritordeums and their respective parents. J Appl Genet. 2014;55:307–12. [DOI] [PubMed] [Google Scholar]
  • 10.Chakraborty A, Mukherjee S, Santra I, Dey D, Mukherjee S, Ghosh B. Secondary metabolite fingerprinting, anti-pathogenic activity, elite chemotype selection and conservation of Curcuma caesia—an ethnomedicinally underutilized species. 3 Biotech. 2024;14:155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Chawla R, Sharma H, Dadheech A, Jattan M, Devi S, Kumar P, et al. Genetic insights into diversity and population structure of ashwagandha (Withaniasomnifera (L.) Dunal) using EST-SSR, ISSR and SSR markers: implications for enhancing agricultural and industrial value. Ind Crops Prod. 2025;224:120242. 10.1016/j.indcrop.2024.120242. [Google Scholar]
  • 12.Chhajer S, Jukanti AK, Kalia RK. Start codon targeted (SCoT) polymorphism-based genetic relationships and diversity among populations of Tecomellaundulata (Sm.) Seem—an endangered timber tree of hot arid regions. Tree Genet Genomes. 2017;13:1–10. [Google Scholar]
  • 13.Collard BC, Mackill DJ. Start codon targeted (SCoT) polymorphism: a simple, novel DNA marker technique for generating gene-targeted markers in plants. Plant Mol Biol Rep. 2009;27:86–93. [Google Scholar]
  • 14.Das A, Kesari V, Satyanarayana VM, Parida A, Rangan L. Genetic relationship of Curcuma species from northeast India using PCR-based markers. Mol Biotechnol. 2011;49:65–76. [DOI] [PubMed] [Google Scholar]
  • 15.Etminan A, Pour-Aboughadareh A, Mohammadi R, Ahmadi-Rad A, Noori A, Mahdavian Z, et al. Applicability of start codon targeted (SCoT) and inter-simple sequence repeat (ISSR) markers for genetic diversity analysis in durum wheat genotypes. Biotechnol Biotechnol Equip. 2016;30:1075–81. [Google Scholar]
  • 16.Fadime Beyazyuz F, Gulbahce-Mutlu E, Kulac S, et al. Evaluation of genetic diversity of chestnut (Castaneasativa Mill.) populations collected from different regions of Türkiye using SCoT and ISSR molecular markers. Biol Bull Russ Acad Sci. 2025;52:153. [Google Scholar]
  • 17.Ghobadi G, Etminan A, Mehrabi AM, Shooshtari L. Molecular diversity analysis in hexaploid wheat (Triticumaestivum L.) and two Aegilops species (Aegilopscrassa and Aegilopscylindrica) using CBDP and SCoT markers. J Genet Eng Biotechnol. 2021;19:56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Gholami S, Vafaee Y, Nazari F, Ghorbani A. Molecular characterization of endangered Iranian terrestrial orchids using ISSR markers and association with floral and tuber-related phenotypic traits. Physiol Mol Biol Plants. 2021;27:53–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Ghonaim MM, Attya AM, Aly HG, et al. Agro-morphological, biochemical, and molecular markers of barley genotypes grown under salinity stress conditions. BMC Plant Biol. 2023;23:526. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Gowda MS, Soundarya D, Hiremath C, Shetty NP. Assessing genetic diversity of indigenous turmeric (Curcumalonga L.) through inter-simple sequence repeat (ISSR) markers. Genet Resour Crop Evol. 2025;72:2413–23. [Google Scholar]
  • 21.Gupta P, Rustgi S. Molecular markers from the transcribed/expressed region of the genome in higher plants. Funct Integr Genomics. 2004;4:139–62. [DOI] [PubMed] [Google Scholar]
  • 22.Gupta R, Verma S, Singh P. Phytochemical and pharmacological properties of Curcumacaesia Roxb.: a review. J Ethnopharmacol. 2020;250:112498.31877366 [Google Scholar]
  • 23.Habiba R, Bashasha J, Haffez SH, Abo Leilah AAA. Assessment of genetic diversity using SCoT markers and some morphological traits in ten lines of barley (Hordeumvulgare L.). Assiut J Agric Sci. 2021;52:53–65. [Google Scholar]
  • 24.Heubl G. New aspects of DNA-based authentication of Chinese medicinal plants by molecular biological techniques. Planta Med. 2010;76:1963–74. [DOI] [PubMed] [Google Scholar]
  • 25.Ibrahim NNA, Wan Mustapha WA, Sofian-Seng NS, Lim SJ, MohdRazali NS, Teh AH, et al. A comprehensive review with future prospects on the medicinal properties and biological activities of Curcumacaesia Roxb. Evid Based Complement Alternat Med. 2023;2023:7006565. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Igwe DO, Afiukwa CA, Ubi BE, et al. Assessment of genetic diversity in Vignaunguiculata L. (Walp) accessions using inter-simple sequence repeat (ISSR) and start codon targeted (SCoT) polymorphic markers. BMC Genet. 2017;18:98. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Igwe DO, Ihearahu OC, Osano AA, et al. Assessment of genetic diversity of Musa species accessions with variable genomes using ISSR and SCoT markers. Genet Resour Crop Evol. 2022;69:49–70. [Google Scholar]
  • 28.Işık Mİ, Türkoğlu A, Demirel F, Aydın A, Eren B, Koç A, et al. Genetic diversity and genetic structure of alfalfa (Medicagosativa L.) genotypes as revealed by start codon targeted (SCoT) markers. Genet Resour Crop Evol. 2025;72:10543–58. [Google Scholar]
  • 29.Jain A, Jain P, Mathur S, Parihar DK. Curcuma species DNA fingerprinting of wild and cultivated genotypes from different agroclimatic zones. Pharmacol Res Mod Chin Med. 2024;12:100474. 10.1016/j.prmcm.2024.100474. [Google Scholar]
  • 30.Jain D, Sunda SD, Sanadhya S, Nath DJ, Khandelwal SK. Molecular characterization and PCR-based screening of cry genes from Bacillus thuringiensis strains. 3 Biotech. 2017;7:1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Kaur G, Joshi A, Jain D, Choudhary R, Vyas D. Diversity analysis of green gram (Vignaradiata (L.) Wilczek) through morphological and molecular markers. Turk J Agric For. 2016;40(2):229–40. 10.3906/tar-1508-59. [Google Scholar]
  • 32.Khang NHM, Quang NT, Mai HNX, Phuong NDN, Thao NP, Quoc NB. Genetic characterization of coconut (Cocos nucifera L.) varieties conserved in Vietnam through SCoT marker-based polymorphisms. Genet Resour Crop Evol. 2022;69(1):385–98. [Google Scholar]
  • 33.Khodaee L, Azizinezhad R, Etminan AR, Khosroshahi M. Assessment of genetic diversity among Iranian Aegilopstriuncialis accessions using ISSR, SCoT, and CBDP markers. J Genet Eng Biotechnol. 2021;19:5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Kulyal P, Acharya S, Ankari AB, Kokkiripati PK, Tetali SD, Raghavendra AS. Variable secondary metabolite profiles across cultivars of Curcuma longa L. and C. aromatica Salisb. Front Pharmacol. 2021;12:659546. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Kumar A, Das G. Antimicrobial activity of essential oils from Curcuma caesia and its potential therapeutic uses. Indian J Nat Prod Resour. 2017;8:215–20. [Google Scholar]
  • 36.Kumar P, Sharma R, Bhargava S, Kumar A, Sharma S, Krishnan RS, Upadhyay A. Molecular profiling of black turmeric (Curcuma caesia Roxb.) germplasm of central India using microsatellite markers. Plant Mol Biol Rep. 2025;43:2053–68. 10.1007/s11105-025-01594-2.
  • 37.Leela NK, Adheeba PK. Curcuma caesia Roxb.: update of phytochemicals and pharmacological properties. J Spices Aromat Crops. 2024;33:1–22. [Google Scholar]
  • 38.Li Y, Zhao W, Zuo X, et al. Habitat changes due to cultivation alter the genetic diversity and secondary metabolic changes of medicinal plants. Sci Rep. 2025;15:36185. 10.1038/s41598-025-20010-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Luo C, He XH, Chen H, Hu Y, Ou SJ. Genetic relationship and diversity of Mangifera indica L. revealed through SCoT analysis. Genet Resour Crop Evol. 2012;59:1505–15. [Google Scholar]
  • 40.Madhumati B. Potential and application of molecular marker techniques for plant genome analysis. Int J Pure Appl Biosci. 2014;2:169–88. [Google Scholar]
  • 41.Mahanta BP, Kemprai P, Bora PK, Lal M, Haldar S. Phytotoxic essential oil from black turmeric (Curcuma caesia Roxb.) rhizome: screening, efficacy, chemical basis, uptake and mode of transport. Ind Crops Prod. 2022;180:114788. [Google Scholar]
  • 42.Mansoory A, Khademi O, Naji AM, Rohollahi I, Sepahvand E. Evaluation of genetic diversity in three Diospyros species collected from different regions in Iran using ISSR and SCoT molecular markers. Int J Fruit Sci. 2022;22:235–48. [Google Scholar]
  • 43.Ministry of Environment, Forest and Climate Change. Status report on endangered medicinal plants of India. New Delhi: Government of India; 2019. [Google Scholar]
  • 44.MohammadiBazargani M, Falahati-Anbaran M, Rohloff J. Comparative analyses of phytochemical variation within and between congeneric species of willow herb, Epilobiumhirsutum and E.parviflorum: contribution of environmental factors. Front Plant Sci. 2021;11:595190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Mohanty S, Panda MK, Acharya L, et al. Genetic diversity and gene differentiation among ten species of Zingiberaceae from Eastern India. 3 Biotech. 2014;4:383–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Nosratifar R, Badakhshan H, Moradi N, Mohammadzadeh H. Unveiling diversity and population structure in bread wheat: insights from SSR, SCoT, and ITAP markers and grain Fe and Zn content. Plant Mol Biol Rep. 2025;43:1897–914. 10.1007/s11105-025-01580-8.
  • 47.Pais AL, Li X, Xiang QYJ. Discovering variation of secondary metabolite diversity and its relationship with disease resistance in Cornus florida L. Ecol Evol. 2018;8:5619–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Pakseresht F, Talebi R, Karami E. Comparative assessment of ISSR, DAMD and SCoT markers for evaluation of genetic diversity and conservation of landrace chickpea (Cicer arietinum L.) genotypes collected from north-west of Iran. Physiol Mol Biol Plants. 2013;19:563–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Pathan AR, Vadnere GP, Sabu M. Curcuma caesia almost untouched drug: an updated ethnopharmacological review. Inventi Rapid Planta Activa. 2013;4:1–4. [Google Scholar]
  • 50.Pritchard JK, Stephens M, Donnelly M. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–59. 10.1093/genetics/155.2.945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Prevost A, Wilkinson M. A new measure of primer efficacy in amplifying polymorphic DNA. Mol Ecol Notes. 1999;8:208–15. [Google Scholar]
  • 52.Qureshi SA, Nadeem MA, Sarıkaya MF, et al. Unveiling genetic diversity and population structure in lentil (Lens culinaris) germplasm through SCoT markers. Mol Biol Rep. 2025;52:767. [DOI] [PubMed] [Google Scholar]
  • 53.Rai MK. Start codon targeted (SCoT) polymorphism marker in plant genome analysis: current status and prospects. Planta. 2023;257:34. [DOI] [PubMed] [Google Scholar]
  • 54.Rajamma S, Rao P, Swamy M. Chemical constituents and medicinal significance of Curcuma caesia. Int J Herb Med. 2012;5:45–52. [Google Scholar]
  • 55.Rohela GK, Jogam P, Mir MY, Shabnam AA, Shukla P, Abbagani S, et al. Indirect regeneration and genetic fidelity analysis of acclimated plantlets through SCoT and ISSR markers in Morusalba L. cv. Chinese white. Biotechnol Rep. 2020;25:e00417. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Rohlf FJ. NTSYSpc numerical taxonomy and multivariate analysis system, version 2.20e. User guide. Setauket, New York: Exeter Software; 2000. [Google Scholar]
  • 57.Sadhu S, Jogam P, Thampu RK, Abbagani S, Penna S, Peddaboina V. High efficiency plant regeneration and genetic fidelity of regenerants by SCoT and ISSR markers in chickpea (Cicer arietinum L.). Plant Cell Tissue Organ Cult. 2020;141:465–77. [Google Scholar]
  • 58.Saha K, Sinha RK, Basak S, Sinha S. Issr fingerprinting to ascertain the genetic relationship of Curcuma sp. of Tripura. Am J Plant Sci. 2016;7:259–68. [Google Scholar]
  • 59.Singh A, Mishra R, Yadav H. Ethnomedicinal significance of Curcuma caesia in Indian traditional medicine. J Med Plants Res. 2018;12:95–102. [Google Scholar]
  • 60.Singh S, Panda MK, Nayak S. Evaluation of genetic diversity in turmeric (Curcuma longa L.) using RAPD and ISSR markers. Ind Crops Prod. 2012;37:284–91. [Google Scholar]
  • 61.Tiwari G, Singh R, Singh N, Choudhury DR, Paliwal R, Kumar A, et al. Study of arbitrarily amplified (RAPD and ISSR) and gene targeted (SCoT and CBDP) markers for genetic diversity and population structure in Kalmegh [Andrographis paniculata (Burm. f.) Nees]. Ind Crops Prod. 2016;86:1–11. [Google Scholar]
  • 62.Varshney RK, Graner A, Sorrells ME. Genomics-assisted breeding for crop improvement. Trends Plant Sci. 2005;10:621–30. [DOI] [PubMed] [Google Scholar]
  • 63.Verma S, Singh S, Sharma S, Tewari SK, Roy RK, Goel AK, et al. Assessment of genetic diversity in indigenous turmeric (Curcuma longa) germplasm from India using molecular markers. Physiol Mol Biol Plants. 2015;21:233–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Yalinkiliç NA, Başbağ S, Altaf MT, Ali A, Nadeem MA, Baloch FS. Applicability of SCoT markers in unraveling genetic variation and population structure among sugar beet (Beta vulgaris L.) germplasm. Mol Biol Rep. 2024;51:584. [DOI] [PubMed] [Google Scholar]
  • 65.Yeken MZ, Emiralioğlu O, Çiftçi V, Bayraktar H, Palacioğlu G, Özer G. Analysis of genetic diversity among common bean germplasm by start codon targeted (SCoT) markers. Mol Biol Rep. 2022;49:3839–47. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (820.5KB, docx)

Data Availability Statement

All data generated or analysed during this study are included in this published article.

Articles from BMC Plant Biology are provided here courtesy of BMC

RESOURCES