Medicinal plants have been given greatsignificance in recent years due to its demand in industry for human and animal welfare and alluring market prices(Lubbe and Verpoorte 2011). India is called as the “Botanical Garden” of theworld due to variegated climatic ecosystem which is suitable for cultivationfor medicinal plants. India being one of theworld’s 12 mega biodiversity countries needs to conserve its resources wherethey are being exploited and should be grown commercially to avoid theirsusceptibility to extinction because of indiscriminate use.Among the various medicinal plants, Withaniasomnifera (L.) Dunal(Winter cherry, Ashwagandha or Asgandh of family Solanaceae is animportant medicinal plant that finds extensive use as a potential herb in thetraditional system of medicine as a ‘rasayana’ and ‘medhya rasayana’.
The similarities betweenroots of Ashwagandha and ginseng roots have led to it being called as Indianginseng (Tripathi et al. 1996).W.somnifera is a geneticallysimple species (2n = 48; n = 24; largely self-pollinated) most suited todevelop cultivars for commercial production of novel sterols and alkaloids(Singh and Kumar 1998). It grows in dry and sub-tropical regions.
The major Ashwagandha cultivating states areMadhya Pradesh, Rajasthan, Punjab, Uttar Pradesh, Haryana, Gujarat andMaharashtra among which Madhya Pradesh alone is having more than 4000 ha areafor cultivation. Due to presence of alkaloids in roots, leaves and seeds, thesesare used in preparation of Ayurvedic and Unani medicines, to combat a widerange of diseases from tuberculosis to arthritis. Important part of ashwagandhais its roots, followed by leaves and berries due to presence of “Withanolides” (Gupta et al.
1996). Themajor biochemical constituents of W. somnifera are steroidal alkaloids andlactones, a class of constituents together known as withanolides (steroidallactones with ergostane skeleton). Ongoing trials and research on animal support therole of ashwagandha’s root and leaf extracts in different disorders anddiseases and possess properties like anticancer, antioxidant etc.
(Chopra etal. 2004; Cooley et al. 2007; Murthy et al.
2010; Rasool et al. 2000;Padmavathi et al. 2005; Bhattacharya et al. 2006) and act as source of arestorative drug (Asthana and Raina 1989). Molecular markers remain unaffected by physiologicalcondition and environmental factors that is the reason for their wideapplication in genetic diversity assessment among W. somnifera (L.
) Dunal genotypes and toidentify duplicated accessions within the germplasm collections. Due to samereason, molecular markers are reliable for informative polymorphisms sincegenetic composition is unique for each species. Most important development hasoccurred in the field of molecular genetics with the emergence of molecularmarker since for breeders it is effective tool for investigating novel sourcesof variations and geneticfactors controlling quantitatively inherited traits. These markers are used for the detection andexploitation of DNA polymorphism (Semagn et al. 2010).
For differentiatingplants at inter- and/or intra-specific level genetic polymorphism playssignificant role, not only in medicinal plants but also in cereals, cash,plantation and horticulture crops. Themost important role of conservation is to preserve the process of geneticdiversity and development in the viable population of ecology and commerciallyviable varieties / genotypes to avoid possible extinction (Rout et al.2010). Different types of marker systems have been used for biodiversityanalysis. These include RFLP, SSR, RAPD and the AFLP. RAPD and ISSR markers are two molecular approaches that have been used to detectvariation among plants.
Systematic evaluation and quantification of the variability from the presentstudy will serve as one step towards providing accurate genetic information forfurther breeding programmes for Withaniaimprovement. The assessment ofvariation would provide us a correct picture of the extent of variation,further helping us to improve the genotypes for biotic and abiotic stresses. The main objective of this study was tocharacterize the Withania genotypesusing morphological and molecular markers in order to evaluate the geneticdiversity and relationships among genotypes lines. In the present investigation, 7 important yieldsrelated morphological and qualitative characters have been studied to evaluatethe pattern and extent of genetic variability and relatedness among 25genotypes of ashwagandha. The results obtained from the mean value of morphologicalcharacters (Table 1) resulted that days to 75% floweringwere showed by UWS-134, UWS 37, UWS 98 and UWS 111, whereas late flowering wasobserved in AWS2B, UWS 10, UWS 15, andUWS 23.
The genotypes JA 20, UWS 23, UWS 37, UWS 67 and UWS 77 mature early whereasgenotypes UWS 11, UWS 13, UWS 22and UWS 98 mature late. The genotype UWS 37 was found as the tallest (50 cm) among all thegenotypes, whereas UWS 67 found as the smallest (28 cm) one. Maximum number ofbranches (4) was found in UWS 13 and UWS 98 while it was minimum (2) in UWS 11, UWS 22, UWS 32, UWS 35, UWS 56, UWS 59, UWS67, UWS 111, UWS 134 and JA20. Maximum root length (22.
4) wasobtained in HWS-8-14, whereas minimum (12) were found in UWS 67, UWS 93 and UWS14. Root diameter was found maximum (14.5 mm) in UWS 32, whereas it was minimum(8.6 mm) in UWS 98. The dry root yield was found to e maximum in UWS 134 andUWS 67 while it was minimum in UWS 10, AWS2B and HWS-8-14.
Comparativeanalysis of 7 morphological characters revealed moderate variation. Pair wise Similaritycoefficient based on SM matrix among the genotypes of ashwagandha ranged from0.01 to 0.43 with an average of 0.22 based on morphological data. A dendrogram generated from morphological datagrouped all 25 genotypes into 2 clusters (Figure 1a). The first cluster was the biggest, comprising 24 genotypeslines, and was subdivided in IA and IB.
SubclusterIA is divided into two cluster viz.,subcluster I A-c and I A-d. Subcluster I A-c comprised 2 genotypes lines UWS10 andUWS-13 showing similarity value of 0.15.
Another subcluster I A-d can be divided into 2 subgroup viz., I A-d1 andI A-d2 with similarity coefficient of 0.16 and 0.14 respectively. Subgroup IA-d1 comprised of 10 genotypes lines UWS11, UWS22, UWS32,UWS59, JA20, UWS35, UWS56, UWS111, UWS67, and UWS134. From this subgroup, UWS 32, UWS 59, UWS 35, UWS 56, UWS111, UWS 67 and UWS 134 are morphologically most similar with value of 0.
43. Subgroup I A-d2 comprised of 11 genotypes lines UWS15,UWS37,UWS93,UWS77,UWS98,UWS28,HWS-8-14, UWS60,UWS92,JA-134 and RVA100. UWS 98 is distinct from UWS 77, UWS 93, UWS 37 and UWS 15with similarity value of 0.18.UWS 60 is distinct from HWS-8-14 and UWS 28 withsimilarity value of 0.21.
Similarly RVA 100 is distinct from UWS 92 and JA-134. Subcluster I-B include only AWS2B genotype with similarity coefficient of0.02.
The minor cluster II include UWS 23 is distinct from all 24genotypes. Basedon Mantel Z-statistics (Mantel 1967), the correlation coefficient (r) wasestimated as 0.81. The r value of 0.81 was considered a good fit of the UPGMAcluster pattern to the data. The two-dimensional plot generated from PCA showed2 clusters that were found to be somewhat distinct from the clustering patternof the UPGMA dendrogram. In the 2-D plot, UWS 23 is included in the samecluster I which is major whereas UWS 98 is found to be distinct from all 24 genotypes(Figure 1b).
Theanalysis gave 6 principal components (PCs), out of which the first 5 principalcomponents contributed 96.73% of the total variability. The first 4 principalcomponents accounted for 91.82% of the total variability, and the first 3accounted for 83.15% of the variance, in which the highest variation wascontributed by the first component (29.
69%), followed by second (64.80%) andthird components (18.35%).
The first PC was influenced by the characteristicsof plant height, root length and root diameter (Table2). In the second PC, the genotypes contributing to plant height, numberof primary branches, days to 75% maturity, root length and dry root yield. Thethird PC was mostly influenced by plant height, days to 75% flowering, rootdiameter and dry root yield shown in Table 2.