Medicinal industry for human and animal welfare and

Medicinal plants have been given great
significance 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 the
world due to variegated climatic ecosystem which is suitable for cultivation
for medicinal plants. India being one of the
world’s 12 mega biodiversity countries needs to conserve its resources where
they are being exploited and should be grown commercially to avoid their
susceptibility to extinction because of indiscriminate use.

Among the various medicinal plants, Withania
somnifera (L.) Dunal
(Winter cherry, Ashwagandha or Asgandh of family Solanaceae is an
important medicinal plant that finds extensive use as a potential herb in the
traditional system of medicine as a ‘rasayana’ and ‘medhya rasayana’. The similarities between
roots of Ashwagandha and ginseng roots have led to it being called as Indian
ginseng (Tripathi et al. 1996).

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W.
somnifera is a genetically
simple species (2n = 48; n = 24; largely self-pollinated) most suited to
develop 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 are
Madhya Pradesh, Rajasthan, Punjab, Uttar Pradesh, Haryana, Gujarat and
Maharashtra among which Madhya Pradesh alone is having more than 4000 ha area
for cultivation. Due to presence of alkaloids in roots, leaves and seeds, theses
are used in preparation of Ayurvedic and Unani medicines, to combat a wide
range of diseases from tuberculosis to arthritis. Important part of ashwagandha
is its roots, followed by leaves and berries due to presence of   “Withanolides” (Gupta et al.
1996). The
major biochemical constituents of W. somnifera are steroidal alkaloids and
lactones, a class of constituents together known as withanolides (steroidal
lactones with ergostane skeleton).

Ongoing trials and research on animal support the
role of ashwagandha’s root and leaf extracts in different disorders and
diseases and possess properties like anticancer, antioxidant etc. (Chopra et
al. 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 a
restorative drug (Asthana and Raina 1989).  

Molecular markers remain unaffected by physiological
condition and environmental factors that is the reason for their wide
application in genetic diversity assessment among W. somnifera (L.) Dunal genotypes and to
identify duplicated accessions within the germplasm collections. Due to same
reason, molecular markers are reliable for informative polymorphisms since
genetic composition is unique for each species. Most important development has
occurred in the field of molecular genetics with the emergence of molecular
marker since for breeders it is effective tool for investigating novel sources
of variations and genetic
factors controlling quantitatively inherited traits. These markers are used for the detection and
exploitation of DNA polymorphism (Semagn et al. 2010).  For differentiating
plants at inter- and/or intra-specific level genetic polymorphism plays
significant role, not only in medicinal plants but also in cereals, cash,
plantation and horticulture crops.
            The
most important role of conservation is to preserve the process of genetic
diversity and development in the viable population of ecology and commercially
viable varieties / genotypes to avoid possible extinction (Rout et al.
2010).  Different types of marker systems have been used for biodiversity
analysis. These include RFLP, SSR, RAPD and the AFLP. RAPD and ISSR markers are two molecular approaches that have been used to detect
variation among plants.
Systematic evaluation and quantification of the variability from the present
study will serve as one step towards providing accurate genetic information for
further breeding programmes for Withania
improvement. The assessment of
variation 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 to
characterize the Withania genotypes
using morphological and molecular markers in order to evaluate the genetic
diversity and relationships among genotypes lines. In the present investigation, 7 important yields
related morphological and qualitative characters have been studied to evaluate
the pattern and extent of genetic variability and relatedness among 25
genotypes of ashwagandha. The results obtained from the mean value of morphological
characters (Table 1) resulted that days to 75% flowering
were showed by UWS-134, UWS 37, UWS 98 and UWS 111, whereas late flowering was
observed in AWS2B, UWS 10, UWS 15, and
UWS 23.
The genotypes JA 20, UWS 23, UWS 37, UWS 67 and UWS 77 mature early whereas
genotypes UWS 11, UWS 13, UWS 22
and UWS 98 mature late. 
The genotype UWS 37 was found as the tallest (50 cm) among all the
genotypes, whereas UWS 67 found as the smallest (28 cm) one. Maximum number of
branches (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, UWS
67, UWS 111, UWS 134 and JA20. Maximum root length (22.4) was
obtained in HWS-8-14, whereas minimum (12) were found in UWS 67, UWS 93 and UWS
14. 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 and
UWS 67 while it was minimum in UWS 10, AWS2B and HWS-8-14.

Comparative
analysis of 7 morphological characters revealed moderate variation. Pair wise Similarity
coefficient based on SM matrix among the genotypes of ashwagandha ranged from
0.01 to 0.43 with an average of 0.22 based on morphological data. A dendrogram generated from morphological data
grouped all 25 genotypes into 2 clusters (Figure 1a). The first cluster was the biggest, comprising 24 genotypes
lines, and was subdivided in IA and IB. Subcluster
IA is divided into two cluster viz.,
subcluster I A-c and I A-d. Subcluster I A-c 
 comprised 2 genotypes lines UWS
10 and
UWS-13 showing similarity value of 0.15. Another subcluster I A-d can be divided into 2 subgroup viz., I A-d1 and
I A-d2 with similarity coefficient of 0.16 and 0.14 respectively. Subgroup I
A-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, UWS
111, 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 15
with similarity value of 0.18.UWS 60 is distinct from HWS-8-14 and UWS 28 with
similarity 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 of
0.02. The minor cluster II include UWS 23 is distinct from all 24
genotypes. 

Based
on Mantel Z-statistics (Mantel 1967), the correlation coefficient (r) was
estimated as 0.81. The r value of 0.81 was considered a good fit of the UPGMA
cluster pattern to the data. The two-dimensional plot generated from PCA showed
2 clusters that were found to be somewhat distinct from the clustering pattern
of the UPGMA dendrogram. In the 2-D plot, UWS 23 is included in the same
cluster I which is major whereas UWS 98 is found to be distinct from all 24 genotypes
(Figure 1b).

The
analysis gave 6 principal components (PCs), out of which the first 5 principal
components contributed 96.73% of the total variability. The first 4 principal
components accounted for 91.82% of the total variability, and the first 3
accounted for 83.15% of the variance, in which the highest variation was
contributed by the first component (29.69%), followed by second (64.80%) and
third components (18.35%). The first PC was influenced by the characteristics
of plant height, root length and root diameter (Table
2). In the second PC, the genotypes contributing to plant height, number
of primary branches, days to 75% maturity, root length and dry root yield. The
third PC was mostly influenced by plant height, days to 75% flowering, root
diameter and dry root yield shown in Table 2.   

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