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International Journal of Bio-Technology

and Research (IJBTR)

ISSN 2249-6858

Vol. 3, Issue 4, Oct 2013, 1-16

TJPRC Pvt. Ltd.

AGRO- MORPHOLOGICAL AND RAPD MARKER BASED CHARACTERIZATION OF

GENETIC DIVERSITY IN DIFFERENT GENOTYPES OF WITHANIA SOMNIFERA L.

DUNAL

SUNITA KHATAK1, SANTOSH DHILLON

2, OM PRAKASH YADAV

3, ANITA GREWAL

4&

RAM NIWAS SHEOKAND5

1,4Department of Biotechnology, University Institute of Engineering and Technology, Kurukshetra University,

Kurukshetra, Haryana, India

2Department of Biotechnology and Molecular Biology, C.C.S.H.A.U, Hisar, Haryana, India

3Medicinal and Aromatic Plants Section, C.C.S.H.A.U, Hisar, Haryana, India

5Department of Computer Science, Computer Section, C.C.S.H.A.U, Hisar, Haryana, India

ABSTRACT

Withania somnifera (Ashwagandha) is an industrially important plant for production of glycowithanolides

consisting of sitoindusides VII to X and withaferin A. In the present study thirty genotypes ofWithania somnifera (L)

Dunal also known as Ashwagandha were collected from three different states of India and genetic diversity analysis was

carried out using agro morphological characters and RAPD markers. Data obtained was used to determine the component

trait variation, magnitude of correlation between root yield and quality traits along with path coefficient during kharif

season. A high magnitude of phenotypic coefficient of variation was observed for root length, plant height, and seed yield

per plant. Heritability in broad sense and genetic advance were found to be moderate to high and highly significant positive

correlation was observed between root yield and plant height followed by root length. Path analysis revealed direct

contribution of plant height followed by root length for root yield. Sixty two RAPD primers were screened out of which,

46 primers showed amplification. Forty two out of the forty six primers showed 87.28 per cent polymorphism. In total 236

bands were obtained of which 206 were polymorphic. Estimates of genetic similarity ranged from 0.18 to 0.90 showing a

wide genetic base. The genotypes collected from different locations did not formed well defined distinct clusters and were

interspersed with each other and was further supported by morphological results. Molecular markers in combination with

morphological traits could be potential source for Ashwangandha improvement as the roots of the plant associated with

rejuvenation makes it medicinally important.

KEYWORDS: RAPD, Path Coefficient, Heritability, Polymorphism, Cluster Analysis, Agromorphological

INTRODUCTION

Out of a large number of medicinal plants known in present scenario, Withania somnifera (L.) Dunal (family

Solanaceae) commonly known as Ashwagandha or Asgandh finds extensive use as a medicinal herb in the traditional

system of medicine as a rasayana and medhya rasayana extends back over 3000 to 4000 years (Atal et al.,1961). It is also

known as winter cherry, Indian ginseng and is an important small woody shrub that grows upto 30 to 50 cm in height

(maximum of 150 cm), usually clothed with whitish stellate hairs, leaves are ovate, entire and thin up to 10 cm long. Roots

and leaf extracts ofW. somnifera indicates that it possesses anti-inflammatory, antitumour, antistress, antioxidant ,

immunomodulatory properties, (Bhattacharya et al., 2006; Padmavati et al., 2005; Rasool et al., 2000). It is an ingredient in

many formulations prescribed for a variety of musculoskeletal conditions e.g., arthritis, rheumatism, used as a general tonic


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2 Sunita Khatak, Santosh Dhillon, Om Prakash Yadav, Anita Grewal& Ram Niwas Sheokand

to increase energy, improve overall health, longevity and prevent diseases. Clinical trials and animal research support the

use of Ashwagandha for anxiety, cognitive, neurological disorders, inflammation and parkinsons disease (Chopra et al.,

2004; Cooley et al., 2007; Murthy et al., 2010).

The world market for herbal medicine including herbal products and raw materials has been estimated to have an

annual growth rate between 5and 15%. Total global herbal drug market is estimated as US$2 billion and is expected to

grow to US $ 5 trillion by the year 2050 (Joshi et al., 2004). Medicinal importance of withania is due to presence of

different alkaloids. Because of its ever increasing demand in pharmaceutical industries, there is pressing need to further

increase the productivity of withania. However, to meet further increases in productivity information regarding genetic

diversity is essential for breeding program. Ashwagandha has been used successfully in Ayurvedic medicine for centuries

therefore proper identification of genotype is important for protection of both the public health and industries as it has

pharmaceutical importance. Various chromatographic techniques and marker compounds have been used to standardize

botanical preparations but chemical complexity and variable sources are the limitations associated with the identification of

genotypes. An understanding of the extent of genetic diversity is critical for the success of a breeding program.

The selection ofWithania somnifera based on genetic information using morphological and molecular markers is

essential as it is more reliable and consistent. DNA markers are not typically influenced by environmental conditions and

therefore can be used to help describe patterns of genetic variation among Withania somnifera genotypes and to identify

duplicated accessions within the germplasm collections.The development and use of molecular markers for the detection

and exploitation of DNA polymorphism is one of the most significant developments in the field of molecular genetics.

DNA markers are reliable for informative polymorphisms as the genetic composition is unique for each species and is not

affected by age, physiological conditions as well as environmental factors (Semagn et al., 2006). Recent developments in

molecular biology have opened the possibility of employing various types of molecular tools to identify and use genomic

variation improvement of various organisms. Randomly amplified polymorphic DNA (RAPD) markers (Williams et al.,

1990; Dogan et al., 2007; Mostafa et al., 2011) is one of the most preferred molecular approaches that have been used to

detect variation among plants. Genetic variations in medicinal plants by RAPD markers and phylogenetic analysis

of Jurinea(Asteraceae) species, Jatropha, Smallanthus and Clusterbean have been reported (Domyati et al., 2011; Milella

et al. 2011; Punia et al. 2009).Research on similar lines has been initiated to use morphological and molecular marker

specifically RAPD to study genetic variation among W. somnifera. Systematic evaluation and quantification of the

variability from the present study will serve as one step towards providing accurate genetic information for further

breeding program for withania improvement.

MATERIALS AND METHODS

Plant Material

Seeds of thirty selected genotypes ofW. somnifera (L.) Dunal (Table 1) were procured from Medicinal Aromatic

& Under Utilized Plant (MA&UUP) Section, Department of Plant Breeding, Chaudhary Charan Singh, Haryana

Agricultural University, Hisar, Haryana. Seeds were sown in, randomized block design. Observations on various

quantitative morphological characters like plant height (cm), root length (cm), root yield (g), no. of berries/ plant, no. of

seeds/ berry, seed yield/plant (g) and biological yield (g) were recorded in three replicates and the mean values were

utilized for statistical analysis. The experimental data was subjected to method given by Panse and Sukhatme (1967).

Genotypic and phenotypic coefficient of variation was computed based on method given by (Burton et al., 1972).

Heritability in broad sense was calculated according to Hanson et al. (1956). The genotypic correlation among different

quality characters were computed based on the method suggested by (Johannson et al., 1955).


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Agro- Morphological and RAPD Marker Based Characterization of Genetic 3Diversity in Different Genotypes ofWithania somniferaL. Dunal

Genomic DNA Extraction

The DNA was extracted from young leaf samples (5 gm) following cetyl trimethyl ammonium bromide(CTAB )

method given by Murray and Thompson (1980) and modified by Saghai-Maroof et al.,(1980) with slight modifications.

The leaf tissue was lyophilized at -65C for 72h and was ground to a fine powder using mortar and pestle. The fine powder

was transferred into polypropylene tubes (about 1 gm), and 10 ml of prewarmed (60C) CTAB (2%) extraction buffer

(100mM Tris base, pH-8.0, 50 mM EDTA; 250mM NaCl; 2% mercaptoethanol) added, inverted several times and

incubated at 65C for 90 min. DNA was extracted twice with chloroform: octanol (24:1) and precipitated by adding 6ml of

ice cold isopropanol. The DNA was dried on sterile hooks after washing with wash buffer- 1 (76% alcohol, 3M NaOAc)

for 20 minutes followed by wash buffer-II (76% alcohol, 2M NH4OAc). It was dissolved in IX TE buffer (10 mM Tris (pH

8.0); 0.1 mM EDTA) and treated with RNase (10 mg/ml) at 37C for overnight. The extracted DNA was washed with 70%

alcohol, dried at room temperature and finally suspended in 100 l of 1X TE overnight. The quality and quantity of

extracted DNA was measured by taking OD at 260-280 nm and agarose gel electrophoresis containing DNA standards.

PCR Amplification

Sixty two (62) 10-mer, oligonucleotide primers synthesized by Operon Technologies were screened and 46

primers out of 62 producing consistent and reproducible banding profile were subjected to PCR amplification. Different

parameters were tested to determine optimal concentrations of template DNA, MgCl2, dNTPs, Taq DNA polymerase,

primer and different temperatures and time intervals during denaturation, annealing and elongation steps which affect

amplification, banding pattern and reproducibility. For this, varying concentrations of template DNA (50 ng, 100 ng, 200

ng), primers (0.10 M, 0.20 M, 0.30 M, 0.40 M, 0.50 M), dNTPs (0.5 mM, 1 mM, 1.5 mM, 2.0 mM) and MgCl 2

(0.5mM, 1.0 mM, 1.5 mM and 2.0 mM ) were used in a reaction volume of 20 l in different combinations at different

annealing temperatures ( 38C, 40C, 43C, 45C, and 48C). In brief, reproducible and clear banding patterns were

obtained in a reaction mixture of 20 l containing 50 ng template DNA, 10 mM of each of the dNTPs, 10X PCR buffer,

1.5 mM MgCl2, 0.50 M of primer and 1 unit ofTaq DNA polymerase.

PCR amplification in the thermocycler (programmable thermal cycler from BIORADTM

International) was

programmed for an initial denaturation step of 5 min at 94C, followed by 30 cycles of denaturation (94C, 1 min),

annealing (38-48C, 1 min) and extension (72C, 2 min) followed by a final extension of 72C for 5 min and a hold

temperature of 4C. The amplified products were electrohoresed on 1.5 % agarose (Sigma chemicals Co., Ltd. India) gels

in 1X TBE buffer (Tris -borate EDTA) at 80 volts for 4 hrs. The electrophoresed gels were stained with ethidium bromide

and visualized under UV transilluminator and photographed. A 1Kb DNA ladder was used as standard (Bangalore, Genei,

India).

RAPD Analysis

Banding pattern for each primer was scored as discrete variables using 1 to indicate presence and 0 to indicate

absence of a band simply by visual observations whereas, faint bands were neglected . This 0/1 matrix was used to

calculate the genetic similarity to estimate all pair-wise differences in the amplification product for all genotypes. The

binary matrix was subjected to statistical analysis using NTSYS-PC (Numerical Taxonomy and Multivariate Analysis)

system. Jaccards similarity coefficient was calculated using Simqual subprogramme of NTSYS-PC and a dendrogram was

constructed to study relatedness among 30 Ashwangandha genotypes produced by means of unweighted pair group method

with arithematic average (UPGMA) analysis, (Sneath and Sokal, 1973). Two dimensional and three dimensional principal

component analysis was estimated by eigen value calculated using NTSYS-PC software.


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RESULTS AND DISCUSSIONS

Agro-Morphological Analysis

Analysis of variance for the studied characters, like plant height, root length, root yield, biological yield, number

of berries/plant, number of seeds per berry revealed highly significant differences among genotypes. A perusal of table 2revealed that phenotypic coefficients of variation were higher than genotypic coefficient of variation for all the characters

indicating environmental influence on phenotypes. Moreover the phenotypic and genotypic coefficient of variation for all

the characters were comparable being highest for root yield (204.5%) followed by biological yield at maturity (199.4%).

As compared to these two characters, a narrow difference was observed among all other characters, which implies low

environment influence and predominant role of genetic factors contributing to the expression of the characters. Heritability

in broad sense includes both fixable (additive) and non fixable (dominant and epistatic) variance.

The estimates of heritability along with genetic advances would help in predicting gain under selection. In present

study root yield (99.93%) and biological yield at maturity (99.99%) recorded high heritability as well as high genetic

advance. Percentage of mean viz. (421.0%) and (410.6%) respectively which indicated that these traits can be easily

improved in breeding programs. Any crop improvement programme requires selection of improved varieties/genotypes

which in turn depends upon a clear understanding of relationship among different quality characters and root yield.

Moreover as environment plays a pivotal role in expression of characters and considered as an important component of

phenotypic variation on which selection would act. Applying both methods was recommended to extract the maximum

amount of information from the matrix data (Sikdar et al., 2010). So, genotypic correlation is not sufficient enough for

predicting the amount of change in one character by selection of other and phenotypic correlation have to be taken under

consideration. A critical observation of correlation coefficients between different traits revealed that root yield was

positively and highly significantly correlated with plant height (+0.9280) and root length (+0.8757). The root length

exhibited positive and more significant correlation with plant height (+0.8436) followed by root yield (+0.7237). These

yield components can be used as selection criteria to enhance root yield. Similarly plant height exhibited significant

positive correlation with number of berries per plant (0.6380). Selection of plants with higher plant height will increase the

root length and ultimately will increase the root yield. The genotypic correlations were partitioned into direct and indirect

effects using path coefficient analysis to know the relative importance of the components. Plant height (+0.7531) a root

length (0.2898) had direct effect on root yield being more contributing factor followed by seed yield per plant (+0.047) and

biological yield at maturity (+0.003). In contrast number of berries per plant (-0.125) and number of seeds per berry (-

0.134) exhibited negative direct effects on root yield. Our results correlated to Amini et al., (2008) who reported lowest

and highest broad sense heritabilities for days to (50 %) emergence and seed yield (83%).

Path coefficient analysis was done to study the genetic correlation between root yield (being of medicinal

importance) and its component traits, to have a comprehensive view of the real contribution of individual characters

towards the root yield. These observations corroborate well with those of Kandalkar et al., (1993) and Misra et al.,

(1998a;1998b). The path coefficient analysis not only reveals the direct effects but as well as indirect effects of one

variable through the other on the dependent character (Table 2). The direct effect of plant height on root yield per plant was

found to be positive and high (0.753) as compared to root length (0.289), seed yield per plant (0.047), followed by

biological yield at maturity (0.034) which were also positive. Thus plant height, root length, biolo gical yield at maturity

and seed yield per plant showed positive direct effect on root yield but negative for number of berries per plant (-0.125)

followed by number of seeds per berry (-0.134). The root length showed positive direct effect on root yield (0.289) but was

less in comparison to plant height (0.753), while the indirect effect via plant height was very high (0.635) followed by seed


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yield per plant (0.018) compared to this number of berries per plant and number of seeds per berry were found to be

negative (-0.047) and (-0.019) respectively.

Euclidean Cluster Analysis

For morphological characterization of withania species, seven characters were selected which does have a

detrimental effect on plant seed and root yield, as the plant is having rejuvenation properties associated with its roots,

genetic coefficient and phenotypic coefficient of variations along with molecular marker based characterization can better

resolve the plant improvement strategies to enhance the root yield. Mean values of morphological characters were used as

representative data for each character. Euclidean distance for each pair was employed to construct cluster with the

unweighted pair group method of arithmetic averages (UPGMA) in the software package NTSYS-pc (rohlf, 1998). The

seven characters chosen at random clustered the thirty genotypes mainly in four major clusters 1,2,3,4 (Figure 1). Cluster 1

consisted of 4 genotypes which includes WS-124, WS-204, WS-223, WS-226 all from Udaipur( Raj.) while genotypes

WS-201,WS-205,WS-206,WS-210,WS-218 from Udaipur (Raj.) and WS-90-125,WS-134,WS-90-135,WS-20 fromMandsaur district (M.P) were clustered together inspite of their different origin into 2 cluster in the dendrogram.

The third major cluster contained almost 15 genotypes which includes genotypes WS-202,WS-213,WS-220,WS-

224, from Udaipur (Raj.) along with WS-90-100,WS-90-103,WS-90-104,WS-90-105,WS-90-117,WS-90-126,WS-90-

129,WS-90-136, from Mandsaur district (M.P), Adinath from Neemuch tehsil (M.P) along with HWS-04-1 and HWS-04-3

from Haryana. Cluster 4 consisted of only two genotypes which have same origin, one genotype is local collection from

research farm areas while HWS-04-2 is cultivated. ANOVA and RAPD data indicated that there were no significant

differences among the geographical regions from which the genotype were derived (data not shown). The relatedness

between the genetic traits based on agro morphological characters and molecular marker based studies were not

statistically significant. Agro trait revealed slight similarity and corroborate at a point that genotypes collected from

different locations although far off still were interspersed with each other as shown in third cluster of Euclidean based

characterization.

Hikmat Ullah et al., (2012) estimated the genetic variability in 20 genotypes of turmeric (Curcuma longa L.)

collected from three different eco geographical areas of Pakistan using only agromorphological traits. All 20 genotypes

were characterized for three qualitative and 18 quantitative traits from flowering till maturity. Euclidean distance

coefficients were carried out for all genotypes. Relatively a low level of variability was observed and phenogram placed 20

genotypes into 2 major clusters. The cluster was unable to differentiate genotypes distinctly into different groups. Instead

variable genotypes of three population were interspersed and further revealed a wider spread across two principal

components. Some of the species were well characterized but others were difficult to distinguishand this may be due to the

fact that most of the cultivars have been generated by breeding local material through simple selection.Furat et al., (2010)

characterized genetic diversity among 103 cultivated sesame (Sesamum indicum L.) landraces from Turkey using 21

morphological and agronomical characters. Cluster analysis assigned 103 germplasm accession into eight main clusters .

The principal component analysis (PCA) apart from Euclidean cluster analysis was performed for grouping of accessions

on the basis of morphological characters. The genotypes tended to cluster mainly in four major clusters. The clustering of

W. somnifera based on the variations across morphological traits indicated that the genotype from different places

interspersed with each other. The first three principal components in the collection with eigen values were able to explain

78.2% variability for morphological characters. The genotypes 5,6,9, 10 and 12 were grouped closer to each other in 2D

and 3D analysis (Figure 2 a&b). The results of PCA analysis corresponded with that of cluster analysis.


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RAPD and Cluster Analysis

A total of 236 scorable, clear and reproducible bands were generated in 30 genotypes ofWithania somnifera using

forty six selected primers having high (G+C) contents. The number of bands per primer ranged from 10 (OPR-7) to 1

(OPR-14, OPR-20) with an average of 5.13 bands per primer (Figure 3a&b). Out of 236 bands obtained using 46 primers,

206 were polymorphic (87.28%) revealing a high degree of polymorphism. The molecular weights of amplification

products ranged from 100 bp (OPQ-07) to 5000 bp (OPR-07) (Table 3). Percentage of polymorphism detected with each

primer was as high as 100% for most of the primers except for 66.6% (primer OPG-08, OPO-09), 71.4% (primer OPO-18),

85.7% (primer OPA-02, OPG-02, OPO-02), 80% (primer OPR-2, OPR-3), 50% (primer OPH-11) and as low as 25%

detected using primer OPU-27. Singh et al., (2012) reported genetic diversity among 55 turmeric (Curcuma longa L.)

accessions from 10 different agro climatic regions using RAPD marker and found 45 to 100 % polymorphism with an

average of 91.4%. Similar findings in 10 acessions of aloevera were observed by Barandozi et al., (2012).

Previous reports of molecular marker based genetic diversity in Ashwagandha are limited and were mainly basedon morphological parameters. Mansya et al., (2010) reported that genetic similarity coefficient from 0.44 - 0.96 between

twenty three accessions of the Garcinia mangostana collections from Sumatra region. Jaccards similarity coefficient

matrix was used to generate a dendrogram using UPGMA clustering method of NTSYS-PC. In the dendrogram, similarity

coefficients between all possible pairs of genotypes ranged between 0.1818 to 0.9092 (Table 5). The dendrogram for all 30

genotypes using RAPD markers showed 2 clusters at 82.5%. The first major cluster was divided into further sub cluster,

out grouping WS-90-136 (M.P.) from WS-224 (Raj.) and WS-90-47 (M.P.) which were separated at a similarity level of

89%, while second sub cluster contains all remaining genotypes. Genotype WS-90-105 and WS-90-125 from Mandsaur

(M.P.) were diversified from WS-124, WS-205, WS-201, WS-202, WS-204 from Udaipur (Raj.) at a similarity level of

83%, while these genotypes from Udaipur were further diversified from WS-206, WS-220, WS-218, WS-213, WS-226,

WS-210, Udaipur (Raj.) alongwith WS-134 (C), WS-90-103 of Mandsaur dist. (M.P). At 71% similarity level genotype

WS-90-104 (M.P.) was again out grouped and formed solitary cluster again grouping all the genotypes from Udaipur

(Raj.), Mandsaur (M.P.) and Haryana. This cluster was divided into two major subclusters at a similarity level of 77%.

Genotype WS-206 and WS-220 of Udaipur Rajasthan were closest to each other followed by WS-218 and WS-220, WS-

206, while WS-90-135, WS-90-104 and HWS-04-3 were most diverse and delineated from all others at a 69% similarity

(Figure 4).

The earlier reports in W. somnifera are based either on morphological or RAPD analysis. Where as combination

of morphological and molecular genetic analysis are more reliable and consistent. RAPD and agro morphological marker

system being employed to assess the genetic diversity of W. somnifera germplasm was quite informative and was able to

generate adequate polymorphism and unique DNA fingerprint. Although a parallel correlation failed to occur between two

marker system used but in contradiction WS-224(12) from Udaipur and WS 90-117(18) from Mandsaur district M.P. were

found to be grouped together in both the clusters, similarly WS-213(8) from Udaipur and WS-90-103(15) from Mandsaur

(M.P.) were grouped together similar to agro marker analysis. Wang et al., (2011) elucidated a combination of RAPD

markers in cultivars of Camellia (Theaceae) and studied their correlation with three morphological traits, cluster analysis

divided 15 cultivar into two major groups at a similarity coefficient of 0.65 and RAPD markers showed no correlation with

petal color. Similar observations were made by Samal et al., (2012) in commercial cultivars hybrids and local mango

(Mangifera indica L.) using RAPD, where no clear cut geographical separation was revealed among East ,West, North and

South Indian mango cultivars


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Similar clustering of 30 genotypes of Ashwangandha was also evident from two and three dimensional principal

component analysis (PCA). The genotypes tended to cluster mainly in two groups. As revealed in the dendrogram WS-90-

135 and WS-90-104 were lying far apart while WS-218 and WS-220 were closest to each other and followed by WS-218

and WS-206 showing a minimum genetic distance . (Figure 5a&b).Principal component analysis revealed a broad variationamong different accessions, the first two principal component justified the dendrogram obtained and genetic relatedness

which further revealed existence of high morphological, agronomical and genotypic variations among 30 genotypes. All

accessions clearly interspersed with each other on scatter diagram and this well correlate with that of cluster pattern

obtained.

Species Specific Alleles

Rare and genotype specific bands were identified table 4 represents a brief description of bands specific to

particular genotypes (Figure 3a&b). The primer OPQ-07 and OPB-16 produced unique allele specific to each genotype. In

primer OPQ-07,700 bp marker was present only in genotypes WS-90-129, WS-134 (C), while 800 bp alleles were specificto WS-90-117 and WS-90-125, HWS-04-1, primer OPB-16 generated a unique allele of 1,150 bp in WS-90-104, WS-90-

129 and of 1400 bp in WS-224 and HWS-04-3. Ginwal et al., (2011) reported species specific alleles using RAPD markers

in Acorus calamus (Sweetflag) an ethnobotanically important medicinal and aromatic plant. Primer OPA-11 and OPA-15

amplified a band of 0.6kb and 087 kb which specifically associated themselves with populations A-44, A-51, A-52, A-57,

A-58 and A-59 while found to be absent in all other populations. In total 14 primers produced 20 unique alleles in 30

Ashwangandha genotypes, Primer OPB-16 gave unique alleles and could distinguish genotypes viz. HWS-04-3, WS-90-

129, WS-224, and WS-90-104. These genotypes which revealed similar pattern could be further distinguished from each

other using primer OPQ-07 by presence of unique allele of 800 bp in WS-90-117, while absent in WS-90-105. Similarly

this primer could differentiate WS-134 (C) and local genotype from Haryana by presence of unique allele of 700 bp in WS-

134 (C) which was absent in Local genotypes from Haryana. Combination of different primers (OPB-16 and OPG-20) was

able to distinguish WS-202 from all other genotypes. Primer OPB-16 in combination with OPQ-07, OPG-20, and OPH-01

were found to be most contributing primers to fingerprint W. somnifera. The genetic diversity was mainly due to diverse

collection. Singh et al., (2012) reported amplicons ranging from 230 to 3000 bp in size similar to our results in turmeric

(Curcuma longa L.) using RAPD which were species specific.

In conclusion the RAPD patterns obtained from our study can serve as a vital input to the conventional method of

varietal identification, future germplasm management, and marker assisted selection to improve the efficiency of new

cultivar development in future breeding programs that relies solely on morphological characters. Interestingly collections

originating from various parts of the country did not form well defined distinct groups and were interspersed with each

other, indicating no association between RAPD pattern and the geographic origin of accession.

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APPENDICES

Table 1: A Brief Description ofW. somni feraGenotypes Used during Present Investigation

Sr. No. Genotypes Source

1 WS-124 Udaipur, Rajasthan

2 WS-201 -do-

3 WS-202 -do-

4 WS-204 -do-

5 WS-205 -do-

6 WS-206 -do-

7 WS-210 -do-

8 WS-213 -do-

9 WS-218 -do-

10 WS-220 -do-

11 WS-223 -do-

12 WS-224 -do-

13 WS-226 -do-

14 WS-90-100 Mandsaur (M.P.)

15 WS-90-103 -do-

16 WS-90-104 -do -.17 WS-90-105 Mandsaur (M.P.)

18 WS-90-117 -do-

19 WS-90-125 -do-

20 WS-90-126 -do-

21 WS-90-129 -do-

22 WS-134(C) -do-

23 WS-90-135 -do-

24 WS-90-136 -do-

25 WS-20 (C) -do-

26 Adinath Neemuch (M.P.)

27 Local Hisar, Research Farm Area

28 HWS-04 -1 Haryana

29 HWS-04 -2 -do-

30 HWS-04 -3 -do-


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Table 2: Range Overall Mean, Phenotypic Coefficient of Variation (PCV), Genotypic Coefficient of Variation

(GCV), Heritability and Genetic Advance in Withania somnifera(L.) Dunal

Sr.

No.Character

Overall

MeanRange

GCV

(%)

PCV

(%)

Heritability

(%)

Genetic Advance

% Mean

1 Plant height (cm) 45.6 35.6-104.6 24.1 28.0 79.1 50.8

2 Root length (cm) 15.8 12.0-39.3 26.8 32.8 66.6 46.2

3 Root yield (g) 40.8 10.0-380.0 20.4 201.5 98.9 420.0

4 No. of berries/ plant 117.9 47.3-242.6 38.4 37.4 97.8 80.1

5 No. of seeds/ berry 28.9 22.0-37.3 23.2 24.6 86.7 44.0

6 Biological yield (g) 325.6 100.0-3800.0 190.3 199.3 98.9 400.6

7 Seed yield/plant (g) 17.7 6.0-40.0 48.4 51.0 93.9 97.8

Table 3: Random Primers Showing Polymorphism among W. somnif era

Primer

No. of

Genotypes

Amplified

Total

Bands

Polymorphic

Bands

Monomorphic

Bands

Percentage

Polymorphsism (%)Sequence (5 3)

OPA-02 27 7 6 1 85.7 TGC CGA GCTG

OPA-06 30 4 0 4 0 GGT CCC TGAC

OPA-09 29 9 9 0 100* GGG TAA CGCC

OPB-09 27 8 8 0 100* TGG GGG ACTC

OPB-16 28 7 7 0 100* TTT GCC CGGA

OPB-18 24 4 4 0 100* CCA CAG CAGT

OPG-02 30 8 7 1 87.5 GGC ACT GAGG

OPG-05 30 5 5 0 100 CTG AGA CGGA

OPG-08 30 3 2 1 66.7 TCA CGT CCAC

OPG-12 28 4 4 0 100 CAG CTC ACGA

OPG-20 30 5 0 5 0 TCT CCC TCAG

OPH-01 26 4 4 0 100* GGT CGG AGAA

OPH-11 30 8 4 4 50 CTT CCG CAGT

OPH-15 29 7 7 0 100* AAT GGC GCAG

OPH-20 25 3 3 0 100* GGG AGA CATC

OPI-01 30 8 8 0 100* ACC TGG ACACOPI-05 30 3 3 0 100* TGT TCC ACGG

OPI-08 29 6 6 0 100* TTT GCC CGGT

OPI-17 28 8 8 0 100* GGT GGT GATG

OPO-02 30 7 6 1 85.7 ACG TAG CGTC

OPO-06 27 4 4 0 100 CCA CGG GAAG

OPO-09 30 3 2 1 66.7 TCC CAC GCAA

OPO-18 30 7 5 2 71.4 CTC GCT ATCC

OPQ-01 30 8 8 0 100* GGG ACG ATGG

OPQ-07 29 5 5 0 100* CCC CGA TGGT

OPR-1 23 7 7 0 100* TTC GAG CCAG

OPR-2 30 5 4 1 80 GTG AGG CGTC

OPR-3 30 5 4 1 80 GGG GGT CTTT

OPR-4 28 3 3 0 100* CCG CAT CTAC

OPR-5 28 9 9 0 100* GAT GAC CGCCOPR-6 30 3 0 3 0 GAA CGG ACTC

OPR-7 29 10 10 0 100* GTC CCG ACGA

OPR-8 29 3 3 0 100* TGG ACC GGTG

OPR-9 30 2 0 2 0 CTC ACC GTCC

OPR-10 28 7 7 0 100* TGT CTG GGTG

OPR-11 28 4 4 0 100* AAA GCT GCGG

OPR-13 26 3 3 0 100* AAG CCT CGTC

OPR-14 26 1 1 0 100* TGG ACG GGTG

OPR-15 28 3 3 0 100* TCC CAC GCAC

OPR-19 28 5 5 0 100* GTT GCC AGCC

OPR-20 28 1 1 0 100* ACT TCG CCAC

OPU-16 29 2 2 0 100* AGC CAG CGAA

OPU-27 30 4 1 3 25* GGA CAC CACT

OPU-37 28 5 5 0 100* TGT TCC AAGGOPU-39 29 5 5 0 100* TCC CAG GCAA

OPU-58 28 4 4 0 100* CTC ACC ATCA

236 206 30 87.28%


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Figure 1: Dendrogram Showing the Clustering Pattern among 30 Genotypes of Ashwagandha as Revealed UsingAgro Morphological Markers. The Numbers (1-30) Corresponding to Genotypes as Per Table -1

Figure (2a): Two Dimensional Principal Component Analysis of Thirty Genotypes Using RAPD Primers. The

Numbers (1-30) Corresponds to Thirty Genotypes as Given in Table-1

Figure 2(b): Three Dimensional Principal Component Analysis of Thirty Genotypes of Ashwagandha Using 46

Primers. The Numbers (1-30) Corresponds to Thirty Genotypes as Given in Table-1


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(a)

(b)

Figure 3 (a, b): RAPD Profiles Generated by Primer OPQ-07and OPB16. The Numbers (1-30) Corresponding to

Genotypes at the Top of Each Lane as Per Table -1. Lane M is the Molecular Weight Marker

Figure 4: UPGMA Dendrogram Based on 46 RAPD Primers Showing Genetic Relationship in 30 Genotypes. The

Numbers (1-30) Corresponds to Thirty Genotypes as Given in Table-1


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(a)

(b)

Figure 5(a, b): Two and Three Dimensional Principal Component Analysis Based on Euclidean Cluster Analysis

Using Agro- Morphological Markers.The Numbers (1-30) Corresponding to Genotypes at the Top of Each Lane asPer Table -1


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