14

CHAPTER 1

PHYLOGENETIC CHARACTERIZATION

REVIEW OF LITERATURE

Genetic diversity or variation is an inherent plant characteristic that enables it

survival in the wild. The importance of plant diversity studies is the

morphological and genetic characterization of the germplasm, and

establishment of a core collection by redundant accession elimination and

identification of lines that may be useful for future breeding programmes.

Genetically informative markers that also provide high through put assays have

found use in comparative and structural genome biology as well as molecular

breeding. Genetic diversity assessment is an integral part of selecting a highly

productive species. Crop improvement is primarily accomplished by

continuous infusion of wild relatives, traditional varieties using contemporary

breeding techniques, which all require genetic diversity assessment at some

level or another. Genetic diversity assessment is very important to identify

groups with similar genotypes and to conserve, evaluate and utilize the genetic

resources. The diversity of the germplasms can be used as a potential basis of

genes that lead to improved performance of the superior cultivars. Further,

genetic diversity assessment can also be used to determine distinctness and

uniqueness of the phenotypes and the genotypes with the objective of

protecting the intellectual property rights of the breeder (Nemera et al. 2006).

Genetic diversity information in the adapted cultivars or selected

breeding materials provides a major impact in the crop improvement. Genetic

diversity information can be obtained from the pedigree analysis,

morphological characters or using molecular markers (Pejic et al. 1998). The

varietal characterization is imperative for the genetic resources documentation

and for protection of breeder’s rights (Selbach and Cavalli-Molina 2000). This

knowledge is particularly helpful in managing of gene bank and breeding

experiments like labelling of germplasm, identification and elimination of

duplicates in the gene stock and a core collection establishment. Genetic

diversity between and within plant populations comes a combination of

15

several factors such as the physical remoteness, the population size, the type of

mating system (selfing/outcrossing), the mode of seed or pollen dispersal, and

the speed of gene flow (Mekuria et al. 2002). DNA fingerprinting has been

made feasible by the new improvments in molecular biology and a number of

techniques exist today to characterize DNA polymorphism.

Due to lack of even the basic set of information about Asparagus

racemosus germplasm in India, investigations on the number of genotypes and

their geographical distributions as well as the potentially useful resources with

desirable traits are necessary. Collection and conservation of A. racemosus

germplasm is essential for breeding purposes as well as for saving the

germplasm, which is at the edge of extermination. The main problem

associated with the A. racemosus plant material is the loss of genetic diversity,

as the local landraces do not meet the market requirements. In order to resolve

this problem, there is a need to conserve and characterize the A. racemosus

genetic resources and initiate breeding programmes so that new cultivars can

be obtained that could broaden the range of available cultivars adapted to the

market demands.

The assessment of genetic diversity within and between populations is

routinely performed at the molecular level using various laboratory-based

techniques such as allozyme or DNA analysis, which measure levels of

variation directly. Genetic diversity may be also gauged using morphological,

and biochemical characterization and evaluation. Morphological traits are often

susceptible to phenotypic plasticity; conversely, this allows assessment of

diversity in the presence of environmental variation.

Morphological Markers

The knowledge of genetic diversity and its relatedness in the germplasm is a

prerequisite for crop improvement programs. Traditionally, characterization of

germplasm collections were based primarily on the morphological descriptors

(Fajardo et al. 2002) which includes phenotypic characteristics like flower

colour, leaf area, leaf length, growth habit etc.

Morphological analysis is the easiest and least complex of the plant

identification and characterization techniques. The technique involves

description and monitoring of easily detectable parts like form and structure.

16

Prior to the progresses made in biotechnology the morphological and physical

characteristics were assessed for characterization of different cultivars. Several

studies have assessed the genetic diversity on the basis of dissimilarity in

morphological and agronomic characteristics or on ancestry information for

different crops (Sneller et al. 1997; Liu et al. 2004; Uysal et al. 2011; Elameen

et al. 2011). Even though genetic diversity assessment based on morphological

characters alone may not be an effective method. The morphological

parameters may be used in conjunction with other methods. The diversity

based on phenological and morphological characters typically varies with

environment. Evaluation of these traits requires comprehensive

characterization of genotypes prior to identification. Morphological and

phenological characters serve as a basis and provide the basic information for

improvement through breeding programme and further evaluation.

Several studies have used characteristics like leaf blade length, width

and shape of leaf blade, thickness and distribution of the lateral and middle

shoots and size and shape of flower clusters to distinguish cultivars (Badenes

et al. 2000; Vinayak et al. 2009). Similarly, foliage colour, spear length,

cladode length, plant height were described as important descriptors to

distinguish different Asparagus cultivars (Cross and Falloon 1996).

In four districts of Himachal Pradesh genetic divergence among the

naturally growing seedling trees of Persian walnut was assessed based on nut

and kernel traits alone. The evaluated parameters used in this study included

weight, width, height, thickness, index of roundness, shell thickness, kernel

weight, width, thickness, fat and protein percentage (Sharma and Sharma

2001).

Traditional methods find application in contemporary research and

their significance is still recognized. Even in modern times the cultivars are

illustrated through traits like leaf blade length, leaf blade width, seed weight,

seed number, flesh/seed ratio, fruit weight, size, shape and colour (Ntundu et

al. 2006; Kumar et al. 2007; Antonius et al. 2010; Santos et al. 2011 and

Blanckaert et al. 2012). Several researchers have used the plant characteristics

related to leaves and root descriptor traits to illustrate and characterize different

varieties of sweet potato (Enameel et al. 2011) and flower, seed and capsule

17

characters (Uysal et al. 2011). Twenty-two morphological traits were used to

examine the genetic variation and polymorphisms in the Iranian Asparagus

populations. (Sarabi et al. 2010).

Although the conventional taxonomic approach has been used for the

varietal identification and also provide a unique identification of the cultivated

varieties, it is not very suitable for perfectly reliable identifications. Some

studies indicated that for the perennial fruit crop species the conventional

methods for characterization and evaluation of genetic variability, based on

morphological and physiological studies, are time consuming and influenced

by the environment (Nicese et al. 1998 and Mondini et al. 2009). Therefore, it

is difficult to distinguish genotypes just on the basis of their external

morphology. Furthermore, these phenotypic characters besides being

influenced by the environmental factors are affected by the growth stage of the

plant as well (Baranek et al. 2006). Depending on the growth stage of the

plant, insufficient variation and the length of time required for appearance of

informative traits particularly in tree crops causes errors. Also, scoring errors

are common and are most likely to be a result of environmental effects on

morphological expression (Marinoni et al. 2003). So, an accurate

identification becomes difficult in the process, lowering the reliability of

morphological markers for germplasm characterization.

Therefore, studies based on methods at the DNA level should be

utilized in the breeding programs in order to accelerate, optimize genotype

fingerprinting, and to study genetic associations among cultivars (Wunsch and

Hormaza 2002; Shiran et al. 2007).

Molecular Markers

In addition to the morphological markers the molecular markers

provide additional tools for germplasm characterization and assessment of

genetic relatedness and diversity in collections. These tools have been found to

be more dependable than the phenotypic observations for evaluating the

variations and in the assessment of the genetic stability (Leroy et al. 2000).

Molecular markers methods are being very rapidly adopted by the researchers

all over the world for the crop improvement. They have been found to

appropriate and valuable tools for several basic and applied studies on the

18

biological mechanism in agricultural production systems (Jones et al. 1997;

Mohan et al. 1997). The molecular marker techniques are diverse and vary in

principle, application and amount of polymorphism observed and in time

requirements (Vilanova et al.2001; Naghavi et al. 2004). Molecular markers

present an efficient tool for fingerprinting of cultivars, and assessment of

genetic resemblance and relationships (Vilanova et al. 2001). The techniques

provide the excellent estimate of genetic diversity as they are independent of

the confusing effects of the environmental factors (Naghavi et al. 2004). Also,

the molecular markers are not influenced by the growth stage of the plant

andcan be detected in all tissues and at all stages of development (Badenes et

al. 2004).

Molecular markers are employed in different fields of genetics like

genetic mapping, and in genome organization, characterization and

identification of plant cultivars. They are very realiable means for genotypes

chracterization in the gene banks (Raddova et al. 2003). Use of molecular

markers finds important applications for the perennial and recalcitrant crops,

where the crop improvement is frequently held up by its long generation time

(Upadhyay et al. 2004; Shiran et al. 2007).

Molecular methods include both biochemical and DNA markers i.e.

molecular markers may be protein in nature or DNA based. Biochemical

markers were introduced in the 1960s and involve protein and enzyme

electrophoresis. Molecular markers such as RAPD (Vilanova et al. 2001; Pan

et al. 2002; Badenes et al. 2004; Luo et al. 2007), SSR (Soriano et al. 2005;

Gisbert et al. 2007b) and AFLP (Feng et al. 2007) have been used for the

genetic diversity studies of the loquat genepools.

A number of molecular markers have been developed and employed in

the analyses of genetic diversity and relatedness. (Schlötterer 2004; Weising et

al. 2005). All the marker systems have their own strength and weaknesses,

therefore, the choice of molecular marker system should be determined by the

objectives of the study, the mating system of the species bring studied, and the

available financial support.

The following two genetic parameters are of considerably importance

in the assessment of genetic diversity using molecular marker data.

19

1. The per cent polymorphic loci;

2. The average number of alleles per locus.

In this context, the ‘diversity index’ designated by Nei (1973), also known as

the ‘Polymorphic Information Content’ (PIC), is an valuable tool. In case of

markers where allelic relationships are known, PIC is measured as 1-xi2,

where xi is the frequency of ith

allele, averaged across loci. For markers such as

AFLP and RAPD with unknown allelic relationships among bands, PIC is

measured as [2fi (1-fi)], where fi is the frequency of the band present. The

PIC value provides an estimate of the discriminating power of a marker. It take

into account both the number of alleles at a locus, and the relative frequencies

of these alleles in the population under study.

Protein-based markers

Protein markers, including structural proteins, seed storage proteins,

and isozymes are one of the first generation molecular markers used for the

assessment of genetic diversity and for the development of genetic linkage map

in the initial studies. Protein (enzyme and non-enzyme) molecular markers

provide indirect information about plant genome structure.

The term ‘isozymes’ was introduced by Markert & Moller (1959) and

refers to protein forms of an enzyme with the same catalytic activity, and the

same substrate conversion, but different in molecular weight or the electric

charge. These markers reveal the differences between storage proteins or

enzymes encoded by different alleles at one (allozymes) or more gene loci

(isozymes) (Rao 2004).

Isozymes are not necessarily the products of the same gene, and can be

active at different life stages of a plant or in different cell compartments.

Isozymes encoded by the same locus but by different alleles are usually

referred to as allozymes (Weising et al. 2005). Isozymes are differently

charged and can be separated by electrophoresis. The main advantage of

isozyme analysis, especially when allozymes are used, is the protein products

encoded by different alleles/genes. It provides the codominant markers, which

allows the discrimination between homozygous and heterozygous genotypes.

Isozyme analysis has been used for identification of cultivars (Bringhurst et al.

1981; Veasey et al. 2002; Weeden and Lamboy 1985); species (Buck and

20

Bidlack 1998) and analysing genetic diversity and population structure in a

large range of plant species (Hamrick & Godt 1989; Fady-Welterlen 2005).

Although, isozymes supply useful information (Chung and Ko 1995;

Chevreau et al. 1997) but have some drawbacks for e.g.the limited number of

polymorphisms detected between close genotypes and inconsistency due to the

physiological stage (Oliveira et al. 1999). The main disadvantage in the

isozyme analysis is that it detects variations only in protein coding loci and

therefore provides fewer markers when compared to DNA-based methods.

This limits the value of isoenzyme analysis for fingerprinting primarily

because of lack of or low level of variation in many cultivars and species (Fang

et al. 1997). Also, their effectiveness has been restricted due to the lack of

isozymes systems existing, the low level of the obtained polymorphism, and

the influence of the environmental factors (Khadari et al. 2005).

The use of more powerful markers like DNA markers can eliminate the

drawbacks of the isozymes. DNA markers are not affected by the

environmental conditions, organ specificity or the growth stage of the plant

and can detect single nucleotide changes. DNA markers provide an access to

fine scale genetic variation. This makes DNA-based methods the marker of

choice for cultivar identification and characterization and for complementing

traditional approaches in genetic characterization.

DNA-based Markers

DNA markers basically detect differences in genetic makeup; in other

words, they are based on polymorphism in DNA sequences carried by different

individuals (Samec 1993). The introduction of DNA-based markers have led to

the construction of whole genome linkage maps in many plant and animal

genomes, a crucial step for several downstream applications such as gene

cloning, genome analyses and marker-assisted selection of agricultural crops

(Cullis 2002; Paterson 1996a). DNA markers are very valuable fundamental

tool that plant breeders use for the pedigree analysis, cultivar identification,

and assessing genetic diversity. DNA-based markers provide an opportunity

for the genetic characterization that allows the direct comparison of different

genetic material without being influenced by the environmental conditions

(Weising et al. 1995; Nicese et al. 1998). Molecular markers that detect

21

variation at the DNA level overcome most of the limitations of biochemical

and morphological markers. As confirmed by their use in a variety of plant

species, molecular markers are most appropriate for assessment of genetic

diversity and identification of varieties (Upadhyay et al. 2004). Use of

analytical techniques based on DNA amplification for the study of genetic

diversity and relationships within the collections of genetic resources of plant

material is very common. Since the evaluation based on the morphological

characteristics is very time consuming and it may take several years in

perennial plants, DNA based methods are more useful and more economical

(Raddova et al. 2003; Shiran et al. 2007)

DNA markers are generally based either on the use of restriction

enzymes that recognize and cut specific short sequences of DNA (Restriction

Fragment Length Polymorphism, RFLP) or on the polymerase chain reaction

(PCR) that involves DNA amplification of target sequences using short

oligonucleotide primers which includes Random Amplified Polymorphic DNA

(RAPD), Amplified Fragment Length Polymorphism (AFLP), Internal

Transcribed Spacer (ITS) region and Simple Sequence Repeats (SSR) or

microsatellites. The choice of markers depends on the study’s objectives,

technical expertise and operational funds. DNA markers are used in many

studies for cultivar identification (Becher et al. 2000; Guilford et al. 1997),

species characterization (Ahmad and Southwick 2003; Graham and McNicol

1995) assessment of genetic variability (Jakse et al. 2001; Zhebentyayeva et al.

2003), evaluation of population structure (Aranzana et al. 2003) and breeding

materials (Hernandez et al. 2003), detection of monogenic (Araujo et al. 2002)

and quantitative trait loci (QTL) (Funatsuki et al. 2006) marker assisted

selection (Yi et al. 2004) and sequence identification of useful candidate genes

(Li and Garvin 2003). Molecular methods are important tools for genebank

management and have been used to develop future collecting strategies (van

Treuren et al. 2001), to identify gaps in the collections (Carvalho and Schaal,

2001), to eliminate redundancies (Dean et al. 1999) and misidentified

accessions (Dangl et al. 2001; Fossati et al. 2001; Khadari et al. 2003) and to

validate core collections (Grenier et al. 2000).

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Restriction Fragment Length Polymorphism (RFLP) was the first DNA-

based marker developed. RFLPs are detected as DNA fragments of different

sizes generated after restriction endonuclease digestion and hybridization to a

known DNA probe like cDNA clones or microsatellites (Staub and Serquen

1996). DNA fragments are transferred by southern blotting to a nitrocellulose

or nylon membranes that are generally hybridized to a radioactively-labeled

DNA probe. RFLPs are co-dominant markers and analysis of band profiles is

straightforward. Sex-linked molecular marker was identified for early sex

diagnosis by RFLP in the dioecious species Asparagus officinalis L. The

usefulness of this molecular tool was compared to morphological markers for

prediction of gender in several genotypes. The level of polymorphism detected

by this probe was high, and the level of incorrect sex attribution, as determined

by this method, was low (»7%). (Falavigna et al. 1995). Two linkage maps of

Asparagus (Asparagus oficinalis L.) were constructed by crossing MW25 x

A19, two heterozygous parents using a double pseudo test cross mapping

strategy with RFLPs, random amplified polymorphic DNAs (RAPDs), and

allozymes as markers in a population. In this case, RFLPs were more frequent

and informative than RAPDs.

PCR-based techniques in contrast require a small amount of DNA and

do not require radioactive labeling. RFLP (restriction fragment length

polymorphism) analysis involves extensive labour, is highly expensive and

time consuming, and is therefore unfeasible when analyzing huge germplasm

samples (Sarkhosh et al. 2006). It has the limitations due to radioactive needs

and complex methodology, in addition to the larger genomic DNA requirement

(Gaafar and Saker 2006).

PCR (polymerase chain reaction) based assays are considered to meet

both the genetic and technical requirements for the characterization of animal

and plant genetic resources (Powell et al. 1995). PCR is a versatile technique

that was invented in mid 1980s. This method is based on the enzymatic in vitro

amplification of DNA. Starting with a very low quantity of template DNA,

millions of copies of one or more particular target DNA fragments can be

produced. This technique is characterised by its high speed, selectivity and

sensitivity. The selectivity of the reaction is determined by the choice of

23

primers (oligonucleotides with sequence complementarity to template

sequences flanking the target region).

PCR involves the use of a polymerase enzyme called ‘Taq polymerase’

that remains stable even at temperatures as high as 94ºC. In the first step, the

template DNA is made single stranded by the increasing temperature

(Denaturation Step). In the second step, lowering the temperature to about 45

ºC results in the primer annealing to their target sequences on the template

DNA (Annealing Step). For the third step, a temperature is chosen where the

activity of the thermostable polymerase is optimal, usually 72ºC (Elongation

Step). The polymerase now extends the 3’ ends of the DNA-primer hybrids

towards the other primer-binding site. In the next cycle, the two resulting

double-stranded DNAs are again denatured and both the original strands as

well as the product strand now act as templates. Repeating these three steps 25-

35 times results in an exponential amplification of the DNA molecules after

each cycle.

One main reason for the versatility of the PCR technique is that any

kind of primer can be chosen depending on the purpose of the study. Specific

primers based on unique sequences that flank a single tandom repeats

(microsatellites) are also increasingly used for mapping purposes and

population genetics. PCR based methods require lesser amounts of genomic

DNA, are non-radioactive, relatively low costing, and can be developed rapidly

(Al-Humaid and Motawei 2004).

An overall GC content near 50% has been also considered desirable

since higher GC content may result in mispriming because of high stability of

imperfectly matched primer–template complexes Excessive melting

temperature (Tm) difference between primers and the targeted product can lead

to low product yield (Kim and Smithies 1988; Dieffenbach et al. 1993). In

addition to primer-template relationships, internal characteristics of single

primers, and the relationship between primers in a set, may influence

repeatability. The melting temperature (Tm) of the left and right primers of a

pair should be similar to avoid nonspecific amplification (Kim and Smithies

1993; Dieffenbach et al. 1993). Primers should have low internal stability at

24

the 3′ end so that false priming due to base pairing with non target sequences is

lessened. Internal complementarity within a single primer enhances hairpin

loop formation and reduces the annealing of the primer to the target sequence

(Rychlik and Rhoads 1989).

Complementarity between primers can give rise to primer-dimer

formation, which in turn gives rise to art factual bands. The possibility of

primer-dimer formation with a primer and itself (intraprimer) or between the

left and right primers of a set (interprimer) is dependent on more than one

factor. Dimer formation can only occur if there are complementary base

pairings between strands, though a single base pair at the 3′ terminus can be

sufficient for dimerization. Additionally, dimer formation is enhanced if one

primer in the potential dimer has a 3′ overhang, which can serve as a template

for extension by Taq polymerase.

The emergence of new polymerase chain reaction (PCR) based

molecular markers, such as randomly amplified polymorphic DNA (RAPD),

amplified fragment length polymorphisms (AFLPs), and simple sequence

repeats (SSR) has produced the opportunity for excellent level of genetic

characterization of germplasm collections because they are very much

polymorphic and are not readily affected by the environmental conditions

(Geuna et al. 2003; Hokanson et al. 2001; Shiran et al. 2007). RAPD (random

amplified polymorphic DNA) is the method which is most commonly used so

far and was introduced by Williams et al. (1990). After a few years, the

somewhat similar ISSR (inter simple sequence repeats) (Zietkiewicz et al.

1994) and to some extent more technically demanding AFLP (amplified

fragment length polymorphism) (Vos et al. 1995) were established. Later on,

STMS (sequence tagged microsatellite sites), which are based on the micro

satellite DNA loci with tandem repeats of one to six nucleotides became

increasingly popular for the population analysis. These loci are analyzed with

PCR, using sequencing information to develop the necessary primers.

Random Amplified Polymorphic DNA (RAPD) The technique is based on

the use of a single arbitrary primer, commonly a 10-mer (10 nucleotides) and a

GC content of atleast 50%, in a PCR reaction to amplify multiple copies of

25

random genomic DNA sequences. To obtain good amplification with a single

arbitrary primer there should be two identical target sequences close to each

other. In addition, distance between the two sides should be within amplifiable

units of 4-5 kb.

Several studies in Asparagus using RAPD markers had been reported. Vijay et

al. (2009) assessed genetic diversity in 7 Asparagus racemosus (Willd.)

covering Madhya Pradesh using RAPD markers. They revealed 54.92%

polymorphism in collected accessions. A total of 39 polymorphic bands were

generated out of the total 71 bands by 4 random primers. Cluster analysis

based on dice coefficient showed 2 major groups. In Iranian Asparagus

evaluation of genetic diversity was carried out using RAPD markers. Analysis

of polymorphic bands using Jaccard’s similarity coefficient indicated that

genetic similarity ranged between 0.71 and 0.29. At a similarity level of 0.64,

the populations were divided in three sub-clusters, containing 34, four and one

populations, respectively (Sarabi et al. 2010).

However, the reproducibility of a RAPD profile is subject to

discussion. There is debate over the genetic basis, reliability and hence

usefulness of this technique (Devos and Gale, 1992; Thorman and Osborn

1992). It has been reported that even minor changes in any aspect of the

amplification reaction such as DNA quality and quantity (Williams et al.

1993), choice of DNA polymerase (Scheirwater and Ender 1993), magnesium

concentration (Turk and Kohel 1994); Williams et al. (1993), choice of

thermocycler (Penner et al. 1993), use of Ethidium bromide versus silver

nitrate for detection of amplification products (Caetano-Anolles et al. 1998)

and presence of RNA (Yoon and Glawe 1993) can affect the outcome (Xu and

Wilson 1995). RAPD analysis would not enable identification of a single

mutation or a small deletion. It would efficiently identify only localize or

dispersed differences, which comprises a significant fraction of a genome. Amplified Fragment Length Polymorphism (AFLP) is a DNA finger

printing technique based on the amplification of subsets of genomic restriction

fragments using PCR (Vos et al. 1995). AFLPs are DNA fragments (80-500

bp) obtained by endonuclease restriction followed by ligation of

oligonucleotide adapters in the fragment and selective amplification by PCR.

26

Polymorphisms are revealed after separating the amplified DNA fragments by

electrophoresis on a sequencing gel, and visualized by silver staining,

radioactive or fluorescent detection. A large number of bands is generated that

facilitates the detection of polymorphisms (Gupta et al. 1999). AFLP reveals a

high level of polymorphism and has a high marker index or diversity index

(Russell et al. 1997). The high marker index or diversity index is a reflection of

the efficiency of AFLPs to simultaneously analyze a large number of bands

rather than the levels of polymorphism detected. The key feature of AFLP is its

capacity for the simultaneous screening of different DNA regions that

distributed randomly throughout the genome (Mueller and Wolfenbarger

1999). The advantage of this technique includes the fact that no sequence data

for primer construction is required. The multiple banding pattern obtained

makes AFLP a powerful marker. The technique permits the detection of

restriction fragments in any background or complexity including pooled DNA

samples and cloned DNA segments. Therefore, AFLP can be applied to DNA

of any origin and complexity. It has a broad taxonomic scope hence, AFLP

markers can be developed in any organism with DNA with no prior knowledge

of the organism’s genomic make-up needed. It has the efficiency of PCR based

markers such RAPD and the specificity and reliability of hybridisation based

markers like RFLP.

Microsatellite or Simple Sequence Repeats (SSR) Microsatellites have

become one of the most useful molecular marker systems in plant breeding.

They are widely used in cultivar fingerprinting, genetic diversity assessment,

molecular mapping, and marker assisted breeding. The development of SSR

markers from genomic libraries is expensive and inefficient (Squirrell et al.

2003). Repetitive DNA is an integral component of eukaryotic genomes and

may comprise upto more than 90% of total DNA in certain plant genomes.

According to the way it is organised, repetitive DNA may be classified as

either ‘interspersed’ or ‘tandemly’ repeated. In interspersed repeats, the repeat

DNA motifs occur at multiple sites throughout the genome.

Tandem repeats on the other hand, consists of arrays of two to several

thousand basic motifs that are arranged in a head to tail fashion. Though this

kind of organisation is also exhibited by some genes (e.g., the transcriptional

27

units for histone and ribosomal RNA), most tandem repeats consist of non

coding DNA. Tandem repeats are classified as ribosomal DNA, telomeric

repeats, satellites, microsatellites and macrosatellites. Microsatellites consist of

tandem repeat units of very short (2-10 bp) nucleotide motifs. They occur

frequently (Tautz and Renz 1984) and are found randomly in most prokaryotic

and eukaryotic genomes analyzed to date (Zane et al. 2002). They are found in

coding and non-coding regions and are highly polymorphic. Advantages of

SSRs include their multi allelic nature, codominant transmission,

reproducibility, ease of detection by PCR, relative abundance and extensive

genome coverage (Powell et al. 1996). These markers are amenable for

automation and are easily shared between labs as primer sequences providing a

common language for collaborative research and acting as universal genetic

mapping anchors (Powell et al. 1996). Polymorphism results mostly from

either the gain or loss of repeat units (Schlotterer and Tautz 1992). Two

mutational mechanisms were proposed to explain the high rates of mutation:

DNA polymerase slippage or recombination (Ellegren 2004). The slippage

model appears as the most probable cause of variability. During this event,

DNA polymerase pauses during replication and dissociates from the DNA

(Levinson and Gutman 1987; Schlotterer and Tautz 1992). On dissociation, the

terminal portion of the newly synthesized strand may separate from the

template and anneal to another repeat unit. As replication continues after

misalignment, repeat units may be inserted or deleted relative to the template

strand. The mismatch repair system of the DNA polymerase may correct the

primary mutation and those that are not repaired end up as microsatellite

mutation events.

Thus, SSR reliability can represent a balance between the generation of

replication errors by slip strand mispairing and the correction of some of these

errors by exonucleolytic proofreading and mismatch repair (Li et al. 2002).

Microsatellite-mutation may also be caused by recombination-like processes

like cross-over or gene conversion. Cross-over is the reciprocal transfer of

genetic information while gene conversion is the non-reciprocal transfer of

information which has recently emerged as the major cause of tandem repeat

instability (Richard and Paques 2000). Environmental conditions affect the

28

efficiency of the two mutational mechanisms. Factors like repeated motif,

allele size, chromosome position, GC content in flanking DNA, cell division

(meiotic vs. mitotic), sex and genotype affect the mutation rate at the SSR loci

(Li et al. 2002).

The high information content (which is a feature of the number and

frequency of alleles) detected and ease of genotyping increased the utility of

SSRs (Powell et al. 1996). SSRs can distinguish between closely related

individuals. This discrimination power is valuable for identification of plant

species that have a narrow genetic base like that found in Asparagus

racemosus.

Caruso et al. (2008) designed EST–SSRs for a new variety released by

University of California, Riverside named DePaoli to assess the level of

genetic diversity among thirty-five Asparagus officinalis cultivars. Allele

frequencies were estimated from the intensity of the bands in two bulks and

two individual plant samples for each variety. Although Asparagus varieties

derive from a limited germplasm pool, eight EST–SSR loci differentiated all of

the analyzed cultivars. Moreover, UPGMA (unweighted pair group method

with arithmetic mean) and neighbor-joining trees, as well as principal

components analysis separated the cultivars into clusters corresponding to the

geographical areas where they originated.

Later on, STMS (sequence tagged microsatellite sites), which are based

on the micro satellite DNA loci with tandem repeats of one to six nucleotides

became increasingly popular for the population analysis. These loci are

analyzed with PCR, using sequencing information to develop the necessary

primers. Variation at the micro satellite loci, also known as simple sequence

repeats (SSR), is generally studied at all the identified loci separately, and can

then be regarded as codominantly inherited (Nybom 2004). Polymerase chain

reaction (PCR) derived markers obtained with non specific primers have

become remarkably popular because they do not require sequence information

for the target species. As a result, these methods are particularly suited to the

circumstances where little or no research on molecular genetics has been

accomplished previously (Nybom 2004).

29

Smith et al. (1997) made a comparison of SSR with data from RFLP

and pedigree in maize. They stated that SSR revealed co-dominantly inherited

multiallelic product of loci that can be readily mapped. SSR profiles can be

interpreted genetically without the need to repeatedly map amplified bands to

marker loci in the different populations. They anticipated that SSR profiling

will replace RFLP and PCR based arbitrary primer methods.

Lunz (1990) and He et al. (1994) reported that a primer pair that

produces a product marking a particular chromosome region for one laboratory

may not produce the same product when the experiment is repeated in another

laboratory. This limited the utility of sharing primer sequences among

laboratories. The relationship of the primer to the template sequence influences

reproducibility of polymerase chain reactions. For instance, high specificity of

the primer to the target sequence decreases mispriming and resultant

amplification of extraneous DNA (Rychlik and Rhoads 1989). Erpelding et al.

(1996) reported that this factor might be especially important in the cereals,

where primer sets are often transferred across cereal species because of

conservation of map order.

High costs are incurred in the process of de novo microsatellite

development. Cross-transportability between species however may reduce the

development costs (Gupta and Varshney 2000). Cross-species transportability

was widely reported in stone fruits (Cipriani et al. 1999; Zhebentyayeva et al.

2003), in pome fruits (Yamamoto et al. 2001) and in hazelnuts (Bassil et al.

2005). Ten peach microsatellite loci amplified in other Prunus species like

plums, apricots, almonds, nectarine, sweet and sour cherry as well as in apple

(Cipriani et al. 1999) while 12 peach SSRs amplified 14 polymorphic loci in

apricot and were able to highly discriminate between 63 of 74 cultivars tested

(Zhebentyayeva et al. 2003). SSRs isolated from apple were highly conserved

in pear (Yamamoto et al. 2001) and used to distinguish 36 pear accessions.

Transferability of Microsatellites

Transferability is the most important feature of the microsatellite

markers as these markers are transferable among distantly related species,

whereas the genomic SSRs are mostly not suitable for this purpose. An

alternative efficient approach for SSR marker development in species that lack

30

large enough sequence databases could be the utilization of SSR markers from

related species or model crop species for which large numbers of SSR markers

have been developed. So transfer of SSR markers is a very efficient approach

for DNA marker development, especially in crops for which genomes have

been not-well characterized such as, Asparagus racemosus.

The ability to use the same set of SSR primers in different plant species

depends on the extent of sequence conservation in the primer site s flanking

the SSR and the stability of SSR during the evolution. Microsatellite primers

developed for one species can be used to detect polymorphism at homologous

sites in related species. The possible way is that repeat sequence and flanking

regions containing the selected priming sites must be conserved across the

taxa. The success of the heterologous PCR amplification will depend on

evolutionary distance between the source and the target species, higher

genomic homology is likely to translate into greater conservation of SSR

flanking regions and as a result in transfer of primer pair (Peakall et al. 1998).

Transferability has great significance in comparative map construction among

related species and also reduces the cost of genotyping, thus opening new

perspectives for the development of population genetic studies. The high rate

of transferability has already been reported for plant species (Brondani et al.

2003; Collevatti et al. 1999) and among animal species, such as human and

chimpanzee (Deka et al. 1994) and dog and fox (Fredholm and Wintero 1995)

using EST-SSRs. Thus transferability of SSR primers across species would

obviously increase the value of such markers.

Different levels of transferability: Transferability of microsatellites to

related species or genera has been demonstrated in several studies (Table1).

Taken together, the results of these studies indicate that SSRs can often be

transferred across relatively large taxonomic distances, spanning not just

species within a genus, but in some instances multiple genera within a family.

For example, Scott et al. (2000) tested the transferability of 10 Vitis EST-SSRs

among grape cultivars, other grape species and related genera, and found high

levels of transferability, with over 60% of markers tested working across taxa.

In the cereals, Gupta et al. (2003) demonstrated extensive transferability of

Triticum aestivum L. (bread wheat) EST-SSRs to 18 related wild species in the

31

Triticum–Aegilops complex and to five cereal species of barley, oat, rye, rice

and maize. Over 80% of primer pairs tested were transferable to the 18 related

species, while nearly 60% showed transferability to one or more of the more

distantly related cereal species. High levels transferability and substantial

polymorphism were observed among 23 cotton (Gossypium) species (Guo et

al. 2006) using EST-SSRs. A set of 100 SSRs were tested for across genera

transfer from Medicago tuncatula to groundnut and about 20% of tested

primers produced single, double or multiple amplicons in both cultivars and

wild species (Wang et al. 2004). Since the model plant Medicago truncatula is

in the process of whole genome sequencing and many sequences will be

available, exploiting its sequence information can probably provide some clues

for efficient characterization of the groundnut genome.

Basis of transferability: Comparative genetic analysis has shown that different

plant species often share orthologous genes for very similar functions (Fatokun

et al. 1992), and gene contents and gene orders among different plant species

could be highly conserved (Bennetzen and Freeling 1993; Gale and Devos

1998). Since EST-SSR markers represent transcribed regions of the genome,

they should be more conserved across species compared to anonymous

sequences. Thus EST-SSR markers would not only have considerable potential

for genome mapping within a species, but also for comparative studies

between species. It is also of interest to investigate the feasibility of utilizing

SSRs from monocots in dicots and vice versa. This could enhance the

resolution of comparative mapping and facilitate gene cloning in different

species. Recently in a study 478 sets of wheat EST-SSR primers were tested

against rice, maize and soybean DNA. Of these 255 (53.3%) prime sets

amplified in all the four, 280 (58.8%) sets got products for wheat, rice and

soybean, 287 (60%) for wheat, maize and soybean, 320 (67%) for the three

monocots, 389 (81.3%) of the primer sets generated products for wheat and

maize, 362 (75.7%) for wheat and rice and 330 (69%) for wheat and soybean.

So 53.3% of the SSRs amplified successfully in all of the four species,

implying that these EST sequences were conserved between monocots and

dicots. EST-SSRs showed high transferability among in three monocots

(81.3%). Common ESTs shared by wheat and maize was more than between

32

wheat and rice. Though common ESTs shared by wheat and soybean were less

compared to wheat in pair with other cereals (Gao et al. 2003), but still there is

high rate of transferability.

Importance of transferability: Transferability of markers increase the value of

markers as it is helpful for comparative mapping and facilitates gene cloning in

different species. There are a growing number of strategies to utilize data bases

to cross-reference the biological and genetic information known in one

organism to another, such as yeast to mammals (Tugendreich et al. 1993). Due

to the relative ease of discovering genes and the determination of gene function

in model species, and well studied crops comparative genomics has become an

important strategy for extending genetic information from model species to

more complicated species (Paterson et al. 1995). Until now, comparative

genomics efforts have relied heavily on the hybridization based RFLP

technique. The resolution of these comparative maps is generally low for

determination of microsynteny (Kilian et al. 1997). The application of a PCR-

based, co-dominant marker system for comparative genomics would be highly

desirable, because such a marker system could increase the efficiency of

transferring genetic information across species.

Rates of transfer: In general terms, this sort of transferability is unique to EST-

SSRs, with anonymous SSRs being significantly less portable (Chagne et al.

2004; Gutierrez et al. 2005; Pashley et al. 2006). Despite their potential to

cause selectively deleterious frame shift mutations, however, EST-SSRs

located in coding regions appear to reveal equivalent levels of polymorphism

as compared to those located in UTRs, most likely due to a general trend

toward trinucleotide repeats in coding regions. In fact, this trend toward

trinucleotide repeats in exons has been observed in a variety of other taxa,

including wheat (Gupta et al. 2003), barrel medic (Eujayl et al. 2004) and tall

fescue (Saha et al. 2004). Regardless of the cause, if this observed tendency

toward higher transferability and equivalent levels of polymorphism turns out

to be a general feature of EST-SSRs located in protein coding regions, the

targeting of exonic trinucleotide repeat motifs might be the best strategy for

developing portable sets of polymorphic EST-SSR markers. A list of rates of

transferability is given in Table1.

33

Summary of reports on the transferability of EST-SSRs among different

plant taxa

Level of

relatedness

Source taxon Recipient taxa % Reference

Subgenus Prunus armeniaca

(apricot)

Prunus

domestica (plum)

100 Decroooq et

al.2003

Genus Helianthus

annuus

H. angustifolius 75

Pashley et al.

2006

H. verticillatus 81

Genus

Hordeum vulgare H. bulbosum

(wild barley)

77 Thiel et al.

2003

Genus

Medicago

truncatula

8 Medicago spp. 89 Eujayl et

al.,2004

Genus

Citrus clementina other Citrus

species

95 Luro et al.

2008

Genus

Coffea canephora 2 Coffea sps 94 Poncet et

al.2006

Genus

G. arboreum 23 Gossypium

species

60 Guo et

al.2006

Genus Pisum sativum

Cicer arietinum

Lentil

Vetch

Chickpea

Lentil

Vetch

Fieldpea

60

39

62

5

3

18

Pandian et al.

2000

Tribe

Saccharum spp. Erianthus spp 100 Cordeiro et al.

2001

(sugarcane) Sorghum

spp.(grass)

100

Maize

Wheat

Sorghum

Rice

Bamboo Barkley et al.

2005

Capsicum annuum Capsicum

annuum

40

(dinucleotide)

31.4

(trinucleotide)

Ince et al.

2009

Capsicum annuum C. frutescens

C. baccatum

C. chacoense

C. pubescens

70

(dinucleotide)

72.3

(trinucleotide)

Ince et al.

2009

Cereal Ryegrass (Lolium

spp)

57 Sim et al.

2009

Tribe

Coffea sp. (coffee) Psilanthus spp. 5 Bhat et al.

2005

Subfamily

Medicago

truncatula

Vicia faba

(fabaean)

43 Gutierrez et

al. 2005

(alpinelady-fern)

Cicer sp.

(chickpea)

39

Subfamily

Festuca

rundinaceae

Sachcharum sps 77 Saha et al.

2004

Family

Athyrium

distentifolium

Diplazium

caudatum

75 Woodhead et

al. 2003

Family Cicer arietinum Cajanus cajan 46 Datta et al.

34

2010

Monocot-

Monocot

Setaria italica 9 Graminae sps et al. Jia et al. 2007

Monocot -

Monocot

Festuca

rundinaceae

12 Graminae sps 43 Mian et al.

2005

Monocot - Dicot

Triticum aestivum

(Wheat)

Glycine max

(soybean)

69 Gao et.al.

2003

35

MATERIAL AND METHODS

1.1 Procurement of Plant Material and Acclimatization

Thirteen accessions of Asparagus racemosus (Table 1.1) collected from

different geographical locations in India were obtained from National Bureau

of Plant Genetic Resources (NBPGR), New Delhi. These accessions were

planted and maintained in the Botanic Garden at Department of Botany,

Dayalbagh Educational Institute, Agra (27.183ºN, 78.167ºE). All the

accessions were maintained, under uniform cultivation conditions, by regular

irrigation, NPK application in the soil beds; pesticide spray was administered

as and when required (once in a season) to prevent any bug damage to the

plant.

Table 1.1: Collection sites of A. racemosus landraces from India* S.No. Accession Location Altitude

(m)

Latitude

(N)

Longitude

(E)

Average

Annual

Rainfall

(mm)

1. FFDC Kannauj, UP 134 27°4'0" 79°55'0" 753

2. SGI Jabalpur, MP 414 23°10'0" 79°57'0" 1386

3. KAU Thrissur, Kerala 14 10°31'12" 76°12'36" 2500

4. CDH Chandigarh,

Punjab

343 30°44'13" 76°47'13" 1100

5. IC471921 Nauni Forest,

Solan, HP

1361 30°55'12" 77°07'12" 1253

6. IC471923 Solan, HP 1447 30°54'14" 77°05'49" 1253

7. IC471924 Ochlaghat, Solan,

HP

1361 30°55'12" 77°07'12" 1253

8. IC471927 Nauni Forest,

Solan, HP

1361 30°55'12" 77°07'12" 1253

9. IC471911 Mandla, MP 450 22°35'52" 80°22'17" 1580

10. IC471910 Mandla, MP 450 22°35'52" 80°22'17" 1580

11. IC471909 Mandla, MP 450 22°35'52" 80°22'17" 1580

12. IC471908 Mandla, MP 450 22°35'52" 80°22'17" 1580

13. JBP Jabalpur, MP 414 23°10'0" 79°57'0" 1386

*Landraces and passport information obtained from NBPGR

1.2 Evaluation of Asparagus racemosus accessions

The phylogenetic relationship amongst the 13 A. racemosus accessions was

evaluated based on 22 morphological parameters and on the basis of diverse

molecular makers such as STMS from chickpea and pearlmillet, SSRs from

Asparagus, ISSRs (UBC) and RAPD.

36

1.2.1 Morphological Parameters

The morphological characteristics to be studied for the thirteen selected

genotypes were selected from the descriptor list by Cross and Falloon (1996)

for Asparagus Genebank Management and e-descriptors for A. racemosus

from Asia Medicinal Plants Database

(http://www.genebank.go.kr/PP_A/desc_view.bo?key=Asparagus%20race-

mosus). The following morphological characteristics of the genotypes were

studied when the plants were in full bloom.

(i) Cluster of Cladodes or Tuft or Whorl: Cladodes are present in

clusters. Number of cladodes in each whorl and such cladode clusters

in a 5ʺ length at a distance of 10ʺ from the top of ten random branches

was counted and recorded. Distances between clusters were also

measured (Table 1.2).

(ii) Cladode: Cladodes are photosynthetic modified stems. Cladodes 10ʺ

from the top were selected from ten randomly selected branches to

record their length, width and thickness for the different genotypes by

photographing in a Nikon Optical Stereozoom attached with Nikon

Digital Sight DS Fi1c camera supported by Nikon software NIS

Elements D 3.2 for taking the dimensions. Fresh and dry weights and

the moisture content of the cladodes were obtained on a moisture

balance (Contech Mositure Analyzer, India).

(iii) Thorn or Spine: Thorns or spines are present at the base of every

branch at the point of attachment with the main stem. Thorns growing

at 10ʺ from the top were collected from ten random branches and their

length recorded.

(iv) Internode Length: Internode lengths (stem between two consecutive

clusters) 10ʺ from the top in ten randomly selected fully elongated

primary spears were measured.

(v) Spear: The length, breadth and circumference of fresh spears arising

from the ground were measured. The length of the spear was noted

from the ground to the tip. Spear diameter and circumference were

taken at the thickest portion (Table 1.2)

(vi) Root: Roots of the respective genotypes were harvested after a period

of 3 years and their dimensions such as length, diameter,

37

circumference, fresh and dry weight were recorded. The Root length,

the circumference and the diameter were recorded using a ruler while

the fresh weight was recorded on a Mettler balance (PL602-S,

Columbus). The dry weight of roots was measured after drying in an

oven (Zenith Ltd., India) at 60 °C for 48 h or until a constant weight

was achieved (Table 1.2). The Moisture content in the roots was

calculated using following formula:

Moisture Content (%)

= [(Fresh Weight – Dry Weight) / Fresh Weight] x 100

Table 1.2: Morphological characters assessed for

Asparagus racemosus accessions

1.2.2 Data Analysis

The data were analyzed for a Randomized Block Design (RBD) as per

procedure described by Panse and Sukhatme (1985) using Windostat version

8.6. Quantitative data for each morphological characterization is presented as

Mean ± Standard Error (SE).

1. LSD: Means for each trait were separated by the least significant

difference (LSD) at P ≤ 0.01 and the mean value of each entry for

different characters was used for statistical analysis.

S. No. Character Code

1 Cladode width (cm) CW

2 Cladode thickness (cm) CT

3 Cladode length (cm) CL

4 Cladode length/width ratio CL/W

5 Cladode fresh weight (g) CFW

6 Cladode dry weight (g) CDW

7 Cladode moisture content (%) CM

8 Cladodes per whorl CPW

9 No. of whorls/5 inches NW

10 Distance between whorls (cm) DW

11 Internode length (cm) IL

12 Spine length (cm) SpL

13 Spear circumference (cm) SC

14 Spear diameter (cm) SD

15 Spear length (cm) SL

16 Root length (cm) RL

17 Root diameter (cm) RD

18 Root length/diameter ratio RL/D

19 Root circumference (cm) RC

20 Root fresh weight (g) RFW

21 Root dry weight (g) RDW

22 Root moisture content (%) RM

38

2. ANOVA: ANOVA was carried out for the 22 quantitative traits and

phenotypic, genotypic and environmental coefficient of variability was

also calculated.

3. Correlation Coefficient: Phenotypic and Genotypic Correlation

Coefficients were computed to examine the degree of association

among the quantitative traits. Multivariate analysis was employed using

Windostat version 8.6.

4. PCA: Principal component analysis (PCA) was done for quantitative

traits.

5. GD: 1. The measure of dissimilarity was Euclidean or straight-line

measure of distance was used for estimating Genetic Distance (GD)

among accessions (Mohammadi and Prasanna 2003). The matrix of

average GD between two individuals i and j, having observations on

phenotypic characters (p) denoted by x1, x2, ..., xp and y1, y2, ..., yp

for i and j, respectively, was calculated using Euclidean distance,

where: GD (i,j) = [(x1-y1)2 + (x2-y2)2 + ... (xp-yp)2]1/2

6. Dendrogram: Dendrogram was constructed based on the Euclidean

distance to examine the resemblance and grouping of genotypes.

1.3 Molecular Markers

For characterization of the 13 landraces of Asparagus racemosus using

molecular markers DNA was isolated from fresh cladodes and analysed using

Sequence Tagged Microsatellite Sites (STMS) from cross species (chickpea

and pearl millet), Asparagus specific Short Sequence Repeats (SSRs),

Asparagus ISSRs, UBC ISSRs and RAPD primers to assess the genetic

similarity/diversity between different landraces of Asparagus.

1.3.1 Genomic DNA Isolation from Asparagus Genotypes

DNA was extracted following CTAB method, with slight modifications (Doyle

and Doyle 1987). The CTAB buffers were freshly prepared, as illustrated in

the procedure below, from the stocks (Please See Annexure I-a, b, c, d) before

the isolation process. Extraction of DNA involved the following steps:

a) 3 g of young cladodes collected early in the morning from the Botanic

Garden were brought to the lab and plunged into liquid nitrogen.

39

b) The frozen cladodes were ground to a fine powder in a pre-chilled

mortar and pestle in liquid nitrogen. During this process the tissue was not

allowed to thaw.

c) The powder was transferred with the help of pre-chilled spatula to 15

ml of pre-warmed (65 °C) extraction buffer (Annexure I-e) in 50 ml Oakridge

centrifuge tubes while transferring ground powder of each landrace care was

taken to avoid cross-contamination by cleaning the spatula with ethanol after

every use. The tissue samples were mixed on a vortex and incubated at 65 °C

for 1 h.

d) Tubes were gently mixed in between and after incubation samples were

cooled to room temperature.

e) An equal volume of chloroform:isoamyl alcohol (24:1) was added and

mixed gently by inversion of the tubes.

f) The tubes were centrifuged at 12,000 rpm for 20 min in a Sorvall (RC

5C) SS rotor at room temperature to separate the phases.

g) The supernatant was pipetted out with a wide bore tip and transferred to

a fresh tube.

h) Steps 5 to 7 were repeated twice.

i) The supernatant was transferred into fresh Oakridge tubes and to

facilitate DNA precipitation 2/3rd

volume of ice-cold isopropanol was added

and left overnight at 4 ºC or kept at -20 ºC for 3-4 h. Care was taken not to

shake the tube vigorously since DNA at this stage is very vulnerable to

fragmentation.

j) The tubes were centrifuged at 12,000 rpm for 20 min and after

discarding the supernatant, the pellets were washed with 5 ml of 70 % ethanol

and centrifuged again for 10 min at 12,000 rpm. The supernatant was discarded

carefully without disturbing the DNA pellets which was collected and air-

dried.

k) The DNA was dissolved in 1000 μl of Tris-EDTA pH-8 (TE)

(Annexure I-f) buffer.

40

1.3.2 Purification of extracted DNA

a) Genomic DNA was purified by treating the above extracted 1 ml DNA

sample taken in a 15 ml centrifuge tube with 10 μl of Rnase (Annexure I-g)

from a stock of 10 mg ml-1

Rnase A and incubated at 37 ºC for an hour.

b) After incubation an equal volume of phenol:chloroform:isoamyl

alcohol mix (25:24:1) was added to the samples and mixed gently by inverting

the tubes and samples were centrifuged at 12,000 rpm for 20 min at room

temperature.

c) The supernatant was taken out carefully in fresh tubes and equal

volume of chloroform:isoamyl alcohol (24:1) mix was added followed by

centrifuging at 12,000 rpm for 20 min at room temperature.

d) The aqueous phase was taken out in fresh tubes and 3.0 M sodium

acetate (pH 4.8) (Annexure I-h) equivalent to half the volume in step 4 was

added, followed by addition of 2½ volumes of ice cold absolute ethanol.

e) The samples were mixed by gentle inversion of tubes and stored at -20

ºC for 2-3 h to facilitate precipitation of DNA. The tubes were then centrifuged

at 12,000 rpm for 20 min at 4 ºC and the supernatant discarded.

f) The DNA pellet was washed with 1 ml 70 % ethanol by centrifuging at

5000 rpm for 5 min. The supernatant was decanted carefully, the DNA pellet

air-dried and finally dissolved in a suitable volume (100-200 μl) of Tris-EDTA

pH-8 (TE) and stored at -20ºC.

1.3.3 Determining quality and concentration of DNA

The quality and concentration of the purified DNA was checked by running it

on a 0.8% agarose gel. Agarose (0.8 g) was dissolved in 100 ml 1X TBE buffer

(Annexure I-i) by slow boiling in a microwave followed by cooling the

solution just enough to hold the gel. A gel tray was prepared by securing the

ends of the tray by a cello tape to prevent any possible leakages and comb was

inserted to form the loading wells. 3 μl of ethidium bromide was added to the

gel thereafter; the gel was poured to the prepared gel tray and allowed to

solidify. The tape was removed after proper solidification of the gel and the

tray placed in a buffer tank, followed by careful removal of the combs. The

buffer tank was filled with 1X TBE buffer to cover the solidified gel. 4 μl of

each DNA sample was mixed with 2 μl of loading dye and loaded into the

41

wells. The gel was run at 60 V for 45 minutes and then visualized in a UV

transilluminator and quality of the DNA was assessed by comparing to the

known marker λ DNA/Hind III (Thermo Scientific-Fermentas) (Annexure I-j).

Based on the quantification results obtained appropriate dilutions of DNA were

made for the further analysis.

1.3.4 Amplification of DNA using Polymerase Chain Reaction

Specific regions of DNA were amplified through PCR using STMS of

chickpea and pearlmillet, Asparagus SSRs and ISSRs, ISSR (UBC) and RAPD

primers in a BIORAD (Mycycler, USA) and BIOER (GenePro) (384 well)

thermal cycler. Amplification involved the following steps:

a) All the primers were dissolved in freshly prepared and sterilized 10

mM Tris-Cl (pH 7.0) to a final concentration of 100 µM stocks.

b) Ten fold diluted stocks of primers in nuclease-free sterile dH2O were

used as working stocks for PCR reactions. 100 chickpea STMS, 36 pearlmillet

STMS primers, 20 Asparagus SSR (Caruso et al. 2008), 7 Asparagus ISSR

(Aceto et al. 2003), 50 ISSR (UBC) and 45 RAPD primers were used for

amplification reactions, however, only the primers showing polymorphic

results were selected for the purpose of diversity and similarity analysis.

c) A total of 10 μl final volume of reaction mixture was used for

amplification. Please see Annexure I-k-r for details of the reaction mixture and

PCR conditions. PCR conditions were optimized for each set of primer.

1.3.4.1 Chickpea STMS: 100 primers of chickpea STMS kindly provided by

Dr. Bharadwaj IARI (Table 1.3) were used for phylogenetic study in A.

racemosus. DNA at 25 and 50 ng and Taq polymerase enzyme at 0.2, 0.3, 0.5

µl of 3U µl-1

were used in different combinations and the ones that gave good

amplification were selected for further studies. Amplifications were performed

in a final volume of 10 μl. Different programs of Touchdown PCR (Don et al.

1991) such as base annealing temperature ranges and number of cycles were

varied for standardization to produce sharp bands without any of spurious

products. In the initial annealing steps, the annealing temperature was

42

decreased by 0.5 ºC for first 18 cycles, the products were thereafter amplified

for 40 cycles of denaturation, annealing and extension at the optimum

annealing temperature with a final extension for 20 min (Annexure I-k, l).

43

Table 1.3: Chickpea STMS sequences used in the present study for amplifying DNA of A. racemosus

S.No. Primer Forward Sequence Reverse Sequence Ta 1 CaSTMS8 GGACTAGAGGCAGAAGCT AGCATACAAATAAATAATAATGCATG 53.2

2 CaSTMS9 CTTCTATATACATAGTCCTACCTACAC ACCTCATAAAGCTGTTAAAG 49.9

3 CaSTMS12 GTATTTGTTACTGCATATACTTAATTA TATTTACTAGGTAAATCCTATTTATTG 51.8

4 CaSTMS21 CTACAGTCTTTTGTTCTTCTAGCTT ATATTTTTTAAGAGGCTTTTGGTAG 55.5

5 CaSTMS23 GATGAAGATAAAAGCATAATTAAGG TTTCTTCTTCTATGATACACACACT 54.4

6 CaSTMS10 ATAACAAAAAGATATCTCATCGACTA AACAATATACAATAAATAACCAAGT 52.8

7 CaSTMS2 ATTTTACTTTACTACTTTTTTCCTTTC AATAAATGGAGTGTAAATTTCATGTA 55.0

8 TA14 TGACTTGCTATTTAGGGAACA TGGCTAAAGACAATTAAAGTT 52.4

9 H2B061 TCTTGAAGCAAAAGAAGTCAAAAG CAAGTGATAAGTAGGAAGGCAGAA 58.7

10 H2I01 AACATTCTGAACAGACACTTTTCTCTA TTTTCTTCTTTTAACACATAGCCTTTT 59.3

11 H3C08 TTGTTTGAGAAGAAGATGGGTTT ATGCACAGACTGCATTAAATGAT 58.2

12 H3F09 AGCATGTAGTAGGAGGCAAGTATG GTAGGTTCCCGCTACATTACTTTTA 58.8

13 H3H07 GAGGCATAGTACCTCAATTTTATTCA AAGAAAGACAGGTTATCTGTGTGGT 59.1

14 NC7 GACCAAGATTAGTAGAACCT CTTGATAAGGATGAGTCATG 45

15 NC11 AGGTGATGTGGAAATGAT AGAAATATGGAGTATCGC 46.95

16 NC12 CCTTGTTAGTGTGTATAGGT GTAATGACCAAGTGAACA 44.4

17 NC13 GTTGTTGCCGTGACTT TGAATCGGACTGACACT 53.5

18 NC19 TCCATTGTAGCTTAGCTTAG TCTTACTCTTAGCTTACCTCTT 43.2

19 NC33 ACATCTTGAAGTGCCCCAAC TGCAAGCAGACGGTTACAAG 55

20 NC50 ATGATGGATTTTCGGAATGT AAAAATGCTGGAAGGAACTG 42.5

21 TA4 CGAATTTTTCAGAAACACAATGTC TTAGTATTGATTATTATGTATTGCGCC 59.4

22 TA18 AAAATAATCTCCACTTCACAAATTTTC ATAAGTGCGTTATTAGTTTGGTCTTGT 59.8

23 TA22 TCTCCAACCCTTTAGATTGA TCGTGTTTACTGAATGTGGA 53.4

24 NC5 GACAATAATGGTGAACGA GGCACAATGTATGTATTG 43.9

25 TA39 TTAGCGTGGCTAACTTTATTTGC ATAAATATCCAATTCTGGTAGTTGACG 59.9

26 TA42 ATATCGAAATAAATAACAACAGGATGG TAGTTGATACTTGGATGATAACCAAAA 59.7

27 TA106 CGGATGGACTCAACTTTATC TGTCTGCATGTTGATCTGTT 53.2

28 H1H011 CATGTGCCCAAATGCTATTA CAAGTTTGAAATGCCAATTTTT 56.8

29 TA167 TGTGTCTACAGAAAGAAATTAGATTGA AATAATTTTTGCGGAGATGACAA 58.5

30 TA103 TGAAATATCTAATGTTGCAATTAGGAC TATGGATCACATCAAAGAAATAAAAT 58.2

31 TA200 TTTCTCCTCTACTATTATGATCACCAG TGAGAGGGTTAGAACTCATTATGTTT 59.2

32 TA196 TCTTTTTAAATTTCATTATGAAAATACAAATTA CCTCGGGAGACGTAAATGTAATTTC 62.1

33 TR3 GAAGTATCAGTATCACGTGTAATTCGT CTTACGGAGAACATGAACATCAA 58.65

34 TA136 AGATCATTGCAGAGAGTAATATTGGTT TGCTGTGTGACCTATACAATACAAAA 59.7

44

35 TA36 TTTAATATTTTACCTTATTAGGAATTGAGA TTCAACTTAAGACATGAAATTTGTTTTT 59

36 TS72 CAAACAATCACTAAAAGTATTTGCTCT AAAAATTGATGGACAAGTGTTATTATG 58.8

37 TS83 AAAAATCAGAGCCAACCAAAAA AAGTAGGAGGCTAAATTATGGAAAAGT 59.7

38 CaSTMS24 AAAGACAGGTTTTAATCCAAAA CTAATCTTTCTTCTTCTTTTGTCAT 54.4

39 TA30 TCATTAAAATTCTATTGTCCTGTCCTT ATCGTTTTTCTAAACTAAATTGTGCAT 58.8

40 TA87 AAGGGTCAACTCTAAGATCAATTAGAA AATCTGTCTGCACCAATACTTAACA 54.8

41 TA108 AAACCATTATCGAGTTGGATATAAAGA TTTCTAAGTGTTCTTTTCTTAGAGTGTGA 59.8

42 TA116 AATTCAATGACGAATTTTTATAAGGG AAAAAGAAAAGGGAAAAGTAGGTTTTA 60.1

43 TR33 TCTGATTTAATTTCCTATCATTAGTTGC ATTTTTGTCGGGGAGTACATAATA 56.8

44 TR55 TTACTCAACCATAATAATAATAATAAT CTCTTCAATCTTCACTTATTCAT 51.5

45 TS11 GAGAGACCAAAACTGTCGAA TCTATTTTAAATCAAGCAATCAA 53.8

46 TA144 TATTTTAATCCGGTGAATATTACCTTT GTGGAGTCACTATCAACAATCATACAT 59.8

47 TA141 AAAAATTGTCTCACAGACCAAAAA AATTAATTTGTTGTTGAAGAGGGAGT 59.3

48 TA186 ACAAAATTCTAAAAGTTCCTTCTACCA GTTGTTAGTCGAATAATTGAGAAAAAGA 59.9

49 TA198 ATCGAGATAAAATTCAAAAG ATTAGACGATTCTCCATAACTGTGAGT 86.3

50 TA127 AAATTGTAAGACTCTCATTTTTCTTTATT TCAAATTAACTACATCATGTCACACAC 57.9

51 TA11 CATGCCATAAACTCAATACAATACAAC TTCATTGAGGACAATGTGTAATTTAAG 55.1

52 TS35 GGTCAACATGCATAAGTAATAGCAATA ACTTTCGCGATTCAGCTAAAATA 54.5

53 TS74 TTACTTCCTTCACATGGGCTTAG AGATTTGTTGGGTGGACTCATT 53.9

54 GA105 TGAGGAAACACAAAACGACG ATGCCAGGATTAACAGCACC 53.4

55 GA22 ATGAGTATCAAGCCAACCTGA GTCCCAACAATTTCTTACATGC 52.4

56 TAA57 ATCAAAGAAAGAAACACTTGTTCA TGGTTGGATACAAAAGACTGGA 52.8

57 TS79 GCTCATGTGTTAAATGAAAAATCTAAA ACGGCTCAAATACAATTGATAAAA 54.1

58 TR56 f TTGATTCTCTCACGTGTAATTC ATTTTGATTACCGTTGTGGT 48.4

59 CaSTMS15 CTTGTGAATTCATATTTACTTATAGAT ATCCGTAATTTAAGGTAGGTTAAAATA 49.4

60 TAA137 CATGATTTCCAACTAAATCTTGAAAGT TCTTGTTTCGTTTAAACAATTTCTTCT 55.3

61 TA59 ATCTAAAGAGAAATCAAAATTGTCGAA GCAAATGTGAAGCATGTATAGATAAAG 55.1

62 TA114 TCCATNTAGAGTAGGATNTTNTTGGA TGATACATGAGTTATTCAAGACCCTAA 55.9

63 TR20 ACCTGCTTGTTTAGCACAAT CCGCATAGCAATTTATCTTC 48.9

64 GA108 GTTTGTGATGGAGGAAGCGT GCCGCATAGCATTGGTAAGT 53.9

65 TA21 GTACCTCGAAGATGTAGCCGATA TTTTCCATTTAGAGTAGGATCTTCTTG 54.5

66 GAA43 TGATCGGAGAGAGAGGAGGA CGTTGATCCACTGCGATAGT 52.7

67 TAA169 CTCAACTTTTCATCTCTTCCACTACTC CTATATTACTTCCAATTTTACCCTTCG 54.8

68 TS84 TTATAACAGCTTCCTTCTATTTGTTTTG AAGGCAAAAGTTTTTATCCCTTAATAG 55.2

69 TAA61 GGTGAAAGACAAGTTAATAAATCAAT CACCTAGGCATAAAAATGGATCA 53.1

70 TAA107 ATAACCACCAAACATACTAATGCCATA ATTCATAATTCAGGACGCAATAGTTAC 55.7

71 TA89 ATCCTTCACGCTTATTTAGTTTTTACA CAAGTAAAAGAGTCACTAGACCTCACA 54.8

45

72 TA104 TGACACCCTAAACCCTAAAA AATTCATTTGTGTCATTGGC 49.1

73 TA144 TATTTTAATCCGGTGAATATTACCTTT GTGGAGTCACTATCAACAATCATACAT 54.3

74 TS5 GTTGAATAGTACTTTCCCACTTGAGTC TGAGACTAAAAATCATATATTCCCCC 54.9

75 GA20 TATGCACCACACCTCGTACC TGACGGAATTCGTGATGTGT 53.1

76 GA33 CAAGCACAATCTTCGTCCAA CTCTCCATTTGCCTCCTTCA 53.7

77 GAA43 TGATCGGAGAGAGAGGAGGA CGTTGATCCACTGCGATAGT 52.8

78 TA78 CGGTAAATAAGTTTCCCTCC CATCGTGAATATTGAAGGGT 49.1

79 TA28 TAATTGATCATACTCTCACTATCTGCC TGGGAATGAATATATTTTTGAAGTAAA 53.9

80 TA34 AAGAGTTGTTCCCTTTCTTTT CCATTATCATTCTTGTTTTCAA 48.5

81 TA37 ACTTACATGAATTATCTTTCTTGGTCC CGTATTCAAATAATCTTTCATCAGTCA 54.7

82 TA43 GGTTGTGTTCTCCAGATTTT AAGAGTTGTTGGAGAGCAA 47.1

83 TA45 ATGCGTATAAAACCCAGAGA TGTTTTTATTGGATTTTCAGTTTCA 50.7

84 TA53 GGAGAAAATGGTAGTTTAAAGAGTACTAA AAAAATATGAAGACTAACTTTGCATTTA 53.0

85 TR26 TCATCGCAGATGATGTAGAA TTGAACCTCAAGTTCTCTGG 48.6

46

1.3.4.2 Pearlmillet STMS: 36 primers (Table 1.4) kindly provided by Dr.

Bharadwaj, IARI were used for phylogenetic studies. DNA at 25 and 50 ng

and Taq polymerase enzyme at 0.2, 0.3, 0.5 µl of 3U µl-1

were checked in

different combinations to get good amplification. Conditions of amplification

were varied as in the case of chickpea STMS to produce sharp bands. In the

initial annealing steps, the annealing temperature was decreased by 1.0 ºC for

first 18 cycles, the products were thereafter amplified for 40 cycles of

denaturation, annealing and extension at the optimum annealing temperature

with a final extension of 15 min (Annexure I-m, n)

1.3.4.3 Asparagus SSRs: 20 Asparagus specific SSR (Table 1.5) primers used

in the present study were adopted from Caruso et al. 2008 and procured from

Sigma chemicals (St. Louis, MO). DNA at 25, 50 ng and Taq polymerase

enzyme at 0.2, 0.3, 0.5 µl of 3U µl-1

were varied in different combinations and

the combination that gave good amplification were selected for further studies.

Standardization of PCR programs was done as in the cases of other primers to

produce sharp bands without much of spurious products (Annexure I-o, p).

1.3.4.4 Asparagus ISSRs: 7 Asparagus specific ISSRs (Aceto et al. 2003)

(Table 1.6) were used in present study procured from Sigma chemicals (St.

Louis, MO). DNA at 25, 50, 75 ng and Taq polymerase enzyme at 0.2, 0.3 and

0.5 µl of 3U µl-1

were varied in different combinations and the combination

that gave good amplification were selected for further studies. PCR

programmes were varied and adopted as in the case of other primers to produce

sharp bands without much of spurious products. (Annexure I-o, p).

1.3.4.5 ISSRs: A total of 50 ISSR primers were tested in A. racemosus. The

UCB-primers originally designed by the University of British Columbia were

obtained from Sigma Co. (USA) by providing the sequence data. DNA at 25,

50, 75 ng and Taq polymerase enzyme at 0.2, 0.5 and 1.0 µl of 3U µl-1

were

varied in different combinations and PCR conditions were standardized to

produce sharp bands (Annexure I-q, r).

47

Table 1.4: Pearlmillet STMS sequences used for amplifying DNA of A. racemosus.

S. No. Primer Forward Sequence Reverse Sequence Ta

1. IPES0009 TTGATCGATCGTCTACGGTT TATACTCACTCACGGCAGCG 62.9

2. IPES0045 CAGCACCATTAGTGGCAAAA CGTAACTTTGGTCAGGCATACA 64.7

3. IPES0071 CGATGCATGTATGTATGAATGA GAAAAGTTCTTTCCTCCCCC 61.7

4. IPES0144 AGATCCCATCTCCCTGTCCT TCCTGTGATTGAACAGCAGC 62.95

5. IPES0147 GAGGAGCACAAAGAAGCACC GACTGAAAAATTGGGAGGCA 63.75

6. IPES0189 AGCAAGCAAGCTCTACCTCG TTGATCAATCACCCCCAAAT 62.85

7. IPES0114 CGTTGTGTTGAATAATGTCGTACC CAATAACCAAACGACGGACA 63.15

8. IPES0127 TGTACAAATGATACTTGATATCCCAAA TGCAGAATTACACTGCCCTG 62.7

9. IPES0127 TGTACAAATGATACTTGATATCCCAAA TGCAGAATTACACTGCCCTG 62.3

10. IPES0141 GCACACTGTATGTCTAGCTGGTG GTCCAGTGGTCGTTGGGTAT 62.9

11. IPES0161 GGATCCATCCATCATCACCT TCAGGGGAACCAATTAACCA 62.9

12 ICMP3069 TAGGAGGGGACTGCTCCTTT AGGAAGAGGATGGTGGTGTG 55

13 ICMP3043 TCCTGTACAAGGACGTGCAG TATCGACGCCAACGATACTG 58

14 ICMP 3031 CACGCTGCTGGAACTTATCA TCTCTCTCTCGGATCGCTGT 58

15 ICMP 3032 CGAATACGTATGGAGAACTGCGCATC CAGTCTCTAACAAACAAACACGGC 58

16 ICMP 3001 ACATGGAGTTGGCACCAGAT GGAATGAAAGGAAGCCAACA 58

17 ICMP 3049 GAGCTGAACACGCTCAAGG CAGATGACATCCATCCGTTG 58

18 ICMP 3068 CTGGCAAAGTTGTAGCGTGA ATGTCGCTCTCTGCCAAGAT 58

19 ICMP3057 ATGTGGAATAACCGCAGAGG AGCAAAAGCTGAGCGACTTC 58

20 ICMP4014 TTCCTTCAATACACAGTTGTTGG

ACCATGAGGACCTTGACCAG 58

21 ICMP3042 TAGTTAATGGGGGTGCGTGT

AAGCACCATCAGCATACCC 54.1

22 ICMP3045 ACAAGGACGACAAGGACCAC

CCTCTCCAAGCACATGTTTC 53.4

23 ICMP3008 GCACGAGGGTTGATTAGGC

CTCAATAAGAGGGGCGAGAA 54.1

24 ICMP3037 CGGCTGCGTTTATTGAAGGAG

GGCGAAACAAAGAGAGTTGG 54.3

25 ICMP3024 ATCGAGGCCAAGTACGTGAT

CGAGCTTCTAGCTCCAATCC 53.3

26 ICMP3018 ACGAGGACAAGCTCTTGGAA

ACGGCGCATACTCGATCATA 54.5

27 ICMP3010 TGTCTCGAGAGCAGGTGATG AGAATGTGGGGGAGACACAC 53.1

48

28 ICMP3063 TCCGGTAGAGACCGTAATGG

GGCACTCCCTAGCAAAATGA 53.8

29

ICMP3058 CGGAGCTCCTATCATTCCAA GCAAGCCACAAGCCTATCTC 54.0

30

ICMP3055 CCCAAACGCAAGTAGGGTTA CCTTCTCCTGCCCCAGAC 54.1

31

ICMP3050 ATGTCCAGTGTTGACGGTGA CGGGGAAGAGACAGGCTACT 53.0

32

ICMP3048 CGGAACTGCTGGAGTGAAAT GCGACTTCGACCCACTTTT 53.9

33

ICMP3066 GGCCCCAAGTAACTTCCCTA TGTCAGACACAGATGCCACA 54.4

34

ICMP3002 AAGATGGATGATGGATTGATGA TACACACACATTGCCACACG 53.0

35

ICMP3004 TGTTACGCAGTGCTCGGTAG ATATAGGGGCGCGCAATAGT 53.7

36

ICMP3047 CGGAGACGCACTAGACTTGG ACCACCATTCCATCACTCCT 54.6

49

Table 1.5 Asparagus SSR Sequences used for amplifying DNA of A. racemosus S.No. Primer Forward Sequence Reverse Sequence Ta 1 DSFR1 AGGTGGAGAACAAATGGCTG CGAGCTCAATTGAAATCCATAA 68.3

2 DSFR3 CCGGTGCTTTGATTACTGCT GATCATCATCTTGCGCATTG 69.1

3 DSFR7 CACCATTTCAAATCCCCACT GAGGCTAGAGCTCCGCTCAT 69.2

4 DSFR14 CATGCCCTAAAATCTCCAAGA GCCAGAGGCTGAAATAAACTG 67.7

5 DSFR16 GGCTAGCCGAAAGAATCTCC TCTTCCTCCTCCTCCTCCTC 68.8

6 DSFR2 CCTCCTCGGCAATTTAATCA CAGCTGCATCACGTTCTTGT 64.1

7 DSFR4 GGCAGGATTAGGGTTTCG TCTCGCTCACCTTCTCATCC 64.5

8 DSFR5 CTTTTGCTTCTGAACGCTCC TTGAAGGAGCCGTAAACTGG 63.9

9 DSFR6 TCCACCCCACAAAAAGAAAG AGAAGTTGACGCCGTTGTCT 63.8

10 DSFR8 GATTAATAAAGCGCCGCTGA ACATAAGCCCATACTTGCGG 63.8

11 DSFR9 TCATCTGAAATGGCATCAGC CGAGGCCTAGTGTGTGTTGA 63.9

12 DSFR10 TTTTGCTCCGATCATTTTCA CCTCTTCGTCTTCATCAGCC 63.9

13 DSFR11 AGAGAGGAAGTTGTCGCTCG TGGGAAAATGGAAGAACCAA 64.0

14 DSFR12 CCCGATCCAAACCCATCC GAAAATTCGATCGGAACCCT 63.9

15 DSFR13 GATTGGGACCAACACAAACA AGCAATGACTTGATCCCCAG 64.0

16 DSFR15 CGCCCCGAATCAACTAATAA TACTGCGGAGGTATGTGGGT 64.2

17 DSFR17 CGCCCTTGTTCTTCTTCTTG CAGTTGTCTGCCGTCTTCAA 64.1

18 DSFR18 AGGGGTCCGGATTAATTCAC GTCCTTGGCCATTAGAGCTG 63.6

19 DSFR19 GTGATTCAAGGGGGAAAGGT TACACCAAAACCAGAAGGGC 63.5

20 DSFR20 GACTAGCGCCATGAGAAAGG TTTTAGGGCATTTTAAACGCAT 63.7

Table 1.6: Asparagus ISSR Sequences used for amplifying DNA of A. racemosus S.No. Primer Forward Reverse Tm 1. DISF1 ACGGTATTTGATGGGAGAG TGTCAATGTAGCCTCTGCA 58.9

2. DISF2 TGTGGAGTATGCCAATGAGTAGC TTGCGTGTAGTCCTCTGATCG 65

3. DISF3 TCATCCTCATCGTCATTTCCTTCAC GCCCACTCTCTAACTCAAATCAAG 69

4. DISF4 GAGGTCAACAAACGGCAAAT TTGCTATTTGTGCTCGTCGT 63.7

5. DISF5 CGTGGATTAGCTGGCAGCTTGGCA CTCGTCGCCTTCATCTCGTCGACT 76.2

6. DISF6 CCGTGAGGAAAGCTTGAAGA CTCTCCCTTGTCCTCATTGC 64.3

7. DISF7 GAGCGGAGAGGGTGTCCTCGACGC GACGGATAAGAGTTTGACCGTACC 78.4

50

Table 1.7: UBC-ISSR Sequences used for amplifying DNA of A. racemosus.

S.No. Primer Sequence Ta 1 DBC801 ATA TAT ATA TAT ATA TT 24.2

2 DBC 802 ATA TAT ATA TAT ATA TG 24.9

3 DBC 803 ATA TAT ATA TAT ATA TC 23.8

4 DBC 804 TAT ATA TAT ATA TAT AA 23.2

5 DBC 805 TAT ATA TAT ATA TAT AC 21.9

6 DBC 806 TAT ATA TAT ATA TAT AG 22.5

7 DBC 807 AGA GAG AGA GAG AGA GT 42.5

8 DBC 808 AGA GAG AGA GAG AGA GC 46.8

9 DBC 809 AGA GAG AGA GAG AGA GG 46.6

10 DBC 810 GAG AGA GAG AGA GAG AT 42.9

11 DBC 811 GAG AGA GAG AGA GAG AC 43.3

12 DBC 812 GAG AGA GAG AGA GAG AA 44.3

13 DBC 813 CTC TCT CTC TCT CTC TT 43.5

14 DBC 814 CTC TCT CTC TCT CTC TA 41.3

15 DBC 815 CTC TCT CTC TCT CTC TG 44.9

16 DBC 816 CAC ACA CAC ACA CAC AT 51.1

17 DBC 817 CAC ACA CAC ACA CAC AA 52.7

18 DBC 819 GTG TGT GTG TGT GTG TA 47.6

19 DBC 820 GTG TGT GTG TGT GTG TC 50.3

20 DBC 821 GTG TGT GTG TGT GTG TT 49.9

21 DBC 822 TCT CTC TCT CTC TCT CA 45.8

22 DBC 823 TCT CTC TCT CTC TCT CC 47.5

23 DBC 824 TCT CTC TCT CTC TCT CG 49.0

24 DBC 836 AGA GAG AGA GAG AGA GYA 43.3

25 DBC 837 TAT ATA TAT ATA TAT ART 25.8

26 DBC 838 TAT ATA TAT ATA TAT ARC 25.4

27 DBC 841 GAG AGA GAG AGA GAG AYC 46

28 DBC 842 GAG AGA GAG AGA GAG AYG 47.2

29 DBC 844 CTC TCT CTC TCT CTC TRC 46.5

30 DBC 845 CTC TCT CTC TCT CTC TRG 47.7

31 DBC 853 TCT CTC TCT CTC TCT CRT 46.5

32 DBC 855 ACA CAC ACA CAC ACA CYT 51.9

33 DBC864 ATG ATG ATG ATG ATG ATG 51.2

34 DBC 869 GTT GTT GTT GTT GTT GTT 50.9

35 DBC 871 TAT TAT TAT TAT TAT TAT 32.1

36 DBC 875 CTA GCT AGC TAG CTA G 41

37 DBC 876 GAT AGA TAG ACA GAC A 36.4

38 DBC 879 CTT CAC TTC ACT TCA 42.2

39 DBC 882 VBV ATA TAT ATA TAT AT 25.5

40 DBC884 HBH AGA GAG AGA GAG AG 41.9

41 DBC885 BHB GAG AGA GAG AGA GA 46.3

42 DBC 886 VDV CTC TCT CTC TCT CT 44.2

43 DBC 887 DVD TCT CTC TCT CTC TC 44.8

44 DBC 888 BDB CAC ACA CAC ACA CA 52.3

45 DBC 889 DBD ACA CAC ACA CAC AC 47

46 DBC 891 HVH TGT GTG TGT GTG TG 51.8

47 DBC 893 NNN NNN NNN NNN NNN 41.9

48 DBC 896 AGG TCG CGG CCG CNN NNN NAT G 73.1

49 DBC897 CCG ACT CGA GNN NNN NAT GTG G 64.3

50 DBC 899 CAT GGT GTT GGT CAT TGT TCC A 67.7

1.3.4.6 RAPD: A total of 45 RAPD primers were used (Table 1.8). RAPD

assays were performed using random 10-mer oligonucleotide primers.

Amplification reaction was carried out in 10 μl volume per reaction. Various

concentrations of DNA (25, 50, 75, 100 ng) and Taq polymerase enzyme (0.2,

0.5, 1.0 and 1.5 µl of 3U µl-1

) were used in different combinations to get

amplification. PCR conditions were standardized to get good amplification

(Annexure I-q, r).

51

Table 1.8: RAPD primer sequences used for amplifying DNA of A. racemosus.

S.No. Primer Sequence Ta

1 OPC9 CTCACCGTCC 33.1

2 OPC10 TGTCTGGGTG 29.1

3 OPC11 AAAGCTGCGG 40.5

4 OPC12 TGTCATCCCC 33.2

5 OPC13 AAGCCTCGTC 32.6

6 OPC14 TGCGTGCTTG 39.1

7 OPC15 GACGGATCAG 27.6

8 OPC16 CACACTCCAG 22.4

9 OPC17 TTCCCCCCAG 43.1

10 OPC18 TGAGTGGGTG 29.1

11 OPC19 GTTGCCAGCC 39.6

12 OPC20 ACTTCGCCAC 33.7

13 OPH12 ACGCGCATGT 41.3

14 OPH14 ACCAGGTTGG 32.9

15 OPH17 CACTCTCCTC 25.0

16 OPH19 CTGACCAGCC 33.6

17 OPH20 GGGAGACATC 25.2

18 OPM1 GTTGGTGGCT 33.2

19 OPM2 ACAACGCCTC 33.7

20 OPM4 GGCGGTTGTC 39.2

21 OPM5 GGGAACGTGT 32.7

22 OPM6 CTGGGCAACT 33.8

23 OPM7 CCGTGACTCA 29.7

24 OPM8 TCTGTTCCCC 32.7

25 OPM9 GTCTTGCGGA 35.2

26 OPM10 TCTGGCGCAC 41.8

27 OPO2 ACGTAGCGTC 29.2

28 OPO3 CTGTTGCTAC 23.0

29 OPO4 AAGTCCGCTC 32.6

30 OPO5 CCCAGTCACT 26.5

31 OPO8 CCTCCAGTGT 26.5

32 OPO10 TCCCACGCAA 42.5

33 OPO11 CCTCCAGTGT 26.5

34 OPO13 GACAGGAGGT 24.6

35 OPQ1 GGGACGATGG 38.5

36 OPQ2 TCTGTCGGTC 27.7

37 OPQ3 GGTCACCTCA 27.2

38 OPQ4 AGTGCGCTGA 36.2

39 OPQ5 CCGCGTCTTG 41.8

40 OPQ6 GAGCGCCTTG 40.5

41 OPQ7 CCCCGATGGT 42.7

42 OPQ9 GGCTAACCGA 35.0

43 OPQ10 TGTGCCCGAA 42.5

44 OPY9 GCAGCGCAC 37.2

45 OPZ1 TCTGTGCCAC 29.4

1.3.5 Separation of PCR amplified Products

The PCR amplified products were separated on a horizontal slab gel. The

following steps involved.

52

a) Gel casting: 15 g metaphor agarose (Lonza) was suspended in 500 ml

1X TBE buffer

in a glass beaker to prepare 96 wells gel and The solution was then heated to

boiling in the microwave oven, in between swirling the beaker to mix the

agarose well, to obtained a final clear solution. The SCIE-PLAS

electrophoresis casting tray was prepared by taping the ends with cello tape or

a stopper. A comb was placed to provide 96 loading wells. 15 µl of ethidium

bromide (G-Biosciences, USA) was added to the cooled metaphor agarose and

mixed by swirling the beaker. Cooled metaphor was poured into the casting

tray to 1 mm thickness and left to solidify. Care was taken not to trap air

bubbles. The comb was carefully removed once the agarose solidified.

a) Setting up the gel tank: The solidified gel was placed in the

electrophoresis tank with the wells towards the negative electrode. 1X TBE

buffer was added till the top of the gel was covered.

b) Loading the samples: 5 µl of loading dye (Thermo Scientific-

Fermentas, USA) was added to each sample prior to loading. The samples

were loaded in the wells with micropipette. Last well of each gel was loaded

with the standard molecular marker of 100 bp DNA ladder (Thermo Scientific-

Fermentas, USA) for determining the size of the amplified products. Loaded

gel was connected to power supply and run at 120 V for 3 hour.

1.3.6 Scoring of Amplified Products

The gels were visualized in UV transilluminator and photographed using a

CCD camera (Sony XC-75 CE, USA) attached to the gel documentation

system, with the Quantity One software (BIORAD Universal Hood II, USA).

Scoring was done manually for each of the gel sections. Band patterns for each

of the microsatellites markers were recorded for the respective genotype by

assigning a letter to each band. Alleles were numbered as ‘a1’, ‘a2’ etc.

sequentially from the largest to the smallest sized band. Any band thought to

be an artefact or bands which were either diffused or highly faint or those that

were difficult to score due to multiple bands were considered as ‘missing data’.

The missing data were designated as ‘AB’ (in comparison with ‘1’ for

presence of a band and ‘0’ for absence of a band) in the data matrix; the

missing data was not considered while analyzing the genetic similarities. ‘Null

53

allele’ for any specific marker in any genotype was again considered as

absence of band (designated as ‘0’). The null alleles were reconfirmed.

Monomorphic data were excluded from the studies except in cases where at

least one genotype showed a null allele, clearly indicating absence of STMS

primer binding site.

1.3.7 Statistical Analysis

Binary matrix was generated for each accession and used to construct a

dendrogram using UPGMA (Unweighted Paired Group Method using

Arithmetic averages) (Sneath and Sokal 1973). Cluster analysis was performed

using Windostat version 8.6 based on Ward’s Minimum Variance Method

(Ward 1963) based on Jaccard’s IJ distance. Genetic diversity, allele

frequencies, allele number, genotype frequency and Polymorphic Information

Content (PIC) values were estimated for different markers used in genetic

diversity study among individuals was done using Windostat version 8.6.

54

1.4 Annexure I

Stock Solutions:

(a) Tris-HCl Buffer (100 mM, pH 8.0): 121.1 g Tris base was dissolved

in 800 ml sterile water and pH was adjusted to 8.0 using 1N HCl. The

volume was made up to 1000 ml using sterile dH2O.

(b) EDTA (0.5M, pH 8.0): 186.12 g Na2EDTA.2H2O dissolved in 800 ml

sterile dH2O the pH of which was raised to 8.0 by adding NaOH

pellets. The solution was stirred vigorously over a magnetic stirrer and

after completing dissociation of the salt the volume was made up to

1000 ml with sterile dH2O.

(c) NaCl (4M): 292.2 g NaCl was dissolved in 800 ml sterile dH2O and

volume made upto 1000 ml with sterile dH2O.

(d) CTAB (10%): 10 g CTAB was dissolved in sterile dH2O and volume

made up to 1000 ml with sterile dH2O.

(e) DNA extraction buffer (100 ml):

Ingredients Volume (ml)

100 mM Tris HCl (pH 8.0) 10.0

0.5 M EDTA 4.0

4 M NaCl 35.0

10% CTAB 20.0

Sterile H2O 30.8

Β-Mercaptoethanol 0.2

Total 100.0

(f) Tris-EDTA (TE) buffer (10 mM Tris, 1 mM EDTA): 1 ml of Tris-Cl

buffer (1 M, pH 8.0) and 0.2 ml EDTA (0.5M, pH 8.0) was mixed with

sterile dH2O and volume made up to 100 ml.

(g) Rnase A (10 mg ml-1

): 100 mg of Rnase A was dissolved in 10 ml of

10 mM Tris (pH 7.5) and 15 mM NaCl. It was boiled in waterbath for

15 min and cooled to room temperature. Solution was dispensed to 1

ml aliquots and stored at -20 °C. Working stock was kept at 4 °C.

55

(h) Sodium Acetate (3M): 24.6 g of sodium acetate was dissolved in 50

ml sterile dH2O; pH was adjusted to 5.2 with glacial acetic acid and

made up to 100 ml with sterile dH2O.

(i) Tris-Borate EDTA Buffer (20X): 216 g of Tris base, 110 g of boric

acid (MW=61.83) and 80 ml of 0.5M EDTA was dissolved in 1000 ml

sterile dH2O. 1X TBE buffer was used for electrophoresis.

(j) Lambda DNA/Hind III Marker

PCR Reaction Mixes and PCR amplification conditions

(k) Reaction Mix for Chickpea STMS S. No. Components Volume (µl)

1. Forward primer 1.0

2. Reverse Primer 1.0

3. dNTP Mix (2.5 mM) 1.0

4. 10X Taq Poly Buffer (With MgCl2) 1.6

5. Taq Polymerase (3U μl-1

) 0.3

6. DNA (50 ng) 1.0

7. Sterile H2O 4.1

8. Total Volume 10.0

(l) PCR amplification Condition for Chickpea STMS S. No. Reaction Steps Temp. (°C) Duration Cycles

1. Initial Denaturation 94 2:30 min

2. Denaturation 94 20 sec

-0.5 ºC per

cycle X 18 3. Primer Annealing AT 1 min

4. Primer Extension 72 50 sec

5. Denaturation 94 20 sec

X 40

6. Primer Annealing AT 1 min

7. Primer Extension 72 50 sec

8. Final Extension 72 15 min

9. Store at 4 Till end

56

(m) Reaction Mix for Pearlmillet STMS S. No. Components Volume (µl)

1. Forward primer 1.0

2. Reverse Primer 1.0

3. dNTP Mix (2.5 mM) 0.5

4. 10X Taq Poly Buffer (With MgCl2) 1.0

5. MgCl2 0.8

5. Taq Polymerase (3U μl-1

) 0.2

6. DNA (50 ng) 1.0

7. Sterile H2O 4.5

8. Total Volume 10.0

(n) PCR amplification Condition for Pearlmillet STMS S. No. Reaction Steps Temp.

(°C)

Duration Cycles

1. Initial Denaturation 94 5 min 1

2. Denaturation 94 15 sec

-1 ºC per

cycle X 10 3. Primer Annealing AT 30 sec

4. Primer Extension 72 30 sec

5. Denaturation 94 15 sec

X 40 6. Primer Annealing AT 30 sec

7. Primer Extension 72 30 sec

8. Final Extension 72 20 min 1

9. Store at 4 Till end

(o) Reaction Mix for Asparagus SSR and ISSR S. No. Components Volume (µl)

1. Forward primer 1.0

2. Reverse Primer 1.0

3. dNTP Mix (2.5 mM) 1.0

4. 10X Taq Poly Buffer (With MgCl2) 1.6

5. Taq Polymerase (3U μl-1) 0.5

6. DNA (50 ng) 1.0

7. Sterile H2O 3.9

8. Total Volume 10.0

(p) PCR amplification Condition for Asparagus SSR and ISSR S. No. Reaction Steps Temp.

(°C)

Duration Cycles

1. Initial Denaturation 95 5 min

2. Denaturation 94 1 min

X 40 3. Annealing AT 1 min

4. Extension 72 1:50 min

5. Final Extension 72 15 min

6. Store at 4 Till end

(q) Reaction Mix for RAPD and UBC-ISSR S. No. Components Volume (µl) 1. Primer 1.0

3. dNTP Mix (2.5 mM) 1.0

4. 10X Taq Poly Buffer (With MgCl2) 1.6

5. Taq Polymerase (3U μl-1) 0.5

6. DNA (50 ng) 1.0

7. Sterile H2O 4.9

8. Total Volume 10.0

57

(r) Touch Down-PCR Condition for RAPD and UBC-ISSR S. No. Reaction Steps Temp.

(°C) Duration Cycles

1. Initial Denaturation 94 2:30 min 1

2. Denaturation 94 20 sec

-1 ºC

per cycle

X 10

3. Primer Annealing AT 1 min

4. Primer Extension 72 50 sec

5. Denaturation 94 20 sec

X 20 6. Primer Annealing 38 1 min

7. Primer Extension 72 50 sec

8. Final Extension 72 10 min 1

9. Store at 4 Till end

Molecular formula and weight of chemicals used

S. No. Chemical Molecular

Formula

Molecular

Weight

Company

1. Boric Acid H3BO3 61.83 Qualigens, India

2. CTAB (Cetyl

Trimethyl Ammonium

Bromide)

C19H42NBr 364.46 SRL, India

3. Sodium-EDTA Na2C10H16N2O8 372.24 HiMedia, India

4. Tris-Base C4H11NO3 121.10 Merck, Genei

5. Sodium Acetate CH3COONa 82 HiMedia,India

7. Iso Amyl Alcohol C5H12O 88.14 Merck, India

8. Chloroform CHCl3 119.38 Merck, India

9. Phenol C6H5OH 94 Qualigens, India

10. Sodium Chloride NaCl 58.5 Qualigens, India

58

OBSERVATIONS

1.1 Plant material: Established plants were prevented from root rot and

fungal diseases by treating them with malathion periodiocally. The undertaken

precautionary measures enabled the plants to remain healthy and produce

several spears during the growing season. Initially, plants were established in

nethouse but poor growth was observed in a few accessions while others

displayed healthy growth. Subsequently, the plants were shifted to an open

field and the plant continued to remain healthy and demonstrated spear growth

and also produced flowers during the growing season.

1.2 Evaluation of Asparagus racemosus accessions: The 22

morphological parameters used to evaluate relationship in the 13 A. racemosus

accessions could successfully establish divergence in the accessions. Similarly,

the molecular markers (STMS of chickpea and pearlmillet that were used

demonstrated the penetrability of cross species STMS. Along with these

markers the other molecular markers, viz, SSRs of Asparagus, ISSR (UBC)

and RAPDs could establish the genetic diversity in the 13 A. racemosus

accessions.

1.2.1 Morphological parameters

The assessment of variability in the collected accessions was estimated by

computing range, mean and coefficient of variation at phenotypic and

genotypic levels. The analysis of variance revealed highly significant

difference among genotypes for all the characters indicating thereby substantial

amount of variability for various characters in the material examined. The

mean values for the accessions as shown in Table 1.9, were highly significant

for phenotypic traits revealing a high level of genetic diversity among collected

accessions.

1.2.1.1 The variation of individual parameters and their collective influence on

genotypic diversity is discussed in the following paragraphs.

(i) Cluster of Cladode or Tuft or Whorl: Number of cladodes in each

whorl was counted in all accessions. Number of cladodes in each whorl ranged

59

from 5 to 10 with the mean of 7 in individual plants. Among all accessions

KAU had higher number of cladodes per whorl (10) (Fig. 1.1A) and IC41908

was found at the bottom for lowest number of cladodes per cluster (6).

Distance between each whorl varied from 0.27 to 0.71 cm with an average of

0.40 cm. IC471921 had least distance between whorls while KAU had highest

distance of 0.707 cm (Fig. 1.1B). It was significantly different from the

genotypes which followed it. Number of whorls ranged from 17 to 31 with an

average of 26 whorls in a 5ʺ length. In all these parameters, IC471921 had

highest number of whorls (31) (Fig. 1.1C) in a 5ʺ length with lowest distance

between whorls (0.276 cm) (Plate.

(ii) Cladode: Various parameters for cladodes were recorded. KAU

showed the highest values for cladode width (0.194 cm) (Fig. 1.1D), thickness

(0.022 cm) (Fig. 1.1E), length (1.92 cm) (Fig. 1.1F), fresh weight (0.14 g) (Fig.

1.1G) and dry weight (0.041 g) (Fig. 1.1H) with lowest values for length/width

ratio (9.93) (Fig. 1.1I) and per cent moisture content (67%) (Fig. 1.1J)

IC471927 had lowest value for cladode width (0.039 cm) and fresh weight

(0.053 g) while IC471924 had lowest value for dry weight (0.0098 g) and

highest value for percent moisture (84.47%). Accession IC471911 revealed

lower mean value for cladode length (1.08 cm) and IC471910 showed highest

value for length/width ratio (31.57) among all genotypes.

(iii) Thorn or Spine: Length of thorns or spines was recorded and it ranged

from 0.76 to 1.66 cm with mean length of 1.13 cm. The results revealed that

KAU had longest spine (1.66 cm) while JBP had shortest spine (0.76 cm) (Fig.

1.1K).

(iv) Internode length: Among all accessions, IC471908 had significantly

higher internode length (2.45 cm), while accession FFDC had the shortest (1.7

cm) (Fig.1.1L)

(v) Spear: Accession from Chandigarh, CDH revealed maximum spear

length (201.5 cm) (Fig.1.1M) while KAU had higher value for spear

circumference (1.74 cm) (Fig. 1.1N) and diameter (0.554 cm) (Fig. 1.1O).

Accession JBP had lowest value for all parameters, spear length (110 cm),

circumference (1.16 cm) and diameter (0.37 cm).

60

(vi) Root: Various parameters were recorded for root. Accession IC471923

had highest value for root length (23.3 cm) (Fig. 1.1P), root fresh weight

(29.38 g) and per cent moisture (90.02%). Accession IC471908 had highest

value for root circumference (6.2 cm) (Fig. 1.1Q) and diameter (1.97 cm) (Fig.

1.1R) with lowest value for root length/diameter ratio (6.78) (Fig. 1.1S). The

lowest root diameter (1.19 cm), circumference (3.76 cm) and fresh weight

(8.43 g) (Fig. 1.1T) were observed in accession KAU. Highest value for root

dry weight was observed in IC471910 (3.13 g) (Fig. 1.1U) and lowest in

accession CDH (1.18 g). Among all accessions, accession IC471927 had

highest value for root length/diameter ratio (14.48) and lowest value for

moisture content (80.24 g) (Fig. 1.1V).

Frequency distribution histograms were also plotted for 22 quantitative

traits in A. racemosus and illustrated a continuous variation among 22

quantitative traits and displayed a normal graph for each trait (Fig. 1.2A-V).

Genotypical correlations (Fig. 1.3A, B and Table 1.10) revealed spear

circumference and diameter were significantly and positively correlated with

cladode width, cladode thickness, cladode length and cladode length/width

ratio. Positive association was also found between cladode width and cladode

thickness, cladode length, cladode length width ratio, cladode fresh and dry

weight, number of cladode per whorl, distance between whorls and number of

whorls in 5 span but negatively correlated with cladode moisture content. Root

fresh weight was positively correlated with root length, circumference,

diameter and root length/diameter ratio while root circumference and diameter

was negatively correlated with number of cladodes per whorl and cladode per

cent moisture. Root diameter was also found to be negatively correlated with

cladode fresh weight. Length of spine was significantly and positively

correlated with cladode width, cladode length/width ratio and number of

cladodes per whorl. Spear diameter was also positively correlated with spine

length.

Phenotypical correlations showed almost all correlation coefficients

involving root characteristics (root fresh weight with root moisture content,

root diameter and root circumference). Root diameter was also positively

correlated (Fig 1.4A, B and Table 1.11). Cladode width was positively and

61

significantly associated with cladode thickness, cladode length, cladode fresh

and dry weight, number of cladodes per whorl and distance between whorls

but negatively correlated with number of whorls in a 5 span.

Phenotypic and genotypic variances were estimated for 22 quantitative

traits. In general, the Phenotypic Coefficient of Variability (PCV) was higher

than the Genotypic Coefficient of Variability (GCV) for all characters (Fig.

1.5). Very high estimate of genotypic Coefficient of Variability (GCV) and

were recorded for cladode width. Root percent moisture and cladode percent

moisture revealed low GCV and PCV while magnitude of GCV and PCV for

cladode length, number of cladodes per whorl, number of whorls in 5 span,

internode length, spear length, circumference and diameter, root length,

circumference, diameter and length/diameter ratio were medium, which

indicated that environment also played a considerable role in the expression of

these traits.

A

B

62

C

D

F

E

63

G

H

I

J

64

K

L

M

N

65

O

P

Q

R

66

Fig 1.1 A-V. Mean value for 22 morphological traits of Asparagus racemosus

accessions.

S

T

U

V

67

A B

C D

E F

G H

68

I J

K

D

L

M N

P O

Q

69

Fig 1.2. Frequency distribution graph for 22 morphological traits of A.

racemosus accessions.

R

S T

U V

Q

70

Fig. 1.3 (A) Genotypical corelations and (B) Shaded similarity matrix for

Genotypic Coefficient of Variance among 22 quantitative traits studied in 13

A. racemosus

71

Fig. 1.4 (A) Phenotypical corelations and (B) Shaded similarity matrix for

Phenotypic Coefficient of Variance among 22 quantitative traits studied in 13

A. racemosus

72

Fig. 1.5 Phenotypic and Genotypic Coefficient of Variance among 22

quantitative traits studied in 13 A. racemosus

73

Table 1.9: Means, %CV, %GCV and %PCV for the 22 quantitative characters studied in A. racemosus#

Character CW CT CL CL/W CFW CDW CPM CPW NOW DBW IL SpL SL SC SD RL RC RD RL/D RFW RDW RPM

FFDC 0.044 0.012 1.22 27.78 0.066 0.018 73.93 6.17 27.92 0.31 1.70 1.38 173.57 1.40 0.45 20.90 5.08 1.62 12.93 20.23 2.55 87.41

SG1 0.051 0.010 1.15 22.55 0.076 0.018 77.71 7.83 21.08 0.41 2.38 1.10 187.11 1.28 0.41 19.98 4.72 1.50 13.29 17.70 2.62 85.22

KAU 0.194 0.022 1.93 9.94 0.138 0.042 67.14 10.25 17.33 0.71 2.10 1.66 163.41 1.74 0.55 14.64 3.76 1.20 12.23 8.43 1.29 84.70

CDH 0.050 0.015 1.25 25.00 0.084 0.016 80.70 8.92 27.83 0.35 2.11 1.16 201.51 1.42 0.45 13.16 4.80 1.53 8.61 11.47 1.18 89.69

IC471921 0.059 0.017 1.52 25.71 0.096 0.023 76.41 6.67 31.92 0.28 1.88 0.76 169.33 1.54 0.49 20.30 4.90 1.56 13.01 17.12 2.43 85.79

IC471923 0.047 0.013 1.19 25.30 0.057 0.011 79.99 8.08 27.67 0.35 2.21 1.30 186.27 1.50 0.48 23.30 5.40 1.72 13.55 29.38 2.93 90.02

IC471924 0.052 0.014 1.21 23.18 0.059 0.010 84.47 7.83 28.33 0.35 1.79 1.28 143.09 1.62 0.52 19.70 5.24 1.67 11.81 23.65 2.84 87.98

IC471927 0.039 0.013 1.09 27.92 0.054 0.011 80.32 8.33 31.58 0.28 1.75 1.06 159.17 1.30 0.41 20.10 4.36 1.39 14.48 15.78 3.12 80.24

IC471911 0.040 0.012 1.08 27.08 0.054 0.014 77.62 6.17 26.17 0.40 2.06 0.78 163.41 1.38 0.44 16.80 4.90 1.56 10.77 14.52 2.23 84.68

IC471910 0.041 0.013 1.29 31.57 0.060 0.014 76.19 6.92 24.83 0.52 1.82 1.20 121.07 1.62 0.52 18.70 5.26 1.68 11.16 26.86 3.14 88.31

IC471909 0.048 0.014 1.45 30.21 0.064 0.012 81.14 6.92 27.58 0.34 2.21 1.16 130.39 1.52 0.48 16.90 4.82 1.54 11.01 18.70 3.02 83.83

IC471908 0.050 0.016 1.31 26.11 0.066 0.013 81.21 5.92 23.08 0.44 2.46 1.06 151.55 1.24 0.39 13.40 6.20 1.97 6.79 21.35 2.74 87.15

JBP 0.049 0.013 1.37 28.00 0.058 0.012 79.41 7.00 27.42 0.35 1.80 0.76 110.07 1.16 0.37 12.03 4.07 1.30 9.28 10.40 1.40 86.58

Mean 0.059*** 0.014*** 1.31*** 25.61*** 0.071*** 0.016*** 78.69* 7.47*** 26.30*** 0.39*** 2.03** 1.12*** 158.88 ***

1.44*** 0.45*** 17.67*** 4.88*** 1.55*** 11.48*** 18.09*** 2.40*** 86.23***

CV% 7.16 13.80 9.56 20.72 27.20 44.36 7.99 9.66 6.47 22.03 15.37 11.67 6.58 9.07 9.08 12.42 7.62 7.62 14.92 10.69 11.38 2.69

GCV% 70.60 23.45 16.88 20.49 30.73 48.61 4.46 16.13 14.84 28.52 10.68 22.42 16.69 11.03 11.04 18.98 12.05 12.05 18.24 33.68 28.31 2.74

PCV% 70.97 27.22 19.40 29.15 41.04 65.81 9.16 18.80 16.20 36.04 18.72 25.27 17.94 14.29 14.30 22.68 14.26 14.26 23.57 35.34 30.51 3.84

74

Table 1.10: Genotypical correlation matrix for 22 quantitative traits among 13 A. racemosus accessions

Character CW CT CL CL/W CFW CDW CMP C/W DBW IL SpL SL SC SD RL RC RD RL/D RFW RDW RMC

CW 1.000

CT 0.923 1.000

CL 0.888 0.959 1.000

CL/W -0.971 -0.817 -0.751 1.000

CFW 0.956 1.004 0.976 -0.960 1.000

CDW 0.992 0.981 0.941 -0.972 1.004 1.000

CMP -0.957 -0.840 -0.840 0.877 -1.006 -1.034 1.000

CW 0.720 0.550 0.504 -0.942 0.704 0.674 -0.552 1.000

-0.673 -0.440 -0.471 0.607 -0.583 -0.654 0.688 -0.365 1.000

DBW 0.846 0.718 0.691 -0.708 0.740 0.819 -0.914 0.500 -0.950 1.000

IL 0.160 0.133 0.070 -0.200 0.205 0.138 0.065 0.060 -0.710 0.443 1.000

SpL 0.607 0.504 0.385 -0.734 0.509 0.508 -0.450 0.610 -0.535 0.587 0.092 1.000

SL 0.083 0.010 -0.188 -0.353 0.331 0.277 -0.229 0.352 -0.046 -0.138 0.375 0.218 1.000

SC 0.576 0.623 0.600 -0.632 0.605 0.613 -0.529 0.472 -0.224 0.502 -0.215 0.654 0.018 1.000

SD 0.574 0.622 0.599 -0.631 0.604 0.611 -0.527 0.471 -0.222 0.500 -0.215 0.653 0.019 1.000 1.000

RL -0.278 -0.388 -0.376 -0.079 -0.287 -0.217 0.050 -0.101 0.335 -0.404 -0.363 0.194 0.326 0.303 0.304 1.000

RC -0.557 -0.345 -0.486 0.519 -0.507 -0.585 0.700 -0.634 0.138 -0.248 0.431 -0.074 0.069 -0.106 -0.105 0.236 1.000

RD -0.556 -0.345 -0.486 0.519 -0.507 -0.585 0.700 -0.634 0.137 -0.248 0.431 -0.074 0.068 -0.106 -0.105 0.236 1.000 1.000

RL/D 0.101 -0.125 -0.051 -0.447 0.068 0.188 -0.429 0.283 0.186 -0.198 -0.549 0.258 0.296 0.337 0.337 0.833 -0.325 -0.325 1.000

RFW -0.477 -0.432 -0.438 0.280 -0.562 -0.554 0.543 -0.407 0.186 -0.222 0.055 0.141 -0.067 0.203 0.203 0.710 0.782 0.782 0.231 1.000

RDW -0.524 -0.497 -0.478 0.345 -0.629 -0.578 0.469 -0.489 0.292 -0.345 -0.009 -0.013 -0.185 0.052 0.053 0.713 0.603 0.603 0.379 0.835 1.000

RMC -0.125 -0.032 -0.043 0.078 -0.009 -0.131 0.378 -0.061 -0.060 0.040 0.144 0.209 0.137 0.223 0.223 0.035 0.536 0.536 -0.348 0.442 -0.107 1.000

75

Table 1.11: Phenotypic correlation coefficient matrix among 22 quantitative morphological characters studied in A. racemosus accessions

#

Character abbreviations as given in Table__. ***

p≤0.001, **

p≤0.01, *p≤0.05.

Character

CW CT CL CL/

W

CFW CDW CPM CPW NOW DBW IL SpL SL SC SD RL RC RD RL/D RFW RDW RPM

CW 1.000

CT 0.771 1.000

CL 0.754 0.758 1.000

CL/W -0.689 -0.483 -0.482 1.000

CFW 0.740 0.506 0.543 -0.528 1.000

CDW 0.763 0.482 0.545 -0.489 0.885 1.000

CMP -0.461 -0.314 -0.356 0.162 -0.414 -0.697 1.000

CW 0.615 0.465 0.304 -0.437 0.417 0.388 -0.176 1.000

-0.610 -0.350 -0.380 0.386 -0.399 -0.473 0.381 -0.295 1.000

DBW 0.666 0.522 0.507 -0.380 0.370 0.480 -0.359 0.356 -0.682 1.000

IL 0.094 -0.007 0.012 -0.103 0.137 0.167 -0.170 0.025 -0.355 0.072 1.000

SpL 0.529 0.353 0.363 -0.370 0.249 0.269 -0.196 0.467 -0.416 0.455 -0.027 1.000

SL 0.078 0.002 -0.145 -0.173 0.200 0.175 -0.126 0.288 -0.049 -0.113 0.217 0.205 1.000

SC 0.444 0.457 0.416 -0.406 0.272 0.252 -0.151 0.253 -0.123 0.378 -0.068 0.459 0.013 1.000

SD 0.442 0.455 0.415 -0.405 0.271 0.251 -0.152 0.252 -0.121 0.377 -0.067 0.459 0.014 1.000 1.000

RL -0.221 -0.292 -0.312 -0.031 -0.152 -0.137 0.074 -0.072 0.300 -0.260 -0.067 0.099 0.268 0.176 0.176 1.000

RC -0.470 -0.252 -0.322 0.262 -0.359 -0.355 0.220 -0.549 0.078 -0.217 0.197 -0.090 0.047 -0.105 -0.105 0.176 1.000

RD -0.470 -0.252 -0.322 0.263 -0.359 -0.355 0.220 -0.549 0.077 -0.216 0.197 -0.089 0.047 -0.105 -0.105 0.176 1.000 1.000

RL/D 0.091 -0.102 -0.106 -0.192 0.088 0.103 -0.075 0.243 0.202 -0.075 -0.132 0.161 0.232 0.221 0.221 0.829 -0.381 -0.382 1.000

RFW -0.456 -0.357 -0.347 0.218 -0.422 -0.405 0.182 -0.337 0.151 -0.184 -0.005 0.147 -0.044 0.159 0.160 0.521 0.649 0.649 0.119 1.000

RDW -0.486 -0.386 -0.377 0.252 -0.429 -0.396 0.206 -0.401 0.276 -0.273 -0.031 -0.004 -0.206 0.055 0.056 0.553 0.476 0.476 0.272 0.751 1.000

RMC -0.100 -0.002 -0.008 0.061 -0.116 -0.176 0.082 -0.007 -0.089 0.059 -0.011 0.178 0.184 0.128 0.128 -0.047 0.349 0.349 -0.273 0.438 -0.232 1.000

76

1.2.1.2 Principal Component Analysis: The Principal Component Analysis

(PCA) was used as a data reduction tool to summarize the information from

the data set so that the influence of noise and outliers on the results is reduced.

PCA also decreases the number of descriptors responsible for the highest

percentage of total variance of the experimental data. It allows the relationship

between variables and observations to be studied, as well as recognizing the

data structure. In the present study, the PCA grouped the 22 morphological

characters into 22 components, which accounted for the entire (100%)

variability among the studied accessions. Components with an eigenvalue of

less than 1 were eliminated so that fewer components are dealt with (Chatfield

and Collins 1980, Hair et al. 1998) and component loadings greater than ±0.3

were considered to be meaningful. Hence, from this study, only the first five

eigenvectors which had eigenvalues greater than one and cumulatively

explained about 86.90% of the total variation among the accessions were used

(Table 1.12). The first Principal component (PC) alone explained 43.08% of

the total variation, mainly due to variation in the cladode width (0.318),

cladode fresh (0.303) and dry weight (0.308). Characters which contributed

more to the second PC accounted for 16.33% of the total variation and was

dominated by traits of root as fresh weight (0.417), dry weight (0.317), length

(0.393) of spear as circumference (0.360), diameter (0.359) and spine length

(0.303). Root fresh weight showed the most variation among the characters in

this PC with a high positive loading. The third PC with 13.45% of the variation

was composed of internode length (0.393), root diameter (0.353),

circumference (0.353), and moisture content (0.322). Internode length showed

the most variation among the characters in this PC with a high positive

loading. Root diameter and circumference had the same weight for this

component. The fourth PC with 8.05% of variance comprised of spear length

and internode length with large negative loadings. The eigenvectors of PC5

showed root dry weight with positive loadings which is responsible for 5.97%

of total variation. Number of whorls also showed negative loading for this

component. The first and the second PCs explained the most variation among

the accessions, revealing a high degree of association among the characters

studied.

77

Table 1.12: Principal component analysis of 22 quantitative characters in 13 A.

racemosus accessions showing eigenvectors, eigenvalues, individual and

cumulative percentage of variation explained by the first five PC axes

Character Eigenvector

PC1 PC2 PC3 PC4 PC5

Cladode Width (cm) 0.318 0.025 0.024 0.018 0.077

Cladode Thickness

(cm) 0.270 0.043 0.101 0.194 -0.148

Cladode Length (cm) 0.278 -0.006 0.057 0.257 -0.006

Cladode Length/Width

Ratio -0.285 -0.037 -0.038 0.226 0.002

Cladode Fresh Weight

(g) 0.303 0.011 0.033 -0.066 -0.093

CladodeDry Weight (g) 0.308 0.022 -0.008 -0.048 0.035

Cladode Moisture

Content (%) -0.254 -0.034 0.079 -0.004 -0.181

Cladode per Whorl 0.231 0.043 -0.122 -0.225 -0.171

No. of Whorl/5 inches -0.207 -0.011 -0.288 0.129 -0.406

Distance between

whorls (cm) 0.251 0.063 0.212 0.102 0.286

Internode Length (cm) 0.029 -0.001 0.390 -0.367 0.263

Spine Length (cm) 0.183 0.303 0.050 -0.082 0.027

Spear Circumference

(cm) 0.179 0.360 -0.051 0.245 -0.165

Spear Diameter (cm) 0.180 0.359 -0.052 0.245 -0.165

Spear Length (cm) 0.043 0.112 0.000 -0.647 -0.291

Root Length (cm) -0.097 0.393 -0.288 -0.183 0.074

Root Diameter (cm) -0.196 0.236 0.353 -0.005 -0.015

Root Length/Diameter

Ratio 0.028 0.247 -0.471 -0.193 0.146

Root Circumference

(cm) -0.195 0.236 0.353 -0.006 -0.015

Root Fresh Weight (g) -0.177 0.412 0.099 0.063 0.080

Root Dry Weight (g) -0.194 0.317 -0.081 0.081 0.388

Root Moisture Content

(%) -0.036 0.170 0.322 -0.010 -0.510

Eigen Value 9.479 3.594 2.961 1.772 1.314

Individual % 43.088 16.334 13.458 8.054 5.973

Cumulative % 43.088 59.422 72.880 80.934 86.907

The existence of wider phenotypic diversity among A. racemosus accessions

studied was further explained by the PCA 2D and 3D plot (Fig 1.6A, B). The

PCA plots provide an overview of the similarities and differences between the

quantitative traits of the different accessions and of the interrelationships

between the measured variables. The loading plot demarcated the accessions

with characteristics explained by the first two dimensions. Accessions which

were having similar relationships in the traits were grouped together in the

Principal Component axes.

78

Fig. 1.6 (A) 2D and (B) 3D plot of Principal Component Analysis for 22

quantitative traits studied in 13 A. racemosus

79

The PCA grouped the accessions into four groups. The group I was comprised

of ten accessions FFDC, SG1, CDH, IC471921, IC471924, IC471927,

IC471911, IC471910, IC471909 and IC471908 while the remaining three

accessions (IC471923, JBP and KAU) remained scattered showing large

genetic variability for the traits studied based on the quantitative traits (Fig.

1.6A, B). Both the loading biplot (Figure 1.7) and the correlation matrix

showed that cladode width, cladode thickness, cladode length and cladode

length/width ratio were close to each other.

PCA 0 PCA 1 PCA 2 PCA 3 PCA 4 PCA 5 PCA 6

Cladode Width (cm) 1 0.003825 0.00264 0.000737 0.022575 0.000314

Cladode Thickness (cm) 0.719879 0.01093 0.04639 0.089618 0.084158 0.390615

Cladode Length (cm) 0.765751 0.000228 0.014862 0.157594 0.000124 0.262769

Cladode Length/Width Ratio 0.804321 0.008225 0.006612 0.121814 1.62E-05 0.006547

Cladode Fresh Weight (g) 0.90467 0.000721 0.004972 0.010506 0.03335 0.408324

CladodeDry Weight (g) 0.935194 0.002817 0.000302 0.005422 0.004786 0.3738

Cladode Moisture Content (%) 0.635027 0.006711 0.02832 3.48E-05 0.126543 0.288104

Cladode per Whorl 0.526155 0.010778 0.067408 0.12148 0.112396 1

No. of Whorl/5 inches 0.423221 0.00065 0.373032 0.040047 0.635536 0.270487

Distance between whorls (cm) 0.622609 0.023744 0.203559 0.024732 0.315815 0.290829

Internode Length (cm) 0.008594 6.93E-06 0.685485 0.32164 0.266953 0.056194

Stipule Length (cm) 0.329312 0.539186 0.011233 0.016088 0.002879 0.704488

Spear Circumference (cm) 0.316089 0.764724 0.01155 0.144052 0.104892 0.00257

Spear Diameter (cm) 0.318814 0.758391 0.01229 0.143567 0.105035 0.002497

Spear Length (cm) 0.018646 0.073838 4.9E-07 1 0.325326 0.257057

Root Length (cm) 0.093229 0.908046 0.374911 0.080404 0.020859 0.077914

Root Diameter (cm) 0.377737 0.328677 0.561289 6.82E-05 0.000841 0.230867

Root Length/Diameter Ratio 0.007547 0.358268 1 0.08909 0.081591 0.010015

Root Circumference (cm) 0.377537 0.329109 0.560785 7.64E-05 0.000914 0.230956

Root Fresh Weight (g) 0.310934 1 0.044161 0.009586 0.024485 0.036687

Root Dry Weight (g) 0.371076 0.59192 0.029511 0.015637 0.579405 0.031479

Root Moisture Content (%) 0.012498 0.170674 0.468669 0.000218 1 0.233775

Fig. 1.7 Loading plot for 22 morphological traits among 13 A. racemosus

1.2.1.3 Genetic Distances and Cluster Analysis: The Euclidean2

distance

matrices of morphological traits for all pair-wise comparisons of the 13

accessions of A. racemosus are presented in Table 1.13. A wide range of

Euclidean2 distances was observed among the accessions evaluated. Genetic

distances from 11.12 to 157.87 were observed in the pair-wise combinations,

indicating that the accessions were diverse for the phenotypic characters

measured. The mean Euclidean2 distance between all pairs of genotypes was

44.0. The minimum genetic distance of 11.12 was recorded between accessions

80

IC471923 and IC471924. On the other hand, the highest genetic distance of

157.87 was recorded between accessions IC471908 with KAU (Fig. 1.8A, B).

Cluster analysis for phenotypic traits revealed a clear demarcation

between A. racemosus accessions (Figure 1.8A). Furthermore, Table 1.13

showed differences among clusters by summarising cluster means for the 22

quantitative traits. Based on these traits, the accessions were grouped into

different clusters. The dendrogram divided the accessions into three main

clusters and two singletons. The main Cluster I was produced at a genetic

distance of 15 and included six accessions FFDC, IC471921, IC471911,

IC471909, SG1 and IC471927.

Accessions FFDC (from Kannauj, Uttar Pradesh) and IC471921 (Nauni

Forest, Himachal Pradesh) were separated within the cluster indicating that

they had some differences in the traits. Of the remaining four accessions three

(IC471911, IC471909 and SG1) were originated from Madhya Pradesh while

IC471927 was from Himachal Pradesh. Cluster I was characterised by high

cladode length/width ratio, highest number of whorls per 5 inch of span,

highest root length to diameter ratio with lesser root moisture content and

lowest distance between whorls.

Cluster II was formed at a genetic distance of 25 and comprised of

three accessions, two from Himachal Pradesh IC471923 and IC471924 and

one from Madhya Pradesh (IC471910). IC471910 was separated as a singleton

accession from the rest two accessions in this cluster. This cluster was

characterized by longest root, highest value for root fresh weight, root dry

weight and root moisture content, shortest spear and internode, lowest value

for cladode width, thickness and length with lesser cladode fresh and dry

weight.

Cluster III consisted of two accessions that originated from Chandigarh (CDH)

and Madhya Pradesh (JBP). This cluster grouped the accessions with shortest

spine and root and lowest root dry weight.

Cluster IV and V contained only one accessions each IC471908 and

KAU respectively. Accession IC471908 and KAU did notCluster with any of

the clusters and grouped as a singletons and stood individually as a separate

cluster, indicating that these were phenotypically dissimilar from the other

81

accessions. Cluster IV formed at a distance of 25 and was from Madhya

Pradesh while cluster V formed at 35 and originated from Kerala. Cluster IV

was characterized by highest moisture content, longest internode, thickest root,

smaller number of cladodes per whorl, lowest value for spear circumference

and diameter and root length to diameter ratio.

Cluster V was characterised by longest cladode, highest value for

cladode, width, thickness, fresh and dry weight, highest number of cladodes

per whorl and highest value for distance between whorls with lesser number of

whorls in 5 span and lowest value for cladode length to width ratio, longest

spine and spear, highest value for spear circumference and diameter and lowest

value for root circumference, diameter, fresh weight and moisture content.

82

Fig. 1.8 (A) Dendrogram and (B) Euclidean2 distance using 22 morphological

trait in 13 A. racemosus

83

Table 1.13: Estimates of genetic distance based on morphological characters for all pair-wise comparisons of 13 A. racemosus accessions

Accession FFDC SG1 KAU CDH IC471921 IC471923 IC471924 IC471927 IC471911 IC471910 IC471909 IC471908 JBP

Average

D²

FFDC 1.0000 31.1453

SG1 19.8127 1.0000 34.2031

KAU 127.2027 113.9031 1.0000 129.3786

CDH 29.3114 26.950 102.6854 1.0000 37.7296

IC471921 16.0309 30.1984 104.7598 28.6783 1.0000 34.0611

IC471923 15.2596 20.6883 143.2315 33.8353 27.8183 1.0000 38.2351

IC471924 15.9822 30.0087 130.5650 29.4056 22.3205 11.1291 1.0000 33.1412

IC471927 21.8724 24.8616 151.6556 43.7805 27.0979 35.1499 27.6172 1.0000 42.3883

IC471911 14.7032 13.5467 133.9233 21.6054 19.2127 26.8406 21.7939 19.4219 1.0000 29.1987

IC471910 16.7124 33.8287 126.3699 41.7974 27.6378 19.2945 11.6559 39.5963 22.0913 1.0000 36.2936

IC471909 18.5342 22.3812 120.4425 28.8227 17.2973 23.0265 13.0542 20.4280 12.5110 14.3308 1.0000 29.0033

IC471908 39.6542 35.1398 157.8733 35.9851 47.8367 38.3742 37.9602 62.7628 25.3042 35.1147 27.8925 1.0000 49.2695

JBP 38.6677 39.1178 139.9314 29.8983 39.8449 64.1732 46.2022 34.4155 19.4301 47.0938 29.3190 47.3357 1.0000 47.9525

84

Table 1.14: The summary of cluster means of 22 quantitative traits for A.

racemosus accessions based on data set

Cluster means were compared for each quantitative traits among genotypes and

it was observed that cluster V (KAU) had highest value for cladode width,

thickness, length, fresh and dry weight and distance between whorls while

remaining clusters had average value for maximum traits (Fig. 1.9A-V).

Character Cluster Means

Cladode Width (cm)

I II III IV V Mean

0.047 0.047 0.049 0.050 0.194 0.059

Cladode Thickness (cm) 0.013 0.013 0.014 0.016 0.022 0.014

Cladode Length (cm) 1.252 1.230 1.311 1.306 1.928 1.312

Cladode Length/Width

Ratio

26.874 26.684 26.502 26.111 9.937 25.411

Cladode Fresh Weight (g) 0.068 0.058 0.071 0.066 0.138 0.072

Cladode Dry Weight (g) 0.016 0.012 0.014 0.013 0.042 0.016

Cladode Moisture

Content (%)

77.854 80.218 80.054 81.212 67.144 78.173

Cladode per Whorl 7.014 7.611 7.958 5.917 10.250 7.462

No. of Whorl/5 inches 27.708 26.944 27.625 23.083 17.333 26.365

Distance between whorls

(cm)

0.336 0.410 0.354 0.438 0.708 0.392

Internode Length (cm) 1.996 1.939 1.954 2.458 2.100 2.020

Spine Length (cm) 1.040 1.260 0.960 1.060 1.660 1.128

Spear Circumference

(cm)

1.403 1.580 1.290 1.240 1.740 1.440

Spear Diameter (cm) 0.447 0.502 0.410 0.394 0.554 0.458

Spear Length (cm) 163.830 150.142 155.787 151.553 163.407 158.457

Root Length (cm) 19.163 20.567 12.595 13.400 14.640 17.685

Root Diameter (cm) 1.527 1.687 1.412 1.974 1.197 1.555

Root Length/Diameter

Ratio

12.582 12.176 8.947 6.788 12.231 11.457

Root Circumference (cm) 4.797 5.300 4.435 6.200 3.760 4.885

Root Fresh Weight (g) 17.342 26.630 10.937 21.350 8.430 18.123

Root Dry Weight (g) 2.660 2.972 1.289 2.743 1.290 2.422

Root Moisture Content

(%)

84.528 88.770 88.135 87.150 84.700 86.277

85

A B

C D

E F E

G H

86

I J

K L

M N

O P

87

Fig 1.9 A-V. Cluster means for 22 quantitative traits of 13 A. racemosus accessions

1.2.2 Molecular Markers

The 13 accessions Asparagus racemosus were extensively analyzed for

phylogenetic relationship using very diverse molecular markers. In this study

Cross species STMS markers from chickpea and pearlmillet showed high

percentage of penetrability and generated polymorphism. The SSRs of

Asparagus were also able to generate polymorphisms. However the UBC-ISSR

and RAPD primers were also studied. In the following paragraphs the

observation are given in details.

1.2.2.1 Transferability and Polymorphism of chickpea STMS: Of a total of 85

STMS loci utilized to characterize and assess the genetic diversity of the

thirteen A. racemosus accessions, 38 markers were found to be polymorphic.

The data from all the 38 STMS loci was used in the statistical analysis. The

Allelic data obtained using the 38 chickpea STMS produced a total of 100

R Q

S T

V U

88

alleles, between a minimum of one and a maximum of 11 alleles (NC5), at 38

loci with an average of 2.63 per locus. Most of the markers showed a high

frequency of null allele amongst the genotypes studied. The occurrence of null

allele was confirmed for every null allele occurrence by reamplification, using

the same primer and resolution of amplification products. The reamplification

was done to rule out the possibility of non-amplification due to experimental

error. Primers H2I01, H3C08, H3F09 and H3H011 generated the maximum

number of null alleles. The data for the allele numbers, with respect to the

polymorphic STMS loci, obtained for the different genotypes is presented in

Table 1.15.

Transferability of chickpea STMS in A. racemosus was evaluated based on

the successful amplification of the respective STMS. A total of 85 chickpea

STMS were tested on A. racemosus, out of which 38 (i.e. 44.70 % of the total)

STMS successfully produced bright and distinct amplicon in A. racemosus

genotypes. A high rate of transfer of the primer pairs produced clear bands

across the diverse set of 13 A. racemosus landraces, indicating a high level of

sequence conservation among these species. Seven primers (i.e. 18.42 % of the

total) viz., TA14, H2B061, NC5, TA39, TA167, TA200 and TR3 produced

amplification in all the genotypes (Table 1.16, Fig. 1.10) and established 100

% transferability in A. racemosus. Nine (i.e. 23.68 % of the total) markers

(H2I01, H3C08, NC7, NC11, TA18, TA42, TA103, TA136 and TS83) showed

more than 90 % transferability while eight markers (i.e. 21.05 % of the total)

(CaSTMS9, CaSTMS10, H3F09, H3H07, NC12, NC33, H1H011 and

CaSTMS24) showed more than 80% transferability. Eight (i.e. 21.05 % of the

total) markers (CaSTMS12, CaSTMS21, NC19, NC50, TA22, TA106, TA36

and TS72) amplified positively between 61% and 80%. Two (i.e. 5.2 % of the

total) markers (CaSTMS8 and CaSTMS23) were found to be more than 50%

transferable; four (i.e. 10.52 %) markers namely CaSTMS2, NC13, TA4 and

TA196, showed transferability between 31 to 50 % of the A. racemosus

accessions (Fig. 1.9). The PIC value of primers ranged between 0.15 (H2I01

and H3C08) and 0.97 (NC13). The average PIC value of the transferable

primers was found to be 0.53 (Table 1.16)

89

Fig. 1.10 Extent of cross-transferability of chickpea STMS markers in A.

racemosus. Numbers depicted on the red line and the blue line on the y-axis

indicate per cent cross-transferability and the number of cross-transferable

markers.

90

Table 1.15: Chickpea STMS allelic profile for 13 A. racemosus accessions

Accession Primer name

CaSTMS8 CaSTMS9 CaSTMS12 CaSTMS21 CaSTMS23 CaSTMS10 CaSTMS2 TA14 H2B061 H2I01

FFDC a1 - a1 a1 a1 a1 - a2,a3 a1 a1,a2

SG1 - a1,a2 a1 a1 a1 a1 - a2,a3 a1 a1,a2

KAU a1 a1,a2 a1 a1 - a1 - a1,a2 a1 a1,a2

CDH a1 a1,a2 a1 a1 - a1 a1,a2 a1,a2,a3 a1,a2 a1,a2

IC471921 a1 a2 a1 - a1 - a1,a2 a3 a1,a2 a1,a2

IC471923 a1 a1,a2 a1 - a1 a1 a1,a2 a1,a2,a3 a1,a2 a1,a2

IC471924 - a1,a2 a1 - - a1 a2 a2,a3 a1,a2 a1,a2

IC471927 a1 - a1 a1 a1 a1 a1,a2 a3 a1,a2 a1,a2

IC471911 - a1,a2 a1 - a1 a1 - a1,a2 a1 a1,a2

IC471910 a1 a2 - a1 - a1 a1 a1,a2 a1,a2 a1,a2

IC471909 - a1,a2 - a1 - a1 - a1,a2,a3 a1 a1,a2

IC471908 - a2 - a1 - - - a3 a1 -

JBP - a1,a2 a1 a2 a1 a1 - a1,a3 a3 a1,a2

H3C08 H3F09 H3H07 NC7 NC11 NC12 NC13 NC19 NC33 NC50

FFDC a1 a1 - a2,a3 a5,a6 a1,a2 a1 - a1 a1

SG1 a1 a1 a1 a2,a3 a5,a6,a9 - a1 a2 - -

KAU a1 a1 a1 a1,a2,a3 a3,a5,a6,a7 a1,a2 - a1,a2 a1 a1

CDH a1 a1 a1 - a4,a5,a6,a7 a1,a2 - a1,a2 a1 a1

IC471921 a1 a1 a1 a1,a2 a4,a5,a6 a1,a2 - a1,a2 - a1

IC471923 a1 a1 a1 a1,a2 a5,a6,a8 a1,a2 - a1,a2 a1 a1

IC471924 a1 a1 a1 a3 a9 a1,a2 a3 - a1 a1

IC471927 a1 a1 a1 a1,a2,a3 a2,a5,a6 a1,a2 - a1,a2 a1 -

IC471911 a1 a1 a1 a1,a2,a3 a1,a4,a5,a6 a1,a2 a1,a2,a3 a1,a2 a1 a1

IC471910 a1 a1 a1 a1,a2,a3 a5,a6,a7 a1,a2 - a1,a2 a1 a1

IC471909 a1 a1 a1 a2,a3 a5,a6 a1,a2 - a1,a2 a1 a1

IC471908 - - - a2 - a1,a2 - - a1 a1

JBP a1 - a1 a2,a3 a5,a6 - - a1,a2 a1 -

91

TA4 TA18 TA22 NC5 TA39 TA42 TA106 H1H011 TA167 TA103

FFDC - a2 a2,a3 a3,a10,a11 a1,a2,a6 a1 a3,a4,a5 a1 a3,a4 a1

SG1 - a1 a1,a2,a3 a3,a11 a1,a2,a5 - a1,a2 a1 a3 a2

KAU - a1 a1,a2,a3 a1,a2,a3,a4,a6,a8,a10 a1,a2,a6 a1 a1,a2,a3,a4,a5 a1 a1,a2,a3 a1

CDH - a1,a2 - a1,a3,a4,a5,a6,a8,a9,a10,a

11 a1,a2,a3,a6 a2 a1,a2,a3,a4,a5 a1 a4 a1

IC471921 - a1,a2 a1,a2,a3 a3,a5,a7,a11 a6 a1,a2 a1,a3,a4,a5 a1 a3,a4 -

IC471923 - a1,a2 - a3,a4,a9,a10,a11 a1,a4,a5 a1 a4 - a3,a4 a1

IC471924 - a1,a2 a3 a4,a9,a10,a11 a2,a6 a2 a4,a5,a6 a1 a3,a4 a1

IC471927 a1,a2 a1 - a2,a4,a6,a9,a10,a11 a1,a6 a1 a1,a2,a3,a5,a6 a1 a3,a4 a1

IC471911 a1,a2 a1 a1,a2 a9,a10 a1,a4,a6 a1 a3,a4,a5 a1 a3,a4 a1

IC471910 - a1 a1,a2 a1,a2,a6,a7,a8,a9,a11 a1,a6 a1,a2 - a1 a2,a3,a4 a2

IC471909 a1,a2 - a1,a2 a3,a4,a7,a10,a11 a1,a4,a6 a1,a2 - a1 a3,a4 a1

IC471908 - a1,a2 a1,a2,a3 a3,a4,a7,a8,a9,a10 a1,a4,a6 a2 a4,a5.a6 - a1,a2,a3,a4 a1

JBP a1,a2 a2 a3 a3,a9,a10 a6 a1 - a1 a2,a3,a4 a2

TA200 TA196 TR3 TA136 TA36 TS72 TS83 CaSTMS24

FFDC a1,a2 a1 a3,a4 a1,a2 a1,a2 a1 a1,a2 a1,a2

SG1 a2 - a1,a2,a3,a4 a1,a2 - a1 a1,a2,a3 a1

KAU a1 - a1,a2,a3 a1,a2 a1,a2,a3,a4 a1 a2,a3 a1,a2

CDH a2 - a3,a4 a1,a2 a3,a4 - a2,a3 a1,a2

IC471921 a2 - a1 a1 a3,a4 - a2,a3 -

IC471923 a1,a2 - a1,a2,a3,a4 a1 a1,a2 a1 a1,a2,a3 a1,a2

IC471924 a1,a2 a1 a3,a4 - a1,a2 - a2,a3 -

IC471927 a1,a2 a1 a1,a2,a3,a4 a1 a1,a2 a1 a2 a1,a2

IC471911 a1,a2 a1 a1,a2,a4 a1 a1,a2 a1 a1,a2,a3 a1,a2

IC471910 a1,a2 a1 a1,a2,a4 a1,a2 a2 a1 a2,a3 a1

IC471909 a1 - a1,a2,a3,a4 a1,a2 a1,a2 a1 a2 a1,a2

IC471908 a1,a2 - a1,a2,a4 a1,a2 - - a2,a3 a1,a2

JBP a2 - a1,a2,a3,a4 a1,a2 - - - a1

92

Table 1.16: Cross-transferability of chickpea STMS primers and PIC in A. racemosus accessions

Primers Accessions FFDC SG1 KAU CDH IC471921 IC471923 IC471924 IC471927 IC471911 IC471910 IC471909 IC471908 JBP %T Alleles PIC

CaSTMS8 + - + + + + - + - + - - - 53.85 1 0.36

CaSTMS9 - + + + + + + - + + + + + 84.62 2 0.45

CaSTMS12 + + + + + + + + + - - - + 76.92 1 0.28

CaSTMS21 + + + + - - - + - + + + + 69.23 2 0.81

CaSTMS23 + + - - + + - + + - - - + 53.85 1 0.32

CaSTMS10 + + + + - + + + + + + - + 84.62 1 0.36

CaSTMS2 - - - + + + + + - + - - - 46.15 2 0.85

TA14 + + + + + + + + + + + + + 100 3 0.55

H2B061 + + + + + + + + + + + + + 100 3 0.64

H2I01 + + + + + + + + + + + - + 92.31 2 0.15

H3C08 + + + + + + + + + + + - + 92.31 1 0.15

H3F09 + + + + + + + + + + + - - 84.62 1 0.28

H3H07 - + + + + + + + + + + - + 84.62 1 0.28

NC7 + + + - + + + + + + + + + 92.31 3 0.53

NC11 + + + + + + + + + + + - + 92.31 9 0.82

NC12 + - + + + + + + + + + + - 84.62 2 0.28

NC13 + + - - - - + - + - - - - 30.77 3 0.97

NC19 - + + + + + - + + + + - + 76.92 2 0.46

NC33 + - + + - + + + + + + + + 84.62 1 0.28

NC50 + - + + + + + - + + + + - 76.92 1 0.41

TA4 - - - - - - - + + - + - + 30.77 2 0.91

TA18 + + + + + + + + + + - + + 92.31 2 0.56

TA22 + + + - + - + - + + + + + 76.92 3 0.68

NC5 + + + + + + + + + + + + + 100 11 0.76

TA39 + + + + + + + + + + + + + 100 6 0.74

TA42 + - + + + + + + + + + + + 92.31 2 0.65

TA106 + + + + + + + + + - - + - 76.92 6 0.79

H1H011 + + + + + - + + + + + - + 84.62 1 0.28

TA167 + + + + + + + + + + + + + 100.00 4 0.57

TA103 + + + + - + + + + + + + + 92.31 2 0.73

TA200 + + + + + + + + + + + + + 100.00 2 0.40

TA196 + - - - - - + + + + - - - 38.46 1 0.85

TR3 + + + + + + + + + + + + + 100.00 4 0.43

TA136 + + + + + + - + + + + + + 92.31 2 0.38

TA36 + - + + + + + + + + + - - 76.92 4 0.81

TS72 + + + - - + - + + + + - - 61.54 1 0.62

TS83 + + + + + + + + + + + + + 92.31 3 0.52

CaSTMS24 + + + + - + - + + + + + + 84.62 2 0.28

93

Table 1.17: Number and per cent of Chick Pea, Pearl Millet STMS and Asparagus SSR

primer pairs that amplified PCR products in A. racemosus along with unique alleles

Unique bands: Four primer pairs produced accession-specific amplicons in A. racemosus

(Table 1.17). For example, CaSTMS2 produced a unique band of 80 bp in the A. racemosus

accession obtained from Jabalpur, Madhya Pradesh. Similarly, Primer H2B061 amplified a

unique amplicon of 60 bp in A. racemosus originated from Jabalpur. NC11 detected five

unique bands of 400, 350, 320, 70 and 50 bp with IC471911, IC471927, KAU, IC471923 and

IC471924, respectively. Primer NC13 amplified a unique band of 200 bp in IC471911 of A.

racemosus.

Chick Pea STMS

Accessions

No. of

marker

amplified

Unique allele

Marker Size (bp)

JBP 42 H2B061 60

IC471911 58 NC11 400

IC471927 62 NC11 350

KAU 63 NC11 320

IC471923 48 NC11 70

IC471924 61 NC11 50

IC471911 58 NC13 200

Pearl Millet STMS

Accessions

No. of

marker

amplified

Unique allele

Marker Size (bp)

IC471909 15 IPES0114 90

IC471910 11 IPES0161 300

Asparagus SSR

Accessions

No. of

marker

amplified

Unique allele

Marker Size (bp)

IC471921 8 DSFR3 100

KAU 14 DSFR3 1000

KAU 14 DSFR3 700

KAU 14 DSFR3 300

IC471910 11 DSFR14 150

94

Table 1.18: Genetic similarity among 13 A. racemosus generated using 38 chickpea STMS primer combinations based on Jaccard’s similarity

coefficient.

S. No. Accessions FFDC SG1 KAU CDH IC471921 IC471923 IC471924 IC471927 IC471911 IC471910 IC471909 IC471908 JBP Average

D²

1 FFDC 1.000 0.4932

2 SG1 0.50 1.000 0.5672

3 KAU 0.4545 0.5195 1.000 0.4870

4 CDH 0.5065 0.5897 0.3951 1.000 0.5113

5 IC471921 0.5429 0.5942 0.5556 0.4306 1.000 0.5586

6 IC471923 0.4366 0.5278 0.4634 0.4359 0.5068 1.000 0.4673

7 IC471924 0.50 0.6571 0.5926 0.5135 0.5942 0.5070 1.000 0.5761

8 IC471927 0.4306 0.5789 0.4390 0.4691 0.5395 0.3784 0.5405 1.000 0.4880

9 IC471911 0.4167 0.5467 0.4268 0.5465 0.5455 0.3649 0.5467 0.4026 1.000 0.480

10 IC471910 0.5333 0.5417 0.3974 0.4875 0.5205 0.4605 0.6184 0.4342 0.4416 1.000 0.5004

11 IC471909 0.4179 0.5147 0.4079 0.5190 0.5946 0.3857 0.5775 0.4247 0.3662 0.40 1.000 0.4699

12 IC471908 0.5821 0.6957 0.5714 0.6053 0.6324 0.5833 0.5938 0.6494 0.60 0.5775 0.5303 1.000 0.6094

13 JBP 0.5970 0.5410 0.6203 0.6364 0.6471 0.5571 0.6716 0.5694 0.5556 0.5915 0.50 0.6923 1.000 0.5983

95

96

(i) Genetic Distance Similarity and Cluster Analysis:

A genetic similarity matrix was produced based on the 100 alleles amplified

by 38 chickpea STMS marker data for all the pair combinations of the 13 A.

racemosus accessions (Table 1.18). The genetic similarity varied from 0.364

to 0.695 with an average of 0.534. The minimum genetic distance of 0.364

was recorded between accessions IC471923 and IC471911. On the other hand,

the highest genetic distance of 0.695 was recorded between accessions

IC471908 with SG1. However, STMS analysis detected no duplications (i.e.

100% similarity) within the tested accessions.

Dendrogram depicted by Ward’s method based on Jaccard’s similarity

coefficient clearly clustered the A. racemosus accessions into four major

groups based on the secondary branching (Fig. 1.11). Cluster I comprised of

two accessions IC471924 and IC471908 were respectively at a genetic

similarity of 0.78 from Solan, Himachal Pradesh and Mandla, Madhya

Pradesh (IC471908). Cluster II consisted of five accessions at a distance of

0.78. This cluster was subdivided into subgroups and as singletons. Two

accessions (IC471923 and IC471911) were grouped together while IC471909,

IC471927 and FFDC were separated as singletons. Cluster II consisted of a

mix of accessions originated from Himachal Pradesh (IC471923 and

IC471927) and Madhya Pradesh (IC471911 and IC471909) except for one

accession (FFDC) originating from Uttar Pradesh.

Cluster III comprised of four accessions at a genetic distance similarity of

0.74. This group contained accessions, KAU, CDH, IC471910 and IC471921

from four different origins i.e. Kerala, Chandigarh, Madhya Pradesh and

Himachal Pradesh respectively while two accessions originating from Kerala

and Chandigarh formed a single cluster, the other two accessions were

separated as singletons from Mandala (Madhya Pradesh) and Solan (Himachal

Pradesh). Cluster IV was also formed at a distance of 0.74 contained only two

accessions. Both accessions SG1 and JBP are from Madhya Pradesh.

97

Fig. 1.11 Dendrogram based on Jaccard’s Similarity Matrix for 38 chickpea

STMS among 13 A. racemosus

(ii) Principal Component Analysis: The Principal component analysis

(PCA) was used as a data reduction tool to summarize the information from

the data set so that the influence of noise and outliers on the results was

reduced. Principal components (PCs) with an eigen value of less than 1 or

negative eigen values were eliminated to determine the optimum number of

clusters in the study (Chatfiedd and Collins 1980, Hair et al. 1998). Hence,

amongst all the accessions a total of twelve eigen vectors which had eigen

values of greater than one were used (Table 1.19). The first Principal

Component (PC) alone explained 33.83% of the total variation, mainly due to

CaSTMS8, H3F09 and NC5. Markers which contributed more to the second

PC accounted for 14.35% of the total variation. The third PC produced

11.91% of the variation while the fourth PC produced 10.86% of variance.

The eigen vectors of PC5, PC6, PC7, PC8, PC9, PC10, PC11 and PC12

contributed less than 10% of total variation (Fig. 1.12).

98

Table1.19: Principal component analysis of 38 Chick Pea STMS loci in 13 A. racemosus accessions showing eigenvectors, eigenvalues,

individual and cumulative percentage of variation explained by the12 PC axes

PC1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 PC 8 PC 9 PC 10 PC 11 PC 12

CaSTMS8 0.189 0.044 0.064 0.003 0.061 0.027 0.134 0.045 0.082 0.019 0.097 0.217

CaSTMS9 0.013 -0.072 0.042 -0.032 -0.087 -0.124 -0.122 -0.247 -0.145 0.020 0.011 -0.139

-0.042 0.031 0.056 0.117 0.003 0.069 -0.164 -0.164 -0.233 0.008 0.050 -0.095

CaSTMS12 0.051 -0.030 0.137 -0.165 -0.070 -0.066 -0.064 -0.062 0.097 0.204 -0.014 0.096

CaSTMS21 0.078 0.010 -0.048 0.160 -0.097 -0.065 0.178 0.097 0.081 -0.127 -0.138 -0.066

-0.142 -0.126 0.093 0.033 0.132 -0.135 -0.071 -0.034 0.020 0.032 0.033 0.090

CaSTMS23 -0.040 -0.136 0.066 -0.150 0.008 0.129 0.042 -0.012 0.148 0.142 -0.024 0.092

CaSTMS10 0.101 -0.147 -0.014 -0.088 -0.072 -0.178 0.004 -0.043 -0.110 -0.105 -0.052 0.065

CaSTMS2 0.130 0.051 0.102 -0.039 0.175 0.103 0.162 0.035 -0.079 0.016 -0.008 0.074

0.066 0.126 0.108 -0.172 0.095 -0.012 0.100 -0.011 -0.065 0.106 0.050 -0.056

TA14 0.102 -0.099 -0.017 0.086 0.121 -0.053 -0.098 -0.206 -0.120 -0.092 0.031 0.043

0.124 -0.020 -0.044 -0.037 -0.191 -0.020 -0.057 -0.121 -0.130 -0.197 0.004 0.065

-0.136 0.057 0.098 -0.105 0.004 -0.043 0.207 -0.046 0.089 -0.042 0.041 -0.149

H2B061 0.142 0.126 -0.093 -0.033 -0.132 0.135 0.071 0.034 -0.020 -0.032 -0.033 -0.090

0.094 0.109 0.094 -0.115 0.128 0.030 0.101 0.080 -0.170 0.015 0.037 0.060

H2B061 -0.142 -0.126 0.093 0.033 0.132 -0.135 -0.071 -0.034 0.020 0.032 0.033 0.090

H2I01 0.139 -0.127 0.118 -0.133 -0.025 -0.040 -0.069 0.025 -0.043 -0.079 0.106 0.009

0.139 -0.127 0.118 -0.133 -0.025 -0.040 -0.069 0.025 -0.043 -0.079 0.106 0.009

H3C08 0.139 -0.127 0.118 -0.133 -0.025 -0.040 -0.069 0.025 -0.043 -0.079 0.106 0.009

H3F09 0.208 0.000 0.018 -0.123 -0.116 0.070 0.002 0.044 -0.047 -0.082 0.054 -0.060

H3H07 0.108 -0.091 0.116 -0.046 0.060 -0.014 -0.086 -0.008 -0.223 0.073 0.047 -0.172

NC7 0.163 -0.039 -0.043 -0.011 0.104 0.147 -0.031 0.059 -0.052 0.207 0.084 0.097

-0.023 -0.157 -0.089 0.095 0.020 0.168 0.064 0.044 0.120 0.094 0.097 0.035

0.013 -0.164 -0.073 -0.030 -0.103 -0.128 -0.106 0.183 -0.024 -0.063 -0.006 -0.014

NC11 0.036 -0.059 -0.107 -0.081 0.027 0.108 -0.242 -0.082 0.020 0.049 -0.198 -0.008

0.065 -0.039 -0.036 -0.088 0.102 -0.072 0.175 0.186 0.056 0.168 -0.110 -0.114

0.124 -0.005 -0.028 0.148 -0.099 -0.117 -0.092 -0.017 0.039 0.184 0.155 0.029

0.083 0.072 0.103 -0.050 0.096 0.135 -0.187 -0.091 0.087 -0.008 -0.150 -0.072

0.147 -0.176 0.095 0.009 0.041 0.068 0.028 -0.045 0.097 -0.057 0.016 0.023

99

0.147 -0.176 0.095 0.009 0.041 0.068 0.028 -0.045 0.097 -0.057 0.016 0.023

0.172 0.044 0.053 0.167 0.012 -0.086 -0.042 0.006 -0.094 -0.068 -0.052 0.138

0.022 -0.016 -0.026 -0.084 0.028 0.077 0.170 -0.230 -0.120 0.091 0.156 0.139

-0.082 0.022 0.065 -0.089 -0.235 -0.037 -0.025 0.078 -0.177 0.030 -0.044 -0.089

NC12 0.142 0.154 -0.143 -0.042 0.079 0.038 -0.002 0.011 0.033 -0.055 0.082 0.006

0.142 0.154 -0.143 -0.042 0.079 0.038 -0.002 0.011 0.033 -0.055 0.082 0.006

NC13 -0.014 -0.092 -0.029 -0.081 -0.201 0.107 -0.076 -0.017 0.135 -0.055 -0.190 0.086

0.036 -0.059 -0.107 -0.081 0.027 0.108 -0.242 -0.082 0.020 0.049 -0.198 -0.008

-0.018 0.039 -0.087 -0.167 -0.039 -0.018 -0.258 0.003 -0.114 0.034 -0.084 -0.022

NC19 0.149 -0.088 0.040 0.034 0.231 0.018 -0.048 -0.067 -0.036 0.034 0.072 -0.065

0.131 -0.149 0.106 0.052 0.102 0.071 -0.006 -0.061 -0.081 0.064 -0.013 -0.134

NC33 0.035 0.008 -0.175 -0.007 0.123 -0.210 0.000 -0.076 -0.030 -0.078 -0.023 0.131

NC50 0.081 0.157 -0.099 0.019 0.003 0.078 -0.112 -0.108 -0.007 -0.153 0.140 0.077

TA4 -0.022 -0.160 -0.073 -0.061 0.162 -0.065 -0.065 0.013 0.057 0.007 -0.072 -0.225

-0.022 -0.160 -0.073 -0.061 0.162 -0.065 -0.065 0.013 0.057 0.007 -0.072 -0.225

TA18 0.093 0.116 0.015 0.005 -0.029 0.107 -0.001 0.029 -0.178 0.242 -0.142 0.016

TA18 -0.125 0.142 0.090 -0.070 0.066 -0.035 0.029 -0.123 0.078 -0.022 0.091 0.179

TA22 0.017 -0.015 -0.061 0.180 -0.068 0.203 -0.125 0.055 -0.029 0.008 0.005 -0.165

0.014 -0.017 -0.083 0.146 -0.128 0.196 -0.102 0.076 0.112 -0.089 0.028 -0.037

-0.146 0.049 0.072 0.058 -0.158 -0.043 -0.082 0.079 0.122 0.117 0.128 0.082

NC5 0.172 0.044 0.053 0.167 0.012 -0.086 -0.042 0.006 -0.094 -0.068 -0.052 0.138

0.154 -0.044 -0.053 0.101 0.043 -0.070 0.056 0.215 -0.065 0.117 0.015 0.082

-0.055 0.007 0.101 0.124 -0.066 0.011 0.097 -0.208 0.171 -0.027 0.142 -0.041

0.057 0.121 -0.087 0.006 0.026 -0.168 0.139 -0.104 -0.075 0.085 0.061 -0.193

0.070 0.127 0.199 0.002 0.092 0.078 -0.040 -0.046 0.087 -0.046 -0.029 -0.078

0.194 0.018 0.027 0.101 0.070 -0.120 0.063 0.112 -0.053 0.034 -0.111 0.060

-0.045 0.069 -0.045 0.143 0.106 0.176 0.015 0.105 -0.028 -0.150 0.114 -0.134

0.077 0.113 -0.020 0.229 0.026 -0.055 0.002 -0.009 -0.061 -0.017 -0.109 0.120

-0.039 0.040 -0.051 -0.064 0.193 -0.072 0.010 -0.041 -0.187 0.068 -0.207 0.145

-0.005 0.023 -0.137 -0.068 0.064 -0.229 -0.003 -0.173 0.100 0.039 -0.006 -0.024

0.070 0.037 0.092 -0.134 -0.049 0.060 0.194 0.091 -0.071 -0.199 0.067 -0.059

TA39 0.127 -0.036 -0.139 0.080 -0.078 0.041 0.160 -0.085 0.030 -0.058 -0.185 0.007

100

0.053 0.066 0.084 -0.015 -0.258 -0.155 -0.032 -0.011 0.039 -0.043 -0.043 0.068

0.092 0.100 0.132 0.017 0.053 -0.096 0.020 -0.145 0.012 -0.125 -0.215 -0.026

-0.045 -0.001 -0.189 -0.001 0.058 0.122 0.013 -0.222 -0.031 -0.009 0.001 -0.136

-0.021 -0.073 0.055 -0.044 -0.156 0.118 0.180 -0.156 -0.137 0.099 0.009 0.030

0.021 0.073 -0.055 0.044 0.156 -0.118 -0.180 0.156 0.137 -0.099 -0.009 -0.030

TA42 0.091 -0.148 -0.059 -0.017 0.139 0.061 -0.032 0.038 0.107 0.003 0.220 0.090

-0.025 0.177 0.022 0.064 0.083 0.042 -0.033 0.065 -0.112 -0.207 0.036 -0.150

TA106 0.128 0.025 0.167 0.047 -0.061 -0.001 0.056 0.069 0.081 0.182 -0.076 -0.159

0.133 -0.015 0.097 0.058 -0.105 -0.117 0.102 0.025 0.024 0.155 -0.182 -0.121

0.167 0.035 0.032 -0.048 0.026 0.002 -0.089 0.033 0.263 0.086 -0.080 0.023

0.038 0.179 -0.032 -0.053 -0.044 0.033 -0.115 -0.161 0.102 0.088 0.051 0.146

0.061 0.167 -0.038 -0.056 -0.004 -0.049 -0.112 0.067 0.198 0.130 -0.094 0.007

-0.085 0.127 -0.104 -0.063 0.030 -0.104 0.086 0.156 -0.047 0.155 -0.083 -0.092

H1H011 0.086 -0.082 0.106 -0.036 -0.039 -0.086 -0.176 0.189 0.057 -0.126 -0.037 -0.096

TA167 -0.048 0.147 -0.099 0.086 -0.098 -0.132 -0.082 0.028 -0.058 0.165 0.084 -0.001

-0.059 -0.018 -0.042 0.238 0.071 -0.077 -0.051 0.055 -0.056 0.074 0.035 0.188

-0.092 -0.100 -0.132 -0.017 -0.053 0.096 -0.020 0.145 -0.012 0.125 0.215 0.026

-0.055 0.065 -0.053 -0.127 0.250 0.026 0.013 -0.002 0.019 -0.167 -0.008 0.051

TA103 0.078 0.094 -0.179 -0.082 -0.018 -0.131 0.038 -0.138 0.080 0.017 -0.025 -0.074

-0.087 -0.149 0.109 0.098 -0.026 0.016 0.006 0.099 -0.155 -0.059 -0.084 0.131

TA200 0.057 0.021 -0.267 -0.034 -0.011 -0.031 0.030 0.045 -0.042 -0.007 0.087 0.066

-0.094 0.043 0.077 -0.131 0.059 0.097 0.049 0.047 -0.031 0.038 -0.224 0.244

TA196 0.048 -0.008 -0.117 -0.157 0.004 -0.022 -0.066 0.217 -0.021 -0.072 -0.111 0.172

TR3 -0.015 -0.162 -0.126 0.123 0.036 0.028 0.066 -0.035 -0.117 0.140 -0.050 -0.066

-0.015 -0.162 -0.126 0.123 0.036 0.028 0.066 -0.035 -0.117 0.140 -0.050 -0.066

0.026 -0.066 0.062 -0.078 -0.110 -0.247 0.139 -0.085 0.018 -0.014 0.086 -0.068

-0.124 0.005 0.028 -0.148 0.099 0.117 0.092 0.017 -0.039 -0.184 -0.155 -0.029

TA136 0.061 -0.112 0.011 0.145 0.080 0.132 0.107 -0.086 0.174 0.003 -0.084 0.022

-0.035 -0.039 0.023 0.226 -0.081 -0.100 0.043 -0.023 0.061 -0.202 -0.060 0.046

TA36 0.097 -0.034 -0.173 -0.156 -0.058 -0.092 -0.012 -0.037 0.045 0.040 0.149 -0.049

0.130 -0.049 -0.189 -0.105 -0.024 -0.051 -0.010 0.056 -0.063 -0.050 0.140 0.067

0.138 0.106 0.153 0.095 0.016 -0.008 -0.093 -0.050 0.099 0.077 0.073 -0.049

101

0.138 0.106 0.153 0.095 0.016 -0.008 -0.093 -0.050 0.099 0.077 0.073 -0.049

TS72 0.136 -0.156 -0.128 -0.013 -0.111 0.066 0.090 0.020 -0.004 -0.025 0.015 0.025

TS83 0.000 -0.093 -0.041 -0.123 -0.167 0.142 0.029 -0.148 0.054 0.003 -0.084 0.159

0.142 0.126 -0.093 -0.033 -0.132 0.135 0.071 0.034 -0.020 -0.032 -0.033 -0.090

0.048 0.128 0.034 0.055 -0.085 0.139 -0.102 -0.081 -0.195 0.124 -0.066 0.081

CaSTMS24 0.043 -0.136 -0.093 0.118 0.006 -0.051 0.134 -0.125 0.050 -0.044 -0.192 0.075

0.107 0.028 -0.163 0.002 0.027 -0.052 0.095 -0.178 0.171 0.018 -0.069 -0.058

EigenValue 13.839 14.354 11.911 10.862 8.524 7.917 6.680 6.572 6.213 5.003 4.486 3.708

% Var.Exp. 13.839 14.354 11.911 10.862 8.524 7.917 6.680 6.572 6.213 5.003 4.486 3.708

Cum. Var. 13.839 28.193 40.104 50.966 59.490 67.407 74.087 80.660 86.873 91.876 96.362 100.070

102

PCA 0 PCA 1 PCA 2 PCA 3 PCA 4 PCA 5 PCA 6 PCA 7 PCA 8 PCA 9 PCA 10 PCA 11 PCA 12

CaSTMS8 0.827179 0.061786 0.057893 0.000214 0.056272 0.01161 0.271938 0.032912 0.097632 0.006167 0.187007 0.790675

CaSTMS9 0.003862 0.160231 0.024743 0.018226 0.113211 0.252798 0.223467 1 0.304616 0.006569 0.002282 0.327474

0.041242 0.030418 0.043351 0.241263 0.000151 0.077478 0.404811 0.43949 0.784782 0.001085 0.049814 0.150894

CaSTMS12 0.059765 0.028227 0.261926 0.481235 0.074185 0.071729 0.060862 0.063503 0.136744 0.715185 0.003706 0.15684

CaSTMS21 0.142204 0.002863 0.031849 0.448639 0.14221 0.069835 0.475877 0.154387 0.094247 0.277006 0.376022 0.072871

0.470952 0.499076 0.122249 0.019313 0.260261 0.29774 0.076018 0.019462 0.005909 0.017228 0.021659 0.137135

CaSTMS23 0.037302 0.576576 0.060391 0.398113 0.001017 0.272388 0.026537 0.002269 0.315462 0.347688 0.011603 0.142334

CaSTMS10 0.234343 0.673108 0.002887 0.134862 0.077355 0.522548 0.000247 0.030178 0.174242 0.188584 0.053319 0.072018

CaSTMS2 0.390027 0.079917 0.146895 0.026318 0.461757 0.174398 0.395201 0.020458 0.091093 0.004597 0.001291 0.091084

0.100773 0.495396 0.162281 0.521236 0.136552 0.002392 0.150831 0.002118 0.061892 0.19415 0.049779 0.053209

TA14 0.239481 0.30785 0.00426 0.13039 0.21899 0.046029 0.144561 0.694367 0.209393 0.143558 0.018996 0.03118

0.353839 0.011931 0.027439 0.023974 0.547605 0.006695 0.048152 0.239088 0.244074 0.665536 0.000263 0.071145

0.431079 0.102208 0.135048 0.192509 0.000264 0.030891 0.647072 0.034389 0.113393 0.029656 0.033762 0.374236

H2B061 0.470952 0.499076 0.122249 0.019313 0.260261 0.29774 0.076018 0.019462 0.005909 0.017228 0.021659 0.137135

0.204782 0.372058 0.123503 0.233394 0.2466 0.014695 0.153066 0.105921 0.419162 0.003655 0.027402 0.06073

H2B061 0.470952 0.499076 0.122249 0.019313 0.260261 0.29774 0.076018 0.019462 0.005909 0.017228 0.021659 0.137135

H2I01 0.445886 0.501256 0.194652 0.311543 0.009697 0.026546 0.071412 0.01045 0.026502 0.107746 0.221992 0.00135

0.445886 0.501256 0.194652 0.311543 0.009697 0.026546 0.071412 0.01045 0.026502 0.107746 0.221992 0.00135

H3C08 0.445886 0.501256 0.194652 0.311543 0.009697 0.026546 0.071412 0.01045 0.026502 0.107746 0.221992 0.00135

H3F09 1 1.3E-06 0.004572 0.265088 0.202055 0.079897 3.92E-05 0.031873 0.031331 0.11517 0.057256 0.060694

H3H07 0.270487 0.257491 0.188351 0.037192 0.05349 0.003271 0.11126 0.001065 0.719667 0.091744 0.044451 0.500777

NC7 0.615363 0.046993 0.025406 0.00218 0.162468 0.353995 0.014406 0.057947 0.039287 0.735303 0.140195 0.157882

0.012293 0.766075 0.11175 0.157314 0.006024 0.466189 0.062414 0.031725 0.20686 0.151764 0.185819 0.021222

0.003744 0.835559 0.074278 0.015508 0.158595 0.270421 0.170423 0.550576 0.008023 0.067393 0.000793 0.003403 PCA 0 PCA 1 PCA 2 PCA 3 PCA 4 PCA 5 PCA 6 PCA 7 PCA 8 PCA 9 PCA 10 PCA 11 PCA 12

NC5 0.684874 0.059603 0.03937 0.488859 0.00229 0.120177 0.026244 0.000514 0.126746 0.079804 0.054074 0.318641

0.553368 0.060624 0.040112 0.177895 0.027905 0.081252 0.047166 0.757293 0.061979 0.233409 0.004276 0.112722

0.06998 0.001431 0.143288 0.271519 0.065392 0.001815 0.142595 0.71083 0.424678 0.012528 0.397537 0.028173

0.074742 0.454697 0.1073 0.000603 0.010058 0.463098 0.290426 0.175656 0.081637 0.124619 0.073263 0.630295

0.113431 0.505805 0.55516 5.1E-05 0.128111 0.099462 0.024278 0.03449 0.108869 0.036039 0.016732 0.10269

0.874721 0.009723 0.010591 0.181291 0.073837 0.234933 0.059062 0.206481 0.040664 0.020352 0.243997 0.059806

0.046689 0.148974 0.02906 0.357718 0.167449 0.511964 0.003438 0.18051 0.011077 0.38693 0.258169 0.30261

0.136857 0.398953 0.005416 0.922598 0.010109 0.049459 4.1E-05 0.001469 0.053363 0.004674 0.234548 0.244122

0.035984 0.050036 0.036308 0.071132 0.55875 0.085735 0.001633 0.027332 0.506312 0.079574 0.846638 0.355577

0.000574 0.01644 0.262101 0.082192 0.061256 0.860914 0.000181 0.491693 0.145456 0.026108 0.000725 0.009313

0.113262 0.043213 0.117844 0.317889 0.035991 0.059053 0.566144 0.137141 0.071844 0.675123 0.089047 0.058423

TA39 0.374489 0.041327 0.269813 0.112181 0.090882 0.02819 0.384198 0.119103 0.013093 0.057856 0.682537 0.000857

0.066268 0.13559 0.099731 0.003869 1 0.396893 0.014985 0.002144 0.022091 0.032318 0.037304 0.078735

0.196903 0.313705 0.244942 0.004983 0.042652 0.151185 0.006013 0.346805 0.001968 0.267072 0.919594 0.011508

0.046742 3.06E-05 0.501565 3.87E-05 0.050214 0.243868 0.002568 0.808604 0.014318 0.001416 1.72E-05 0.313138

0.010041 0.164657 0.042137 0.0347 0.364216 0.227553 0.48783 0.397014 0.269434 0.167758 0.001447 0.015522

0.010041 0.164657 0.042137 0.0347 0.364216 0.227553 0.48783 0.397014 0.269434 0.167758 0.001447 0.015522

TA42 0.19205 0.682954 0.049406 0.004883 0.28856 0.060609 0.015537 0.023414 0.164006 0.000178 0.961537 0.135243

0.014353 0.981694 0.007076 0.071113 0.104122 0.028318 0.01601 0.069927 0.181998 0.735897 0.0251 0.377889

TA106 0.378852 0.019637 0.392454 0.039346 0.055319 6.36E-06 0.047157 0.078015 0.094663 0.566649 0.114459 0.42355

0.411793 0.007206 0.132223 0.059664 0.166864 0.22438 0.156404 0.00992 0.008462 0.414111 0.654727 0.247442

0.644652 0.038111 0.014324 0.040387 0.010092 4.09E-05 0.118586 0.017357 1 0.125213 0.128338 0.008966

0.033678 1 0.014481 0.050318 0.029486 0.017478 0.197382 0.425354 0.149271 0.132363 0.052141 0.357114

0.087718 0.865194 0.020213 0.0543 0.00019 0.039047 0.188574 0.072614 0.565898 0.287861 0.176065 0.000763

0.168967 0.49977 0.153243 0.069722 0.013227 0.176428 0.111722 0.397372 0.032207 0.409107 0.136311 0.141925

H1H011 0.172281 0.209862 0.157178 0.023153 0.022953 0.122385 0.467718 0.58288 0.046387 0.272403 0.027352 0.156081 PCA 0 PCA 1 PCA 2 PCA 3 PCA 4 PCA 5 PCA 6 PCA 7 PCA 8 PCA 9 PCA 10 PCA 11 PCA 12

NC5 0.684874 0.059603 0.03937 0.488859 0.00229 0.120177 0.026244 0.000514 0.126746 0.079804 0.054074 0.318641

0.553368 0.060624 0.040112 0.177895 0.027905 0.081252 0.047166 0.757293 0.061979 0.233409 0.004276 0.112722

0.06998 0.001431 0.143288 0.271519 0.065392 0.001815 0.142595 0.71083 0.424678 0.012528 0.397537 0.028173

0.074742 0.454697 0.1073 0.000603 0.010058 0.463098 0.290426 0.175656 0.081637 0.124619 0.073263 0.630295

0.113431 0.505805 0.55516 5.1E-05 0.128111 0.099462 0.024278 0.03449 0.108869 0.036039 0.016732 0.10269

0.874721 0.009723 0.010591 0.181291 0.073837 0.234933 0.059062 0.206481 0.040664 0.020352 0.243997 0.059806

0.046689 0.148974 0.02906 0.357718 0.167449 0.511964 0.003438 0.18051 0.011077 0.38693 0.258169 0.30261

0.136857 0.398953 0.005416 0.922598 0.010109 0.049459 4.1E-05 0.001469 0.053363 0.004674 0.234548 0.244122

0.035984 0.050036 0.036308 0.071132 0.55875 0.085735 0.001633 0.027332 0.506312 0.079574 0.846638 0.355577

0.000574 0.01644 0.262101 0.082192 0.061256 0.860914 0.000181 0.491693 0.145456 0.026108 0.000725 0.009313

0.113262 0.043213 0.117844 0.317889 0.035991 0.059053 0.566144 0.137141 0.071844 0.675123 0.089047 0.058423

TA39 0.374489 0.041327 0.269813 0.112181 0.090882 0.02819 0.384198 0.119103 0.013093 0.057856 0.682537 0.000857

0.066268 0.13559 0.099731 0.003869 1 0.396893 0.014985 0.002144 0.022091 0.032318 0.037304 0.078735

0.196903 0.313705 0.244942 0.004983 0.042652 0.151185 0.006013 0.346805 0.001968 0.267072 0.919594 0.011508

0.046742 3.06E-05 0.501565 3.87E-05 0.050214 0.243868 0.002568 0.808604 0.014318 0.001416 1.72E-05 0.313138

0.010041 0.164657 0.042137 0.0347 0.364216 0.227553 0.48783 0.397014 0.269434 0.167758 0.001447 0.015522

0.010041 0.164657 0.042137 0.0347 0.364216 0.227553 0.48783 0.397014 0.269434 0.167758 0.001447 0.015522

TA42 0.19205 0.682954 0.049406 0.004883 0.28856 0.060609 0.015537 0.023414 0.164006 0.000178 0.961537 0.135243

0.014353 0.981694 0.007076 0.071113 0.104122 0.028318 0.01601 0.069927 0.181998 0.735897 0.0251 0.377889

TA106 0.378852 0.019637 0.392454 0.039346 0.055319 6.36E-06 0.047157 0.078015 0.094663 0.566649 0.114459 0.42355

0.411793 0.007206 0.132223 0.059664 0.166864 0.22438 0.156404 0.00992 0.008462 0.414111 0.654727 0.247442

0.644652 0.038111 0.014324 0.040387 0.010092 4.09E-05 0.118586 0.017357 1 0.125213 0.128338 0.008966

0.033678 1 0.014481 0.050318 0.029486 0.017478 0.197382 0.425354 0.149271 0.132363 0.052141 0.357114

0.087718 0.865194 0.020213 0.0543 0.00019 0.039047 0.188574 0.072614 0.565898 0.287861 0.176065 0.000763

0.168967 0.49977 0.153243 0.069722 0.013227 0.176428 0.111722 0.397372 0.032207 0.409107 0.136311 0.141925

H1H011 0.172281 0.209862 0.157178 0.023153 0.022953 0.122385 0.467718 0.58288 0.046387 0.272403 0.027352 0.156081

Fig 1.12. Loading plot for 38 chickpea STMS in Asparagus racemosus

accessions

103

Cluster analysis was done based on PCA using K-clustering method which

revealed four clusters among the 13 accessions (Fig. 1.13). Cluster I consisted

of two accessions of very diverse origins, Kerala (KAU) and Chandigarh

(CDH). Cluster II comprised of six accessions from various origins: three from

Mandla, (IC471911, IC471910 and IC471909); two from Solan, (IC471923

and IC471927) and one from Kannauj, Uttar Pradesh (FFDC). Cluster III had

two accessions (SG1 and JBP) of same origins i.e. Jabalpur while cluster IV

consisted of three accessions from Nauni Forest. (IC471921), Ochlaghat,

(IC471924) and Mandla, (IC471908).

104

Fig.1.13 Principal Component Analysis (A) 2-D and (B) 3-D for chickpea

STMS primers

105

1.2.2.2 Transferability and Polymorphism of Pearl Millet STMS: The

genetic diversity of the 13 A. racemosus accessions was evaluated using a total

36 pearlmillet STMS and 11 markers were found to be polymorphic with one

monomorphic (IPES0189). Data from all the eleven polymorphic STMS loci

was utilized for the statistical analysis. Allelic data generated using the 11

pearl Millet STMS revealed a minimum of one (marker IPE50147) and a

maximum of three alleles IPES0147 and IPES0189, respectively with a total

of 22 alleles at eleven loci leading to an average of 2 alleles per locus (Table

1.20). A majority of the markers displayed a high frequency of null allele

occurrence in the genotypes being assessed. The occurrence of null allele was

verified in each case by reamplification. The possibility of flawed

amplification was eliminated by using the same primer and resolution of

amplification products.

Transferability of pearlmillet STMS in A. racemosus was evaluated based on

the successful amplification. Of the total of 36 pearlmillet STMS tested on A.

racemosus 12 (33.33%) markers successfully produced bright and distinct

amplicon in 13 accessions of A. racemosus. A high level of sequence

conservation amongst the species was indicated by the transfer of the tested

primer pairs in production of clear bands across a very diverse set of the 13 A.

racemosus accessions. Out of 11 loci, a single primer IPES0114 generated

amplification in all the genotypes (Table 1.21, Fig. 1.14). This primer

displayed 100 % transferability in A. racemosus. Two primers (IPES0045 and

IPES0127) showed more than 90% transferability; four markers (IPES0009,

IPES0071, IPES0147 and IPES0141) showed more than 75 % transferability;

two primers (IPES0166 and IPES0189) showed more than 60 % transferability

while remaining two primers IPES0161 and IPES014 showed 38%

transferability in A. racemosus. The PIC value of primers ranged from 0.38

(IPES0127) to 0.9 (IPES0161). The average PIC value of transferable primers

was observed to be 0.67 (Table 1.21). Two primer pairs produced accession-

specific amplicons in A. racemosus (Table1.17). IPES0114 and IPES0147

106

produced unique band of 90 bp in the cultivar IC471909 and 300 bp in

IC471910, both originated from Mandla, Madhya Pradesh.

Fig. 1.14 Extent of cross-transferability of pearlmillet STMS markers in A.

racemosus. Numbers depicted on the red line and the blue line on the y-axis

indicate per cent cross-transferability and the number of cross-transferable

markers

107

Table 1.20: Pearlmillet STMS allelic profile for 13 A. racemosus accessions.

Table 1.21: Cross transferability of pearlmillet STMS primers in A. racemosus accessions.

Accessions

Primer name

IPES0009 IPES0045 IPES0071 IPES0127 IPES0114 IPES0166 IPES0144 IPES0147 IPES0161 IPES0189 IPES0141

FFDC a2 a1,a2 a1 a1,a2 a2 - - a1 a1,a2 a3 a2

SG1 - a2 - a1,a2 a2 - a1 a1,a2 - a3 -

KAU a1,a2 a2 a1 a1,a2 a2 a1,a2 a1 a1 - - a2

CDH a1,a2 a2 a1 a2 a2 a1,a2 - a1,a2 - a2,a3 -

IC471921 - - - - a2 - a1 - - a3 a2

IC471923 a2 a2 a1 a1,a2 a2 a2 - a1,a2 a1,a2 - a1

IC471924 a1,a2 a2 a1 a1,a2 a2 a1,a2 a1 a1,a2 a1,a2 - -

IC471927 a1 a1,a2 a1 a1,a2 a2 a1,a2 - a1,a2 a2 a1,a3 a2

IC471911 a1 a1,a2 a1 a2 a2 a1,a2 - a1 a2 a2,a3 a2

IC471910 a1 a2 a1 a1,a2 a2 a2 - a1,a2,a3 - - a1

IC471909 a1 a1,a2 a1 a1,a2 a1 a1,a2 - a1 - a1,a2,a3 a1,a2

IC471908 a1 a2 a1 a2 a2 - - - - a3 a1

Accessions FFDC SG1 KAU CDH IC471921 IC471923 IC471924 IC471927 IC471911 IC471910 IC471909 IC471908 JBP % T Alleles PIC

IPES0009 + - + + - + + + + + + + + 84.62 2 0.69

IPES0045 + + + + - + + + + + + + + 92.31 2 0.50

IPES0071 + - + + - + + + + + + + - 76.92 1 0.41

IPES0127 + + + + - + + + + + + + + 92.31 2 0.38

IPES0114 + + + + + + + + + + + + + 100.00 2 0.57

IPES0166 - - + + - + + + + + + - - 61.54 2 0.70

IPES0144 - + + - + - + - - - - - + 38.46 1 0.85

IPES0147 + + + + - + + + + + + - - 76.92 3 0.73

IPES0161 + - - - - + + + + - - - - 38.46 2 0.90

IPES0189 + + - + + - - + + - + + - 61.54 3 0.85

IPES0141 + - + - + + - + + + + + + 76.92 2 0.82

108

Table 1.22: Genetic similarity among 13 A. racemosus generated using Pearl Millet STMS primer combinations based on Jackard’s similarity

coefficient

Accessions FFDC SG1 KAU CDH IC471921 IC471923 IC471924 IC471927 IC471911 IC471910 IC471909 IC471908 JBP Average D²

FFDC 1.000 0.5626

SG1 0.5714 1.000 0.6058

KAU 0.50 0.5714 1.000 0.5091

CDH 0.5882 0.5714 0.40 1.000 0.5229

IC471921 0.7692 0.6667 0.7692 0.8571 1.000 0.8196

IC471923 0.40 0.5714 0.50 0.50 0.9333 1.000 0.5555

IC471924 0.4706 0.5333 0.2667 0.3750 0.8750 0.2667 1.000 0.5090

IC471927 0.4118 0.5625 0.4118 0.4118 0.8125 0.50 0.3889 1.000 0.4903

IC471911 0.4375 0.6875 0.4375 0.3333 0.7857 0.6111 0.50 0.250 1.000 0.5185

IC471910 0.6471 0.5385 0.4667 0.4667 0.9286 0.3571 0.4375 0.4706 0.5882 1.000 0.5477

IC471909 0.5789 0.7222 0.50 0.50 0.8824 0.650 0.6190 0.3333 0.3529 0.5556 1.000 0.5854

IC471908 0.6429 0.6364 0.6429 0.5385 0.7778 0.6429 0.6875 0.6250 0.5714 0.50 0.6250 1.000 0.6112

JBP 0.7333 0.6364 0.6429 0.7333 0.7778 0.7333 0.6875 0.7059 0.6667 0.6154 0.7059 0.4444 1.000 0.6736

109

(i) Genetic Distance Similarity and Cluster Analysis: Estimation of

genetic similarity matrices was done based on the twenty two alleles amplified

by the 11 pearlmillet STMS marker data for all the pairwise combinations of

the 13 A. racemosus accessions (Table 1.22). The genetic similarity varied

between 0.250 and 0.933, with an average of 0.568. The minimum genetic

distance of 0.250 was recorded between accessions IC471927 and IC471911

originating from Mandla district. On the other hand, the highest genetic

distance of 0.933 was recorded between accessions IC471923 and IC471921

originated from Solan, and Nauni Forest, respectively. However, STMS

analysis detected no duplications (i.e. 100% similarity) within the tested

accessions.

Dendrogram depicted by Ward’s method based on jaccard’s similarity

coefficient grouped the 13 A. racemosus accessions in to three main clusters

(I, II, III) and one single one appeared in the fourth cluster (IV) (Fig.1.15).

Fig. 1.15 Dendrogram of 13 A. racemosus accessions based on pearlmillet

STMS primers

Cluster I included single accession with a genetic similarity of 0.56 and

originated from Nauni Forest, (IC471921). Cluster II comprised of two

accessions (IC471908 and JBP) at a distance of 0.56 and are arose from

Mandla and Jabalpur, respectively. Cluster III consisted of four accessions

originated from Himachal Pradesh, Madhya Pradesh and Uttar Pradesh at a

distance of 0.72. This cluster was subdivided into groups within the cluster.

110

Two accessions (IC471927 and IC471911 originating from Nauni Forest,

Himachal Pradesh and Mandla, Madhya Pradesh, respectively were placed

together while IC471909 (Mandla, Madhya Pradesh) and FFDC (Kannauj,

Uttar Pradesh) were placed as singletons within this cluster.

Cluster IV grouped six accessions at a genetic distance similarity of 0.74. This

group contained accessions from six different origins, viz., Thrissur, Ochla

Ghat, Chandigarh, Solan, Mandla and Jabalpur. This cluster was also sub-

classified into two groups of two accessions and remaining two as singletons.

Accessions KAU and IC471924 were placed together in a group originated

from Kerala and Himachal Pradesh, respectively, while IC471923 (Himachal

Pradesh origins) and IC471910 (Madhya Pradesh origins) comprised the other

group. Accession CDH and SG1 were incorporated as singletons within the

cluster originated from Chandigarh and Madhya Pradesh, respectively.

(ii) Principal Component Analysis: The Principal Component Analysis

(PCA) was used to summarize the information and data reduction and

therefore diminishing the influence of noise and outliers on the results.

Optimum number of clusters was determined by eliminating the principal

components with an eigenvalue of less than one (Chatfied and Collins 1980;

Hair et al. 1998). Amongst the accessions a total of eight eigen vectors with

eigen values greater than one were used (Table 1.23). The first Principal

Component (PC) explained 25.44% of the total variation. A 20.01% of the

total variation was observed in the markers that contributed more to the second

PC while a 13.03% variation was accounted for in markers that involved more

of the third PC. Remaining five components (each of PC4, PC5, PC6, PC7 and

PC8) contributed less than 10% of total variation (Fig. 1.16).

111

Table 1.23: Principal component analysis of 11 pearlmillet STMS loci in 13 A.

racemosus accessions showing eigenvectors, eigenvalues, individual and

cumulative percentage of variation explained by the first eight PC axes

PC1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 PC 8

IPES0009 0.205 0.096 0.274 0.383 0.061 0.194 0.182 0.222

0.101 -0.307 -0.227 0.056 -0.016 0.283 -0.367 -0.010

IPES0045 0.156 0.243 -0.124 -0.179 0.341 0.070 0.355 0.112

0.280 -0.160 0.195 -0.010 0.291 0.160 0.206 -0.261

IPES0071 0.337 -0.110 -0.007 0.065 0.136 -0.096 -0.240 0.282

IPES0127 0.198 -0.195 -0.048 -0.392 -0.334 0.013 0.222 -0.026

0.280 -0.160 0.195 -0.010 0.291 0.160 0.206 -0.261

IPES0114 0.206 0.301 0.096 -0.273 -0.201 0.173 -0.214 -0.111

-0.206 -0.301 -0.096 0.273 0.201 -0.173 0.214 0.111

IPES0166 0.303 0.089 -0.154 0.366 -0.171 0.178 0.066 0.015

0.336 -0.091 0.038 0.206 -0.246 -0.045 -0.088 0.220

IPES0144 -0.245 -0.047 -0.073 0.091 -0.337 0.473 0.263 -0.093

IPES0147 0.323 -0.174 -0.129 -0.019 -0.197 -0.150 0.046 -0.170

0.097 -0.298 0.083 0.046 -0.246 -0.341 0.107 -0.330

0.019 -0.117 0.364 -0.041 -0.178 -0.394 0.058 0.349

IPES0161 0.058 -0.305 -0.236 -0.318 0.126 0.152 -0.245 0.069

0.166 -0.184 -0.342 -0.124 0.255 -0.100 0.124 0.191

IPES0189 0.244 0.255 -0.021 -0.200 -0.161 -0.087 0.263 -0.019

0.225 0.241 -0.013 0.261 0.062 -0.056 -0.346 -0.210

0.004 0.291 -0.228 0.032 0.155 -0.394 -0.064 -0.366

IPES0141 0.012 0.046 0.467 -0.308 0.149 0.085 -0.200 0.148

0.094 0.260 -0.363 -0.056 -0.085 -0.026 0.126 0.371

Eigene

Value

5.597 4.402 2.867 1.996 1.702 1.390 1.219 1.117

% Var.

Exp.

25.440 20.011 13.033 9.074 7.736 6.316 5.540 5.075

Cum.

Var.

25.440 45.451 58.484 67.558 75.293 81.609 87.150 92.225

112

PCA 0 PCA 1 PCA 2 PCA 3 PCA 4 PCA 5 PCA 6 PCA 7 PCA 8 PCA 9

P9 0.370356 0.096907 0.345542 0.955952 0.031483 0.167762 0.246844 0.35892 0.087626

0.090932 1 0.236348 0.020279 0.002062 0.357207 1 0.00078 0.115382

P45 0.213992 0.626276 0.071135 0.208677 1 0.021886 0.93633 0.091626 0.004977

0.692895 0.270666 0.173902 0.000649 0.72885 0.114285 0.315315 0.493788 0.236759

P71 1 0.128815 0.000208 0.027753 0.158314 0.041434 0.427024 0.578751 0.055581

P127 0.345625 0.404419 0.010643 1 0.957177 0.000807 0.36575 0.004792 0.283866

0.692895 0.270666 0.173902 0.000649 0.72885 0.114285 0.315315 0.493788 0.236759

P114 0.374344 0.959783 0.042407 0.485674 0.345376 0.134266 0.340358 0.088907 0.001008

0.374344 0.959783 0.042407 0.485674 0.345376 0.134266 0.340358 0.088907 0.001008

P166 0.809505 0.084056 0.108932 0.872632 0.251099 0.141349 0.032774 0.001641 0.105278

0.999286 0.087767 0.006471 0.275888 0.521269 0.008942 0.057656 0.350035 0.223188

P144 0.528304 0.02338 0.024328 0.054505 0.975682 1 0.51489 0.063211 7.57E-06

P147 0.923486 0.322872 0.076899 0.002298 0.33233 0.100116 0.015459 0.209768 0.509586

0.082293 0.940448 0.031725 0.014019 0.521105 0.520184 0.085157 0.791492 1

0.00335 0.145458 0.608615 0.010915 0.270848 0.692703 0.024623 0.884404 0.542324

P161 0.029548 0.987266 0.254768 0.657482 0.136632 0.103431 0.444128 0.034468 0.228644

0.24315 0.358527 0.53746 0.099591 0.558841 0.044413 0.114925 0.263303 0.788135

P189 0.527098 0.692083 0.001949 0.261172 0.221957 0.033651 0.512591 0.002694 0.70983

0.447688 0.617338 0.000781 0.443559 0.032957 0.013798 0.889199 0.321099 0.006171

0.000165 0.901904 0.239292 0.006534 0.206422 0.693243 0.030407 0.973506 0.056133

P141 0.001333 0.022618 1 0.617975 0.190485 0.032426 0.297328 0.158864 0.259128

0.07772 0.71581 0.604376 0.020352 0.061761 0.003081 0.117735 1 0.563154

Fig. 1.16 Loading plot for 11 pearlmillet STMS marker in A. racemosus

Four clusters could be distinctly observed amongst the A. racemosus

accessions following a cluster analysis based on PCA, using K-clustering

method (Fig 1.7). Cluster I consisted of four accessions from two diverse

regions of Kannauj, Uttar Pradesh (FFDC), Kerala (KAU), Chandigarh (CDH)

and Mandla, Madhya Pradesh (IC471910). Cluster II included two accessions

from very proximate regions, Solan and Ochlaghat, i.e. IC471923 and

IC471924, respectively. Cluster III comprised of three accessions, one from

Nauni Forest, Himachal Pradesh (IC471927) and remaining two from Mandla,

Madhya Pradesh (IC471911 and IC471909). Cluster IV had four accessions

(IC471921, IC471908, SG1 and JBP) from Nauni Forest, Himachal Pradesh,

Mandla and Jabalpur, Madhya Pradesh, respectively.

113

Fig.1.17 Principal Component Analysis (A) 2-D and (B) 3-D for pearlmillet

STMS primers

1.2.2.3 Transferability and Polymorphism of Asparagus SSR:

Characterization and assessment of the genetic diversity of the 13 accessions

of A. racemosus was performed using a total of 20 SSRs of Asparagus

officinalis. Six (30 %) out of the 20 Asparagus SSRs displayed good

114

amplification. Out of these, five markers produced polymorphic products and

one marker (DSFR18) produced monomorphic products; the data from the five

SSRs was used in the statistical analysis. The generated allelic data illustrated

a minimum of two and maximum of seven alleles (DSFR16 and DSFR3,

respectively) with a total of 19 alleles at 5 loci. An average of 6.3 alleles per

locus was observed (Table 1.24).

Asparagus SSRs transferability in A. racemosus was gauged based on

successful amplification of the twenty Asparagus SSRs. Six markers (30 %)

generated bright, and clear amplicon in the tested genotyoes. High level of

sequence conservation was confirmed by transfer of primer pairs and clear

bands across a diverse range of the thirteen A. racemosus species. DSFR7

showed the highest transfer rate (92.31%) among accessions while the

remaining four markers (DSFR1, DSFR3, DSFR14 and DSFR16) amplified

positively and showed more than 50 % transferability to A. racemosus

accessions The PIC value of primers ranged from 0.65 (DSFR16) to 0.9

(DSFR14). The average PIC value of transferable primers was observed to be

0.555 (Table 1.25).

Two primer pairs illustrated accession-specific amplicons (Table 1.17).

DSFR3 and DSFR14 formed unique bands in the accessions. Marker DSFR3

produced one (100 bp) and three (1000, 700 and 300 bp) unique amplicons in

IC471921 and KAU, respectively. Marker DSFR14 generated one unique

band of 150 bp in accession IC471910 originated from Mandla, Madhya

Pradesh.

Table 1.24: Asparagus SSR allelic profile for 13 A. racemosus accessions.

Accessions Primer name DSFR1 DSFR3 DSFR7 DSFR14 DSFR16

FFDC a3 a6 a1,a2,a3 a1 a1

SG1 a3 a3,a4,a5,a6 a1,a2,a3 - a1

KAU a1,a2,a3 a1,a2,a4,a5,a6,a7 a1,a2,a3 - a1,a2

CDH a3 a4,a5,a6 a3 - a1,a2

IC471921 a1,a2,a4 a3,a4,a5,a6 a3 - -

IC471923 a1,a2,a3 a4,a5,a6 a1,a2,a3 a1 a1

IC471924 a3 a4,a5,a6 a3 - -

IC471927 a1,a2,a3 a3,a4,a5,a6 a1,a2 - a1,a2

IC471911 a1,a2,a3 - a1 a1,a3 a1,a2

IC471910 a1,a2,a3 a3,a4,a5,a6 - a1,a2 a1,a2

IC471909 - - a1 a3 a1,a2

IC471908 - - a1 a1,a3 -

JBP - - a1 a1,a3 -

115

Fig. 1.18 Extent of cross-transferability of Asparagus SSR markers in A.

racemosus. Numbers depicted on the red line and the blue line on the y-axis

indicate percent cross-transferability and the number of cross-transferable

markers

(i) Genetic Distance Similarity and Cluster Analysis: A total of 19 alleles

amplified by five Asparagus SSR marker data for all pairwise combinations of

the thirteen A. racemosus accessions was used in estimation of genetic

similarity matrices (Table 1.26). The minimum genetic distance of 0.250 was

recorded between accessions IC471908 and JBP originated from Madhya

Pradesh (Mandla and Jabalpur, respectively). The maximum genetic distance

of 0.983 was recorded between accessions IC471924 and IC471909 originated

from Ochlaghat, and Mandla, respectively. The genetic similarity average was

found to be 0.638.

Dendrogram method illustrated by Ward’s method based on Jaccard’s

similarity coefficient evidently clustered the A. racemosus accessions in to

four clusters (Fig. 1.26). Cluster I formed at a genetic similarity of 0.90 and

included two accessions IC471908 and JBP that originated from Madhya

Pradesh (Mandla and Jabalpur, respectively) while Cluster II grouped at a

distance of 0.78 comprised of IC471911 and IC471909 belonged to same

location i.e. Mandla, Madhya Pradesh. Cluster III formed at a distance of 0.65

was subdivided to one subgroup of two accessions and four accessions as

singletons within the cluster. Two accessions IC471923 and IC471927 from

116

Himachal Pradesh (Solan and Nauni Forest, respectively) were placed together

in the subgroup while accessions SG1 (Jabalpur, Madhya Pradesh), KAU

(Thrissur, Kerala), IC471910 (Mandla, Madhya Pradesh) and FFDC (Kannauj,

Uttar Pradesh) were placed as singletons in third cluster. Cluster IV formed at

a distance of 0.62 and subdivided into one subgroup of two accessions and one

as singleton. Accessions IC471924 and CDH were placed together originated

from Ochlaghat (Himachal Pradesh) and Chandigarh and accession IC471921

from Nauni Forest (Himachal Pradesh) was placed as singleton within the

Cluster IV.

Fig.1.19: Dendrogram of 13 A. racemosus accessions based on Asparagus

SSR primers

(ii) Principal Component Analysis: The Principal Component Analysis

(PCA) was used for data reduction to summarize the information from the data

set so that the influence of noise and outliers on the results is reduced.

Principal components with an eigenvalue of less than 1 were eliminated to

determine the optimum number of clusters in the present study (Chatfied and

Collins 1980, Hair et al. 1998). Hence, from this study, six eigenvectors which

had eigenvalues greater than one among the accessions was used (Table 1.27).

The first Principal Component (PC) alone explained 32.59 % of the total

variation. Markers which contributed more to the second PC accounted for

18.96 % of the total variation and the third and fourth PC with 14.88 % and

11.51 %, respectively of the total variation. Remaining two components (each

of PC5 and PC6) contributed less than 10 % of total variation (Fig. 1.20).

117

Table 1.27: Principal component analysis of Asparagus SSRs in 13 A.

racemosus accessions showing eigenvectors, eigenvalues, individual and

cumulative percentage of variation explained by the first eight PC axes PC1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7

DSFR1 0.233 0.065 0.330 0.226 0.335 0.045 0.272

0.233 0.065 0.330 0.226 0.335 0.045 0.272

0.260 0.086 0.072 -0.408 -0.078 0.161 0.273

0.065 -0.282 -0.065 0.441 0.266 -0.148 0.134

DSFR3 0.201 0.375 -0.136 0.258 -0.083 0.118 -0.157

0.201 0.375 -0.136 0.258 -0.083 0.118 -0.157

0.186 -0.270 0.207 0.041 0.173 -0.314 -0.540

0.358 -0.174 -0.041 0.007 -0.087 -0.026 0.008

0.358 -0.174 -0.041 0.007 -0.087 -0.026 0.008

0.352 -0.155 -0.117 -0.167 0.024 0.079 -0.022

0.201 0.375 -0.136 0.258 -0.083 0.118 -0.157

DSFR7 -0.128 0.371 -0.029 -0.164 0.385 -0.263 -0.187

0.213 0.204 -0.139 -0.314 0.388 -0.175 -0.198

0.129 -0.073 -0.485 -0.038 0.089 0.237 0.166

DSFR14 -0.184 0.063 0.209 -0.143 0.347 0.597 -0.006

0.094 -0.123 0.399 -0.018 -0.169 0.427 -0.490

-0.352 0.155 0.117 0.167 -0.024 -0.079 0.022

DSFR16 0.165 0.228 0.230 -0.359 -0.067 -0.211 0.116

0.107 0.213 0.365 0.057 -0.413 -0.233 0.192

Eigen

Value

6.193 3.604 2.829 2.188 1.502 1.150 0.679

% Var.

Exp

32.596 18.969 14.889 11.516 7.905 6.054 3.572

Cum. Var. 32.596 51.565 66.454 77.970 85.875 91.929 95.500

Cluster analysis was done based on PCA using K-clustering method revealed

four clusters among the A. racemosus accessions (Fig. 1.21). Cluster I

consisted of a single accession KAU from Thrissur (Kerala) while Cluster II

consisted of seven accessions, two from Madhya Pradesh, SG1 and IC471910

(Jabalpur and Mandla, respectively), four accessions from Himachal Pradesh,

IC471921, IC471927, IC471923 and IC471924 (Nauni Forest, Solan and

Ochlaghat) and one accession from a different location Chandigarh (CDH).

Cluster III contained two accessions, one from Kannauj, Uttar Pradesh

(FFDC) and another from Mandla, Madhya Pradesh (IC471911). Cluster IV

had three accessions from Madhya Pradesh (IC471909, IC471908 and JBP).

118

PCA 0 PCA 1 PCA 2 PCA 3 PCA 4 PCA 5 PCA 6 PCA 7

DSFR1 130 0.424752 0.030044 0.462217 0.262982 0.658333 0.005746 0.254296

DSFR1 120 0.424752 0.030044 0.462217 0.262982 0.658333 0.005746 0.254296

DSFR1 110 0.52728 0.052665 0.021942 0.855081 0.035436 0.072612 0.255735

DSFR1 100 0.033365 0.564733 0.017937 1 0.414399 0.061706 0.061233

DSFR3 1000 0.315813 1 0.078984 0.343348 0.040162 0.039134 0.084502

DSFR3 700 0.315813 1 0.078984 0.343348 0.040162 0.039134 0.084502

DSFR3 600 0.269266 0.519293 0.181527 0.008561 0.175074 0.277611 1

DSFR3 500 1 0.216761 0.006984 0.000242 0.044188 0.001911 0.000209

DSFR3 400 1 0.216761 0.006984 0.000242 0.044188 0.001911 0.000209

DSFR3 350 0.966932 0.172204 0.058729 0.14334 0.003252 0.01733 0.001641

DSFR3 300 0.315813 1 0.078984 0.343348 0.040162 0.039134 0.084502

DSFR7 300 0.127463 0.980476 0.003538 0.138574 0.869224 0.194297 0.119747

DSFR7 200 0.353035 0.297785 0.081985 0.50741 0.880923 0.086014 0.134483

DSFR7 60 0.130154 0.038084 1 0.007362 0.04677 0.158308 0.094207

DSFR14 180 0.265144 0.028058 0.186734 0.105387 0.703436 1 0.000129

DSFR14 150 0.068453 0.107595 0.676602 0.001739 0.166779 0.512612 0.824115

DSFR14 80 0.966932 0.172204 0.058729 0.14334 0.003252 0.01733 0.001641

DSFR16 260 0.212308 0.371878 0.225095 0.664011 0.025915 0.124686 0.04635

DSFR16 180 0.089646 0.324229 0.567469 0.016513 1 0.152776 0.125697 Fig. 1.20 Loading plot for Asparagus SSR primers among 13 A. racemosus

accessions

119

Fig.1.21 Principal Component Analysis (A) 2-D and (B) 3-D for pearlmillet

STMS primers

120

Table 1.25: Cross transferability of Asparagus SSR primers in A. racemosus accessions.

Table 1.26: Genetic similarity among 13 A. racemosus accessions generated using Asparagus SSR primer combinations based on Jackard’s

similarity coefficient

FFDC SG1 KAU CDH IC471921 IC471923 IC471924 IC471927 IC471911 IC471910 IC471909 IC471908 JBP %T Alleles PIC

DSFR1 + + + + + + + + + + - -

- 76.92 4

0.77

DSFR3 + + + + + + + + + + - -

- 69.23 7

0.81

DSFR7 + + + + + + + + + -

+ + + 92.31 3

0.66

DSFR14 + - -

- - +

- - + + + + + 53.85 3

0.90

DSFR16 + + + + - + -

+ + + + - - 69.23 2

0.65

FFDC SG1 KAU CDH IC471921 IC471923 IC471924 IC471927 IC471911 IC471910 IC471909 IC471908 JBP Average

D²

FFDC 1.000 0.6329

SG1 0.40 1.000 0.5720

KAU 0.60 0.4667 1.000 0.6057

CDH 0.60 0.40 0.50 1.000 0.6125

IC471921 0.8462 0.5833 0.6250 0.6364 1.000 0.7190

IC471923 0.3636 0.3333 0.3333 0.50 0.5385 1.000 0.5265

IC471924 0.6667 0.4444 0.6429 0.2857 0.5556 0.5455 1.000 0.6888

IC471927 0.6154 0.3333 0.3333 0.50 0.5385 0.3077 0.6667 1.000 0.5619

IC471911 0.6364 0.7857 0.6250 0.750 0.8571 0.5385 0.9167 0.5385 1.000 0.6648

IC471910 0.7143 0.5714 0.5294 0.50 0.5385 0.4286 0.6667 0.3077 0.5385 1.000 0.6244

IC471909 0.7778 0.8182 0.80 0.7778 0.8791 0.8462 0.9831 0.750 0.50 0.8462 1.000 0.7819

IC471908 0.750 0.9091 0.9375 0.9430 0.8923 0.8333 0.9154 0.9231 0.6250 0.9231 0.60 1.000 0.8126

JBP 0.6250 0.8182 0.8750 0.90 0.9091 0.750 0.8750 0.9286 0.6667 0.9286 0.6667 0.250 1.000 0.7661

121

1.2.3 Combined Marker Analysis

Dendrogram was constructed based on genetic distance similarity.

Dendrogram was constructed using NTSYSpc 2.02e based on Jaccard’s

similarity coefficient evidently clustered the A. racemosus accessions in to

eight clusters, out of which six formed singletons (FFDC, IC471924, SG1,

IC471921, IC471908 and JBP (Fig. 1.22) while two accessions (KAU and

CDH) formed a cluster. The other cluster had five accessions, two accessions

(IC471923 and IC471927) formed a subgroup within the cluster originated

from Himachal Pradesh. Remaining three accessions (IC471910, IC471911

and IC471909) were originated from Madhya Pradesh. Principal Component

Analysis was also performed for combined molecular data and PCA revealed

similar results to dendrogram (Fig. 1.23)

Fig. 1.22 Dendrogram constructed for microsatellite markers among 13 A.

racemosus accessions

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Fig. 1.23 Principal Component Analysis (A) 2-D and (B) 3-D for

Microsatellite markers

123

DISCUSSION

Asparagus racemosus grows in the tropical and subtropical parts of India. Due

to its important medicinal properties the plant is in demand as a

pharmaceutical drug or as a derivative in different pharmaceutical

applications. Poor cultivation and over exploitation of rhizomes for

pharmaceutical use has lead to depletion of the species. Genetic diversity

analysis in germplasm collection can enable reliable classification of

accessions and identification of subsets of core accessions with a potential

utility for specific breeding purposes. The genetic parameters play an

important role in predicting the expected genetic gain from a certain

improvement programme (Kumar 2007; Kumar et al. 2010). The present study

utilizes both the morphological and molecular diversity to characterize A.

racemosus germplasm collections. To our knowledge the study is the first

investigation to characterize A. racemosus germplasm collections using a

combination of morphological and molecular diversity. Phenotypic analysis

revealed a considerable variation in the A. racemosus accessions for the 22

morphological characters. The present analysis indicated the potential for the

selection of potential morphological markers with greater genetic gain for

commercially useful characteristics. In this study the morphological variation

observed in all the 22 traits offers scope for a breeder to carry out further

genetic improvement. The literature review indicated the lack of research on

the genetic improvement of A. racemosus. The reported morphological

estimates can enable the selection of the germplasms on the basis of its

anticipated positive genetic response.

The major goals of the plant improvement programs include a

substantial amount of genetic gain at a reasonable cost while preserving

adequate genetic variability in the breeding population, which helps to

safeguard future gain (Zobel and Talbert 1984). This can be attained by

selecting the plants with phenotypic traits of economic importance and their

use as parents in a breeding programme. Hodge et al. (2002) used GCV to

express the genetic gain in case of Bombacopsis quinata. The study indicated

a significant correlation between morphological characteristics and

124

chromosomal regions among the collected accessions. Knowledge of

correlations among the characters is useful in designing an effective breeding

program for any crops. Complex plant characters such as yield are

quantitatively inherited and influenced by genetic effects, as well as by

genotype and environment interaction. Due to these reasons, selections may be

difficult and time consuming to improve yield directly especially for perennial

plant such as Asparagus. Therefore, identification and use of highly correlated

characters is prerequisite for genetic diversity.

In this study, cladode width was positively and significantly correlated

with cladode thickness, length and fresh and dry weights. Cladode being

photosynthetic units, their dimensions should directly correlate with

photosynthetic efficiency. Therefore, genotype such as KAU that had bigger

cladode dimensions could be photosynthetically efficient as well. These

statistically significant correlations of morphological may be useful for

improvement. Similar selection processes were reported by Shabanimofrad et

al. (2013), Rao et al. (2008), Jindal et al. (1999), Kundu and Tigerstedt (1997).

Selection for any of these characters, which are highly heritable and easy to

measure, will help to improve the A. racemosus population.

Breeding and crop improvement cannot be accomplished solely on the

basis of variability but variance should be divided between phenotypic and

genotypic coefficient of variation, which are more dependable parameters for

effective selection. Hodge et al. (2002) expressed the genetic gain in

Bombacopsis quinata using Genetic Coefficient of Variation (GCV). Their

study showed that significant associations existed between morphological

characteristics and chromosomal regions. The phenotypic and genotypic

coefficients of variability were used in this study because the absolute

variability of the different cultures could not establish which characters

showed high variability; the genotypic and phenotypic coefficients of variation

for the characters revealed a similar trend. The reported PCA values were

higher than the GCV values. The cladode width, fresh and dry weight,

distance between whorls and root fresh and dry weight had higher GCV and

PCV values, which was an indication that the substantial variability was

125

present for these characters. In addition, it also indicated the greater scope for

selection of these characters for better expression. Low GCV and PCV values

were obtained for the characters like cladode per cent moisture, spear

circumference and diameter and root moisture content. The selection of these

characters was found to be difficult because of their low variability in the

genotypes. The (PCA) was carried out for data reduction tool and to

summarize the data set information so as to reduce the influence of noise and

outliers on the results. In addition the PCA also contributed by reducing the

number of descriptors causing the highest percentage of total variance in the

experimental data. It facilitated in the study of relationship between variables

and observations and recognition of data structure. Studies have shown that

multivariate analyses can be used to deal with germplasm collections,

evaluation, and choosing parent plants for hybridzation (Falcinelli et al. 1988;

Dasgupta Das 1984; Chozin 2007). Furthermore, the PCA has also been used

to understand the pattern of variability in a specific character among

individual accession in a population (Chozin, 2007). In another study, the

characterisation of accessions and clustering them on the basis of the

morphological traits and the genetic similarity enabled identification and

selection of the best parents for hybridisation (Souza and Sorrells 1991). The

grouping of accessions by multivariate methods of analysis based on their

similarity is a valuable tool for Asparagus breeders as well. It will enable the

selection of the most important accessions in the population from the different

clusters for use in the improvement programmes.

Therefore, the present study is an important attempt in this respect. The

results show significant differences between all the morphological traits

among the collected landraces. This genetic variability would be useful for

further genetic improvement. The cluster analysis was done for the 22

morphological characters based on similarity distance and did not reflect their

geographic region of origin. Similar conclusions were reported for sweet

potato by Veasey et al. (2007).

Molecular markers find applications in construction of linkage maps

examining genetic relationships between individuals and identification of crop

126

cultivars. They are scattered throughout the genome and their association with

various agronomic traits is influenced by the cultivator under selection

pressure induced by domestication. The microsatellite or STMS markers are

highly popular and are the preferred markers for the diverse crop plants due to

their abundance in the genome, robustness, reproducibility, hypervariability

and codominance (Powell et al. 1996). In addition the availability of a variety

of DNA markers, like restriction fragment length polymorphism (RFLP),

amplified fragment length polymorphism (AFLP), random amplified

polymorphic DNA (RAPD), simple sequence repeat (SSR) and intersimple

sequence repeat (ISSR) has allowed researchers to study genetic diversity

between different plant species across natural habitats.

A robust set of informative markers are essential for a marker assisted

breeding programme. SSR markers are a power tool for genetic mapping,

diversity analysis and gentyping due to their properties like locus specificity,

co-dominant nature, their potential to amplify multiple alleles, and

transferability. At present a limited number genomic tools and molecular

resources exist for A. racemosus and therefore, there is a critical need to

increase the number of genome-wide polymorphic SSR markers for the

available germplasm. The contemporary development of molecular markers

utilizes gene-based PCR markers, also called functional markers instead of the

anonymous DNA fragments. At present the ESTs are used for the systematic

development of SSR and SNP markers. Transferability is one of the important

and valuable feature of SSRs. Transferability of markers helps in comparative

genetic analysis such as comparative mapping and evolutionary studies

between species with lot of genetic studies and genomic resources and the

ones that lack enough genetic study and genomic resource. Transferability

study has been attempted in several plants across different levels. Theoretical

likelihood of monocots to dicot transferability was proposed by Varshney et al

(2005), but to a lesser extent. However, it should be noted that no

comprehensive sequence information for A. racemosus is available, and

attempts of development of new markers will benefit from the availability of

sequence information from other crop species. In this regard, chickpea STMS,

127

pearlmillet STMS and Asparagus SSRs were found to be a good resource for

genetic studies in A. racemosus. Results of exploring the A. racemosus

landraces for transferability of chickpea STMS, pearlmillet STMS and

Asparagus SSRs and their use in genetic diversity studies in A. racemosus are

discussed here. Microsatellite genotypic data from a number of loci can

provide distinctive allelic profiles or DNA fingerprints for instituting

genotypes identity. This comprehensive characterization of potentially useful

and elite lines of Asparagus is essential to counter the need for plant varietal

registration and conservation. Detailed and finer discrimination of elite lines

of Asparagus as well as the utility of STMS marker in the diversity analysis

has been illustrated at a larger scale in Asparagus (Aceto et al. 2003).

In the current study, a reasonably high rate of polymorphism was

observed for at least eight markers namely H2B061, NC11, NC13, NC5,

TA39, TA167, TA200 and TR3 for out of 38 STMS markers loci of chickpea

and four markers i.e. IPES0045, IPES0127, IPES0114 and IPES0161 for 11

pearlmillet STMS; the two Asparagus SSRs markers DSFR3 and DSFR14

also displayed high level of polymorphism. The results of the study point

towards the scope for further utilization of these markers for Asparagus

accessions characterization. The occurrence of unique alleles or rare STMS

alleles presents a prospect for generation of a comprehensive fingerprint

database. The resources of several unique STMS alleles may be an indication

of addition or deletion of small number of repeats (Goldstein and Pollock,

1997) and most rational explanation for high mutation rate is polymerase

slippage (Levison and Gutman, 1987). The null alleles were also observed in

the study, which could be due to mutations in the primer binding site leading

to non-amplification.

In the present study STMS primers consistently amplified 2 bands or

more than two bands in many of the A. racemosus landraces at the same locus.

Considerable heterozygosity can be present in cross pollinated crop like

Asparagus (Nichols 1989) and can be the cause for occurrence of two bands.

A high frequency of double bands and more were also observed with

Asparagus SSRs in all the accessions. It can again be attributed to the higher

128

amount of heterozygosity, in the genome of these lines. Alternatly, mutations

may have occurred in the parental stock at a specific STMS locus, leading to

two different alleles on homologous chromosomes. The occurrence of

heterozygosity indicates the diversity in the respective lines from the rest of

the lines studied. On comparing the genetic relationship pattern obtained by

STMS matrix data with the geographical information available for the 13 A.

racemosus lines clearly indicated that geographic diversity cannot correlate to

genetic diversity. The clustering pattern suggests that substantial diversity was

present in the material studied. Apart from this no clear cut pattern, especially

for different clustering (i.e. genetic dissimilarities) and source population

diversity could be identified (Table D1). The diverse and broad base of the

source population can be attributed as one of the parameters for the non-

conformity of genetic relationship and source of collection In addition, The

diversity of Asparagus lines can also be due to the selection drift and

mutation, which is an ignored parameter when finding the association between

genotypes on the basis of the place of origin. It may represent alleles identical

in state which may not be identical by descent.

Up to 44.70% transferability of chickpea STMS, 33% of pearlmillet

STMS and 30% Asparagus SSR markers to A. racemosus in our study suggest

the potential for transferability of markers from other species where a large

number of markers are available (Gepts et al. 2008, Gupta et al. 2008).

Transferability of microsatellites in the present study was found to be lower

when compared to the study of Gao et al. (2003). The study by Gao et al.,

2003 demonstrated 69% transferability from wheat (monocot system) to

soybean (dicot system) and also reported similar per cent transferability (43%)

from wheat to rice, maize and soybean in the same study. EST-SSRs are

relatively more transferable across closely related species than distant ones,

i.e. proportion of transferable markers is negatively correlated to the

phylogenetic distance (Thiel et al. 2003).

In the present study, 38 chickpea STMS, 11 pearl millet STMS, and 5

Asparagus SSR markers produced amplification across all the landraces of A.

racemosus, indicating their high rate of transferability. A study by Peakall et

129

al. (1998) reported 65% cross amplification within Glycine. Another study by

Choumane et al. (2004) reported up to 70% transferability of chickpea

genomic SSR primers to pea. Pandian et al. (2000) studied the transferability

of SSR primers derived from chickpea in pea and lentil, and observed 5% and

18% chickpea primers amplification in lentil and pea, respectively. The

genetic relatedness in chickpea, lentil and pea was also studied by Weeden et

al. (1992) using RFLP markers, and found 40% of linked loci in the lentil

linkage map were conserved in pea. Some of the markers produced

amplification only in one or a few species. Several primer pairs displayed

complex banding patterns with minor bands showing weak amplification

which were not considered for further analysis. The size of amplicons

generated by the transferable SSRs was highly variable in A. racemosus,

indicating that the chickpea primers resulted in amplifications of different size.

An earlier study has reported the appearance of multiple bands as the result of

amplification of more than one locus by each SSR (Holton et al. 2002). The

presence of cryptic site upstream, downstream and between the primer binding

sites can cause occurrence of multiple bands from the same locus (Winter et

al. 1999). The generation of amplification products from a distinct locus needs

that the 3' terminal nucleotides of the target sequence be perfectly

complementary to the primers (Choumane et al. 2004). The respective loci are

expected to be conserved between the two genera if the amplification across

genera boundary can be achieved. However, amplification of a SSR locus in

one genus/species with primers from other species does not essentially

confirm the conservation and identity of the loci. In the presented study, many

chickpea, pearlmillet, and Asparagus SSRs failed to produce any amplification

in any accession. This may have been caused by the mutation in the primer

binding site or the absence of locus itself. Despite high polymorphism,

transferability with genomic SSR markers is usually low when cross-species

analyses are performed. On the other hand, the EST-SSR markers derived

from transcribed regions of the DNA generate a higher rate of transferability

even with low polymorphism (Cordeiro et al. 2001, Gutierrez et al. 2005). For

example, in a study the transferability of genomic SSR markers from wheat

(Triticum aestivum) to rye (Secale cereale M. Bieb.) was found to be 17%

130

(Kuleung et al. 2004) however, based on EST-SSR markers, the transferability

from wheat to 18 Triticum-Aegilops species was as high as 84%. In the

presented study an extensive polymorphism among the accessions of A.

racemosus was detected by the difference in size and number of amplification

products. The evolutionary distances between source and target species and

the rate of evolution of the genomic sequence where the primer sequences are

located both dictate the success of heterologous PCR amplification. The

clustering of the genotypes clearly indicates that the microsatellites can be

efficiently used in the A. racemosus diversity analysis. The phylogenetic

relationship among accessions indicates the conservation of true locus and

therefore, eliminating the need for efforts to verify PCR fragment identity by

sequencing them. Thus, transferability study of microsatellite loci from other

species to A. racemosus genotypes would be very beneficial.

Comparison of PIC values for three marker systems (a parameter

associated with the discriminating power of markers) indicated that the range

of PIC values for chickpea STMS primers was ranged from 0.15 (H2I01 and

H3C08) and 0.97 (NC13) had the highest PIC among used markers. Among

the pearlmillet STMS markers surveyed across the A. racemosus genotypes,

eleven markers showed better resolving power. The PIC value ranged from

0.38 (IPES0127) to 0.9 (IPES0161) while the average PIC values of

Asparagus SSRs primers was from 0.65 (DSFR16) to 0.9 (DSFR14). Mean

polymorphic information content (PIC) for each of these marker systems (0.53

for chickpea STMS, 0.67 for pearlmillet STMS and 0.55 for Asparagus SSR

markers) suggests that all the marker systems were effective in determining

polymorphisms in collected landraces.

The clustering of morphological data resulted in a dendrogram, which

also was in conflict with miscrosatellite data. However, the lack of correlation

between genetic markers and morphological characters has been observed in

other species; ryegrass (Roldan-Ruiz et al. 2001), mandarin (Campos et al.

2005), barley (Abdellaoui et al. 2007), potatoes (Solis et al. 2007) and

grapevine (Zdunic et al. 2008). Similar to the morphological data,

microsatellite data was found to be discordant with geographic regions.

131

Morphological traits, by themselves, cannot provide a thorough

evaluation of genetic diversity as they represent a limited number of

segregating loci within the whole genome and are influenced by the

environmental factors (Gepts 1993; van Kleunen et al. 2002). The results

support the concept that phenotypic analyses are not as effective as molecular

analyses to detect duplicates. Furthermore, the results showed that by using

the microsatellite marker technique, a large set of informative data could be

generated in less time than with morphological traits. Also when

simultaneously using DNA markers and morphological traits to classify

genotypes, it is possible to obtain a relevant minimum subset of marker-

fragments that can be used in conjunction with available morphological data to

better classify genotypes compared to using only the quantitative or only the

qualitative traits. However, the importance of morphological characters for

both consumers and breeders cannot be denied and exclusion of some

accessions based on genotype characterization alone is not advisable. This

result is important for association mapping that have many advantages and

could be used as a primary and probable method for QTLs. This is the first

evaluation of genetic diversity of A. racemosus using morphological and

molecular markers. For preserving this valuable plant, more samples of this

species should be gathered, cultivated and domesticated in collections. The

present analysis gives an understanding of the interrelationship between the

genotypes and illustrates the urgency for effective supplementation of

morphological data with the database generated by STMS marker to

comprehensively understand the genetic inter-relationship among the

genotypes. It can also be concluded that genetic similarity is not linked with

geographical similarity. The result of cluster analysis and Principal

Component Analysis for different marker types showed that estimated values

of genetic relationship given for chickpea and pearlmillet STMS, Asparagus

SSR and morphological markers were not corelated. Powell et al. (1996) also

reported that SSR similarity estimates were not significantly correlated to

RFLPs, RAPDs, or AFLPs in soybean.

132

RAPD markers have been used to study genetic diversity in A.

racemosus. Accessions collected from Madhya Pradesh, India (Vijay et al.

2009), Himachal Pradesh and Tamil Nadu (Ginwal et al. 2009) have been

reported to be have high level of genetic similarity. The studies also stressed

the need for survey, selection, and preservation of genetically divergent

genotypes. However in the present study RAPD primers, including those

reported in the earlier studies on this plant, failed to produce any

polymorphism.

This study confirmed the outstanding differences observed between the

landraces from different and same locations. Further studies are needed for the

dissection of relevant agronomic characters for future introgression in the

cultivated species through conventional breeding or advanced techniques.

Phenotypic information can be used in a variety of ways. It can be used for

description and characterization purposes as well as to derive valuable

information about genomic structure and genetic control of useful traits

without specifically developing mapping populations for QTL analyses.

Therefore, the phenotypic information is also beneficial in conserving

resources and enhancing the scientific value of germplasm-related activities

(Clement et al. 2003; Marcano et al. 2007).

Detailed studies on genetic diversity in germplasm can be performed

by studying morphological traits or by employing marker systems. The

microsatellite markers used in this study were polymorphic, and can be used to

distinguish accessions of A. racemosus. In addition, this study indicated that

although morphological characterisation is influenced by the environment and

is time consuming in general, it can still be an important and practical means

of making progress in A. racemosus germplasm evaluation. The low similarity

value among the accessions could indicate that there is a high level of genetic

diversity among the test materials for these markers systems. Microsatellite

markers can be utilized as a method of choice for revealing genetic variation

and identifying slightly different genotypes in Asparagus breeding program.

133

The present study also highlighted the fact that molecular markers

could segregate the population were vividly than morphological parameters.

The morphological parameters could identify three clusters with one of the

clusters grouping as many as ten of the thirteen accessions through PCA while

the Eucledean2 method clustered six accessions together. On the other hand,

molecular markers were able to resolve the subtle differences in the accessions

by segregating them into four clusters with the biggest subgroup having not

more than four accessions.

134

Table D1. Table to depict the comparative clustering of different types of molecular markers used.

*Subgroup 1, #Subgroup 2

FFDC I, SG1 II, KAU III, CDH IV, 921 V, 923VI, 924 VII, 927 VIII, 911 IX, 910 X, 909 XI, 908 XII, JBP XIII

Clusters

Markers

Cluster 1 Cluster 2 Cluster 3 Cluster 4

Genetic

Similarity

K-

clustering

Genetic

Similarity

K-

clustering

Genetic

Similarity

K-

clustering

Genetic

Similarity

K-

clustering

Chickpea

STMS

VII XII III IV I VIII XI

VI* IX

*

I VI VIII

IX X XI

V X IV *

III*

II XIII II XIII V VII XII

Pearl millet

STMS

V I III IV X XII, XIII VI VII I IX VIII*

XI*

VIII IX

XI

II X* VI

*

IV VII#

III#

II V XII

XIII

Asparagus

SSR

XII* XIII

* III IX

* XI

* II IV V

VI VII

VIII X

I X III II

VI* VIII

*

I XI V VII* IV

* XI XII XIII

14