chapter 1 phylogenetic characterization review of...
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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
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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.
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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
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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
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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.
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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
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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
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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
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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
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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
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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.
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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
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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
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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).
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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