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Introduction
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1. INTRODUCTION
Species of Cucurbitaceae are grown widely around the world as crops. The
family is comprised of about 118 genera and 825 species that are primarily cold-
sensitive, annual / perennial vines (Jeffrey, 1990). Liberty Hyde Bailey coined the
term ‘Cucurbit’ in reference to cultivated species in the Cucurbitaceae (Robinson and
Decker-Walters, 1997). Cucurbit is now commonly used as a general term for all taxa
in the family. The four major food crops of the Cucurbitaceae are watermelon
(Citrullus lanatus (Thunb.) Matsum and Nakai), cucumber (Cucumis sativus L.),
melon (Cucumis melo L.) and squash (Cucurbita spp.). Other important Cucurbit
crops include loofa (Luffa acutangula (L.) Roxb.), bottle gourd (Lagenaria siceraria
(Molina) Stand.), chayote (Sechium edule (Jacq.) Swartz), wax gourd (Benincasa
hispida (Thunb.) Cogn.) and bitter gourd (Momordica charantia L.) (Robinson and
Decker-Walters, 1997). Jeffrey (1980) has classified the genus Cucumis in
accordance with the practical breeding view point into two subgenera i.e. subgenus
sativus and subgenus melo. However Cogniaux as early as 1881 had described 26
species which was later increased to 36 species (Cogniaux and Harms, 1924).
According to Chakravarty (1982) the genus Cucumis consists of about 25 species of
which only 6 are reported to occur in India. Thulin (1991) added four new species:
Cucumis hastatus (wide-spread in Southern Somalia), C. pubituberculatus (found in
Central Somalia on open coastal dunes), C. jeffreyanus (South Western Somalia and
Eastern Ethiopia abundantly growing on alluvial soils), and C. baladensis (South-
Central Somalia on fixed dunes). All these belong to the anguria subgroup of
subgenus melo. Kirkbride (1993) in his monograph recognizes 32 species, 4 attributed
15
to India. As per his treatment two Indian species i.e. Cucumis callosus and C. setosus
are synonymous with Cucumis melo and Cucumis sativus respectively and thus the
number of species is reduced and differs from Chakravarty's work. Further C.
hardwickii and other varieties under C. sativus are not accorded formal taxonomic
status by him. The status of C. prophetarum remains unchanged while two Indian
varieties of C. melo are elevated to subspecific rank. Kirkbride (1993) has accepted
and followed the work of Jeffrey (1980), but he segregated the supraspecific (between
subgenus and species) ranks and proposed two new sections and five new series
under the subgenus melo. The section raised by him based on presence or absence of
aculei on the female hypanthium and ovary, while the series were proposed on the
basis of biosystematic data supported by morphological characters.
1. Subgenus: sativus - 2 species, adapted to tropical to temperate regions of Asia.
Plant monoecious, aculeate. Species available in India are - i) C. sativus L. ii) C.
hystrix Chakr. (syn. C. muriculatus Chakr.)
2. Subgenus: melo - 30 species with tropical distribution. The subgenus includes 2
sections and 6 series. Taxa available in India are - i) C. prophetarum ssp.
prophetarum ii) C. melo ssp. melo iii) C. melo ssp. agrestis
Thus Kirkbride has reduced from 4 species to 2 species under the subgenus
Cucumis and 30 species (comprising with 2 sections and 6 series) against 31 species
(consisting of 4 groups) under the subgenus melo to the contrary of Jeffrey's
classification. More recently, Pitrat et al. (2008) divided C. melo into two subspecies,
ssp. agrestis and ssp. melo, which included five and eleven varieties, respectively. In
16
addition, some germplasm are difficult to classify. Thus C. melo is considered as the
most variable species in the genus Cucumis (Jeffrey, 1980; Mallick and Masui, 1986).
According to Ghebretinsae et al. (2007) genus Cucumis contains 33 species, of which
Cucumis sativus and Cucumis melo are the two most economically important species.
1.1 Cucumis sativus
Cucumbers, Cucumis sativus L. (2n = 2x = 14), are considered to be of Asiatic
origin and are thought to have descended from the closely related, wild Cucumis
sativus var. hardwickii (Royle) Alef., found in the foothills of Nepal and Northern
India (Harlan, 1975; Whitaker and Davis, 1962). Although the cucumber is thought
to have originated in India or Southern Asia, evidence from Northern Thailand
suggests the earliest use of cucumber by humans was approximately 9,750 B.C.
(cucumber history reviewed by Lower and Edwards, 1986; Meglic and Staub, 1996;
Staub and Bacher, 1997; Wehner, 1989; Tatlioglu, 1993). The initial domestication of
cucumber, however, is thought to have occurred in India 3,000 years ago (Lower and
Edwards, 1986), which makes it one of the oldest cultivated vegetable crops (Shetty
and Wehner, 2002). The domestication of cucumber spread East from India to
Western Asia, then West to Asia Minor, North Africa, and Southern Europe before
written history (Tatlioglu, 1993). Cucumber was cultivated by the Chinese (200
B.C.), Sumerians (2,500 B.C.), ancient Greeks and Romans (300 B.C.), ancient
Egyptians, French (9th
century), before being carried to Haiti and New England by
Christopher Columbus at the end of the 15th
century (Lower and Edwards, 1986;
Meglic and Staub, 1996; Tatlioglu, 1993; Wehner, 1989). After its introduction into
the U.S., cucumber was grown in colonial gardens and by several North American
17
Indian tribes (Meglic and Staub, 1996; Staub and Bacher, 1997). Cucumber is now
grown in nearly all countries in temperate zones (Tatlioglu, 1993). Today, cultivated
cucumbers are distributed throughout most temperate and tropical climates and are
the fourth most widely grown vegetable crop behind tomato (Lycopersicon
esculentum Mill.), cabbage (Brassica oleracea var. capitata L.) and onion (Allium
cepa L.) (Tatlioglu, 1993).
Although there are 33 species in Cucumis, cucumber is genetically isolated
within the genus, since it is not readily cross-compatible with any other species
(Ghebretinsae et al., 2007). Chromosome number (x = 7) is a major crossing
impediment, since cucumber deviates from other Cucumis species, which posses 12
(or its multiples) haploid chromosomes (x = 12; Lower and Edwards, 1986).
Although cucumber is cross-compatible with a feral, sympatric, botanical variety of
the same species [C. sativus var. hardwickii (R.) Alef. (x = 7)], cross-compatibilities
between cucumber and x = 12 Cucumis species are extremely rare.
Cucumbers have both culinary and non food uses. Some cosmetic products,
including lotions, perfumes and soaps contain cucumber extracts. Cucumbers are
consumed as fresh or processed forms. Cucumber cultivars are classified as slicers,
picklers, gherkins, middle-Eastern, trellis and European greenhouse types (Shetty and
Wehner, 2002). In Asia, cucumber seeds are eaten as well as tender leaves and stems.
Cucumber seed oil is sometimes used in French cuisine (Robinson and Decker-
Walters, 1997). Pickling cucumbers are the most widely grown type in the United
States.
Genetic diversity of C. sativus in the primary center of origin (India) and
secondary center of diversity (China) has been described (Staub et al., 1997a; 1999).
18
Germplasm from these geographic areas are genetically different from each other, and
distinct from all other C. sativus germplasm in the U.S. National Plant Germplasm
System (NPGS) (Staub et al., 1999). Within the species, wide variation with respect
to fruit bearing habits, maturity, yield, shape, size, colour, spines and vine habit of the
crop has been observed in India (Robinson and Decker-Walters, 1997). In 1992, the
U.S. and Indian governments sponsored an expedition to collect Cucumis species in
the states of Rajasthan, Madhya Pradesh, and Uttar Pradesh, India. There has been no
comprehensive program for the collection and characterization of C. sativus from
Southern India.
1.2 Cucumis melo
Melon, Cucumis melo L. (2n = 2x = 24), is a morphologically diverse, out
crossing horticultural crop of broad economic importance that belongs to the family
Cucurbitaceae. Africa has been generally regarded as the center of origin of C. melo,
while India has been considered an important center of diversification. Strong
viewpoints and arguments on African versus Indian origin are moot in the light of
continental drift, South Eastern Africa and peninsular India were likely continuous or
contiguous. The species C. melo is a polymorphic taxon encompassing a large
number of botanical and horticultural varieties or groups. Melon is divided
into two subspecies, C. melo ssp. agrestis and C. melo ssp. melo,
differentiated by the pubescence on the hypanthium (ovary; Jeffery, 1990).
Furthermore, the former has been subdivided into conomon, makuwa,
chinensis, acidulus and momordica groups, the latter into ten groups:
cantaloupe, reticulatus, adana, chandalak, ameri, inodorus, flexuosus, chate,
tibish, dudaim and morren (Pitrat et al., 2008). They exhibit tremendous
variation in fruit traits such as size, shape, colour, taste, texture, and biochemical
19
composition. The increasing number of varieties and morphological
similarities among melons has necessitated the use of precise system for their
identification and characterization. There are several local varieties of melon
grown in different regions of India. Melons of India have large variability for
fruit shape, size, skin characters, flesh colour, keeping quality and reaction
towards insect pest and disease incidence. The non-dessert or culinary forms
of C. melo is a distinct group distributed and adapted well essentially under
humid tropics of South India (Fergany et al., 2010; Seshadri and More, 1996).
Snapmelon (Cucumis melo L. var. momordica (Roxb.) Duthie et Fuller; 2n =
2x = 24) is native to India, where it is commonly known as ‘phut’ or ‘phoont’ which
means to split. Its fruits invariably crack at maturity and the flesh tastes mealy.
Immature fruits are cooked or pickled, eaten as salad (Karnataka) and the mature, low
sugared flesh is eaten raw. Snapmelon is cultivated in many parts of India and in the
two Japanese islands (Hachijo and Fukue; Fujishita, 2004), where it was used as food
during the two world wars. Snapmelon germplasm has been found to be a very good
source of disease (Cucumber mosaic virus, Zucchini yellow mosaic virus, Powdery
mildew (races 1, 2, 3, 5) and Fusarium wilt (races 1, 2)) and insect resistance (Aphis
gossypii and leafminer) (Fergany et al., 2010).
Culinary melon (Cucumis melo L. var. acidulus; 2n = 2x = 24)
commonly called “vellari” is being cultivated in Karnataka, Andhra Pradesh,
Tamil Nadu and Kerala states of India. This is a popular vegetable crop in
humid tropical region of South India, with a variety of common names viz.,
vellari, melon, pickling melon, preserving melon, culinary melon etc. A
20
modest gene bank of the culinary melon has been established by N. P. S.
Dhillon in the Department of Vegetable Crops, Punjab Agriculture
University, Ludhiana, through explorations in Tamil Nadu and Kerala.
1.3 Genetic diversity
Biological diversity is a commonly recognized value in natural
resource management. This diversity is often represented as a hierarchy of
discrete units such as species, ecosystem and landscapes. Genetic diversity is
a measure of the possible choices of information provided by a gene, when all
or nearly all the members of a population have the same allele at that gene. If
many variants exist for a gene sequence, that population has high genetic
diversity at that gene.
Study of genetic diversity is the process by which variations among
individuals or populations are analyzed by a specific method or a
combination of methods. The data often involves numerical measurements
and in many cases, combinations of different types of variables (Mohammadi
and Prasanna, 2003). Diverse data sets have been used by researchers to
analyze genetic diversity in crop plants; most important among such data sets
are pedigree data (Bernardo, 1993; Messmer, 1993; Van Hintum and Haalman,
1994), passport data, morphological data (Bar-Hen et al., 1995; Smith and
Smith, 1992), biochemical data obtained by the isoenzymes (Hamrick and
Godt, 1997), storage proteins (Smith et al., 1987), and DNA based markers
data that allows more reliable differentiation of genotypes.
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1.3.1 Morphological variability
Genetic diversity in plants has traditionally been established using
morphological and biochemical markers. Phenotypic characterization is the
first step in the description and classification of genetic resources (Smith et
al., 1987). With respect to diversity in characters among populations, cluster
analysis has been used to identify morphological variability in different crop
species (Balkaya et al., 2010; Balkaya and Karaagac, 2005; Cartea et al.,
2002; Decker and Willson, 1986; Escribano et al., 1991). The assessment and
description of trait variation are important tasks in the start-up of programmes aimed
at the selection of genotypes with high-yield performance and qualitative traits useful
to markets. In addition, studies on genetic variation of genetic resources are necessary
to avoid storage of redundant germplasm that contributes to increase in the cost of
germplasm management (Kumar, 1999; Ricciardi and Filippetti, 2000). Therefore,
development of both procedures for characterization of genetic diversity and reducing
collection size to manageable and accessible levels (core size) are important issues in
genebank studies (Brown, 1989; Frankel, 1984; Marshall, 1990). Bio-agronomic
characterization carried out by means of appropriate statistical methods continues to
be a useful tool for the initial description and classification of germplasm, since it
enables plant breeders to identify and select valuable genetic resources for direct use
by farmers or in breeding programmes. Although morphological (visualized as a
phenotype, such as flower color) and biochemical markers (allelic variants of
functional enzymes, also referred to as isozymes) were historically valuable, their
paucity and variability due to environmental conditions and developmental stages
22
limit their effectiveness in plant genetics and breeding. The large majority of
currently utilized markers are DNA-based because they are relatively abundant, not
influenced by the environment and do not effect phenotype (Collard et al., 2005;
Gupta et al., 1999; Staub et al., 1996).
1.3.2 Molecular markers
During the early period of research, classical strategies including
comparative anatomy, physiology and embryology were employed in genetic
analysis to determine inter- and intra-species variability. In the past decades,
however, molecular markers sometimes called as DNA markers were taught
as signs along the DNA trail that pinpoint the desirable genetic trait or
indicate specific genetic differences. Molecular markers include biochemical
constituents (e.g. secondary metabolites in plants) and macromolecules, viz.
proteins and deoxyribonucleic acid (DNA). Environment and management
practices do have an effect on the biochemical and protein markers used,
hence, amongst the molecular markers used, DNA markers are more suitable
and ubiquitous to most of the living organisms (Joshi et al., 2000).
Genetic engineering and Biotechnology holds great potential for plant
breeding as it promises to expedite the time taken to produce crop varieties
with desirable characters. Polygenic characters which were previously very
difficult to analyze using traditional plant breeding methods, are easily
tagged using molecular markers. Techniques which are particularly promising
in estimation of genetic diversity involve the use of two types of molecular
markers: hybridization based molecular markers (RFLP) and PCR based
23
molecular markers (RAPD, AFLP, ISSR, SCAR, STS, SNP and SSR).
Molecular markers have developed into powerful tool to analyze genetic
relationship and genetic diversity (Tatineni et al., 1996). In the hybridization
based molecular markers, the DNA profiles are visualized by hybridizing the
restriction enzyme-digested DNA to a labeled probe, which is a DNA
fragment to known origin or sequence. PCR-based markers involves the in
vitro amplification of particular DNA sequence or loci, with the help of
specifically or arbitrarily chosen oligonucleotide sequences (primers) and a
thermo stable DNA polymerase enzyme. The amplified bands are separated
electrophoretically and banding patterns are detected by different methods
such as staining and autoradigraphy. PCR is a versatile technique invented
during the mid-1980s. Ever since thermo stable DNA polymerase was
introduced in 1988 (Saiki et al., 1988), the use of PCR in research and
clinical laboratories has increased tremendously. PCR is extremely sensitive
and operates at very high speed. The primer sequences are chosen to allow
base-specific binding to the template in reverse orientation.
The first widely used DNA-based markers were Restriction Fragment
Length Polymorphisms (RFLP; Tanksley, 1993). Although RFLPs are co-
dominant, fairly robust, and more prevalent than isozymes, they are costly,
time-consuming, laborious (not high-throughput), and not as abundant as
other marker systems. They also require large amounts of DNA, as well as the
use of radio labeled isotopes, and cloning is a necessary part of marker
development.
24
To overcome the time and labor requirements of RFLP markers,
Random Amplified Polymorphic DNA (RAPD) markers were developed
(Williams et al., 1990). As their name implies, RAPDs are much quicker and
easier to develop and utilize than RFLPs, and they are comparatively more
abundant, much less expensive, require less DNA, and in many cases provide
multiple markers per assay. RAPDs, however, are typically dominant, not
robust, and often methodologically non-problematic (Paran and Michelmore,
1993; Staub et al., 1996).
Sequence Characterized Amplified Region (SCAR) markers were
initially designed by Paran and Michelmore (1993) to convert a polymorphic
RAPD marker into a robust, single-copy marker. SCAR markers are produced
by sequencing the RAPD band and using the sequence at both ends of the
fragment to extend the 10 bp (base pair) RAPD primer an additional 14 bp to
produce a specific pair of primers. Since a SCAR marker is defined as a
fragment from genomic DNA generated from specific primers through PCR
(Paran and Michelmore, 1993), SCARs can also be derived from markers
other than RAPDs (e.g., RFLPs). The only requirement is that cloning and
sequencing are needed to design primers to specifically amplify a single
product. Once developed, however, SCARs are much more robust and
repeatable than RAPDs, and are easy and inexpensive to use (Polashock and
Vorsa, 2002; Randig et al., 2002). Although SCAR markers are usually
dominant, co-dominant SCARs are not uncommon (Staub et al., 1996).
Because most SCAR markers produce a single band, they are amenable to
25
multiplexing (including two or more markers simultaneously in the same PCR
reaction), which further increases their efficiency during genotyping
(Polashock and Vorsa, 2002; Randig et al., 2002), and makes them amenable
to high-throughput systems.
Inter Simple Sequence Repeats (ISSR) was reported by Zietkiewicz et
al. (1994). These primers based on microstaellites are utilized to amplify
inter-SSR DNA sequences. Here, various micro satellites anchored at the 3’
end are used for amplifying genomic DNA, which increases their specificity.
These are mostly dominant markers, though occasionally few of them exhibit
co-dominance. These markers are DNA sequence delimited by two inverted
SSR composed of the same units which are amplified by single PCR primer,
composed of few SSR units with or without anchored ends. ISSR markers
give multi-locus patterns, which are very reproducible, abundant and
polymorphic in plant genomes (Bornet and Branchard, 2004).
Amplified Fragment Length Polymorphism (AFLP) markers are
dominant, more robust than RAPDs, and can provide several markers per
assay (Vos et al., 1995). Although the AFLP methodology is more
technologically complicated than RAPDs, no cloning or prior sequence
knowledge is required. Initially, AFLPs required polyacrylamide gel
electrophoresis and labeling with radio-labeled isotopes, but they have been
adapted for automated sequencing platforms with fluorescent labeling
(fAFLP; Desai et al., 1998). AFLP markers are more expensive than RAPDs,
26
and, except for RFLPs, require only slightly more DNA than other marker
systems for utilization.
There are several types of markers that require sequence information
for development in addition to SCARs, Simple Sequence Repeat (SSR or
microsatellite) markers take advantage of the fact that small (usually di-, tri-,
tetra-, or penta-nucleotide) tandemly repeated sequences tend to vary in
length among haplotypes in a population (Gupta et al., 1999). These repeats
are relatively abundant and highly polymorphic in plants (Staub et al., 1996).
SSRs are usually developed by creating a library enriched with genomic
fragments containing repeats, sequencing the fragments, then designing
primers flanking the repeats which are expensive and time consuming. SSRs
are co-dominant by nature, and can have multiple alleles per locus because
the tandem repeats vary in length in genetically diverse populations. Once
developed, SSRs are robust, but small differences in molecular weight among
band morphotypes often necessitate their visualization by polyacrylamide gel
electrophoresis. Like fAFLPs, SSRs can be visualized in automated
sequencing platforms, but unlike AFLPs or RAPDs, they can be multiplexed
in high-throughput systems.
Sequenced Tag Site (STS) markers were originally proposed as a
standard for simple PCR-based markers created from RFLP probes in humans
(Olson et al., 1989). An STS is a short, single-copy marker that is associated
with a specific locus and can be amplified by PCR. Although STS and SCAR
have been used synonymously in the literature at times, STS is conventionally
27
reserved for PCR markers made from RFLPs (Gupta et al., 1999). STS
markers are robust, relatively inexpensive, easy to use, and amenable to high-
throughput systems through multiplexing. STSs are usually dominant, but can
be co-dominant depending on their design and use.
Markers based on Single Nucleotide Polymorphisms (SNP) are gaining
popularity and are the current marker of choice for several species including
crop plants (Gupta et al., 2001). This popularity is based on the idea that as
more genomic resources are being made available, SNPs are best able to fit
the ideal marker for use in plant breeding. SNPs are usually co-dominant and
robust markers. The number of SNPs in any given genome is much higher
than any other marker type (estimated at 1 in 100 to 1 in 1000 bp), including
an order of magnitude higher than SSRs (Gupta et al., 2001). The rise in SNP
popularity has lead to several different methods of discovery and genotyping.
Some of these methods, such as pyrosequencing for SNP detection, are
focused on high-throughput systems. These and other non-gel based assays
such as TaqMan, Molecular Beacons, and array-based assays, are usually
supported by proprietary technologies which may be cost prohibitive to many
plant breeding programs. SNP genotyping, however, can be adapted to low
cost methods using basic laboratory equipment such as PCR followed by
agarose gel electrophoresis in allele-specific PCR (AS-PCR) or single-
nucleotide amplified polymorphism (SNAP) assays (Drenkard et al., 2000;
Moreno-Vazquez et al., 2003). The major disadvantages to the development
of SNPs markers are that sequence information is necessary for their design,
28
and SNPs are bi-allelic unlike SSRs, which usually have multiple alleles per
locus. The abundance of SNPs, however, compensates for the limited number
of alleles, making their development cost-effective.
The selection of marker types for use in plant breeding depends on
several factors including project objectives, population and mating structure,
genomic complexity, the intended use of the markers, and the resources
available (Gupta et al., 1999; Staub et al., 1996). For example, RAPD and
AFLP are useful technologies for new marker identification and molecular
map construction because multiple markers can be identified in each sample
and no prior sequence knowledge is needed (Brugmans et al., 2003; Paran
and Michelmore, 1993). Once established, however, SCAR, SNP, STS, and
SSR markers are much more useful in genotyping populations because of
their robustness and potential ability to be mutliplexed. The continued
increase in sequence availability and EST databases, allows for the creation
of SNP, SSR, CAPS, and SCAR type markers without having to generate
sequence date. Furthermore, markers created from EST databases are based
upon transcribed loci, and may, therefore, be more suited to gene tagging.
Genetic diversity in crops plants may be analyzed at different levels:
individual genotypes such as inbred lines or clones, populations, germplasm
accessions and species. Sampling strategies in each of the above cases would
vary, primarily because of the differences in nature of the genetic material.
Genetic distance is “the extent of gene difference between populations or
species that is measured by some numerical quantity” (Nie, 1987). Genetic
29
distance or similarity between two genotypes, populations or individuals may
be calculated by various statistical measures depending on the data set.
1.3.3 Genetic variability analysis
Multivariate analysis
With the increase in the sample sizes of breeding materials and
germplasm accessions used in the crop improvement programs, methods to
classify and order genetic variability are of considerable significance. The
use of established multivariate statistical algorithms is an important strategy
for classifying germplasm and ordering variability for large number of
accessions, or analyzing genetic relationships among breeding materials.
Multivariate analytical techniques, which simultaneously analyze multiple
measurements on each individual under investigation, are widely used in
analysis of genetic diversity irrespective of data set (morphological,
biochemical or molecular marker data). Among these algorithms, cluster
analysis, Principle Component Analysis (PCA), Principal Coordinate
Analysis (PCoA), and Multi Dimensional Scaling (MDS) are presently the
most commonly employed and appear particularly useful (Brown et al., 2000;
Johns et al., 1997; Melchinger, 1993).
Cluster analysis
Cluster analysis refers to “a group of multivariate techniques whose
primary purpose is to group individuals or objects based on the characteristics
they possess, so that individuals with similar descriptions are mathematically
30
gathered into the same cluster” (Hair et al., 1995). There are broadly two
types of clustering methods (i) distance based methods, which a pair-wise
distance matrix is used as an input for analysis by a specific clustering
algorithms, leading to graphical representation (such as tree or dendrogram)
in which cluster may be visually defined and (ii) model based methods, in
which observations from each cluster are drawn from some parametric model.
Inferences about clusters and each member are performed using some
statistical methods as maximum likelihood or Bayesian method (Johnson and
Wichern, 1992). Distance based clustering methods can be categorized into
two groups hierarchical and nonhierarchical. Hierarchical methods are found
to be most commonly used for genetic diversity study in crop species. Among
hierarchical methods UPGMA (Un-weighed Paired Group Method using
Arithmetic averages; Panchen, 1992; Sneath and Sokal, 1973) is most
commonly adopted method. Data analysis of genetic relationship in crop
species is an important component of crop improvement programme. Many
software packages are available for analyzing genetic diversity. Each data set
has its own strength and constraints and there is no single or simple strategy
to address effectively various complex issues related to genetic diversity and
genetic relatedness.
1.3.4 Disease resistance
Plants use a variety of mechanism to defend themselves against
pathogen attack. In many cases, plant disease resistance gene (R gene) has
been shown to confer resistance against a pathogen in accordance with the
31
“gene-for-gene” model originally described for flax-flax rust interaction by
Flor (1956). Many different types of R-genes encoding proteins with different
functional domains, have been characterized in varieties of species (reviewed
by Hammond-Kosack and Parker, 2003). The largest class of functionally
defined R-genes encodes products that have a Nucleotide Binding Site (NBS)
domain and a Leucine Rich Repeat domain (LRR). Resistance Gene
Homologues (RGH) of the NBS-LRR class occurs in large numbers in plant
genome. RGHs of the NBS-LRR type are often organized in clusters in plant
genomes, as demonstrated for various plant species (Leeuwen et al., 2005)
including Cucumis sps. (Leeuwen et al., 2003). The cultivated Cucumis is
susceptible to a variety of disease, so the identification and mapping of
resistance genes can contribute to identifying varieties of increased
agronomic values. Many genes have been marked by various researchers for
identification of disease resistance in Cucumis (Liu et al., 2008; Park et al.,
2004a; Zheng and Wolff, 2000) by various kinds of molecular markers. These
markers can be used for screening and identification of disease resistant
varieties and use for further breeding purpose.
1.3.5. Fatty acid profile
The demand for vegetable oils is ever increasing, and the world relies
mostly on the popular vegetable oils for the preparation of many products.
India has a wide variety of plants that can produce oil. However, there is little
information on the composition and utilization of the many oilseeds in India.
Many Cucurbitaceae seeds are rich in oil and protein and although none of
32
these oils has been utilized on an industrial scale, many are used as cooking
oil in some countries in Africa and the Middle East (Al-Khalifa, 1996).
Several authors (Al-Khalifa, 1996; Applequist, 2006; Badifu, 1991; Kamal et
al., 1985) have reported studies on species of the Cucurbitaceae family and
compared the physicochemical characteristics of their oils with those from
conventional sources.
The Cucurbitaceae consists of an important genus known as Cucumis,
which consists of two species with great commercial importance: melon
(Cucumis melo) and cucumber (Cucumis sativus). These two crops represent 7
% of the world’s total cultivated vegetable surface in 2001, ranking third
after tomato and watermelon (www.fao.org). Other Cucumis species are
cultivated for food or ornamental use, but are of less economical importance.
Cucumber is genetically isolated and low in polymorphism within the genus since it
is not readily cross-compatible with any other species (Kirkbride, 1993).
Chromosome number (x = 7) is a major crossing impediment since cucumber deviates
from other Cucumis species, which posses 12 (or its multiples) haploid chromosomes
(x = 12; Lower and Edwards, 1986), while melons (x = 12) are morphologically
diverse and out crossing horticultural crop with high polymorphism. Due to these
facts there is an unquestionable need for more highly polymorphic genetic markers.
DNA markers like RAPD and ISSR have been used intensively to
characterize cucumber and melon germplasm to define different classes and
relationships (Garcia et al., 1998; Katzir et al., 1996; Lopez-Sese et al., 2002;
Mliki et al., 2001; Monforte et al., 2003; Silberstein et al., 1999; Staub et al.,
33
1997a, b; Stepansky et al., 1999a). But the relative genetic distances among
different melons and between individual accessions of culinary melon
endemic to India, have still not been defined and the screening for various
disease resistance and fatty acid profile has to be analyzed to confirm their
variations at various levels. Fergany et al. (2010) have analyzed the variation
of melons in Tamil-Nadu and Kerala, but Karnataka was not included in their
study. So, present study was undertaken with the following objectives:
(1) Phenotypic characterization of cucumber and melons collected from
different regions of Karnataka.
(2) Analysis of DNA variation (RAPD and ISSR) in cucumber and melons
collections and their genetic relatedness.
(3) Screening for disease resistance using molecular markers (SCAR and
STS) in the cucumber and melon collections of Karnataka.
(4) Analysis of variation in fatty acid composition of seed oil among the
cucumber and melons collections of Karnataka.