13

Introduction

14

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

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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.