FINGERPRINTING OF RICE VARIETIES

RELEASED FROM TAMIL NADU RICE

RESEARCH INSTITUTE, ADUTHURAI USING

MORPHOLOGICAL AND SIMPLE SEQUENCE

REPEATS (SSR) MARKERS

By

SHIV DAYAL MEENA

Degree : Master of Science in Biotechnology

Chairman : Dr. M. RAVEENDRAN

Associate Professor,

Department of Plant Molecular Biology and Biotechnology

Centre for Plant Molecular Biology,

Tamil Nadu Agricultural University,

Coimbatore-641 003

2008

Abstract:

Genetic diversity assessment and identification of superior genotypes are the basic

objectives of any crop improvement programme. Morphological traits have been used

extensively for this purpose till now. But now a days DNA based markers has opened up

new horizons for detecting variation at the molecular level. Of all the DNA markers,

Simple Sequence Repeat (SSR) markers provided a rapid approach to analyse the genetic

diversity. In the present investigation, an attempt has been made to assess the genetic

diversity among 35 rice varieties released from TRRI, Aduthurai using 27 morphological

traits and 72 SSR alleles which are generated by 25 SSR primers. A wide range of

morphological diversity was noticed for 7 quantitative and 20 qualitative traits in the rice

varieties released from TRRI, Aduthurai. The duration of the varieties ranged from 90

(ADT 30) to 220 (ADT 6) days. The plant height varied from 100 cm (ADT 45) to 150cm

(ADT 27). Thirty five varieties were grouped into 5 clusters based on morphological

traits using principle component analysis and hierarchical cluster analysis. The cluster I

has maximum varieties and consisted of ADT 1, ADT 2, ADT 25, ADT 7, ADT 10,

ADT 26, ADT 30, ADT 22, ADT 35 and ADT 14 with long duration, tall nature and

short bold grains. The Cluster II consisted of ADT 4, ADT 15, ADT 12, ADT 6, ADT 8,

ADT 20, ADT 27 and ADT 32 which are having tall nature and long bold grains. The

Cluster III consists of ADT 16, ADT 47, ADT 38, ADT 39, ADT 36 and ADT41 with

medium duration, dwarf in nature and medium slender grains. The Cluster IV consisted

of ADT 11, ADT43, ADT48 and ADT45 with short duration, semi dwarf nature and long

slender grains.

Genetic diversity was also assessed using set of 25 SSR primers which generated

72 polymorphic alleles. Polymorphism information content values ranged between

0.382 (RM 420) and 0.711 (RM 4955). Dendrogram was constructed using Jacquard’s

similarity coefficient and rice varieties were grouped into 12 clusters based on SSR

markers. The cluster XII has maximum varieties and consisted of varieties ADT 43,

ADT 47, ADT 28, ADT 44, ADT 45 and ADT48. The narrow genetic base of aduthurai

varieties also evidenced from the cluster XI which includes ADT 36 (Triveni/IR20) and

ADT 39 (IR8/IR20). Highest diversity was found between ADT 35 (Bhavani/Jaya) and

ADT 6 (pureline) with similarity level 12% and lowest diversity was found between ADT

22 ( pureline selection) and ADT 20 (ADT 3/ADT 2) (cluster VII) with similarity level 68

percent. The iron content analysis of ADT varieties resulted in identification of highest

iron content variety ADT 25(15.76 ppm) and the lowest iron content variety was the

ADT 42 (3.77ppm).

INTRODUCTION

Rice, Oryza sativa (2n = 24) belonging to the family Graminae and subfamily

Oryzoidea is the staple food for one third of the world’s population and occupies almost

one-fifth of the total land area covered under cereals. It is grown under diverse cultural

conditions and over wide geographical range. Most of the world’s rice is cultivated and

consumed in Asia, which constitutes more than half of the global population.

Approximately 11% of the world’s arable land is planted annually to rice, and it ranks

next to wheat. The world’s rice production has doubled during last 25 years, largely due

to the use of improved technology such as high yielding varieties and better crop

management practices (Byerlee, 1996). Further scope of crop improvement depends on

the conserved use of genetic variability and diversity in plant breeding programmes and

use of new biotechnological tools. There is wide genetic variability available in rice

among and between wild relatives and varieties leaving a wide scope for future crop

improvement.

Moreover, rice is also an ideal model plant for the study of grass genetics and

genome organization due to its diploid genetics, relatively small genome size 430 Mb

(Causse et al., 1994; Kurata et al., 1994), significant level of genetic polymorphism

(McCouch et al., 1998; Tanksley, 1989; Wang et al., 1992), large amount of well

conserved genetically diverse material (approximately 200,000 accessions of rice

germplasm worldwide) and the availability of widely collected and compatible wild

species. Characterization and quantification of genetic diversity has long been a major

goal in evolutionary biology. Information on the genetic diversity within and among

closely related crop varieties is essential for a rational use of genetic resources. The

analysis of genetic variation both within and among elite breeding materials is of

fundamental interest to plant breeders. It contributes to monitoring germplasm and can

also be used to predict potential genetic gains. Diversity based on phenological and

morphological characters usually varies with environments and evaluation of these traits

requires growing the plants to full maturity prior to identification. Protein or isozyme

marker studies are also influenced by environment and reveal low polymorphism. Now,

the rapid development of biotechnology allows easy analysis of a large number of loci

distributed throughout the genome of plants. Molecular markers have proven to be

powerful tools in the assessment of genetic variation and in the elucidation of genetic

relationships within and among species. Several molecular markers viz. RFLP (Becker et

al., 1995; Paran and Michelmore, 1993;), RAPD (Tingey and Deltufo, 1993; Williams et

al., 1990), SSRs (Levinson and Gutman, 1987), ISSRs (Albani and Wilkinson, 1998;

Blair et al., 1999), AFLP (Mackill et al., 1996; Thomas et al., 1995; Vos et al., 1995; Zhu

et al., 1998) and SNPs (Vieux et al., 2002) are presently available to assess the variability

and diversity at molecular level (Joshi et al., 2000). Information regarding genetic

variability at molecular level could be used to help, identify and develop genetically

unique germplasm that compliments existing cultivars. The fingerprinting technique has

been successfully applied in the field of phylogeny, taxonomy, genetic diversity, forensic

science, determination of paternity, plant varietal protection, seed certification etc.

Among all the DNA markers currently available, microsatellites are considered to

be the marker of choice for varietal identification because of their co-dominant

segregation and their ability to detect large number of discrete alleles repeatedly,

accurately and efficiently (Olufowote et al., 1997). SSR markers generate enough allelic

diversity to differentiate cultivars within a subspecies or ecotype (Yang et al., 1994). The

information obtained from SSR markers is also useful for making predictions about

crossing and selection aimed at increasing the efficiency of parental selection and variety

development (Panaud et al., 1996).

Several rice varieties have been released by Tamil Nadu Rice Research Institute,

Aduthurai to cater the needs of the farmers. The molecular characterization and

fingerprinting of these released varieties using microsatellite markers will provide

sufficient knowledge on diversity among them at molecular level, which will help the

breeders to develop strategies for the future, and the variety specific fingerprints will

enable to identify and characterize each variety released.

Hence, the present study was undertaken with the following objectives.

1) To assess the genetic diversity among TRRI, Aduthurai rice varieties through

morphological traits.

2) To assess the level of polymorphisms among TRRI, Aduthurai varieties through

microsatellite markers

REVIEW OF LITERATURE

Recent development in the field of DNA technology has resulted in the

development of several molecular markers, which are linked to many traits that are used

in characterizing, true species and genera. Out of all the known DNA markers,

microsatellites are regarded as the markers of choice. Some of the recent applications of

microsatellite markers in various fields in different crops are reviewed briefly here under.

2.1 Rice

The genus Oryza belongs to the tribe Oryzae of the family Poaceae and subfamily

oryzoideae. Oryza has two cultivated species, Oryza sativa and Oryza glaberrima. Oryza,

the common cultivated rice is grown worldwide. Rice (Oryza sativa L.) is a true diploid

(2n=24) with twelve chromosome pairs and contains 5.8x105 kb/haploid genome (Bennet

and Smith, 1976). There is ample polymorphism in rice DNA and it is highly

recombinogenic compared to other plants. One centimorgan of rice equals approximately

250 kb, compared to more than 500 kb in tomato and 750 kb in potato (Tanksley et al.,

1989). Most of the species in the genus Oryza have been characterized in terms of their

chromosome number, genome symbols, phenotypic characters and geographical

distribution (Khush and Kinoshita, 1991). The DNA content per map unit in rice, the

most important food crop in the world is only 2-3 times greater than Arabidospsis

thaliana, the ideal plant for molecular genetics. There is a vast reservoir of germplasm

(2, 00,000 accessions) of rice worldwide. Rice is considered as the most ideal monocot

for molecular mapping and map based cloning of agriculturally important genes.

2.2. Molecular Markers

Genetic studies over the past 70 years have led to the establishment of several

different types of molecular markers, which include biochemical markers like isozyme

markers and DNA markers. DNA markers include Restriction fragment length

polymorphism (RFLP) (Botstein et al., 1980), Oligonucleotide polymorphism (OP)

(Beckmann, 1988), Single strand confirmation polymorphism (SSCP) (Orita et al., 1989).

Minisatellites (Jarman et al., 1989), Random amplified polymorphic DNA (RAPD)

(Williams et al., 1990), Allele specific PCR (AS-PCR) (Sarket et al., 1990), DNA

amplification fingerprinting (DAF) (Caetano-Anolles et al., 1991), Sequence

characterized amplified region (SCAR) (Williams et al., 1990), Simple sequence repeats /

Short tandem repeats (SSR/STR) (Hearne et al., 1992), Arbitrarily primed PCR (AP-

PCR) (Welsh and Mackill, 1992), Cleaved amplified polymorphic sequences (CAPs)

(Lyamicher et al., 1993), Inter simple sequence repeat amplification (ISA), Random

amplified multiple polymorphism (RAMP) (Saghai Maroof et al., 1994), Single

nucleotide polymorphism (SNP) (Saghai Maroof et al., 1994), Sequence tagged sites

(STS) (Fukuoka et al., 1994) Amplified fragment length polymorphism (AFLP)

(Vos et al., 1995), Amplicon length polymorphism (ALP) (Ghareyazie et al., 1995) and

Retrotransposon based insertion polymorphism (RBIP) (Flavell et al., 1998).

The estimation of genetic diversity among different genotypes is the first and

foremost process in any plant breeding programme. Briquet et al., (1996) studied the

applications of molecular markers to assess plant genetic diversity. Since a variety of

molecular markers have become available in recent years, efforts are being made to

identify the most efficient and cost effective markers that can be used by plant breeders

(Mohan et al., 1997 and Gupta et al., 1999). Different marker types revealed contrasting

genetic diversity relationships in rice (Parsons et al., 1997).

RAPD and isozyme markers were used to study the genetic diversity of forty rice

accessions (Anjana Sree, 1999). RAPD Markers have been used for evaluating the

genetic diversity and establishing DNA fingerprints of 78 rice varieties developed by

IRRI from 1965 to 1995, their 58 ancestral lines, farmer’s varieties and gene bank

collections. The study on genetic diversity using molecular markers has been successfully

conducted in various cultivars of Oryza sativa L. by Mathew (1999) and Ravi (2000).

PCR based molecular markers (e.g. RAPDs, SCARs, CAPs, STS, STMS and

AFLPs) are preferred over hybridization based markers like RFLPs. Among the

PCR-based markers, microsatellites have been exploited in many ways like in genome

mapping, DNA fingerprinting, study of genetic diversity etc. (Gupta and Varshney, 2000).

2.3. Microsatellite markers

Microsatellites are also known as Simple Sequence Repeats (Hearne et al., 1992)

or short Tandem Repeats (Edwards et al., 1996). Microsatellites are simple tandemly

repeated di to tetra nucleotide sequence motifs flanked by unique sequences and are

found mostly confined to telomeres. (McCouch et al., 1997).

Microsatellite sequences are abundant, dispersed through out the genome, and are

highly polymorphic in plant genomes even among closely related cultivars, due to

mutations causing variation in the number of repeating units in genomes (Condit and

Hubbell, 1991, Akkaya et al., 1992: Morgante and Oliveri, 1993). A number of strategies

have been designed to exploit microsatellite sequences for the study of DNA

polymorphism in eukaryotes. They involve both hybridization and PCR based

approaches. Oligonucleotide fingerprinting, a hybridization based approach represents

polymorphism due to variation in the length of the restriction fragments that carry the

microsatellites while PCR based approaches detect variation in the length of

microsatellites.

Microsatellite markers have become available in several individual crops due to

production of genomic libraries enriched for microsatellites (Ostrander et al., 1992;

Edwards et al., 1996; Fisher et al., 1996). The frequencies of microsatellites vary

significantly among different organisms (Morgante and Oliveri, 1993; Wang et al., 1994,

Gupta et al., 1996). In a survey of published DNA sequences in 54 plant species, Wang et

al. (1994) observed that the (AT)n sequences are the most abundant in plants.

Microsatellites are abundant and occur frequently and randomly in all eukaryotic nuclear

DNAs (Gupta et al., 1996). The microsatellites are valued highly as genetic markers

because they are co dominant, detect high levels of allelic diversity and are easily and

economically assayed by the PCR (McCouch et al., 1997).

The known DNA sequence (microsatellites) may be searched from the databases

like EMBL and GenBank and flanking sequences can be determined. Once the flanking

sequences are known, primers may be designed either by manual inspection or with the

help of computer programs keeping in view that the GC content should be around 50 per

cent (Tm 60°C), low frequency of primer dimmers and 3' end should be AT rich (Gupta

et al., 1996). DNA polymorphisms are detected by PCR at individual loci using locus

specific primers flanking the microsatellites. PCR assays using microsatellite primers

carry high information content and have been used for mapping and gene tagging

purposes (Morgante et al., 1994). Variation in the number of tandemly repeated core

sequence of nucleotides at SSR loci among different genotypes provides the basis for

polymorphism that can be used in plant genetic studies (Condit and Hubbell, 1991).

Recent reports indicate the SSR loci for a number of core repeat units are highly

polymorphic between species and more importantly, between individuals within species

and populations (Akkaya et al., 1992). New microsatellites can be cloned directly from

total genomic DNA libraries (or) libraries enriched for specific microsatellites (Ostrander

et al., 1992). Polymorphism can also be detected by using the synthetic oligonucleotides

as primers each complimentary to a microsatellite motif randomly distributed through out

the genome (Meyer et al., 1993). (AT)n repeats occur frequently in plants while (GT)n

repeats are frequent in animals (Gupta et al., 1996). SSRs are among the DNA marker

types effective in assessing genetic diversity at the DNA level (Moreno

et al., 1999). Advantages of SSRs over other molecular markers have been reported by

Botha and Venter (2000) and Gupta and Varshney (2000).

2.4 Microsatellites in rice

Microsatellite primers have been developed in a number of crops. In rice, they are

commercially available as "Rice Map Pairs" (RM pairs) through Research Genetics, AL

35801, USA.

Reports of rice microsatellite linkage maps show a range of 1-300 mapped

microsatellite loci (Yang et al., 1994, Akagi et al., 1996; Panaud et al., 1996; McCouch

et al., 1997; Cho et al., 2000; Temnykh et al., 2000). Panaud et al., (1995) investigated

the relative frequency of 13 different SSR motifs in rice based on the screening of both

genomic and cDNA libraries and the results suggested that there are 5,700-10,000

microsatellites in rice. In the same study it was also seen that 1360 poly (GA)n and 1230

poly (GT)n occurred in rice genome and the frequency of repeats decreased with

increasing size of the motif. Reports by Akagi et al., (1996) showed that 35 per cent of

the rice chromosomes were covered by 56 microsatellite markers.

It has been found that genetically mapped microsatellite markers cover the entire

rice genome with at least one microsatellite for every 16 to 20 cM (Chen et al., 1997). A

map consisting of 120 microsatellite markers demonstrates that they are well distributed

through out the 12 chromosomes of rice. The current level of genome coverage provided

by SSLPs in rice is sufficient to be useful for genotype identification, gene and QTL

analysis, and marker assisted selection in breeding (McCouch et al., 1997). A total of 312

microsatellite markers provide whole genome coverage in rice with an average density of

one SSLP per 6 cM (Temnykh et al., 2000).

Amongst eight microsatellite loci tested over twenty rice accessions, an average

number of 3-11 alleles are detected per microsatellite locus with a diversity index of

0.64-0.90. Many studies have reported significantly greater allelic diversity of

microsatellites over RFLPs and high numbers of alleles for rice microsatellite markers

(Yang et al., 1994; Olufowote et al., 1997; McCouch et al., 1997). Yang et al., (1994)

reported 3 to 25 alleles for 10 microsatellite markers among 238 accessions of indica and

japonica cultivars and landraces. Panaud et al., (1996) identified 2 to 9 alleles for

microsatellite markers in 22 japonica and indica cultivars. Akagi et al., (1997) found 5 to

10 alleles among 59 closely related japonica cultivars. The allelic diversity of

microsatellite markers in cultivated rice varieties has been reviewed by Gupta and

Varshney (2000) as 2-25 alleles per microsatellite locus.

Rice microsatellites have been demonstrated to be polymorphic between rice

varieties (Yang et al., 1994; Panaud et al., 1996; Akagi et al., 1997; Chen et al., 1997;

Olugowote et al., 1997; Bligh et al., 1999). Seventy one rice cultivars were evaluated for

intra-specific variation using RFLP and SSLP polymorphisms by Olufowote et al.,

(1997). The results suggested that six well chosen SSLPs were sufficient to discriminate

between all the seventy one closely related lines of rice confirming that these hyper

variable SSLP markers provide a means for uniquely identifying specific inbred lines,

varieties and hybrids.

Ramakrishna et al. (1994) used five microsatellite primers to identify genetic

diversity amongst twelve rice varieties. Mackill et al., (1996) found that microsatellites

have average polymorphisms 1.5 times higher than AFLP and RAPD markers in a

comparison of 12 japonica cultivars. Microsatellite markers provide high levels of

polymorphism needed to allow genomic segments through the narrow crosses and closely

related pedigrees of a rice breeding program (Panaud et al., 1996). Microsatellites

consisting of AT repeats were found to be highly polymorphic in rice genomes and were

used to distinguish 59 japonica cultivars (Akagi et al., 1997). Provan et al., (1997) using

SSR markers detected intra and inter cultivar polymorphism between the cultivated and

wild rice and the extent of chloroplast genomic differentiation was quantified. Isozymes

and microsatellites were used for varietal classification in rice, which revealed two major

groups corresponding to indica and japonica varieties (Quilloy et al., 1998). Thirty two

inter simple sequence repeats were used for DNA fingerprinting of 59 rice accessions

(Blair et al., 1999), Use of microsatellite polymorphisms for the identification of

Australian breeding lines of rice (Oryza sativa L.) was investigated and most of the

cultivars could be uniquely identified by at least one microsatellite marker (Garland et

al., 1999). Microsatellite DNA assays were found to be most appropriate tools in

classifying and grouping genotypes (Lu et al., 1999).

2.5 Various applications of microsatellite markers

The abundance and amount of information derived from microsatellite markers

make them ideal markers for plant genetic linkage mapping, population studies and

varietal identification (Schwarzacher, 1994).

2.5.1. Gene tagging and marker assisted selection

Microsatellite markers have become the markers of choice in gene tagging

(Powell et al., 1996).A number of genes in rice, wheat, and soybean have been tagged

using microsatellite markers.

2.5.2 DNA fingerprinting and genetic diversity studies

Genetic diversity is desirable for long term crop improvement and reduction to

vulnerability to important crop pests and pathogens (Liu et al., 2000). Microsatellites

have been considered the marker of choice for assessment of genetic diversity among

cultivars and their wild relatives in many crops including rice, sorghum, maize, wheat,

soybean etc.

2.5.3. Genetic fidelity and characterization of germplasm

Microsatellite markers have also been utilized successfully to find out whether or

not the germplasm accessions maintain their genetic fidelity during storage and

conservation. SSR markers were used to assess the genetic purity among the accessions

i.e., to detect duplication, seed mixture, inadvertent out crossing and genetic drift (Powell

et al., 1996: Olufowote et al., 1997; Gupta and Varshney, 1999).

2.5.4. Cytogenetic research

Use of microsatellites to ascertain chromosome constitution of tetraploid hybrids

in Musa was reported by Crouch et al. (1998). ISSR markers were used for analysis of

the origin of genomes of finger millet (Salimath et al., 1995). The chromosome

constitution of anther derived plants of dihaploid potato clones (Solanum chacoense /

Solanum phureja) have been characterized using a set of STMS primers. Microsatellites

have been used to characterize cytogenetic stocks (Peil et al., 1998). Microsatellites have

increased future prospects in cytogenetic studies facilitating tagging and isolation of

genes located on specific chromosomes (Gupta and Varshney, 2000).

2.5.5 Comparative genome mapping

Sequence tagged microsatellite (STMS) primers designed for a particular crop

species could be successfully utilized for a study involving the related wild species /

widely divergent species or the related genera. The primers designed for cultivated rice

were successfully used in wild Oryza species and vice versa (Panaud et al., 1996). In a

study conducted by Chen et al., (1998), 66 per cent of the genomic microsatellites, out of

one hundred and twenty four microsatellite markers developed from a genomic library

and cDNA sequences in rice, were amplified when tested with three dicots (Brassica,

tomato and tobacco) and three monocots (maize, sorghum and wheat). When 15 wheat

primer pairs were tested with barley and rye, 10 primer pairs gave amplification products

for barley and 9 primer pairs gave amplification products for rye when used under less

stringent conditions (Roder et al., 1995).

From the research work carried out by many workers reviewed in this chapter, it

can be presumed that the microsatellite markers will have a clear edge over other

molecular markers currently being used in genetic studies in the coming years.

MATERIALS AND METHODS

All the experiments for the microsatellite analysis were carried out at Department

of Plant Molecular Biology and Biotechnology, CPMB and Paddy Breeding Station,

Tamil Nadu Agricultural University, Coimbatore, India.

Methods

The experimental method consists of four parts:

1. Genetic diversity studies based on morphological characters.

2. Genetic diversity studies based on simple sequence repeats (SSR).

3. Genetic diversity studies based on Iron content.

3.1 Plant Material

A total of thirty five varieties of cultivated rice (Oryza sativa L.) released from

Tamil Nadu Rice Research Institute, Aduthurai, India was used for this study.

3.2 Methods

3.2.1. DNA extraction

Leaf samples were harvested from 15 days old seedlings grown in fields of Paddy

Breeding Station, Coimbatore. Leaves from plants within each cultivar were harvested

separately, frozen in liquid nitrogen and stored at 80°C until total genomic DNA was

isolated. DNA was extracted by following a modified method of Dellaporta et al., (1983).

• Two gram of fresh or frozen leaf tissue was grinded to a fine powder with

a pre-chilled mortar and pestle in liquid nitrogen.

• The powder was transferred to a 25 ml polypropylene centrifuge tube

containing 10 ml of cold extraction buffer (500 mM NaCl, 100 mM Tris-

HCl pH 8.0, 5.0 mM EDTA pH 8.0 and 10 mM ß- mercaptoethanol).

• One ml of 20% SDS pH 8.0 was added and the tube was incubated for 10

min at 65°C with occasional swirling to mix the contents.

• Five ml of 5 M potassium acetate was added, mixed and placed in ice for

20 min. The tube was centrifuged at 5000 rpm for 20 min at 4°C.

• The upper clear aqueous phase was removed and transferred to a new

sterile polypropylene tube.

• Equal volume of cold isopropanol was added and gently inverted several

times to precipitate the DNA.

• The precipitated DNA was hooked out and carefully transferred to a 1.5

ml micro centrifuge tube and dissolved in 700 µl of 1 x TE buffer (10 mM

Tris HCl pH 8.0 and 1 mM EDTA pH 8.0).

• To remove RNA, 7.5 µl of RNase A was added and incubated for 30 min

at 37°C. Equal volume of chloroform / isoamyl alcohol (24:1) solution

was added and the tube was gently inverted 20 times before centrifugation

at 12000 rpm for 10 min at 4°C.

• The upper aqueous phase was removed and transferred to a 1.5 ml micro

centrifuge tube.

• Two volumes of absolute alcohol and 1/10 volume of 3 M sodium acetate

(pH 5.2) were added and the tube was incubated overnight at – 20°C.

• The tube was centrifuged at 12,000 rpm for 5 min at 4°C and the

supernatant was discarded.

• The pellet was washed once with 70% ethanol, dried well and resuspended

in 100 µl of 1 x TE buffer. The DNA isolated was stored at –20°C.

3.2.2. Quality and quantity check of DNA

DNA was checked for its purity and intactness and then quantified. The crude

genomic DNA was run on 0.8% agarose gel stained with ethidium bromide following the

protocol of Sambrook et al., (1989) and was visualized in a gel documentation system

(Alpha Imager 1200, Alpha Innotech Corp., CA, USA). Intact and pure genomic DNA as

assessed by agarose gel electrophoresis was quantified with spectrophotometer. Based on

the quantification data, DNA dilutions were made in 1 x TE buffer to a final

concentration of 40 ng per µl and stored at –20°C for further use. The absorbance for all

accession was measured at 260 nm.

DNA has maximal absorbance at 260 nm. An optical density (OD) of 1.0

corresponds to 40 µg / ml for double stranded DNA.

If the reading = ‘a’ OD

And the DNA is diluted 1000 times multiply ‘a’ by the dilution factor.

‘a’ x 1000 = ‘b’

To determine the µg / ml, multiply by 40

‘b’ x 40 = ‘c’ µg / ml

To convert µg / ml to µg / µl, divide the value ‘c’ by 1000

‘c’

= ------------ = ‘d’ µg / µl

1000

Based on the quantification data, DNA dilutions were made in TE buffer to a final

concentration of 40ng/µL and stored in -20°C for further use.

3.2.3. SSR analysis

3.2.3.1. Microsatellite amplification

A total of twenty five microsatellite primer pairs (designated RM) obtained from

Research Genetics, AL, USA as Rice Map Pairs was used to analyze the thirty five

cultivars in this study. The sequence and the details of the primers used are given in

Table 6.

PCRs were performed in 15 µl reactions as described by Panaud et al. (1996)

containing 1.0 µM of each forward and reverse primers, 2.5 mM of each dNTPs, 50 mM

KCl, 10 mM Tris HCl (pH 8.3), 1.5 mM MgCl2, 0.01 per cent gelatin, 40 ng of DNA and

5 unit of Taq DNA polymerase (Bangalore Genei, Bangalore). The PCR profile was :

94°C for 5 min, followed by 40 cycles of 94°C for 1 min, 55°C for 1 min, 72°C for 2 min

and finally by 5 min at 72°C for the final extension. Annealing temperature was adjusted

based on the specific requirement of each primer. The PCR reaction was carried out in a

PTC 100TM Thermocycler (MJ Research, Sanfrancisco, USA).

PCR Cocktail:-

S. No. Components

Volume for one

reaction. in µl

Stock concentration

Final concentration in 15µl PCR

cocktail

Volume for 50

reactions. in µl

1. dNTPs 0.6 2.5mM 0.1mM 30

2. PCR buffer 1.5 10x 1x 75

3. Taq-polymerase 0.2 5Unit/µl 0.06 Unit 10

4. Sterile water 8.2 ------------ ------------- 410

5. Total 10.5 ------------ -------------- 525

6. Template DNA 1.5 40ng/µl 4.0ng 75

7. PRIMER (R/F) 1.5 each 10µM 1.0 µM 150.0

8. Grand Total 15.00 ------------ ------------- 12750

3.2.3.2. Polyacrylamide gel electrophoresis (PAGE) and silver staining

3.2.3.2.1. Materials

a. 40% Acrylamide (38:2)

Reagent Amount

Acrylamide 38 g

Bis-acrylamide 2 g

Total volume (by adding distilled water) 100 ml

b. 8% Polyacrylamide gel solution:

Reagent Final concentration Quantity

10X TBE buffer 1X 10 ml

Urea 7M 42 g

40% Acryl amide 8% 20 ml

10% APS ----- 600 µl

TEMED ----- 60 µl

Total volume (by adding distilled water) ----- 100 ml

c. 10X TBE (Tris- borate EDTA) buffer:

Tris base 107.8g

Boric acid 55.03g

EDTA (Na2 2H2O) 8.19g

Dissolved in 800 ml double distilled water, filtered through 0.22 µm filter paper,

made up to 1000 ml and stored at 4º C

d. PAGE Loading Dye (Total volume 50 ml):

Formamide 49ml

Xylene cyanol 50mg

Bromophenol blue 50mg

0.5M EDTA 1ml

e. Silver staining Chemicals:

Fixer:

200 ml Acetic acid + 1800 ml Distilled water

Staining solution:

2 gm Silver nitrate (AgNO3) + 2000 ml Distilled water +3 ml Formaldehyde

Developer:

60 gm Sodium carbonate (Na2CO3) + 2000 ml Distilled water + 400 µl Sodium

thiosulfate + 3 ml (37%) Formaldehyde

3.2.3.2.2. Method

• The large and small glass plates were cleaned with distilled water and 3 mL racin or

repellent was applied on large plate.

• Three ml bind saline was applied on small plate and wiped with Kim wipes.

• Unit was assembled according to manufacturer’s instructions. 100 mL of gel

mixture prepared by mixing 15 mL of 40% per cent polyacrylamide denaturation

solution with 42 gm urea (7M urea), 600µL 10 per cent ammonium persulfate

solution and 60 µL N’, N’, N’, N’, Tetra-methyl-ethylene-diamine (TEMED).

After 60 min polymerization it was assmebled in a electrophoresis unit.

• After flushing the well with 1X TBE buffer, the gel was pre-run for at least 45

minutes at 100 constant watt. 15 µL of PCR amplified product was mixed with 3

µL loading dye and added 3 µL in each well.

• The electrophoresis was resumed and allowed to proceed at 100 Watt constant till

bromophenol blue dye reached at the bottom of the gel.

• Finally unit were dismantled and gel was subjected to silver staining.

Silver staining

• First gel was soaked in fixer solution with mild shaking till the dye disappeared.

• Then plate was soaked in double distilled water with mild shaking for 5 min. Gel

was soaked in staining solution for 20 min. with mild shaking followed by

washing in double distilled water for 10 sec.

• For devoloping colour, gel was soaked in developer solution with mild shaking

till band appeared.

• Again gel was soaked in fixer solution for 1 min. followed by brief wash with

double distilled water.

• Then the gel was air dried and scanned by scanner.

3.2.3.3 Analysis of polymorphism

Clearly resolved, unambiguous polymorphic bands were scored visually for their

presence or absence. The scores were obtained in the form of a matrix with ‘1’ and ‘0’,

which indicate the presence and absence of bands in each species respectively. The binary

data scored was used to construct a dendrogram.

3.2.3.3.1 Polymorphism information content

Polymorphism information content (PIC) or expected heterozygosity scores for each

SSR marker was calculated based on the formula

Hj = 1-Σpi2

Where Pi is the allele frequency for the i-th allele (Nei, 1973).

3.2.3.4 Cluster analysis

The binary data scored was used to construct a dendrogram. The genetic

associations between varieties were evaluated by calculating the Jaccard’s similarity

coefficient for pair wise comparisons based on the proportions of shared bands produced

by primers (Jaccard, 1908). Similarity matrix was generated by using the NTSYS-pc

software (Rohlf, 1994). The similarity coefficients were used for cluster analysis and

dendrogram was constructed by the unweighted pair-group method with arithmetic

average (UPGMA) (Mathew et al., 2000).

3.2.3.5 Statistical analysis

The data on 7 quantitative traits for all 35 varieties were subjected to multivariate

hierarchical cluster analysis and principal component analysis (PCA) using the computer

software NTSYSpcv2.02i (Rohlf, 1998). The mean values were subjected to hierarchical

cluster analysis performed by un-weighted pair-group arithmetic average method (Sneath

and Sokal, 1973) using sequential agglomerative hierarchical nested cluster analysis

(SHAN) programme. A phenetic tree was constructed using the TREEPLOT programme

of NTSYS pc.

3.2.3.5.1 Principal component analysis (PCA)

The PCA was performed to confirm the diversity pattern brought out by cluster

analysis with respect to both quantitative (7) and qualitative (20) traits. Using SIMIT

option, similarity indices for quantitative data were obtained for all the genetic

accessions. Through EIGEN programme, Eigen vector and Eigen value matrix were

obtained. The extracted vectors were plotted in a 2D graph using the PROJ programme.

The cumulative percentage of contribution to variation of different traits to Eigen vectors

was also computed.

3.3 Comparison of dissimilarity matrix derived from morphological and SSR marker data

The product-moment correlation (r) and the Mantel test statistic (Z) were

calculated to measure the degree of relationship between the dissimilarity matrix

generated from morphological and SSR marker data using the MXCOMP programme of

NTSYS-pc software 2.02.

3.4 Iron Quantification

1. Sample (rice grains) was grinded into powder form with the help of grinder.

2. 0.5 gram of the powdered sample was weighed and transferred into a 150 ml

conical flask.

3. 15 ml of the triple acid mixture was added to this sample containing conical flask

and kept for overnight.

4. The three acids Nitric acid, Sulphuric acid and Hydrochloric acid, are in the ratio

of 9:2:1.

5. Then the sample was digested over sand bath at 90°C, till the colour of the

mixture turns white.

6. Then the volume was made up to 100 ml by adding distilled water and was

filtered with filter paper.

7. Then the quantity of iron was analyzed by using Atomic Absorption

Spectrophotometer.

EXPERIMENTAL RESULTS

The study was designed to characterize and assess the degree of genetic diversity

among thirty five rice varieties released from Tamil Nadu Rice Research Institute,

Aduthurai, India. The available thirty five rice varieties were evaluated and characterized

with respect to their quantitative and qualitative characters as well as molecular markers.

4.1 Descriptive statistics

The data recorded on seven quantitative characters were subjected to descriptive

statistics of quantitative traits such as mean, and measures of dispersion (Range, variance,

standard deviation, standard error and coefficient of variation). The descriptive statistics

of the quantitative traits is present in table 2.

4.1.1 Mean

Among all the characters the highest mean value was recorded by duration

(132.62) and the least value by yield (4.59)

4.1.2 Range

The highest value was exhibited by duration (118) and the least value by number

of leaves in primary tillers (4)

4.1.3 Variance

The maximum value of variance was shown by duration (967.42) and the

minimum value of variance was shown by number of leaves in primary tillers (0.93)

4.1.4 Coefficient of variation

The highest value was shown by days to flowering (30.30) and least for number

of productive tillers (9.54).

4.2 Correlation analysis

The correlation coefficients of 7 quantitative traits were used in characterizing the

35 rice varieties. The correlation coefficients of 7 quantitative traits estimated are

presented in Table 3. The mean values of 27 morphological traits for 35 varieties of rice

are presented in table 1.

4.2.1 Correlation between yield and its components

Among the various characters studied number of productive tillers (0.967) and

thousand grain weight (0.635) were positively and significantly associated to the yield.

Number of leaves in primary tillers (0.139) showed non-significant and positive

association with yield. Plant height (-0.543) showed significant and negative association

with yield. Days to flowering (-0.624) and duration

(-0.624) were negatively and significantly associated with the yield.

4.2.2 Inter correlation among yield components

Plant height

The plant height was negatively and non-significantly associated with thousand

grain weight (-0.272). It was negatively and significantly associated with yield (-0.544).

Number of leaves in primary tillers

The number of leaves in primary tillers (0.139) was positively and non-

significantly associated with yield.

Days to 50% flowering

The days to 50% flowering was negatively and non-significantly associated with

thousand grain weight (-0.355) and was negatively and significantly associated with yield

(-0.624) and positive and significant association with duration (0.993).

Thousand grain weight

The thousand grain weight was positively and significantly associated with yield

(0.635). It was negatively and non-significantly associated with duration (-0.358).

Yield

The yield was negatively and significantly associated with days to 50% flowering

(-0.624) and plant height (-0.544).

Number of productive tillers

The Number of productive tillers was positively (0.967) and significantly

associated with yield.

4.2.3 Factor analysis

The first three factors were selected for principal component analysis on the basis

of factor loadings (Table 4) of the 27 morphological traits that were contributing to

maximum variability. The first factor had high contributing factor loadings from plant

height, grain length, and days to flowering, duration and contributed to 20.3 percent of

the total variation. The second factor had high contributing loadings from milling

percentage, hulling percentage, number of tillers, and contributed to 12.4 percent of the

total variation. The third factor had high contributing loadings from grain breadth, fertile

glumes, apiculus, and contributed to 10.4 percent of total variation.

4.2.3.1 Principal component analysis

A principal component was performed using 19 morphological traits. The values

of the Eigen vectors and their contribution to variation are presented in Table 5. The first

three principal components accounted for 55.4 percent of the total variance. The first

principal component (PC1) accounted for 28.4 percent of total variance, and had high

contributing factor loadings from yield, junction, auricle and ligule. The second principal

component (PC2) had high contributing factor loadings from axil and translucency and

contributed to 16.9 percent of the total variation. The third principal component (PC3)

accounted to 10.2 percent of the total variation, with high factor loadings from hulling

percentage and milling percentage.

4.2.4 Cluster analysis

4.2.4.1 Dissimilarity index

The Euclidean distances computed for the morphological traits were presented in

Table 6.

4.2.4.2 Clusters based on dendrogram

Agglomerative hierarchical clustering performed on the Euclidean distance matrix

utilizing the Ward’s linkage method and resulting dendrogram is presented in Fig 4a.

The 35 ADT varieties formed 5 clusters at 3.61% similarity level. The list of all the 5

clusters along with the varieties included is presented in Table 7. Among the different

clusters, the cluster size varied from 4 to 10. The maximum number of varieties were

included in cluster I having 10 varieties and the minimum number in cluster VI having 4

varieties. The cluster I consisted of ADT 1, ADT 2, ADT 25, ADT 7, ADT 10, ADT 26,

ADT 30, ADT 22, ADT 35, and ADT 14. The cluster II consisted of ADT 4, ADT 15,

ADT 12, ADT 6, ADT 8, ADT 20, ADT 27, and ADT 32. The cluster III consisted of

ADT 16, ADT 47, ADT 38, ADT 39, ADT 36, and ADT41. The cluster IV consisted of

ADT 11, ADT43, ADT48, and ADT45. The cluster V consisted of ADT 29, ADT 31,

ADT 37, ADT 42, ADT 44, ADT28, and ADT 40.

4.3 Genetic diversity analysis using molecular marker

In the present study, 35 ADT varieties were evaluated for genetic diversity using

simple sequence repeat (SSR) markers.

4.3.1. SSR analysis

To assess the quality, all the genomic DNA samples were run on 0.8% agarose gel

and the gel was stained with ethidium bromide and the quantity of DNA present in each

sample was determined by reading the absorbance at 260 nm in an ELICO SL 159 UV-VIS

spectrophotometer. The quantity of DNA in different samples varied from 860-1550 ng /µl.

This variation was due to difference in the quantity of leaf sample taken for DNA extraction.

After quantification, all the samples were diluted to 40ng µl-1 and used for PCR reactions.

The 25 SSR primers used in the present study produced scorable, unambiguous

markers. The details of markers amplified by the 25 SSR primers among 35 varieties are

given in Table 8. A total of 72 alleles were detected. All were polymorphic.

Polymorphism percentage was 100. The number of alleles detected per primer pair

ranged from 2 to 4 with an average 2.96. The maximum number of four amplified

products was observed in the profiles of the primer RM 1302, RM 4955 and RM 5341.

The minimum number of amplified product (2) was observed in the profiles of primer

RM 420, RM 591, RM 515, RM 475 and RM 247. The SSR products size ranged from 92

to 270 bp. The SSR marker profiles of 35 ADT varieties generated by the primer RM 3630,

RM 420, RM 515, RM 6424 and RM 457 are given in Plates 5, 6, 7, 8 and 9 respectively

4.3.2 Marker analysis for polymorphism

The polymorphism information content (PIC) was calculated for SSR

markers. PIC was the highest for the SSR primer RM 4955 (0.711), and was

the lowest for the primer RM 420 (0.382). The results are presented in Table

9. The higher the PIC value, the more informative is the SSR marker. Hence,

primer RM 4955 was found to be highly informative.

4.3.3 Principal component analysis

A principal component was performed using 73 alleles of 25 SSR

primers.

The score plot of 35 ADT varieties based on the first two principal

components is presented in Fig 6 and the score plot of 35 ADT varieties

based on the first three principal components is presented in Fig 7.

4.4 Cluster analysis

4.4.1 Similarity index

The binary data from the polymorphic primers were used for

computing Jaccard’s similarity indices. The similarity index values obtained

for each pair wise comparison among the 35 ADT varieties and presented in

the Table 10. The similarity coefficients based on SSR markers ranged from

0.28 to 0.68. Among the 35 ADT varieties the highest similarity index (0.68)

was observed between ADT 20 and ADT 22 and the lowest similarity index

(0.12) was observed between ADT 35 and ADT 6.

4.4.2 Clusters based on dendrogram

The similarity values obtained for each pair wise comparison of SSR markers

among the 35 ADT varieties were used to construct dendrogram based on Jacquard’s

coefficient and the results are presented in Fig 4b. The 35 varieties formed 12 clusters at

nearly 40 % similarity levels. Among the different clusters, the cluster size varied from 6

(cluster XII) to 1 (Cluster VIII). The list of all the 12 clusters along with the ADT

varieties included is presented in Table 11. The cluster I consisted of ADT 1, ADT 2,

and ADT 25. The cluster II consisted of ADT 4 and ADT 6. The cluster III consisted of

varieties ADT 7, ADT 15, ADT29 and ADT 26. The cluster IV consisted of varieties

ADT 12 and ADT 30. The cluster V consisted of varieties ADT 16 and ADT 31. The

cluster VI consisted of varieties ADT 14 and ADT 40. The cluster VII consisted of

varieties ADT 20, ADT 22 and ADT 27. The cluster VIII consisted of varieties ADT 10.

The cluster IX consisted of varieties ADT 32, ADT 35 and ADT 38. The cluster X

consisted of ADT 8, and ADT 41. The cluster XI consisted of ADT 11, ADT 36, ADT

37, ADT 39 and ADT 42. The cluster XII consisted of varieties ADT 43, ADT 47, ADT

28, ADT 44, ADT 45 and ADT48.

4.4.3 Comparison of dissimilarity matrix derived from morphological and SSR

marker data

The product-moment correlation (r) and the Mantel test statistic (Z) were

calculated to measure the degree of relationship between the dissimilarity matrixes

generated from morphological and SSR marker data. The matrix correlation value ‘r’ was

0.0606 for morphological and SSR data and Mantel t value 0.420. There was an

agreement of 3.5% between dendrogram derived from morphology and SSR techniques

compared using the Mantel matrix correspondence test.

4.5 Iron content analysis

Iron quantification was done for all the 35 ADT varieties to know the

difference in iron content among the 35 ADT varieties. A wide variation was

found in iron content of all the 35 varieties. The iron content varies from

3.77 ppm to 15.76 ppm. Among the 35 varieties the highest iron content

(15.76 ppm) was observed for ADT 25 and the lowest iron content (3.77

ppm) was observed for ADT 42. The iron content of all the 35 ADT varieties

is presented in the table 12 and in the figure 8.

Legend for Table 1

Qualitative traits Number Name

Leaf sheath colour

1

2 3 4 5 6 7

Light green

Medium green Dark green Purple lines at the base Light purple at the base Green with light purple base Green purple at base

Axil colour 1

2 3 4 5 6

Light green

Dark green Light purple Medium purple Dark purple Purple lines at the base

Junction ,Auricle, Ligule colour 1

3 5 7

Light green

Dark green Light purple white

Septum colour 1 3 5

7

Light cream Medium cream Dark cream

White

Leaf blade colour intensity 1 3 5

Light green Medium green Dark green

Flag leaf type 1 2

Acute Erect

Fertile glumes type 1

3 5 7

Green when fresh & straw on ripening

Green when fresh & light gold on ripening Gold when fresh & medium gold on ripening Gold when fresh & dark gold on ripening

Qualitative traits Number Name

Apiculus colour 1 5 7

Light purple Purple Green

Awns 1

2

Absent

Present

Panicle type 1

3 5 7 9

Very short

Short Medium Long Very long

Exsertion 3 5

7

Full Exerted

Well exerted

Rice grade 1 3 5 7 9

Short bold Long bold Short slender Medium slender Long slender

Rice colour 1 9

White Red

Abdominal white 1

9

Present

Absent

Translucency

1 9

Translucent Opaque

Table 2. Descriptive statistics of quantitative traits

Legend: PH= Plant height, NT= Number of productive tillers, NLPT= No. of leaves in

primary tillers, DF = Days to flowering, TGW= 1000 grain weight (gm), TD= Total

duration of crop, YLD= Yield,

PH NT NLPT DF TGW TD YLD

Mean 124.24 14.68 5.34 99 24.09 132.62 4.59

Range 70 5 4 113 12 118 4.7

Minimum 85 12 3 72 18 102 2.2

Maximum 155 17 7 185 30 220 6.9

Sample Variance

458.54 1.98 0.93 942.82 8.60 967.41 1.30

Coefficient of variation

17.24

9.54

19.98

30.30

12.17

23.46 24.84

Standard Error

3.61 0.23 0.16 5.19 0.49 5.25 0.19

Standard Deviation

21.41 1.40 0.96 30.70 2.93 31.10 1.14

Table 3. Pearson correlation coefficient of quantitative traits

PH NT NLPT DF TGW TD YLD

PH 1

NT -0.562 1

NLPT 0.154 0.016 1

DF 0.317 -0.613 -0.060 1

TGW -0.272 0.631 0.141 -0.355 1

TD 0.283 -0.604 -0.086 0.993 -0.358 1

YLD -0.543 0.967 0.139 -0.624 0.635 -0.624 1

Legend: PH= Plant height, NT= Number of productive tillers, NLPT= No. of leaves in

primary tillers, DF = Days to flowering, TGW= 1000 grain weight (gm), TD= Total

duration of crop, YLD= Yield,

*(Level of significance: 5%)

Table 4. Sorted rotated factor loading of quantitative traits

Variable Factor1 Factor2 Factor3

A -0.770 -0.033 -0.352

B 0.501 0.728 0.269

C -0.522 -0.170 -0.500

D 0.035 0.105 -0.518

E 0.183 -0.580 -0.104

F -0.677 0.038 -0.492

G 0.198 0.355 -0.061

H 0.006 -0.002 -0.061

I -0.544 0.382 0.034

J -0.178 0.119 0.350

K -0.592 0.235 0.332

L -0.069 0.448 0.183

M 0.812 -0.129 -0.150

N 0.091 -0.008 -0.606

O 0.562 0.683 0.284

P 0.072 -0.488 0.041

Q 0.387 0.153 -0.077

R -0.657 0.058 -0.041

S 0.574 0.064 -0.212

T -0.046 0.257 0.393

U 0.337 -0.028 -0.146

V -0.708 -0.078 0.280

W 0.066 0.010 -0.284

X -0.617 0.297 0.289

Y 0.325 -0.611 0.218

A1 -0.355 0.550 -0.568

A2 0.114 0.567 -0.499

Variance 5.4879 3.3609 2.8040

% Variance 20.3 12.4 10.4

Table 5. Principal component analysis showing the contribution of

quantitative characters among the 35 ADT varieties

Variable PC1 PC2 PC3

A 0.345 0.059 0.199

B -0.206 -0.437 -0.130

C 0.244 0.140 0.332

E -0.096 0.350 0.160

F 0.321 0.030 0.317

I 0.249 -0.178 -0.060

K 0.245 -0.131 -0.352

L 0.027 -0.258 -0.018

M -0.335 0.045 0.172

O -0.236 -0.419 -0. 131

P -0.044 0.266 -0.095

Q -0.153 -0.084 0.123

R 0.287 -0.038 -0.108

S -0.236 -0.044 0.295

V 0.284 0.053 -0.250

X 0.254 -0.133 -0.182

Y -0.163 0.314 -0.158

A1 0.202 -0.277 0.385

A2 -0.013 -0.305 0.376

Eigenvalue 5.3937 3.2061 1.9352

Percent 28.4 16.9 10.2

Cumulative 28.4 45.3 55.4

Table 7. Cluster composition of ADT varieties for morphological traits

Cluster No.

Number of

varieties

List of varieties included

I II III IV V

10 8 6 4 7

ADT 1, ADT 2, ADT 25, ADT 7, ADT 10, ADT 26, ADT 30, ADT 22, ADT 35, ADT 14 ADT 4, ADT 15, ADT 12, ADT 6, ADT 8, ADT 20, ADT 27, ADT 32 ADT 16, ADT 47, ADT 38, ADT 39, ADT 36, ADT41 ADT 11, ADT43, ADT48, ADT45 ADT 29, ADT 31, ADT 37, ADT 42, ADT 44, ADT28, ADT 40

Table 9. SSR marker profile across ADT varieties

Primer Number of alleles

Number of polymorphic

alleles Polymorphism

(%) PIC

RM 8110 RM 3143 RM 3630 RM 6424 RM 1002 RM 570 RM 5951 RM 1302 RM 405 RM 480 RM 4173 RM 528 RM 473 RM 420 RM 4955 RM 515 RM 6475 RM 2885 RM 1236 RM 591 RM 1124 RM 457 RM 247 RM 5341 RM 17

3

3

3

3

3

3

3

4

3

3

3

3

3

2

4

2

2

3

3

2

3

3

2

4

3

3

3

3

3

3

3

3

4

3

3

3

3

3

2

4

2

2

3

3

2

3

3

2

4

3

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

0.660

0.654

0.647

0.659

0.666

0.652

0.662

0.690

0.552

0.538

0.647

0.584

0.625

0.383

0.712

0.484

0.663

0.626

0.619

0.497

0.484

0.652

0.456

0.662

0.666

Table 11. Cluster composition of rice varieties for SSR markers

Cluster No.

Number of varieties List of varieties included

I

II

III

IV

V

VI

VII

VIII

IX

X

XI

XII

3

2

4

2

2

2

3

1

3

2

5

6

ADT 1, ADT 2, ADT 25 ADT 4, ADT 6 ADT 7, ADT 15, ADT29, ADT 26 ADT 12, ADT 30 ADT 16, ADT 31 ADT 14, ADT 40 ADT 20, ADT 22, ADT 27 ADT 10 ADT 32, ADT 35, ADT 38 ADT 8, ADT 41 ADT 11, ADT 36, ADT 37, ADT 39, ADT 42 ADT 43, ADT 47, ADT 28, ADT 44, ADT 45, ADT48

Table 12. Iron content of ADT varieties

Rice varieties Iron content (mg/kg)/ ppm ADT 1 4.63 ADT 2 6.27 ADT 4 6.90 ADT 6 8.01 ADT 7 5.92 ADT 8 5.49

ADT 10 9.70 ADT 11 6.12 ADT 12 10.67 ADT 14 14.39 ADT 15 5.45 ADT 16 6.95 ADT 20 8.77 ADT 22 11.48 ADT 25 15.76* ADT 26 7.03 ADT 27 12.04 ADT 28 8.74 ADT 29 9.78 ADT 30 8.19 ADT 31 8.02 ADT 32 10.29 ADT 35 7.73 ADT 36 7.67 ADT 37 5.64 ADT 38 5.58 ADT 39 8.71 ADT 40 7.38 ADT 41 7.75 ADT 42 3.77* ADT 43 5.33 ADT 44 7.75 ADT 45 10.55 ADT 47 5.95 ADT 48 5.73

SSR locus Chromosome

Location cM position in Chromosome Forward primer sequence (5'->3') Reverse primer sequence (3'-->5') Allele

size(bp) RM 8110 RM 3143 RM 3630 RM 6424 RM 1002 RM 570 RM 5951 RM 1302 RM 405 RM 480 RM 4173 RM 528 RM 473 RM 420 RM 4955 RM 515 RM 6475 RM 2885 RM 1236 RM 591 RM 1124 RM 457 RM 247 RM 5341 RM 17

1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9

10

10

11

11

12

12 12

30.5

113.0

62.2

125.6

36.1

158.2

56.1

81.7

24.7

111.3

32.7

100.8

93.9

118.3

24.9

72.2

42.5

93.5

16.4

83.0

19.8

78.8

26.7

65.3

107.4

TGTGCGGTCCGAATTATTATGG AGCCTGGATAAGATGGTTCG CAGCTACTGTTCCATGGTGG AGCGAATCAGGTGACTCCAC GAACCAGACAAGCAAAACGG GTTCTTCAACTCCCAGTGCG TGATCCCAGAACTGAACACG TCTTCCCCTGAACGTGAAAG TCACACACTGACAGTCTGAC GCTCAAGCATTCTGCAGTTG TAGATTTGTCTTGGAAAATA GGCATCCAATTTTACCCCTC TATCCTCGTCTCCATCGCTC GGACAGAATGTGAAGACAGTCG GCATCCAGCAATATAATCAA TAGGACGACCAAAGGGTGAG AGATCAAAGCAACGGCTAGC GGCGTCATACATTAAAATAC AGCATCCAACGAGGTACAAG CTAGCTAGCTGGCACCAGTG AAGCTATCCCCCTTTTTGGC CTCCAGCATGGCCTTTCTAC TAGTGCCGATCGATGTAACG TGCATTTTCCATACAATACG TGCCCTGTTATTTTCTTCTCTC

GCCTCCACATTCATCAGTGTCC CGAGAAGACCCAGTTTCTGC GCCATCAACTCCCGGATC ACACCATCCATCTCCAGTCC AGCATGGGGATTTAGGAACC TGACGATGTGGAAGAGCAAG AAGACGTGTCGTGTGGTGTG CCTTCTCTCCCAACATCTCG AATGTGGCACGTGAGGTAAG GCGCTTCTGCTTATTGGAAG AACATAACTTTGACTTCTTG AAATGGAGCATGGAGGTCAC AAGGATGTGGCGGTAGAATG ACTAATCCACCAACGCATCC CAAGGATTTTGTTAAGTGGG TGGCCTGCTCTCTCTCTCTC GAACAGAGAGGGGACGTGTC GTTTCTATATGCATGTGTCC GGAGTGCTAGGGATGTCGAC TGGAGTCCGTGTTGTAGTCG AGGGATCGGTAGACCCAATC ACCTGATGGTCAAAGATGGG CATATGGTTTTGACAAAGCG ATTTGATACATGGACGATGC GGTGATCCTTTCCCATTTCA

270

98

137

101

140

208

92

173

110

225

127

232

97

197

164

211

209

174

113

258

180

228

131

149

184

DISCUSSION In recent years, there are an increased number of releases of genetically related varieties by rice breeders, which resulted in limited levels of genetic diversity. Information on the extent of genetic diversity among cultivars is

needed to exploit the available genetic resources to create new genotypes. The information on genetic diversity helps in choosing parents for generation of new varieties which needs continuous evaluation of germplasm for useful characters based on morphological data. Morphological markers reflect not only the genetic contribution of the cultivar, but also the interaction of the genotype with the environment (G x E) in which it is expressed. Hence the descriptions based on morphological data are inadequate in providing reliable information for the calculation of genetic distance or the validation of pedigrees. Advances in biochemistry, genetics and molecular biology have provided descriptors based on protein markers (Isozymes) which were later replaced by DNA markers due to the availability of unlimited number of polymorphic loci which proved to be sufficient for cultivar identification and better understanding of pedigree. The first generation DNA markers include RFLP markers, were used for evaluation of variation within cultivar of rice accessions (Olufowote et al., 1997). RFLP was later replaced by PCR based markers, as it is cumbersome, time consuming, labour intensive and costly affair. The PCR based markers proved to be highly evolved over RFLP, which include RAPDs, AFLPs and Microsatellites. RAPDs and AFLPs were used for assessing genetic diversity in rice (Fukuoka et al., 1992; 1994; Virk et al., 1994; Agarwal et al., 1999). The use of RAPDs is of less importance when compared to microsatellites and AFLPs as they are dominant and non-reproducible. AFLP technique is reaching better grounds at present but yet the drawback underlying behind their usage is that they are dominant markers and are difficult to reproduce due to the quality of DNA that is needed to be extracted from the samples. Now a days, microsatellite markers are gaining a lot of importance as they provide a rapid approach to analyse the genetic diversity and reflect the genetic relationships among the selected genotypes. 5.1.1 Morphological trait variability Variability refers to the presence of differences among the individual of a species. It is due to differences either in the genetic constitution of the individual plants or in the environment in which they are grown. The success of genetic improvement depends on the nature of variability present in the gene pool for a given character. Hence, assessment of existing variability for any character present in the gene pool of a crop species is of utmost importance to a plant breeder for starting a judicious plant breeding programme. In the present study, the morphological variation did exist

among the 35 rice varieties with respect to the 27 morphological traits recorded. The extent of variability was found to be maximum for plant height, days to flowering followed by yield and total duration with regard to quantitative traits. Elemery et al., (1998) reported maximum variability for plant height. Yolanda and Das (1995) and Khanghah and Sohani (1999) reported maximum variability for yield. Among all the traits investigated, days to 50% flowering recorded maximum value of coefficient of variation (30.30). 5.1.2. Correlation studies Correlation provides information on the nature and extent of relationship among the characters. The estimation of correlation coefficient among the different characters indicates the extent of direction of association. Correlation between characters are important for three reasons in connection with the genetic causes of correlation through the phenotypic action of genes, the changes brought about by selection and natural selection, where the relation between quantitative traits and fitness is the primary agent that determines the genetic properties of that character in a natural population (Falconer, 1981). Yield is a complex character influenced by a large number of other component traits. A knowledge of the association between yield and its component traits and also between the component traits helps in improving the efficiency of selection In the present investigation, correlation coefficients were worked out between seven quantitative characters. The highly positive and significant correlation value was recorded for number of productive tillers (0.967), and thousand seed weight (0.635) with yield. It indicates that the selection in any one of these yield attributing traits will lead to increase in the other traits, there by finally enhancing the grain yield. Karmarkar et al., (1998) reported that seed length, width and thickness showed strong and positive correlation with seed weight. 5.1.3. Factor analysis Factor analysis was performed in order to reduce a large set of phenotypic traits (27) to a more meaningful smaller set of traits and to know which trait is contributing to maximum variability because genetic improvement depends on the magnitude of genetic variation. Factor analysis provides an exact picture of variability contributed to by each trait. On the basis of factor loadings of the 27 morphological traits that are contributing maximum variability to the first three factors are selected for principal component

analysis. The first three factors are contributing to 41.3% of the total variance observed. The first factor had high contributing factor loadings (20.3% of the total variation) from plant height, grain length, and days to flowering and duration. The second factor had high contributing loadings (12.4% total variation) from milling percentage, hulling percentage and number of tillers. The third factor had high contributing loadings (10.4% of total variation) from grain breadth, fertile glumes and apiculus. 5.1.4. Principal component analysis The coverage of variation in the collection was further analyzed spatially through ordination of 35 collection using two principal component axes. The widespread distribution of accessions of the sample confirmed effective representation of variation in the population. The first three principal components in the collection with Eigen values more than one were able to explain 55.4 per cent of total variation for morphological traits. The variance accumulated by the last components of the base collection a small amount (28.4%) of variation. According to Mardia et al., (1979), total variance accumulated by principal component close to 80 per cent explains satisfactorily the variability manifested between individuals. 5.1.5 Morphological trait diversity and cluster analysis The clustering of genotypes based on morphological traits resulted in five clusters. Maximum number of varieties were included in cluster I (10 varieties) and the minimum number is in cluster VI (4 varieties). The cluster I consisted of ADT 1, ADT 2, ADT 25, ADT 7, ADT 10, ADT 26, ADT 30, ADT 22, ADT 35, and ADT 14. These varieties included in the Cluster I having long duration, tall and short bold grains. Similarly the cluster II consisted of ADT 4, ADT 15, ADT 12, ADT 6, ADT 8, ADT 20, ADT 27, and ADT 32 which are having tall nature and long bold grains. The cluster III consists of ADT 16, ADT 47, ADT 38, ADT 39, ADT 36, and ADT41 with medium duration, dwarf in nature and medium slender grains. The cluster IV consisted of ADT 11, ADT43, ADT48, and ADT45 with short duration, semi dwarf nature and long slender grains. The varieties having long duration, tall compact and short bold grains are included in the cluster V (ADT 29, ADT 31, ADT 37, ADT 42, ADT 44, ADT28, and ADT 40). Earlier Jaylal (1994) grouped forty genotypes of rice into nine clusters on the basis of yield attributes. 5.2.1 Genetic diversity studies using SSR markers Microsatellites are found to provide high PIC and found to be highly efficient and cost effective for cultivar identification and hence chosen as efficient markers for evaluating

the heterogeneity of rice accessions (Yang et al., 1994; Olufowote et al., 1997; Bligh et al., 1999). The SSR markers can detect discrete loci, they are co-dominant, segregate in a Mendelian fashion and an ideal genetic markers. Hence, in the present investigation, microsatellite polymorphisms were utilized for genetic diversity analysis of rice varieties released from Tamil Nadu Rice Research Institute, Aduthurai. The present SSR survey detected high levels of polymorphism among the rice cultivars with multiple alleles (2-4 alleles per primer). The number of alleles detected in the present study is in consistent with the reports (2-11 alleles per microsatellite locus) of Panaud et al., (1996). Earlier Yang et al., (1994) observed an allelic range of 3-25 per SSR locus using different rice landraces and cultivars of both japonica and indica origin which is larger than that of Panaud et al., (1996). Saghai et al., (1994) observed 37 alleles in one SSR locus of barley, which was significantly a higher number when compared to the present study. In the present investigation, the average number of alleles observed was 2.8. This average allelic number is much smaller when compared with the reports of Saghai Maroof et al., (1994) (average of 17.7 alleles in barley and 11 alleles in wheat). All the 72 SSR markers obtained in the present study proved to be highly informative with 100 per cent polymorphism which was in accordance with the result of Saghai-Maroof et al., (1994) and Russell et al., (1997) in barley, Dje et al., (2000) in sorghum, Prasad et al., (2000) in wheat and Ravi (2000) in rice. 5.2.2 Polymorphism Information Content (PIC) Polymorphism Information Content (PIC) is a measure of diversity for SSR marker. PIC provides an estimate of discriminatory power of a locus by taking into account not only the number of alleles expressed, but also the relative frequency of those alleles. PIC value ranges from 0 (monomorphic) to 1 (very high discriminative with many alleles in equal frequencies. The average PIC value for all 25 microsatellite loci in the present study was 0.611, with a range from 0.382-0.711. PIC was highest for the SSR primer RM 4955 (0.711), and was lowest for the primer RM 420 (0.382). Hence, primer RM 4955 is highly informative in the present study. The above markers were found to be highly informative in revealing the genetic diversity among the varieties and will be useful in future genetic diversity analysis. The current PIC value is higher than the previous reports of Panaud et al., (1996) and Olufowote et al., (1997). The high PIC value obtained in the present investigation might be due to high genetic diversity among the ADT varieties. The PIC values are usually dependent on the genetic diversity of the accessions chosen (Garland, 1999) for the specific study. 5.2.3 Principal component analysis The principal component analysis study was undertaken in order to confirm the clustering pattern obtained from UPGMA cluster analysis and exploit the resolving power of ordination. The PCA results showed that the PC1 contributed 12.10% followed by PC2 9.10% and PC3 8.10% respectively and cumulative variance of first three PCA was 29.30%. The RM 1302 (0.224), RM 4955 (0.281), RM 5341 (0.226), RM 8110 (0.252),

RM 3630 (0.261) and 5951 (0.263) are more informative among alleles which explained 29.30 percent variability through Principle component. The three dimensional ordination of the thirty five ADT varieties confirmed the clustering pattern obtained by the Cluster analysis. 5.2.3 Cluster analysis based on SSR markers The similarity matrix was computed using SSR markers based on Jaccard’s coefficient following the UPGMA method using SHAN programme of NTSYS-pc. The Jaccard’s similarity coefficient for the SSR data set varied from 0.28 to 0.68. The SSR marker profiles resulted in twelve clusters at nearly 40.2 % similarity. The cluster I consisted of ADT 1, ADT 2, and ADT 25(similarity level 42 percent. The cluster II consisted of ADT 4 and ADT 6 (similarity level 51 percent). The cluster III consisted of varieties ADT 7, ADT 15, ADT29 and ADT 26 (similarity level 64 percent. The cluster IV consisted of varieties ADT 12 and ADT 30 (similarity level 50 percent. The cluster V consisted of varieties ADT 16 and ADT 31 (similarity level 46 percent. The cluster VI consisted of varieties ADT 14 and ADT 40 (similarity level 48.5 percent. The cluster VII consisted of varieties ADT 20, ADT 22 and ADT 27 (similarity level 48 percent. The cluster VIII consisted of varieties ADT 10 (similarity level 40.2 60 percent. The cluster IX consisted of varieties ADT 32, ADT 35 and ADT 38 (similarity level 59.2 percent. The cluster X consisted of ADT 8, and ADT 41 (similarity level 47 percent. The cluster XI consisted of ADT 11, ADT 36, ADT 37, ADT 39 and ADT 42 (similarity level 51 percent 39. The cluster XII consisted of varieties ADT 43, ADT 47, ADT 28, ADT 44, ADT 45 and ADT48 (similarity level 52 percent). Wang et al., 1992 constructed dendrogram based on SSR markers using 129 accessions of rice which showed wide genetic variation in the rice accessions. The six varieties (ADT 43, ADT 47, ADT 28, ADT 44, ADT 45 and ADT48) were grouped into a single cluster (Cluster XIII) based on SSR profile with the similarity level of 40 percent . The above varieties also have short duration nature with medium slender grains in nature. These results indicated that there is a narrow genetic base of above ADT varieties in short duration group based on SSR profile. Further the narrow genetic base of aduthurai varieties also evidenced from the cluster XI. The cluster XI consisted of varieties having one of the parent is common in their pedigree which includes ADT 36 (Triveni/IR20), ADT 39 (IR8/IR20), ADT 29 (T (N) 1/ADT 27) and ADT 30 (IR 262/ADT 27). Highest diversity was found between ADT 35 (Bhavani/Jaya) and ADT 6 (pureline) (similarity level 12%) and lowest diversity was found between ADT 22 (selection from Vadan samba) and ADT 20 (ADT 3/ADT 2) (cluster VII) (similarity level 68%). The SSR marker data able to differentiate the ADT 10 (cluster VII) into separate cluster when compare to the morphological data. This shows the potentiality of SSR markers for the characterization of germplasm accessions. Ghatge and Kadu (1993) studied 48 rice genotypes from different eco-geological regions of India and grouped into seven clusters. The clustering pattern revealed that genetic diversity was not associated with geographical diversity. The correspondence between the dissimilarity matrix generated by SSR data and morphological data was evaluated by calculating product-moment correlation (Mantel’s

test). There was no close correspondence between the dissimilarity matrix of SSR and morphological data. There was a very low correlation [r = 0.0606 (p< 0.6241)]. A similar disparity has been reported in rye grass (Roldan-Ruiz et al., 2001) and taro (Okpul et al., 2006). The lower agreement between phenotypic and molecular distances may be due to the fact that the variation observed at SSR level might have not been expressed at phenotypic level and also there is a considerable effect of environment on morphological traits, thus there is less agreement between the diversity pattern of phenotypic traits and molecular markers. 5.3 Analysis of iron content variation in ADT varieties Iron quantification was done for all the 35 ADT varieties. A wide variation was found in iron content (3.77 ppm to 15.76 ppm). Among the 35 varieties the highest iron content (15.76 ppm) was observed in ADT 25 and lowest in ADT 42 (3.77ppm). The similar kind of high iron content in rice varieties was reported by Prom-u-thai et al., (2003) in IR68144 (15.67 ppm) . The lowest iron content 2.87 ppm in Bogan was reported by Prom-u-thai et al., (2007).

SUMMARY

The present day gene pool accommodate high yielding rice varieties every year which is evolved through breeding programmes continuously which results in difficulty in variety identification and cataloguing . Hence it is essential to assess the genetic diversity and finger printing of these rice varieties by using various tools such as morphological and DNA based markers. Of the several DNA markers, microsatellites are co dominant, cost effective and most reliable. Hence the present study was conducted with an objective to assess the genetic diversity among the thirty five rice varieties released from Tamil Nadu Rice Research Institute, Aduthurai, Tamil Nadu, using morphological as well as SSR markers.

1. A wide range of morphological diversity was noticed for 7 quantitative and 20

qualitative traits in the rice varieties released from TRRI, Aduthurai. The duration

of the varieties ranged from 102 (ADT 48) to 220 (ADT 6) days. The plant height

varied from 85cm (ADT 45) to 155cm (ADT 27). This indicated the existence of

wide morphological diversity in the selected varieties.

2. Factor analysis (factor1) revealed that plant height (0.812), days to flowering

(0.562), number of productive tillers (0.501), and thousand seed weight (0.574)

contributed maximum variability among the morphological traits. The cumulative

variability explained by factor1 is 28.4 percent through principle component

analysis.

3. Correlation studies showed that number of productive tillers (0.967) and thousand

seed weight (0.635) had significant and positive association with yield.

4. Thirty five varieties were grouped into 5 clusters based on morphological traits

using principle component analysis and hierarchical cluster analysis. The cluster I

has maximum varieties and consisted of ADT 1, ADT 2, ADT 25, ADT 7, ADT 10,

ADT 26, ADT 30, ADT 22, ADT 35, and ADT 14 with long duration, tall nature

and short bold grains. The Cluster II consisted of ADT 4, ADT 15, ADT 12, ADT

6, ADT 8, ADT 20, ADT 27, and ADT 32 which are having tall nature and long

bold grains. The Cluster III consists of ADT 16, ADT 47, ADT 38, ADT 39, ADT

36, and ADT41 with medium duration, dwarf in nature and medium slender grains.

The Cluster IV consisted of ADT 11, ADT43, ADT48, and ADT45 with short

duration, semi dwarf nature and long slender grains.

5. Genetic diversity was assessed using set of 25 SSR primers which generated 72

polymorphic alleles. The number of alleles produced per primer ranged from 2-4

with a mean of 2.8. Polymorphism information content values ranged between

0.382 (RM 420) and 0.711 (RM 4955).

6. Dendrogram was constructed using Jaccard’s similarity coefficient and rice

varieties were grouped into 12 clusters based on SSR markers. The cluster XII has

maximum varieties and consisted of varieties ADT 43, ADT 47, ADT 28, ADT 44,

ADT 45 and ADT48. The above varieties are having short duration with medium

slender in nature.

7. The narrow genetic base of aduthurai varieties also evidenced from the cluster XI.

The cluster XI consisted of varieties having one of the parent is common in their

pedigree which includes ADT 36 (Triveni/IR20), ADT 39 (IR8/IR20).

8. Highest diversity was found between ADT 35 (Bhavani/Jaya) and ADT 6 (pureline)

with similarity level 12% and lowest diversity was found between ADT 22 (selection

from Vadan samba) and ADT 20 (ADT 3/ADT 2) (cluster VII) with similarity level

68%.

9. The RM 1302 (0.224), RM 4955 (0.281), RM 5341 (0.226), RM 8110 (0.252), RM

3630 (0.261) and 5951 (0.263) were found to be more informative among alleles

which explained 29.30 percent variability in factor 1 through principle component

analysis.

10. The iron content analysis of ADT varieties also indicated that there was a wide

variation in the iron content among the different rice varieties. The iron content

varies from 15.76 ppm to 3.77 ppm. The highest iron content (15.76 ppm) was

found in ADT 25 and the lowest (3.77 ppm) in ADT 42.

REFERENCES

Agarwal, R. K., D. S. Brar, S. Nandi, N. Huang and G. S. Khush. 1999. Phylogenetic

relationship among Oryza species revealed by AFLP markers. Theor. Appl.

Genet., 98: 1320-1328.

Akagi, H., Y. Yokozeki, A. Inagaki and T. Fujimura. 1996. Microsatellite DNA markers

for rice chromosomes. Theor. Appl. Genet., 93: 1071-1077.

Akagi, H., Y. Yokozeki, A. Inagaki and T. Fujimura. 1997. Highly polymorphic

microsatellites of rice consist of AT repeats, and a classification of closely related

cultivars with these microsatellite loci. Theor. Appl. Genet., 94: 61-67.

Akkaya, M. S., A. A. Bhagwat and P. B. Cregan. 1992. Length polymorphism of simple

sequence repeat DNA in soybean. Genetics, 132: 1131-1139.

Albani, M. C. and M. J. Wilkinson. 1998. Inter simple sequence repeat polymerase chain

reaction for the detection of somaclonal variation. Plant Breeding, 117: 573-575.

Anjana Sree, K. N. 1999. Fingerprinting of rice varieties using molecular markers. M.Sc

Thesis, Tamil Nadu Agricultural University, Coimbatore, India.

Becker, J., P. Vos, M. Kuiper, F. Salamini and M. Heun. 1995. Combined mapping of

AFLP and RFLP markers in barley. Mol. Gen. Genet., 249: 65-73.

Beckmann, J. S. 1988. Oligonucleotide polymorphisms: a new tool for genomic genetics.

Nature Biotechnol., 6: 1061-1064.

Bennet, M. D. and J. B. Smit. 1976. Nuclear DNA amounts in angiosperms. Philos.

Trans. Roy. Soc., London, Ser. B., 272: 227-274.

Blair, M. W., O. Panaud and S. R. McCouch. 1999. Inter simple sequence repeat (ISSR)

amplification for analysis of microsatellite motif frequency and fingerprinting in

rice (Oryza sativa L.). Theor. Appl. Gent., 98: 780-792.

Bligh, H. F. J., N. W. Blackhall, K. J. Edwards and A. M. McClung. 1999. Using

amplified fragment length polymorphisms and simple sequence length

polymorphisms to identify cultivars of brown and white milled rice. Crop Sci.,

39: 1715-1721.

Botha, A. M. and E. Venter. 2000. Molecular marker technology linked to pest and

pathogen resistance in wheat breeding. S. Afr. J. Sci., 96: 233-240.

Botstein, B., R. L. White, M. Skolnick and R. Davis. 1980. Construction of a genetic

linkage map in man using restriction fragment length polymorphisms. Am. J.

Hum. Genet., 32: 314-331.

Briquet, M., M. C. Eloy and D. Scheepers. 1996. Plant genetic biodiversity and molecular

markers. Agricontact, 282: 17-21.

Byerlee, D. 1996. Knowledge-Intensive Crop Management Technologies: Concepts,

Impacts, and Prospects in Asian Agriculture. International Rice Research

Conference, Bangkok, Thailand, 3-5 June, 1996.

Caetano-Anolles, G., B. J. Bassam and P. M. Gresshoff.1991. DNA amplification finger

printing using very short arbitrary oligonucleotide primers. Bio/Technology, 9:

553-557.

Causse, M. A., T. M. Fulton, Y. G. Cho, S. N. Ahn, J. Chunwongse, K. Wu, J. Xiao, Z.

Yu., P.C. Ronald, S.E. Harrington, G. Second, S. R. McCouch and S. D.

Tanksley. 1994. Saturated molecular map of the rice genome based on an

interspecific backcross population. Genetics, 138: 1251-1274.

Chen, X., S. Temnykh, Y. Xu, Y. G. Cho and S. R. McCouch. 1997. Development of a

microsatellite framework map providing genome-wide coverage in rice (Oryza

sativa L.). Theor. Appl. Genet., 95: 553-567.

Chen, X., V. Hedatale, S. Tenmykh, W. Park, N. Ayres, S. Cartinhour and S. R.

McCouch. 1998. Amplification and sequence divergence of rice microsatellite

markers in three monocot and three dicot plant species. In: International Plant and

animal Genome (VII) Conference, San Diego, CA, Jan. 18-22, 1999.

Cho, Y. G., T. Ishii, S. Temnykh, X. Chen, L. Lipovich, S. R. McCouch, W. D. Park, N.

Ayres and S. Cartinhour. 2000. Diversity of microsatellites derived from genomic

libraries and Gene Bank sequences in rice (Oryza sativa L.). Theor. Appl.

Genet., 100: 713-722.

Condit, R. and S. P. Hubbell. 1991. Abundance of DNA sequences of two base repeat

regions in tropical tree genomes. Genome, 34: 66-71.

Crouch, H. K., J. H. Crouch, R. L. Jarret, P. B. Cregan and R. Ortiz. 1998. Segregation at

microsatellite loci in haploid and diploid gametes of Musa. Crop Sci., 38:

211-217.

Dellaporta, S. L., J. Wood and J. B. Hick. 1983. A plant DNA mini preparation; Version

II. Plant Mol. Biol. Rep., 1: 19-21.

Dje, Y., M. Heuertz, C. Lefebvre and X. Vekemans. 2000. Assessment of genetic

diversity within and among germplasm accessions in cultivated sorghum using

microsatellite markers. Theor. Appl. Genet., 100: 918-925.

Edwards, K. J., J. H. A. Barker, A. Daly, C. Jones and A. Karp. 1996. Microsatellite

libraries enriched for several microsatellite sequences in plants. Biotechniques,

20: 758-760.

Elemary, M. I., N. A. Nourel-bin., M. S. Ej-habbal and A. H. Selim.1998. Identification

of some soybean genotypes grown in Egypt through growth seed and seedling

characters. Annals of Agrl. Sci. (Cairo), 43(1):159-171.

Falconer, D. S. 1981. Introduction to quantitative genetics. Second Edition Longman,

Newyork.

Fisher, P. J., R. C. Gardner and T. E. Richardson. 1996. Single locus microsatellites

isolated using 5' anchored PCR. Nucl. Acids Res., 24: 4369-4371.

Flavell, A. J., M. R. Knox, S. R. Pearce and T. H. M. Ellis. 1998. Retrotransposon based

insertion polymorphisms (RBIP) for high throughput marker analysis. Plant J.,

16: 68-71.

Fukuoka, S., K. Hosaka and O. Kamijima. 1992. Use of random amplified polymorphic

DNA (RAPD) for identification of rice accessions. Jpn. J. Genet., 67: 247-252.

Fukuoka, S., T. Inoue, A. Miyao, L. Monna, H. S. Ahong, T. Sasaki and Y. Minobe.

1994. Mapping of sequence tagged sites in rice by single strand conformation

polymorphism. DNA Res., 1: 271-277.

Garland, S. M., L. Lewin, M. Abedinia, R. Henry and A. Blakency. 1999. The use of

microsatellite polymorphisms for the identification of Australian breeding lines of

rice (Oryza sativa L.) Euphytica, 108: 53-63.

Ghareyazie, B., N. Huang, S. G. Bennet and G. S. Khush. 1995. Classification of rice

germplasm. I: Analysis using ALP and PCR-based RFLP. Genome, 91: 218-227.

Ghatge, R.D. and R.N. Kadu. 1993. Genentic diversity in soybean. Annual Agric. Res.,

14: 143-148.

Gupta, P. K. and R. K. Varshney. 1999. Molecular markers for genetic fidelity during

micropropagation and germplasm conservation. Curr. Sci., 76: 1308-1310.

Gupta, P. K. and R. K. Varshney. 2000. The development and use of microsatellite

markers for genetic analysis and plant breeding with emphasis on bread wheat.

Euphytica, 113: 163-185.

Gupta, P. K., H. S. Balyan, P. C. Sharma and B. Rajesh. 1996. Microsatellites in plants:

A new class of molecular markers. Curr. Sci., 70: 45-54.

Gupta, P. K., R. K. Varshney, P. C. Sharma and B. Ramesh. 1999. Molecular markers

and their applications in wheat breeding. Plant Breed., 118: 369-390.

Hearne, C. M., S. Ghosh and J. A. Todd. 1992. Microsatellites for linkage analysis of

genetic traits. Trends Genet., 8(8): 288-294.

Jaccard, P. 1908. Nouvelles researches sur la distribution florale. Bull. Soc. Vaud. Sci.

Nat., 44: 233-270.

Jarman, A. P., G. S. Johal and R. A. wells. 1989. Hypervariable minisatellites:

recombinators or innocent bystanders? Trends Genet., 5: 367-371.

Jaylal. 1994. Genetic divergence in soybean for physiological and yield attributes under

rainfed conditions. Indian J. Genet., 54: 418-424.

Joshi, S. P., V. S. Gupta, R. K. Aggarwal, P. K. Ranjekar and D. S. Brar. 2000.

Genetic diversity and phylogenetic relationship as revealed by Inter simple

sequence repeat (ISSR) polymorphism in the genus Oryza. Theor. Appl. Genet.,

100: 1311-1320.

Karmakar, P. G., S. M. Husain and P. S. Bhatnagar. 1998. Seed size parameters of soybean

varieties of commercial importance. Soybean Genetics Newsletter, 25: 86-87.

Khanghah, H. Z. and A. R. Sohani.1999. Genetic evaluation by multivariate analysis

methods of some important agronomic traits of soybean related to seed yield.

Iranian J. of Agril. Sciences., 30(4) : 807-816.

Khush, G. S. and T. Kinoshita. 1991. Rice karyotoype, market genes and linkage groups.

In: Rice Biotechnology, G.S. Khush and G.H. Toenniessen (eds). IRRI

Philippines, pp. 83-108.

Kurata, N., Y. Nagamura, K. Yamamota, Y. Harushim, N. Sue, J. Wu, B. A. Antonio, A.

Shomura, T. Shimizu and S. Y. Lin. 1994. A 300 kilobase interval genetic map of

rice including 883 expressed sequences. Nature Genet., 8: 365-372.

Levinson, G. and G. A. Gutman. 1987. Slipped-strand mispairing: a major mechanism of

DNA sequence evolution. Mol Biol Evol., 4: 203-221.

Liu, S., R. G. Cantrell, J. C. McCarty and J. McD. Stewart 2000. Simple sequence

repeat-based assessment of genetic diversity in cotton race stock accessions. Crop

Sci., 40: 1459-1469.

Lu, W., J. E. Harnandez, S. S. Virmani, A. L. Carpena, L. S. Sebastian, E. D. Redona

and S. R. Obien. 1999. Genetic variation within and between maintainer lines and

restorer lines of tropical hybrid rice measured by conventional and molecular

methods. Philippine J. Crop Sci., 24: 6.

Lyamichev, V., M. A. Brow and J. E. Dahlberg. 1993. Structure-specific endonucleolytic

cleavage of nucleic acids by eubacterial DNA polymerases. Science, 260: 787-

783.

Mackill, D. J., Z. Zhang, E. D. Redona and P. M. Colowit. 1996. Level of polymorphism

and genetic mapping of AFLP markers in rice. Genome, 39: 969-977.

Malosetti, M. and T. Abadie. 2001. Sampling strategy to develop a core collection of

Uruguayan maize landraces based on morphological traits. Genetic Resources

and Crop Evolution, 48:381-390.

Mardia, K. V., J. T. Kent and J. M. Bibby. 1979. Multivariate analysis. Academic press,

London.

Mathew, L. 1999. RAPD markers for genetic variation for drought response in rice

(Oryza sativa L.). M.Sc Thesis, Tamil Nadu Agricultural University, Coimbatore,

India.

Mathew, L., R. C. Babu, J. Souframanien, P. Chezhian P. Shanmugasundaram, P.

Nagarajan and S. Sadasivam. 2000. DNA polymorphism among rice (Oryza

sativa L.) accessions differing in drought tolerance. J. Plant Biol., 27: 145-152.

McCouch, S. R., X. Chen, O. Panaud, S. Temnykh, Y. Xu, Y. G. Cho, N. Huang, T. Ishii

and M. Blair. 1997. Microsatellite marker development, mapping and applications

in rice genetics and breeding. Plant Mol. Biol., 35: 89-99.

Meyer, W., T. G. Michell, E. Z. Freedman and R. Vilgalys. 1993. Hybridization probes

for conventional DNA fingerprinting used as single primers in the polymerase

chain reaction to distinguish strains of Cryptococcus neoformans. J. Clinical

Biol., 31: 2274-2280.

Mohan, M., S. Nair, A. Bhagwat, T. G. Krishna, M. Yano, C. R. Bhatia and T. Sasaki.

1997. Genome mapping, molecular markers and maker-assisted selection in crop

plants. Mol. Breed., 3: 87-103.

Moreno, L. S., S. A. Ordonez and E. D. Redona. 1999. Molecular marker heterozygosity

and heterotic performance in Philippines rice hybrids. Philippine J. Crop Sci.,

24:61-65.

Morgante, M. A. Rafalski, P. Biddle, S. Tingey and X. M. Oliveri. 1994. Genetic

mapping and variability of seven soybean simple sequence repeat loci. Genome,

37: 763-769.

Morgante, M. and A. M. Oliveri. 1993. PCR amplified microsatellites as markers in

plants genetics. Plant J., 3: 174-182.

Nei, M. 1973. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad.

Sci. USA, 70: 3321-3323.

Okpul, T., E. S. Mace, I. D. Godwin, D. Singh and M. E. Wagih. 2006. Evaluation of

variability among breeding lines and cultivars of taro (Colocasia esculenta) in

Papua New Guinea using ISSR fingerprinting and agro-morphological

characterization. Plant Genetic Resources Newsletter, 143: 8-16.

Olufowote, J. O., Y. Xu, X. Chen, W. D. Park, H. M. Beachell, R. H. Dilday, M. Goto

and S. R. McCouch. 1997. Comparative evaluation of within cultivar variation of

rice using microsatellite and RFLP markers. Genome, 40: 370-378.

Orita, M., H. Iwahana, H. Kanazawa, K. Hayashi and T. Sekiya. 1989. Detection of

polymorphisms of human DNA by gel electrophoresis as single-strand

conformation polymorphisms. Proc. Natl. Acad. Sci. USA., 86: 2766-2779.

Ostrander, E. A., P. M. Jong, J. Rine and G. Duyk. 1992. Construction of small-insert

genomic DNA libraries highly enriched for microsatellite repeat sequences. Proc.

Natl. Acad. Sci. USA., 89: 3419-3423.

Panaud, O., X. Chen and S. R. McCouch. 1995. Frequency of microsatellite sequences in

rice (Oryza sativa L.). Genome, 38: 1170-1176.

Panaud, O., X. Chen and McCouch. 1996. Development of microsatellite markers and

characterization of simple sequence length polymorphism (SSLP) in rice (Oryza

sativa L.). Mol. Gen. Genet., 252: 597–607.

Paran, I. and R. W. Michelmore. 1993. Development of reliable PCR based markers to

downy mildew resistance genes in Lettuce. Theor. Appl. Genet., 85: 985-993.

Parsons, B. J., H. J. Newbury, M. T. Jackson and B. V. Ford-Lloyd. 1997. Contrasting

genetic diversity relationships are revealed in rice (Oryza sativa L.) using

different marker types. Mol. Breed., 3: 115-125.

Peil, A., V. Korzun, V. Schubert, E. Schumann, E.W. Weber and M.S. Roder. 1998. The

application of wheat microsatellites to identify disomic Triticum aestivum-

Aegilops markgrafii addition lines. Theor. Appl. Genet., 96: 138-146.

Powell, W., G. C. Machray and J. Provan. 1996. Polymorphism revealed by simple

sequence repeats. Trends Plant Sci., 1: 215-222.

Prasad, M., R. K. Varshney, J. K. Roy, H. S. Balyan and P. K. Gupta. 2000. The use of

microsatellites for detecting DNA polymorphism, genotype identification and

genetic diversity in wheat. Theor. Appl. Genet., 100: 584-592.

Prom-u-thai, C., D. Bernie, G. Thomson and B. Rerkasem. 2003. Easy and rapid

detection of iron in rice grain. Science Asia., 29: 203-207.

Prom-u-thai, C., F. Shu, D. G. Ian and H. Longbin. 2007. Genotyping variation of rice

partitioning in rice grain. J. Sci. Food Agric., 87: 2049–2054.

Provan, J., G. Corbett, J. W. McNiol and W. Powell. 1997. Chloroplast DNA variability

in wild and cultivated rice (Oryza spp.) revealed by polymorphic chloroplast

simple sequence repeats. Genome, 40: 104-110.

Quilloy, S. M., J. L. Pham, J. S. Garcia, L. R. Hipolito, P. L. Sanchez, M. C. Calibo and

L.S. Sebastian. 1998. Varietal classification in rice, do DNA – markers help?.

Philippine J. Crop Sci., 23: 62-68

Ramakrishna, W., M. D. Lagu, V. S. Gupta and P. K. Ranjekar. 1994. DNA

fingerprinting in rice using oligonucleotide probes specific for simple repetitive

DNA sequences. Theor. Appl. Genet., 88: 402-406.

Ravi. M. 2000. Molecular marker based varietal profiling and monitoring in rice (Oryza

sativa L.) M.Sc. Thesis. Tamil Nadu agricultural University, Coimbatore, India.

Roder, M. S., J. Plaschke, S. U. Konig, A. Borner, M. E. Sorells, S. D. Tanksley and M.

W. Ganal. 1995. Abundance, variability and chromosomal location of

microsatellites in wheat. Mol. Gen. Genet., 246: 327-333.

Rohlf, F. J., 1994. NTSYS-PC: Numerical taxonomy and multivariate analysis system,

version 2.2 State University of New York, Stony Brook, NY.

Rohlf, F. J., 1998. NTSYS-pc. Numerical taxonomy and multivariate

analysis system, version 2.02. Exter Software, Setauket, NY.

Roldan-Ruiz, L., F. A. V. Eeuwijik, T. J. Gilliland, P. Dubreuil, C. Dillmann,

J. Lallemand, M. de Loose and C. P. Baril. 2001. A comparative study of

molecular and morphological methods of describing relationships between

perennial ryegrass (Lolium perenne. L.) varieties. Theor. Appl. Genet., 103:

1138-1150.

Russell, J., J. Fuller, G. Young, B. Thomas, G. Taramino, M. Macaulay, R. Waugh and

W. Powel. 1997. Discriminating between barley genotypes using microsatellite

markers. Genome, 40: 442-450.

Saghai Maroof, M. A., R. M. Biyashev, G. P. Yang, Q. Zhang and R. W. Allard. 1994.

Extraordinarily polymorphic microsatellite DNA in barley; species diversity,

chromosomal location and population dynamics. Proc. Natl. Acad. Sci. USA, 91:

5466-5470.

Salimath, S. S., A. C. de Oliveira, I. D. Godwin and J. L. Bennetzen. 1995. Assessment

of genomic and genetic diversity in the genus Eleusine with DNA markers.

Genome, 38: 757-763.

Sambrook, J., E. F. Frotsch and T. Maniatis. 1989. Molecular cloning, A Laboratory

Manual, 2nd ed. Cold Spring Harbor, NY.

Sarket, G., J. Cassady, C. D. K. Bottema and S. S. Sommer. 1990. Characterization of

polymerase chain reaction amplification of specific alleles. Anal. Biochem., 186:

64-68.

Schwarzacher, T. 1994. Mapping in plants: progress and prospects. Curr. Opin. Genet.

Develop., 4: 868-874.

Sneath, P. H. A and R. R. Sokal. 1973. Numerical Taxonomy. Freeman San

Francisco.573.pp

Tanksley, S. D., N. D. Young, A. H. Paterson and M. W. Bonierbale. 1989. RFLP

mapping in plant breeding: new tools for an old science. Biotechnol., 7: 257-264.

Temnykh, S., G. DeClerck, A. Lukshova, L. Lipovich, S. Cartinhour and S. McCounch.

2000. Computational and experimental analysis of microsatellites in rice (Oryza

sativa L.): Frequency, length variation, transposons associations and genetic

marker potential. Genome Res., 11: 1441-1452.

Thomas, C. M., P. Vos, M. Zabeau, D. A. Jones and K. A. Norcottet. 1995. Identification

of amplified restriction fragment polymorphism (AFLP) markers tightly linked to

the tomato Cf-9 gene for resistance to Cladosporium fluvum. Plant J., 8: 785-794.

Tingey, S. V. and J. P. Deltufo. 1993. Genetic analysis with Random Amplified

Polymorphic DNA. Plant Physiol., 101: 349-352.

Vieux, E. F., P. Y. Kwok and R. D. Miller. 2002. Primer design for PCR and sequencing

in high-throughput analysis of SNPs. Biotechniques, 32: 28–30

Virk, P. S., B. V. Ford-Llyod, M. T. Jackson and H. J. Newbury. 1994. Use of RAPD for

the study of diversity within the plant germplasm collections. Heredity,

74: 170-179.

Vos, P., R. Hogers, M. Bleeker, M. Reijans, T. van de Lee, M. Hornes, A. Frijters, J. Pot,

J. Peleman, M. Kuiper and M. Zabeau. 1995. AFLP: A new technique for DNA

fingerprinting. Nucleic Acids Res., 23: 4407-4414.

Wang, Z. Y., G. Second and S. D. Tanksley. 1992. Polymorphism and phylogenetic

relationships among species in the genus Oryza as determined by analysis of

nuclear RFLPs. Theor. Appl. Genet., 83: 565-581.

Wang, Z., J. L. Weber, G. Zhong and S. D. Tanksley. 1992. Survey of plant short tandem

repeats. Theor Appl. Genet., 88: 1-6.

Welsh, J. and M. Mackill. 1992. Finger printing genomes using PCR with arbitrary

primers. Nucleic Acids.Res., 18: 7213-7218.

Williams, J. G. K., A. R. Kubelik, K. J. Livak, J. A. Rafalski and S. V. Tingey. 1990.

DNA polymorphisms amplified by arbitrary primers are useful as genetic

markers. Nucleic Acids Res., 18: 6531-6535.

Yang, G. K., M. A. Saghai Maroof, C. G. Xu, Q. Zang and R. M.

Biyashev. 1994. Comparative analysis of microsatellite DNA

polymorphism in landraces and cultivars of rice. Mol. Gen. Genet.,

245: 187-194.

Yolanda, J. L. and L. D. V. Das. 1995. Correlation and path analysis in rice (Oryza sativa

L.). Madras Agri. J., 82: 576-578.

Zhu, J., M. D. Gale, S. Quarrie, M. T. Jackson and G. J. Bryan. 1998. AFLP markers

for the study of rice biodiversity. Theor. Appl. Genet., 96: 602-611.