poaceae family and oryzoidea subfamily


Rice (Oryza sativa (2n = 24) is a monocot plant and belongs to the Poaceae family and Oryzoidea subfamily. It occupies almost one-fifth of the total land area under world cereals. It covers about 148 million hectares annually that is roughly 11 percent of the world-cultivated land. It is life for more than half of humanity and in past, it shaped the cultures, diets, and economies of billions of people in the world (Farooq et al. 2009). More than 90 percent of the world's rice is grown and consumed in Asia where 60 percent of the world population lives. The world major rice consuming countries are China, India, Egypt, Indonesia, Malaysia, Bangladesh, Vietnam, Thailand, Myanmar, Philippines, Japan, Brazil, South Korea and USA that consume 135, 85, 39, 37, 26, 18, 10, 10, 9.7, 8.7, 8.1, 5.0 and 3.9 million metric ton, respectively (USDA, 2010).

Biochemical and nutritional aspects of rice

Rice is a major source of macro and micronutrients for human being. It is used as feed for more than two billion people worldwide and one of the staple food in Asia. It provides over 21 percent of the calorific needs of the world's population and up to 76 percent of the calorific intake of the population of South East Asia (Fitzgerald et al. 2009). It is mostly consumed as a polished grain, which usually lacks its nutritional components such as minerals and vitamins 41 P. Lucca et al., Genetic engineering approaches to enrich rice with iron and vitamin A, Physiol. Plant. 126 (2006), pp. 291-303. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (7)( Lucca et al. 2002). Since the advent of molecular techniques, recently genetically modified rice varieties have been developed, which contain more nutritional aspects like minerals and vitamins in endosperm (Vasconcelos et al. 2003; Paine et al. 2005; Fitzgerald et al. 2009). The major value-added nutritional protein constituents of the rice are summarized in Table 1.1.

Rice Position in Pakistan

In Pakistan, besides its importance as a food crop, rice is the second important component of daily diet of bulk of the population after wheat. About 23% of the total foreign exchange earnings is shared by rice and thus called as 'Golden Grain of Pakistan' (Shah et al. 1999). Around one third of total production is annually exported and two third is locally consumed to meet food needs. Rice is also used in dishes for special occasions (Sagar et al. 1988). Pakistan is the third largest rice exporting country.

Rice crop is of great economic importance in Pakistan, as it is the 2nd most important food crop (next to wheat) not only in respect of local consumption but also in view of large export (next to cotton). It occupies 10% of the total cultivated area, presently grown on approximately 3.0 million hectares, with a total production of nearly 7 million tons in 2008-09 (Figure 1A). Rice accounts for 5.5% of value added in agriculture and 1.1% in gross domestic production in Pakistan. Although rice production in Pakistan is on increasing trend (Figure 1A), still the country has all its potential to improve yield and quality to expand its export.

High quality Basmati rice, for its strong aroma, is more economical, highly valuable and priced in both domestic and international markets (Fitzgerald et al. 2009). Due to its superior grain quality it has become an important source of revenue for the country and has always been highly priced in the international market as compared to the non-basmati (Figure 1).The country fetched $2.044 billion by exporting record 2.930 million tons of rice during the last fiscal year 2008-09; Basmati rice export of about 0.924 million tons earned $1.018 billion and $1.025 billion was fetched by exporting 2.005 million tons of non-Basmati rice. Pakistan appeared to be the third largest rice exporting country with 13.81% share in the world rice trade (Figure 2).

Rice growing areas of Pakistan

Depending upon the irrigation water availability, rice can be grown in any part of the country from sea level up to 2500m height. Pakistan has a climate and a potential in soil that permits the expectations of a most bright future for the production of rice. Considering temperature difference, optimum sowing seasons and the varietal performance, rice growing areas can be divided in four ecological zones (Salim et al. 2003; Table 1.3).

Major rice varieties in Pakistan

More than 40 rice varieties have been released for general cultivation in Pakistan (Bashir et al. 2007). A general description of agronomical and physiochemical characteristics of these varieties has been summarized in Table 1.4.

Importance of Basmati rice in Pakistan

Quality of rice may be considered from the view point of size, shape and appearance of grain, milling quality and cooking properties (Khush and dela Cruz, 2001). Pakistan is famous for the production and export of Basmati rice. The origin of the word "Basmati" can be trade to the word "Basmati" meaning earth recognized by its fragrance. The Hindi word "Bas" was derived from the Pakrit word "BAS" and has a Sanskrit root" Vassy" (Aroma), while "Mati" originated from "Mayup" (ingrained from the origin). In common usage Vas is pronounced as "Bas" and while combining "Bas and Mayup", the later changed to "Mati' thus the word Basmati (Ahuja et al. 1995).

The fragrance of basmati rice is most closely associated with the presence of 2-acetyl-1-pyrroline (Lorieux et al. 1996; Widjaja et al. 1996; Yoshihashi et al. 2002). Although many other compounds are also found in the headspace of fragrant rice varieties (Widjaja et al. 1996) possibly due to secondary effects related to the genetic background of the rice variety, 2-acetyl-1-pyrroline is widely known to be the main cause of the distinctive basmati and jasmine fragrance. The desirability of fragrance has resulted in strong human preference and selection for this trait. Non-fragrant rice varieties contain very low levels of 2-acetyl-1-pyrroline, while the levels in fragrant genotypes are much higher (Widjaja et al. 1996).

Basmati rice occupies a prime position in the Indian subcontinent and is becoming increasingly popular in Middle East, Europe, USA and even in non-traditional rice growing countries such as Australia (Bhasin, 2000). High-quality, traditional Basmati rice varieties command premium prices, more than three times that of non-Bamati rices in the world market due to its exquisite aroma, superfine grain characteristics and excellent cooking (extra elongation, soft and flaky texture) qualities (Bhasin, 2000; Singh et al. 2000a; Khush and dela Cruz, 2002). Basmati rice traditionally grown in the Himalayan foothills regions of Pakistan and India, and the name is traditionally associated with this region. Basmati rice is the result of centuries of selection and cultivation by farmers (Khush, 2000).

Cultivation of basmati rice in mainly confined to the Kallar tract (Gujranwala, Sheikhupura and Sialkot districts) of Punjab province. Basmati rice always fetch a higher price in the domestic as well as in the international market due to their peculiar quality features such as pleasant aroma, fine grain, extreme grain elongation (7.6mm long) and soft texture on cooking.

Genetic Diversity in Rice

Diversity among organisms is a result of variations in DNA sequences and of environmental effects. The diversity in crop varieties is essential for agricultural development for increasing food production, poverty alleviation and promoting economic growth. The available diversity in the germplasm also serves as an insurance against unknown future needs and conditions, thereby contributing to the stability of farming systems at local, national and global levels (Singh et al. 2000). In crop improvement program, genetic variability for agronomic traits as well as quality traits in almost all the crops is important, since this component is transmitted to the next generation (Singh, 2000). Study of genetic divergence among the plant materials is a vital tool to the plant breeders for an efficient choice of parents for plant improvement. In early 1970's, public authorities felt the need that genetic resources should be collected, maintained and conserved, especial focus was on important food crops e.g wheat, rice, barley etc (Hawkes 1983; Bellon et al. 1998; Barry et al. 2007). This was the first official attempt to preserve genetic diversity. Currently different genetic diversity assessment methods including morphological, biochemical and molecular markers are available.

Morphological markers for the analysis of genetic diversity

Morphological evaluation is the oldest and considered as the first hand tool for detection of genetic variation in germplasm (Smith and Smith, 1989). It is cheap and convenient. It requires not any in depth knowledge at genomic or proteomic level. However, morphological markers are relatively less effective for genetic diversity analysis due to sensitivity to environmental influences and developmental stage of the plant (Werlemark et al. 1999). It takes long time, requires seasonal changes and quite laborious. The genetic variability for some of the traits needed for high yield performance and stress tolerance is limited in cultivated germplasm. This is because a small core of adapted progenitors has been used repeatedly in rice breeding programs such that the genetic base of rice has become narrow (Hargrove et al. 1980; Dilday 1990; Moncada et al. 2001). Introgression of genes from other rice species can provide genetic variation to improve rice and meet the challenges affecting rice production. Morphological traits including both qualitative and quantitative ones are used to evaluate genetic relationship among genotypes (Goodman 1972; Bajracharya et al. 2006). Abbasi et al. (1995) reported the evaluation of elite rice genotypes for agronomic traits during 1992 at NARC, Islamabad. All the genotypes possessed similar grain quality. Agronomic evaluation was used for screening of lines with desired performance by Akram et al. (1995) in field leading to the identification of varieties possessing longer and fine grains as donors for utilization in breeding programs aimed for the improvement of grain length in Basmati rice. Iqbal et al. (2001) morphologically evaluated selected landraces for paddy yield and other important agronomic traits as a purpose to select parents for hybridization program. All the landraces possessed some desirable agronomic traits so these proved effective in rice breeding programs. Koutroubas et al. (2004) described variation in grain quality traits among some European rice lines. They concluded that these lines could be used as parents for introgression of desired traits into different rice cultivars grown in Europe. They also suggested that the interrelations among grain quality traits found in these lines could be useful to study the relationship among their grain quality components and for improving selection criteria. Nabeela et al. (2004) evaluated landrace genotypes of rice collected from various parts of Pakistan for fifteen agronomical important traits. A significant amount of genetic variation was displayed for most of the traits examined. The coefficient of variation was more than 10% for all the characters with exception of grain length. Seven accessions with best performance for individual character were identified, by exploiting their genetic potential. These genotypes can have a beneficial use in the breeding programs. Nepali rice landrace diversity was evaluated by Bajracharya et al. (2005) using morphological traits as one of the parameter for selection. The genotypes varied only for few quantitative traits controlled by major genes; husk color, seed coat and panicle traits. Agronomic characterization also helped to decide which traits need to be improved for further crop improvements. Zaman et al. (2005) studied fifteen different rice varieties which showed that the different morphological characteristics such as the yield, tiller number per hill and filled grains per panicle did not contribute towards the total divergence. This suggested that the breeding improvement of these morphological characteristics have the little possibility. Little phenotypic variation at farm level was observed in Vietnamese rice by Fukuoka et al. (2006), which were considered to be the result of genetic drift and selection by the farmers, on farm conservation of the landraces of rice is considered to be under a force to decrease phenotypic diversity. Different phenotypic profiles contribute to the conservation of regional genetic diversity of the landraces of rice. Veasey and colleagues (2008) investigated the genetic variability among different rice species from South in a greenhouse experiment. They showed a significant difference (p<0.001) among different investigated species. The highest phenotypic variability was observed in two different rice species by multivariate analyses. Bisne and Sarawgi investigated Badshah bhog group accessions from Bangladesh for thirty morphological traits including eight quality characters. Eight agronomical unique accessions were selected for further hybridization program for desired segragant population.

Keeping in view these benefits, morphological variation is a selection criterion for plant scientists among landrace genotypes. Though the environmental factors also play an important role in morphological variation but the knowledge of agro-morphological diversity and the distribution pattern of variation among crop species could be an invaluable aid in germplasm management and crop improvement strategies. Zeng et al. (2007) studied ecogeographic and genetic diversity based on morphological characters of rice landraces (Oryza sativa L.) in Yunnan, China. A great difference in ecological diversity index of rice resources between prefectures or counties in Yunnan province exists. Sanni et al. (2008) studied the relationship in geographical pattern and morphological variation of 880 rice landrace in Cote d'Ivoire for 13 agro-morphological characters. Result of the phenotypic frequency showed differential distribution of landraces with height, heading and maturity period which reflected the distribution pattern of different Oryza sativa landraces in Cote d'Ivoire that proved useful in germplasm management and breeding programs. The altitudinal distributions of grain length, grain width, grain length to width ratio and grain weight were evaluated by Siddiqui and coworkers in 2007. It was noticed that grain length decreased with the increase in altitude, while the grain width increased with the increase in altitude, resulting into a decrease in length to width ratio with the increase in altitude. Considering the change in altitude as a difference in habitat and environment, it can be assumed that Pakistan rice cultivars show a wide variation between and within locations. It may be concluded that the Pakistan rice genetic resources comprise of great diversity for grain morphological characteristics. The prevailing diversity for grain type (shape and size) and pericarp color has distinct correlation to its geographical distribution in terms of altitude.

Morpho-physiological traits are an important tool in hands of plant breeders for identification and purity testing of rice varieties. Sharief et al. (2005) investigated the genetic purity of four different rice varieties on the basis of morphological characteristics at their different growth stages. All of the varieties were identified by different morphological characteristics in terms of flag leaf area, grain color, seed width, number of tillers, time of heading, absent awing, slemma, palea pubescence, plant height, and culm diameter.

Biochemical markers for the analysis of genetic diversity

Seed proteins are very helpful in genetic diversity evaluation in cereal crops because the seeds of these crops have nutritional value. Glutelin, globulin and prolamin are important seed proteins in rice. Variation in these proteins at subunit level changes the quality of rice. Various tools were used to assess variability at peptide level. Biochemical markers have some disadvantages being tissue specific and affected by environmental and developmental changes. These disadvantages could be eliminated by the use of seed storage protein as they are conservative in nature and least effected by environmental changes. Sodium Dodecyl Sulphate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) is useful method not only for revealing variations but also for identification of a variety in seed storage proteins. Four protein fractions (albumin, globulin, gliadin and glutenin) separated by SDS-PAGE as biochemical marker for evaluating polymorphism in three spelt wheat varieties. Very significant difference was observed at protein profile level in old cultivars and new breeding lines (Dvoracek and Curn 2003). Sengupta and Chattopadhyay (2000) identified twelve rice varieties on the basis of banding pattern obtained by SDS-PAGE. Padmavathi et al. (2002) evaluated seven aromatic and five non-aromatic rice cultivars using SDS-PAGE. Two bands of 60.3 and 51.3KDa were polymorphic for their presence in both aromatic and non-aromatic genotypes and suggested that these polymorphic bands can be used as markers for verification of hybridity in crossing program. Thanh et al. 2006 in a study highlighted the importance and application of SDS-PAGE for evaluation of genetic relatedness and purity of several plant varieties including a glutinous rice variety. A new glutelin gene, GluD-1, has been discovered by comparing the seed storage proteins from 48 japonica and indica inbred rice cultivars on SDS-PAGE gels (Kawakatsu, 2008).

Different workers used protein profile to evaluate rice genetic diversity from different regions. Aung et al. (2003) investigated 350 local rice cultivars from different regions of Myanmar. These were analyzed by using SDS-PAGE and IEF. Various cultivars differed in their SDS-PAGE profiles. Rehana et al. (2004) investigated twenty accessions of Pakistani rice germplasm for total seed protein by using SDS-PAGE, to determine the magnitude of genetic variation with respect to geographical distribution. Variation in protein banding pattern with respect to various geographical regions was evaluated and it was suggested that the inter-specific variations were more pronounced as compared to intra-specific variations. Variation in banding profile of globulin and glutelin was used as identification tool for differentiating coarse, fine and super fine rice cultivars by Thind and Sogi (2005). Chandi and Sogi (2006) studied two rice cultivars for albumin, globulin, prolamin and glutelin which were in different ratios in both cultivars. Thanh and Hirata (2002) used seed storage protein profiles of different rice species from Mekong delta in China for evaluation of genetic purity and variability. Two lines were observed in evaluations which were high in waxy protein contents. These variations are valuable in further conservational practices for wild species of rice. Jahan et al. (2005) studied protein diversity in 576 rice cultivars from Bangladesh and SDS-PAGE was used for separation. One hundred and fifteen japonica rice genotypes from China and Japan were evaluated on the basis of SDS-PAGE profile. Very low proteomic differences were observed in japonica rice species and the polymorphism which was observed in some genotypes cannot distinguish different ecotypes (Wei-dong et al. 2006).

Molecular markers for analysis of genetic diversity

Variation in a DNA sequence is known as DNA polymorphism. This quality of DNA can be used as a marker to assess diversity in the genome of any organism. An ideal DNA marker must have any of the following qualities: Highly polymorphic in nature, co-dominant inheritance, frequent occurrence in genome, selective neutral behaviour, easy access/availability, easy and fast assay, high reproducibility and easy exchange of data between laboratories (Joshi et al. 1999). The term DNA fingerprinting used by Alec Jeffery (1985), can increase screening efficiency in breeding programs in a number of other ways. For example, they provide: the ability to screen in the seedling stage for traits that are expressed late in the life of a plant (i.e. grain or fruit quality, male sterility, photoperiod sensitivity), the ability to screen for traits that are extremely difficult, expensive, or time consuming to score phenotypic ally (i.e. root morphology, resistance to quarantined pests or to specific races or biotypes of diseases or insects, tolerance for certain abiotic stresses such as drought, high salinity, or mineral deficiencies or toxicities), the ability to distinguish the homozygous versus heterozygous condition of many loci in a single generation without the need for progeny testing (since molecular markers are co-dominant), and the ability to perform simultaneous marker-aided selection for several characters at one time. Restriction Fragment Length Polymorphism (RFLP) were the first developed non-PCR based, single locus markers used (Botstein et al., 1980) which were further converted to PCR based markers e.g. STS(Sequence Tagged Sites), ASAP( Allele Specific Associated Primer), EST( Expressed Sequence Tags) and SSCP( Single Strand Confirmation Polymorphism). Afterwards, microsatellite markers based on repetitive regions in the genome were introduced (Litt and Luty, 1989). Transposable elements based markers were also reported e.g Alu repeats in human (Nelson and Caskey, 1990). These entire DNA based markers provide in depth knowledge about the variation in genetic make up of any organism.

Random Amplified Polymorphic DNAs (RAPDs) for Analysis of genetic diversity in rice

Randomly-amplified polymorphic DNA markers (RAPD) are arbitrary sequence markers developed by Welsh and McClelland in 1991. This procedure detects nucleotide sequence polymorphisms in DNA using a single primer of arbitrary nucleotide sequence. A single primer directs amplification of several discrete loci in the genome, making the assay useful for efficient screening of nucleotide sequence polymorphism between individuals. However, due to the stochastic nature of DNA amplification with random sequence primers, optimization and consistency for reaction conditions are important factors. Being dominant markers, RAPD's hence have limitations in their use as markers for mapping, which can be eliminated by selecting markers linked in coupling. RAPD assay has been used by several groups as efficient tools for identification of markers linked to agronomically important traits, which are introgressed during the development of near isogenic lines. Though it is less popular due to problems such as poor reproducibility faint or fuzzy products, and difficulty in scoring bands, which lead to inappropriate inferences but it is still applied as markers in variability analysis and individual-specific genotyping. Further modifications in Modifications in Random markers lead to development of DNA Amplfication Fingerprinting (DAF), Sequence Characterized Amplified Regions (SCAR) for Sequence Tagged sites, Randomly Amplified Microsatellite Polymorphism (RAMPO) and Cleaved Amplified Polymorphic Sqeuences (CAPS) etc. Raghunathachari et al. (2000) differentiated a set of 18 accessions from Indian scented rice by random amplified polymorphic DNA (RAPD) analysis. The RAPD analysis offered a rapid and reliable method for the estimation of variability between different accessions, which could be utilized by the breeders for further improvement of the scented rice genotypes. Porreca et al. (2001) reported confirmation of genetic diversity among 28 rice cultivars, different for biometric traits, biological cycle and suitability to water limitation, using RAPD markers. High level of polymorphism was found between japonica and indica subspecies, whereas japonica cultivars with long grains (tropical) resulted to be genetically different from the short grains genotypes (temperate). Genetic relationships among indica and japonica cultivars and between tropical and temperate japonica was estimated. Variability among the varieties could lead to good heterotic combinations between japonica genotypes. Neeraja et al. (2002) determined genetic diversity in a set of landraces in comparison to a representative sample of improved rice varieties, using random amplified polymorphic DNA (RAPD). Analysis of 36 accessions using 10 arbitrary decamer random primers, revealed 97.16% polymorphism. Similarity values among the landraces ranged from 0.58 to 0.89 indicating wide diversity. The landraces and improved varieties formed separate clusters at 0.65 similarity suggesting that genetically distant landraces could be potentially valuable sources for enlarging and enriching the gene pool of improved varieties. Kwon et al. (2002) evaluated genetic divergence among 13 Tongil type rice cultivars and the relationship between genetic distance and hybrid performance in all possible nonreciprocal crosses between them assessed. These results indicate that genetic distance based on the microsatellite and random amplified polymorphic DNA (RAPD) markers may not be useful for predicting heterotic combinations in Tongil type rice and support the idea that the level of correlation between hybrid performance and genetic divergence is dependent on the germplasm used. Rabbani et al. (2008) evaluated the genetic polymorphism and identities of several Asian rice cultivars by using random amplified polymorphic DNA technique. Results showed grouping of cultivars to aromatic, non-aromatic and japonica group, and a few independent cultivars and high level of genetic relatedness among aromatic cultivars. No significant association between the RAPD patterns and the geographic origin of the cultivars was found. Amita et al. (2005) performed molecular and hybridization studies to investigate variation patterns in O. meridionalis by producing 119 polymorphic RAPD markers which showed speciation in O. meridionalis with respect to its geographic distribution in northern Australia and Irian Jaya. Santhy et al. (2003) tested application of RAPD markers for the identification of three rice (Oryza sativa L.) hybrids and their parental lines using random markres. The results are discussed in view of its application for the purpose of Plant Variety Protection and for testing the genetic purity of A line and hybrid seed lots.

Microsatellite or Simple Sequence Repeats (SSR) Analysis

Microsatellites or simple sequence repeats (SSRs) are simple tandemly repeated di- to penta-nucleotide sequence motifs. Microsatellite data are also commonly used to assess genetic relationships between populations and individuals through the estimation of genetic distances (e.g. Joshi et al. 2004; Sodhi et al. 2005; Tapio et al. 2005). The most commonly used measure of genetic distances is Nei's standard genetic distance (DS) (Nei, 1979). Because of microsatellite abundance and even distribution in nuclear genomes of eukaryotes and some prokaryotic genomes, they offer valuable good source of polymorphism, which make them a promising class of genetic markers. The high levels of polymorphism performed by these markers; they are mostly referred as SSLP (simple sequence length polymorphism). Li et al. (2004) examined genetic diversity and relationship among indica and japonica subspecies, including 22 accessions of indica and 35 of japonica rice by using five microsatellite loci from all 12 chromosome having total 60 loci. Evaluating on chromosome-based comparisons it is concluded that nine chromosomes (1, 2, 3, 4, 5, 8, 9, 10 and 11) accumulate higher of genetic diversity within the indica rice than the japonica rice. By applying chromosome-based comparisons they suggested that the extent of the indica-japonica differentiation varied substantially, ranging from 7.62% in chromosome 3 to 28.72% in chromosome 1. Garris et al. (2004) classified 234 accessions of rice into five distinct groups corresponding to indica, aus, aromatic, temperate japonica, and tropical japonica rices using 169 microsatellite markers. Genetic differences among new rice lines (NERICA), developed by cross breeding of African rice (Oryza glaberrima) with high yielding Asian rice (Oryza sativa subsp. japonica), were explored by using simple sequence repeat markers (Semagn et al. 2006). Michael et al. (2006) characterized 330 Indonesian landraces and improved cultivars of rice, by studying 30 fluorescently-labeled microsatellite markers. The resulted analysis grouped all improved cultivars into indica type. Giarrocco et al. (2007) studied variation in Argentinean rice cultivars that were important in breeding and production point of view and grouped those into two major groups, indica and japonica on the basis of 26 microsatellite markers. This result in generation of database for cultivar identification and conservation of germplasm for breeding propose. Alvarez et al. in 2007 evaluated Cuban traditional rice varieties using microsatellite markers and divided those into three genetic groups. Genetic distance studies inferred that traditional varieties have no significant relationship with the improved cultivars. Barry et al. (2007) worked on farm specific diversity in rice varieties and genetic diversity partition between varieties, farms and within varieties of Maritime Guinea with contrasting agro-ecological conditions. The molecular variance was evenly distributed between and within genotypes and agro-ecological effect was nil as the farms did not host any specific genetic variability among varieties. Lapitan et al. 2007 detected variation in twenty four Philippine quality rice cultivars at one hundred and sixty four microsatellite loci. Grouping of cultivars was seen on the basis of aroma and cooking quality. Highest genetic diversity for indica species was observed on chromosome 11. These findings were further utilized in genetic mapping studies and marker assisted selection programs. Jayamani et al. (2007) used microsatellite markers to detect a significantly high degree of polymorphism in Portuguese rice (Oryza sativa L.) using DNA profile at 24 SSR loci covering two loci per chromosome, differentiating three major groups, japonica, basmati, and indica. Hence, identification of genetic distances among the accessions should improve their use in breeding programs. A set of genetically distant parents was identified for further breeding program. Herrera et al. (2008) studied 11 Venezuelan rice varieties by microsatellite markers to assess the genetic diversity. Disimilarity coefficients were used to separate genotypes and the genetic diversity was low. Thomson et al. (2009) investigated genetic diversity among Indonesian rice cultivars using 30 microsatellite markers. They showed genetic variations between the indica and japonica varietal groups. These variations were correlated with the field-level ecotypes also. Microsatellite analysis was used to evaluate diversity and relationship among thirty-five Asian rice varieties. Basmati rice varieties amplified different alleles at 15 of the SSR loci than those in the japonica and/ or indica rice varieties. A number of SSRs were identified that could be utilized to differentiate between basmati and other non-basmati rice varieties (Pervaiz et al. 2009).

Comparative study of different marker systems in genetic diversity analysis

Comparative studies were also reported which explained the strength and nature of these markers systems in genetic diversity assessments. Yunbi et al. (2004) evaluated diversity in rice accessions by applying restriction fragment length polymorphism (RFLP) and simple sequence repeat (SSR) loci at DNA level. Higher value of polymorphism information contents (0.66) was recorded for SSR markers as compared to RFLP (0.36). A subset was selected useful in developing core collections and an efficient source of genetic diversity for future crop improvement. Ravi et al. (2003) studied the genetic diversity among 40 cultivated varieties and five wild relatives of rice, Oryza sativa L. involving simple sequence repeat (SSR) and randomly amplified polymorphic DNA (RAPD) markers. The test indicated that clusters produced based on RAPD and SSR markers were not conserved since matrix correlation value was 0.582 as against the minimum required value of 0.800. The two marker systems contrasted most notably in pair-by-pair comparisons of relationships. SSR analysis resulted in a more definitive separation of clusters of genotypes indicating a higher level of efficiency of SSR markers for the accurate determination of relationships between accessions that are too close to be accurately differentiated by RAPD markers. Qian et al. (2001) investigated genetic variation within and between five populations of Oryza granulata from two regions of China using RAPD (random amplified polymorphic DNA) and ISSR (inter-simple sequence repeat amplification) markers. This was the first report of the partitioning of genetic variability within and among populations of O. granulata at the DNA level, which is in general agreement with a recent study on the same species in China using allozyme analysis. These results also indicated that the percentage of polymorphic bands (PPB) detected by ISSR is higher than that detected by RAPD. It seems that ISSR is superior to RAPD in terms of the polymorphism detected and the amplification reproducibility. Fugang et al. (2003) estimated genetic relationships of the AA-genome Oryza species using RAPD and SSR analyses with 45 accessions. It is also demonstrated from this study that both RAPD and SSR analyses are powerful methods for detecting polymorphisms among the different AA-genome Oryza accessions. However, the RAPD analysis provides a more-informative result in terms of the overall genetic relationships at the species level compared to the SSR analysis. The SSR analysis effectively reveals diminutive variation among accessions or individuals within the same species, given approximately the same number of primers or primer-pairs used in the studies. Jeung et al. (2005) studied comparative behavior of different DNA markers including RAPD and SSR in addition to AFLP in temperate japonica and indica rice genotypes. Significant similarity coefficients were observed for all marker system but AFLP proved to be the very effective. Bajracharya et al. (2006) estimated genetic diversity of rice landraces collected from different locations of Nepal based on agro-morphological variability and microsatellite marker polymorphism. They 39 microsatellite (simple sequence repeats, SSR) markers among these collected accessions using 10 different names. Both agronomic and microsatellite marker analysis revealed very low diversity, only single locus was polymorphic among these accessions.

Statistical tools to evaluate genetic diversity

Various statistical tools have been used by the geneticists and molecular biologists to evaluate diversity among crop populations. These tools depend upon pedigree data, morphological data, agronomic performance in field, biochemical as well as molecular (DNA-based) data to estimate genetic relatedness for crop improvement strategies. First key for analysis of genetic variability is sampling individual genotypes may be in the form of populations or accessions, pure or inbred lines (Mohammadi and Parasana, 2003). Sampling effects genetic variation through evenness (frequency of different alleles in a population) and richness (total number of alleles in a population) (Frankel et al. 1995). Most of the workers reported a large sample size to estimate genetic variation in a population with 95 percent probability of detecting all alleles in a population (Marshel and Brown, 1975; Warburton et al. 2002). Genetic distance or similarity between two individuals in a population or between two populations can be measured using different statistical measurements. Choice of distance measurement depends upon the type of variable and scale of measurement used. Commonly used statistics for distance measurement among genotypes or populations is Euclidean or straight line measure of distance on the basis of morphological data (Lopez et al. 2008), while the distance measure for molecular data is different as the data used is in the form of binary matrix and measures of genetic distance for such type of data are mostly Nei and Li,s coefficient of similarity (1979), Jaccard coefficient (1908), Simple matching coefficient (Sokal and Michener, 1958), and modified Roger distance. In order to detect genetic variation in large populations, for better understanding multivariate analysis tools are being employed. The multivariate tool used currently is cluster analysis (Michael et al. 2006; Jayamani et al. 2007; Rabbani et al. 2008; Pervaiz et al. 2009; Thomson et al. 2009). Hierarchical and nonhierarchical methods for clustering are mostly employed to evaluate genetic diversity. Among various agglomerative hierarchical methods, the UPGMA (Unweighted Paired Group Method using Arithmetic averages) (Sneath and Sokal, 1973; Panchen, 1992) is the most commonly adopted clustering algorithm, followed by the Ward's minimum variance method (Ward, 1963). Second mostly applied multivariate analysis tool is principal component analysis (PCA), which not only allows a number of comparisons between treatments, but also enhances the meaningfulness of these comparisons (Sneedon, 1970). PCA is a useful technique, which enables inter-correlations among variables. Additionally, a useful data reduction technique that removes interrelationship among variables (Broschat, 1979). By using PCA, not only the number of comparisons between treatment means is reduced but interactions among two or more variables may be pointed out by such analysis. In taxonomy, it can be used to express multidimensional inter-OTU (Operational Taxonomic Unit) distances in 3 or fewer dimensions, which can readily be conceptualized. Additional applications of this technique would certainly be found in fields of biological sciences, where it has been used extensively. Multivariate approaches have been used in analysis of genetic diversity of different crop species (Chandra et al. 2007). Principle Coordinate Analysis (PCoA) and Multidimentional scaling (MDS) are similar types of multivariate configurations as PCA (Rohlf, 1972) used by various workers for diversity analysis (Barrett and Kidwell, 1998; Lombard et al. 2000). Bootstrap and Jackknife techniques are being employed to get confidence intervals and standard errors. Bootstrapping can be effectively utilized for estimating the statistical support to the internal branches in a tree (Felsenstein, 1985). For instance, if a specific branching pattern is observed 80% of the time, this branching pattern is said to have 80% bootstrap support. The exact statistical interpretation of bootstrap result is still an active subject of study, but the rule of thumb is that internal tree branches that have 70% bootstrap are likely to be correct at the 95% level (Hillis and Bull, 1993).


To meet the increasing demands for food supply, the human race has to significantly enhance crop productivity, for which fuller exploitation and utilization of genetic resources in crop species will provide many more opportunities. Serving as a vast genetic reservoir, landraces provide elite germplasm for improving crop varieties by transferring beneficial genes to the crops (Zhiping Song, 2005). In Pakistan research activities on rice are targeted for increase in yield, and resistance to disease and pest. In this regard mechanization of rice cultivation, adaptation of improved varieties and more recently, use of biotechnology for the incorporation of gene for disease resistance have come up in PARC (Anon., 2000a). Salt tolerance studies are also in progress, but no studies have been marked for grain quality evaluation of local rice genetic resources; though grain quality of some improved varieties was carried out (Ahmad & Akram, 2005). However, recently it was realized at national level in Pakistan that rice with better grain quality should be produced (Anon., 2000b). Germplasm is a vital source in generating new plant having desirable traits. It helps in increasing crop quality and production as well, that improve the level of human nutrition. It is stated that germplasm collection and conservation is meaningless if it is not evaluated for the traits of concern. The present research project was initiated with the following objectives:

  1. Evaluate the extent of polymorphism in landrace genotypes of rice from Pakistan using morphological traits, biochemical and molecular markers.
  2. Determine the level of genetic relatedness among improved varieties and local landraces at DNA level.
  3. Study the association among various traits in rice.
  4. Identify promising accessions having traits for future rice breeding program.

Experiment I

Morphological Basis of Genetic Diversity

Collection of Plant material

Plant material for the study comprised of 174 landraces of Rice (Oryza sativa L.) collected from Gene Bank, Institute of Agricultural Biotechnology and Genetic Resources (IABGR), National Agricultural Research Center (NARC) Islamabad, which were acquired from various parts of the country that represent a wide ecological variation from dry mountains to irrigated plains.. The field work was carried out during May, 2006 to January, 2007 and May, 2007 to January, 2008 under field conditions at Institute of Agricultural Biotechnology and Genetic Resources (IABGR), National Agricultural Research Center (NARC) Islamabad.

All the experimental accessions (Appendix 1) of Oryza sativa were first planted in a small field for nursery growing (Fig 2.1). The seeds were sown in the pots for raising nursery at the end of May during both years and seedlings were transplanted into the field in an augmented design after one month of growth. Each cultivar as well as germplasm accession was planted in a three-row plot of four meters length with a spacing of 20cm x 20cm. One seedling was transplanted per hill and the inter-plot spacing was kept 40cm. Each experimental unit consisted of 60 plants, while five plants were selected at random from the central row for recording observations as reported by Satoh et al. (1990c, d, e, and f). The mean values of each character for each entry were used for statistical analysis according to Adair et al. (1973). Recommended cultural practices for rice evaluation were carried out from transplanting till harvest of the crop to get healthy and vigorous crop. Proper water treatment was applied to avoid water stress, flooded irrigation was continued after every 15 days till maturity of crop. Experimental field received two hoeings, one during nursery transplantation and other after one month. Fungicide Capton was sprayed twice to save the crop from fungal infections.

All the cultivars were characterized for 18 quantitative and 9 qualitative traits from flowering till maturity and harvest of the crop during both years. Traits selection and measurement techniques were based on IRRI Standard Evaluation System of Rice (Table 2.1). Quantitative traits included days to 50% flowering, days to maturity, leaf length, leaf width, total and productive tillers per plant, plant height, panicle length, number of branches per panicle, seed setting (%), grain yield per plant, straw yield per plant, harvest index, 100-seed weight, grain length, grain width and grain length/width ratio, while qualitative data was observed for flag leaf angle, flag leaf shape, leaf appearance, lodging incidence, panicle type, panicle exertion, awning, awn color and seed coat color.

Data Analysis

Data were subjected to simple statistical analysis like mean, minimum, maximum, standard deviation, coefficient of variation, etc. for all the quantitative traits to assess the amount of genetic diversity present in the local germplasm as well as cultivars. Qualitative traits were categorized into different classes and frequency percentage was calculated. Simple correlation coefficients between all pairs of quantitative characters were also calculated according to Steel and Torrie (1981) using plot mean values.

All recorded morphological traits were also analyzed by numerical taxonomic techniques using two complementary procedures: cluster and principal component analyses (Sneath and Sokal, 1973). To avoid effects due to scaling differences, means of each character were standardized prior to cluster and principal component analyses using Z-scores. Estimates of Euclidean distance coefficients were made for all pairs of varieties. The resulting Euclidean dissimilarity coefficient matrices were used to evaluate the relationships between the entries with a cluster analysis using complete linkage method (NTSys, version 2.1). Principal component analysis was also performed with the same data matrix. Scatter plots of first three principal components were produced to provide a graphical representation of the pattern of variation among all the traditional varieties and improved cultivars, and landrace genotypes of rice (Statistica, version 6.0).


Biochemical basis of genetic diversity


Molecular evaluation involves the use of molecular techniques for assessing genetic diversity of plant germplasm and identification of molecular markers for crop improvements. Healthy and mature seed of 35 commercial varieties and primitive cultivars including two control varieties (Appendix 2) was used for molecular analysis of total seed protein. SDS-PAGE technique was used to identify molecular diversity of rice commercial varieties available. Different molecular level characteristics were studied.

Diversity of total seed protein of all 40 varieties and primitive cultivars were checked in laboratory phase. Electrophoresis was carried out in the discontinuous Sodium Dodecylsulphate Polyacrylamide Gel Electrophoresis (SDS-PAGE) system of Leammli (1970) using 15% (w/v) separating gel and 4.5% (w/v) stacking gel (Walter et al., 2003).

Total Seed Protein Analysis: SDS-PAGE Electrophoresis

In Sodium Dodecylsulphate Polyacrylamide Gel Electrophoresis (SDS-PAGE) separations of polypeptides, migration is determined by molecular weight. Sodium Dodecylsulphate (SDS) is an anionic detergent that denatures proteins by wrapping the hydrophobic tail around the polypeptide backbone. For almost all proteins, SDS binds at a ratio of approximately 1.4g SDS per gram of protein, thus conferring a net negative charge to the polypeptide in proportion to its length. The SDS also disrupts hydrogen bonds, blocks hydrophobic interactions, and substantially unfolds the protein molecules, minimizing differences in molecular form by eliminating the tertiary and secondary structures. The proteins can be totally unfolded when a reducing agent is employed. The SDS denatured and reduced polypeptides are flexible rods with uniform negative charge per unit length. Thus, because molecular weight is essentially a linear function of peptide chain length, in sieving gels the proteins separate by molecular weight.

In a discontinuous system, a non restrictive large-pore gel called a stacking gel is layered on top of a separating (resolving) gel. The two gel layers are each made with a different buffer, and the tank buffers differ from the gel buffers. In this system the protein mobility, a quantitative measure of the migration rate of a charged species in an electric field, is intermediate between the mobility of the buffer ion of the same charge (usually negative) in the stacking gel (leading ion) and the mobility of buffer ion in the upper tank (trailing ion). When electrophoresis is started, the ions and the proteins begin migrating into the stacking gel. The proteins concentrate in a very thin zone, called the stack, between the leading ion and trailing ion. The proteins continue to migrate in the stack until they reach the separating gel. At that point, due to a pH or an ion change, proteins become the trailing ion and "unstuck" as they separate on the gel. Denaturing gel electrophoresis can resolve complex protein mixtures into hundreds of bands on a gel.

Preparation of Buffers

Following buffers were utilized for protein extraction and SDS-PAGE electrophoresis.

Preparation of Seed Samples

Single seed of each variety and primitive cultivars was taken, crushed and grinded in mortar and pestle. 10mg (0.01g) seed flour was weighed by an electronic balance and put into 1.5ml micro tube. After each sample weighing mortar and pestle were cleaned with great care so that there should not be even a single particle of last seed flour. To extract proteins from flour, 500l of the protein extraction buffer was put into the micro tube and mixed well by the test tube mixer (vortex). This sample was preserved in a freezer (- 20C).

Glass plates used for electrophoresis were cleaned up from internal side with 80% Ethanol and Kim wipe. Gaskets were used for sealing to the glass plates with spacer; it was kept in mind that gaskets should not overlap with spacer of plates. Sets of glass plates were fixed with double clips and marked 2cm from the top. To make sure that there is no leakage; glass plate set ups were filled wit water and placed for some time (Fig 2.2).

When separation gel was fixed, distilled water was removed from its top and stacking gel solution poured on it. Combs were fixed into the stacking gel. Combs were put with special care and it was confirmed that there was no any air bubble at the bottom of the combs. The set up was left for 15 minutes so that the stacking solution became gel. Combs, clips and gaskets were removed from glass plates carefully and confirmed there was no any air bubble at this stage. Gel plates were freshly used for electrophoresis but is was also possible that these would be wrapped in aluminum foil and could be used even for one week.


Electrophoresis procedure was carried out using slab type SDS-PAGE model: AE-6530M, ATTA Japan, with 15% polyacrylamide gel. The molecular weight of dissociated proteins was estimated by using molecular weight standard proteins "MW-SDS-70 Kit".

Electrode buffer solution was put into the bottom pool of the apparatus. Gel plates were placed in the apparatus, here again air bubble formation was avoided. Electrode buffer solution was also put into the top pool of the apparatus; wells formed by combs were washed by syringe. Seed samples were centrifuged at 15,000 rpm for 10 minutes, 15 l of supernatant was put into wells with the help of micropipette. Protein molecular weight marker was put in first well of each glass plate. The numbering of seed samples and wells were noted to avoid repetition. The apparatus was connected with + (red) and - (black) electrodes of power supply. The voltage of apparatus was kept constant at 100V and apparatus was left until a blue line of BPB came at the bottom of the gel plates.

Detection of Proteins

(Staining and De-staining of Separation gel)

When blue line reached at the bottom of the gel plates, electric supply was disconnected. Gel plates were taken out from the apparatus and separated by spatula. Stacking gel was removed with the help of same spatula. Separation gel was put in the box which contained staining solution. Box was put on the shaker for two hours. Staining solution was exchanged by destaining solution and the box was shaked gently almost overnight until the background of the gel disappeared to absorb excess CBB, a piece of Kim wipe was put in the destaining solution to check absorbance.

Drying of separation Gel

Wet filter paper was placed on the plate of gel dryer. Separation gel was carefully placed on the paper and covered with a wrap. It was dried in a drier for about 1.5 hours at 60C. When gel sheet was completely dried it was taken out while the pump was still running. All gels were dried with the same manner.

Data Analysis

Depending upon the presence or absence of polypeptide bands, similarity index was calculated for all possible pairs of protein types. To avoid taxonomic weighing, the intensity of bands was not taken into consideration rather only the presence of bands was taken as indicative. The score was '1' for presence and '0' for the absence of bands. Presence and absence of bands were entered in a binary data matrix. Based on result of electrophoresis band spectra, Nei & Li's similarity matrix was calculated for all possible pairs of protein type's electrophoregrams by the following formula (Sneath and Sokal, 1973).

S=W/ (A+B-W)

Where 'W' is the number of bands of common mobility, 'A' the number of bands in protein type A and B is the number of bands in protein type B. The similarity matrix thus generated was converted to a dissimilarity matrix (Dissimilarity = 1- Similarity) and used to construct dendrogram by the un-weighed pair group method with arithmetic means (Sneath and Sokal, 1973). All computations were carried out using the NTSYS-pc, Version 2.1 package (Rohlf 2000, Applied Biostatistics Inc., Exeter Software, NY, and USA).

Molecular basis of genetic diversity

Plant Materials

Initially around 75 selected accessions (Appendix 2) and 35 commercial varieties of rice were used as starting material for molecular characterization.

DNA Extraction from Dry Seed Samples

Total genomic DNA was also extracted from dried seeds of each cultivar according to the method described by Kang et al. (1998) with minor modifications which appears to be more useful in saving cost of extraction, labor and time being used while extracting DNA from seedling samples:

  • Remove seed coat and place 3-5 seeds containing the storage tissue in a micro centrifuge tube (1.5ml).
  • Add 400l of extraction buffer (200mM Tris-HCl (pH 8.0), 25mM EDTA, 200mM NaCl, 0.5% SDS) containing Proteinase K (50g).
  • Incubate at 37oC for 1 hour. Grind seeds in the buffer with a glass rod.
  • Add 400l of 2%CTAB solution (100mM Tris-HCl (pH 8.0), 20mM EDTA (pH 8.0), 1.4M NaCl, 2% CTAB (w/v), 1% PVP "polyvinylpyrrolidone 40,000).
  • Gently extract using chloroform: isoamyl alcohol (24:1) with 5% phenol.
  • Centrifuge at 12,000rpm for 10 min at 4oC and transfer supernatant into new tubes.
  • Add ? volume of Isopropanol and incubate tubes at room temperature for 10 minutes to precipitate DNA.
  • Centrifuge tubes at 12,000 rpm for 5 minutes and remove supernatant.
  • Wash DNA pellet with 70% Ethanol (500l). Centrifuge at 12,000 rpm for 5 minutes at room temperature and pour off 70% Ethanol.
  • Air dry DNA pellet for 5-10 minutes and re-suspend in 100l of TE buffer.
  • Remove RNA by adding 1l of RNase (10mg/ml).
  • After isolation of DNA from dried seed samples, DNA concentration and purity of each variety and primitive cultivar genotype was determined spectrophotometrically at a wavelength of 260 and 280nm using NanoDrop
    ND-1000 Spectrophotometer. The ratio between absorbance at 260 and 280nm (260/280) was used to estimate DNA purity. DNA of each cultivar was diluted to a working concentration of 20ng/l for PCR analysis.


RAPD PCR Analysis

A modified RAPD method based on Williams et al (1990) was used with a model 9700 thermal cycler (Applied Biosystems, USA). To establish RAPD protocols for rice, PCR analysis was performed by changing and checking the concentrations of total genomic DNA from 5~50ng/20l reaction volume, MgCl2 from 1.5~3.0mM, dNTPs mixture from 100~400M each, random primer from 0.1~1.0M and Taq DNA polymerase from 0.2~1.25 units. After standardization of PCR, 20l reaction mixture containing 1x PCR buffer [10mM Tris HCl (pH 8.3), 50mM KCl], 1.5mM MgCl2, 200M each deoxynucleotide triphosphate (dNTP), 0.4M of 10-mer primer (Operon Technologies Inc., Alameda, CA), 1 unit AmpliTaq Gold DNA polymerase and approximately 20ng of template DNA was found optimum for the amplification of rice genomic DNA (Table 2.2). Taq DNA polymerase and reaction buffer were purchased from Applied Biosystems, Japan. DNA amplification was performed in a DNA thermal cycler (Perkin Elmer Cetus, Norwalk, USA). The thermal cycler was programmed to 1 cycle of 5 minutes at 94oC for initial strand separation. This was followed by 45 cycles of 1 minute at 94oC for denaturation, 1 minute at 36oC for annealing and 2 minutes at 72oC for primer extension. Finally, 1 cycle of 7 minutes at 72oC was used for final extension, followed by soaking at 40oC (Table 2.3). The reproducibility of the amplification products was checked twice for each experiment.

Primer Selection

Initially, three cultivars one each from aromatic, non-aromatic and japonica type was used to optimize the RAPD protocols and select the suitable primers which exhibit polymorphisms between the three cultivars. Altogether, forty arbitrary decamer oligonucleotides, belonging to kit OPA and OPB from Operon Technologies Inc. (Alameda, California, USA), were tested as single primers to identify the most promising ones for detecting polymorphism. After an initial screen, thirty-two primers were ultimately chosen for further use on the basis of their ability to detect the polymorphism and produce the reliable and easily scorable banding patterns in rice cultivars. Among them, 7 primers could not amplify the DNA from some of the cultivars used. Therefore, finally the data of twenty-five primers were used and compiled to examine the genetic diversity and relationship among 40 commercial varieties and primitive cultivars of Pakistani rice.

Electrophoresis of Amplified Products

After amplification, 3l of gel loading dye buffer (0.02% Bromophenol blue, 0.02% xylene cyanol FF, 50% glycerol and 1% SDS) were added directly to the reaction tubes and spun for few seconds in a micro centrifuge after mixing with the entire reaction mixtures. Aliquots of 15l of amplification products plus loading dye were then loaded in 1.5% agarose gels for electrophoresis in 1 x TBE (10mM Tris-Borate, 1mM EDTA) buffer and run at 100V for 40 minutes to separate the amplified products. 1kb plus was used as a molecular size weight marker. After electrophoresis, the gels were photographed under UV light using black and white film # 667 (Polaroid, Cambridge, Mass., USA).

Data Analysis

Photographs from ethidium bromide stained agarose gels were used to score the data for RAPD analysis. Each DNA fragment amplified by a given primer was treated as a unit character and the RAPD fragments were scored as present (1) or absent (0) for each of the primer-cultivar combinations. Bands were scored from the top of the gel (band number 1) to the bottom. The left lane of the gel was considered as lane-1. Since DNA samples consisted of a bulk sample of DNA extracted from 5~10 seeds, a low intensity for any particular fragment may be explained by the lesser representation of that specific sequence in the bulk sample of DNA. Therefore, the intensity of the bands was not taken into account and the fragments with the identical mobility were considered to be the identical fragments. Only major bands were scored and faint bands were not considered. The molecular size of the amplification products was calculated from a standard curve based on the known size of DNA fragments of a 1kb plus molecular size weight marker. The presence and absence of the bands was scored in a binary data matrix. Pair-wise comparisons of the cultivars based on the presence or absence of unique and shared amplification products were used to generate similarity coefficients. Estimates of genetic similarity (F) were calculated between all pairs of the cultivars by the Dice algorithm. The Dice algorithm is identical to that of Nei and Li (1979) as follows:

Similarity (F) = 2Nab/ (Na + Nb)

Where Na = the number of scored fragments of individual 'a',

Nb = the number of scored fragments detected in individual 'b' and

Nab = the number of shared fragments between individuals 'a' and 'b'.

The resulting similarity coefficients were used to evaluate the relationships among commercial varieties and primitive cultivars with a cluster analysis using an un-weighted pair-group method with arithmetic averages (UPGMA) and then plotted in the form of a dendrogram. All computations were carried out using the computer program NTSYS, version 2.1 (Applied Biostatistics Inc., USA).


Microsatellite or Simple Sequence Repeat (SSR) Analysis

Thirty five primer pairs covering all twelve chromosomes were selected for the genetic diversity analysis on the basis of published rice microsatellite framework map. Three primers (RM5, RM210 and RM229) exhibited monomorphic fragments and were therefore excluded from further analysis. The original source, repeat motifs, primer sequences and chromosomal positions for these markers can be found in the rice genome database (http://www.gramene.org). Microsatellite primer pairs were obtained from Hokkaido Science System (Sapporo, Hokkaido, Japan).

SSR analysis was performed following the protocol of Ravi et al. (2003) with minor modifications. PCR amplification reactions were carried out in a total volume of 20l containing; 10mM Tris HCl (pH 8.3); 50mM KCl; 1.5mM MgCl2; 200M each of deoxynucleotide triphosphate (dNTP); 0.2M of each forward and reverse primer; 1 unit Taq DNA polymerase (Fermentas Life Sciences); and 20ng of template DNA. The PCR amplifications were carried out using a MyGene Series Peltier Thermal Cycler (UniEquip GmbH, Munich, Germany). Thermal cycler was programmed to 1 cycle of 5 min at 94oC as an initial hot start and strand separation step. This was followed by 35 cycles of 1 min at 94oC for denaturation, 1 min for annealing temperature depending on the marker used (55oC - 65oC) and 2 min at 72oC for primer elongation. Finally, 1 cycle of 7 min at 72oC was used for final extension. Amplified products were stored at -20oC until further use. The reproducibility of the amplification products was checked twice for each primer.

Electrophoresis of amplified products: After amplification, a 15l aliquot of the amplified SSR samples was combined with 3l of a loading buffer (0.4% (w/v) bromo-phenol blue, 0.4% (w/v) xylene cyanole and 5 ml of glycerol) and was analyzed directly on 3% (w/v) Gene Choice High Resolution agarose (CLP, USA) gels in 1xTBE buffer (10mM Tris-Borate, 1mM EDTA) containing 0.5g per ml of ethidium bromide. A 25bp DNA ladder (Biolabs, New England, UK) was used as a size marker to compare the molecular weights of amplified products. After electrophoresis, the gels were documented using an UVI Doc Gel Documentation System (UVITEC, Cambridge, UK).

Allele scoring and data analysis: Ethidium bromide staining of agarose gels generally showed several bands. The size of the most intensively amplified band for each microsatellite marker was determined based on its electrophoretic mobility relative to molecular weight markers (increments of 25bp). Amplified products from SSR analysis were scored qualitatively for presence and absence of each marker allele-genotype combination. Each SSR band amplified by a given primer was treated as a unit character. Data was entered into a binary matrix as discrete variables, 1 for presence and 0 for absence of the character. The most informative primers were selected based on the extent of polymorphism. The polymorphic information content (PIC) value of a marker was calculated according to Anderson et al. (1993). Mean allele numbers, PIC values, and genetic similarities were calculated on the basis of different rice landraces, chromosomes and microsatellite classes. Pair-wise comparisons of the genotypes based on the proportion of unique and shared amplification products (alleles) were used to measure the genetic similarity by Dice coefficients using PAST(Paleontological Statistical Software Package for Education and Data Analysis) program (Hammer et al., 2001). Genetic similarities (F) between all pair of the landraces were calculated according to Nei and Li (1979). A dendrogram was constructed using pair-group method to get genetic relationships among landraces. The reliability of the dendogram was tested by bootstrap analyses with 10,000 replications to assess branch support. Some workers consider that the confidence limits obtained in bootstrap must be over 95% in order to consider the grouping of taxa (a group of genetically similar organisms that are classified together as e.g. species, genus, or family) at a branch to be statistically significant (Felsenstein, 1985). Others use a lower limit (above 50% or at least 50%) as indicating statistical support for the topology at a node (Highton, 1993). In our study we used the lower limits to assess grouping of taxa to be statistically significant because we observed that as the number of test sample increases the confidence interval decreases.

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