Estimation of Genetic Diversity in Rice (Oryza sativa L.) Genotypes using Simple Sequence Repeats  

Muhammad Jamil1 , Iqrar Ahmad Rana2 , Zulfiqar Ali1 , Faisal Saeed Awan2 , Zaigham Shahzad1 , Abdus Salam Khan1
1. Department of Plant Breeding and Genetics, Faculty of Agriculture, University of Agriculture Faisalabad, Pakistan
2. Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
Author    Correspondence author
Molecular Plant Breeding, 2013, Vol. 4, No. 36   doi: 10.5376/mpb.2013.04.0036
Received: 04 Nov., 2013    Accepted: 20 Nov., 2013    Published: 22 Nov., 2013
© 2013 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Jamil et al., Estimation of Genetic diversity in Rice (Oryza sativa L.) Genotypes using Simple Sequence Repeats, Molecular Plant Breeding, Vol.4, No. 36 285-291 (doi: 10.5376/mpb.2013.04.0036)

Abstract

Simple Sequence Repeats (SSRs) are co-dominant DNA markers and have a wide range of applications in the field of genetics. In the present investigation, 24 rice genotypes, belonging to Pakistan and imported from IRRI, Taiwan were examined for the determination of genetic diversity using twenty four SSRs.  Our results showed that the primers used had polymorphism when applied to the tested genotypes. The distinguishing factor with respect to both locations were primers designed from chromosome 2,3,4,5,7 and 8.  A total of 76 loci were produced by twenty four SSRs among 24 rice genotypes with an average of 3.17 loci per genotype. All markers produced polymorphic fragments among most of the genotypes. A total of 995 alleles were detected with an average of 13.87 alleles per locus. Among all the primers, RM-1 produced highest number of bands, “120” followed by RM-154 who developed 108 bands, RM-124 produced 65 bands shown and lowest number of bands (8) produced by primer RM-489. The similarity matrix clearly revealed that most closely genotypes were SRS-62 and SRS-505, securing the similarity value of 0.9234. It mean that these genotypes are 92.34 % similar, likewise genotypes Bas-370 and Bas-Pak, Bas-370 and S.S.Shaheen had the score of 0.8966 and 0.8840 respectively. The most dissimilar genotypes are IRRI-12 and EF-1-2-51-2002, scoring the value of 0.5601; it means they are 56.15% dissimilar. Similarity coefficient ranged from 0.54 to 0.9234. The average genetic similarity index was calculated as 0.73. Dendrogram divided twenty four rice genotypes into four main clusters and each cluster was subdivided into two furthers groups. Among all the genotypes, IRRI-4 showed distinct behavior in dendrogram, proving it to be useful for breeding program.

Keywords
Genetics diversity; Rice; SSRs; Polymorphism

Rice is one of the most ancient food crops and has been feeding large population of the world, probably more than any other cereal crop. Rice belong to the diploid species having 2n=24 chromosome and classified as monocotyledon of the grass family poaceae (Gramineae) under the tribe known as Oryza that fall in order cyperales. Oryza sativa is the dominantly grown rice species divided into sub species/strains indica and japonica. Rice is grown under diverse condition and over wide geographical range. It is grown at the latitude ranging from 50o North to 36o South to 25000-meter high altitude in northern upland valleys and terraces in the arid hot zones to the tropical humid (Rangare et al., 2012). It is categorized as the largest staple food of the world. Only in Asia, rice ensures the supply of 35%~75% calories to more than 1/3rd population and due to over population, there is needs to produce 45% more rice by 2030 (Khush, 2005). One cup of rice provides 216 calories, coming from proteins (17.2), carbohydrates (185) and fat (14.6). Apart from that, it is rich source of minerals and vitamins (Anonymous, 2013). In order to keep the world food security intact, continuous increase in per acre yield is required. An annual yield increase of 2.4% is required from maize, rice, wheat and soybean, worldwide, while only 1.6%, 1.0%, 0.9%, and 1.3% is achieved annually. With this rate only 42% of needed rice and 67% of needed maize will be produced in 2050 (Ray et al., 2013). There are many reasons to this slow increase in yield including climate change and the development of genotypes lacking the ability to cope with changing environmental conditions.

Pakistan, for example, is the major producer of rice including aromatic basmati and non aromatic rice. Pakistani is famous in world for Basmati rice, because it is predominantly cultivated in “Kallar tract” which is well known for producing of good quality aroma rice (Zahid et al., 2006, Rasheed et al., 2002). Though Pakistan is among the top ten producers of rice but per hectare yield is the lowest in the area. China has been producing almost double and neighboring, India and Bangladesh have been slightly better (Ahmad, 2012). The major reason of this lower yield in Pakistan seems to be the uneven climatic changes which resulted in low and heavy rainfalls, melting of glaciers and ultimately floods.
The varietal development in rice is a continuous process and 10~12 years are required to develop a genotype with some distinct character. In order to shorten the breeding cycle molecular approaches have been developed for rice during the last two decade. It is possible now to generate transgenic rice by introducing desired gene. Molecular markers have facilitated to establish genetic diversity at first and then select and purify desired combinations at the earliest. Genome of rice has vast variation so more than thousand varieties have been produced around the world (Ashfaq et al., 2012). Characterization and quantification of genetic diversity has been a major goal in evolutionary biology. The information on genetic diversity within and among closely related varieties is essential for rational use of genetic resources. The analysis of genetic variability within and among breeding materials is of fundamental and very important to plant breeders (Chakravarthi and Naravaneni, 2006). Now the developments of biotech- nology allow easy analysis of large number of loci distributed throughout the genome of plants. Molecular markers have proven to be powerful in the estimation of genetic variability and in the elucidation of genetic relationships within and among species. Among PCR based markers, microsatellites are highly polymorphic, more reproducible, co-dominant and distributed throughout the rice genome. These markers have been used for many purposes such as genome mapping, gene tagging, estimation of genetic diversity, differences among the varieties and purity testing (Nagaraju et al., 2002). The objective of this research is to study the genetic diversity between the germplasm developed locally and imported from IRRI. The study helped us in knowing whether genetic diversity is present between different genotypes of this germplasm and whether this diversity is sufficient for planning breeding program for the evolution of high yielding and better quality rice varieties in Pakistan.
Materials and Methods
Twenty four genotypes (ten local and 14 imported from IRRI- Philippines) were sown into plastic cups in growth room of the Center of Agriculture Biochemistry and Biotechnology, University of Agriculture, Faisalabad in controled conditions. Healthy and fresh fifteen day old leaves were cut and stored at -40 ºCfor good quality DNA extraction.
DNA Extraction
The total genomic DNA was isolated from fifteen days old fresh leaves following C-TAB procedure (Clarke, 2009) with minor modifications. 3g fresh leaves were taken and ground in pre-heat 2× CTAB (cetyl tri-methyl ammonium bromide) buffer in already autoclaved mortar pestle. Water bath was switched on to reach a temperature of 65 for incubation. Homogeneously ground material was transferred to 1.5 mLmicro-centrifuge tube. Gently mixed ground material was incubated at 65 for 30 minute in pre heated water bath. An equal volume of chloroform: isoamyl alchohal (24:1) was added and mixed thoroughly to make emulsion. Emulsion was spun at 13500 rpm for 10 minutes and the supernatant (upper phase) was added to new microfuge. An equal volume of ice-chilled isopropnol, mix slowly and centrifuged at 13000 rpm for 10 minutes at 4℃ temperature. The supernatant was discarded and DNA pellet settled in the bottom of the tube. To wash DNA pellet, an equal volume of 70% ethanol was added and air dried. 200 µL of pure ethanol (100%) was added in micro-centrifuge tube for further washing of pellet and spun at 13000 rpm for 5 minutes at 4. DNA pellet was air dried at room temperature. Finally 100 µL R-40 (TE buffer, 8.0 pH pluss 40 µg/mL RNAse A) was in micro-centrifuge tube to dissolve DNA pellet.
PCR
Each PCR reaction contained 2.5 μL of 10× buffer (200 mM Tris-HCl pH 8.3, 500 mM KCl, 15 mM MgCl2), 45ng of DNA template, 2.5μL MgCl2, 1 mM dNTPs, 1 μL of each forward and reverse primer, and 1 U/μL of Taq DNA polymerase (Yepuri et al., 2013). 24 primer pairs were chosen on the basis of the published rice microsatellite framework map (McCouch et al., 2002; Temnykh et al., 2000). The motifs for these markers can be found in a public domain (http://www.gramene.org/markers.microsat/). The PCR cycles consisted of an initial denaturation at 94 for 10 min, followed by 35 cycles of 94 for 45s, 55 for 45 s, and 72 for 45 s, and a final extension step at 72 for 10 min.
DNA Quantification
DNA concentration was determined by using Nano-drop (ND-1000) spectrophotometers. The desired concentration (15 ng/μL) of the DNA diluted samples were prepared.
Agarose Gel Electrophoresis
PCR products were resolved on 2% (w/v) Agarose gel electrophoresis in 0.5% TAE buffer. 160 mL TAE buffer of 0.5× concentration was taken in flask with the help of cylinder. 3.2 g of Agarose was weighed with the help of electric balance and discharged into the flask. The mixture was heated in microwave oven until a transparent solution appeared. Transparent solution was cooled down slowly, adding 6ul of ethidium bromide in flask, solution was shaken in the flask and poured into the gel casting tray. Gel was allowed to solidify at room temperature under fume hood. 10 uL was loaded from each sample (7 uL sample and 3 uL loading dye) in each well of the gel and 2 uL lambda DNA (1 kb) on both sides to measure the amplificate sizes.
Data Analysis
The fingerprints were examined under ultra violet translluminator and photographed using gel doc Bio Rad (Universal II). The SSR band were counted and designated as present 1 and absent 0. The data was collected and aligned for the construction of cluster analysis and similarity matrix. The cluster analysis of 24 genotypes was constructed with help of Popgene32 software version 1.44 (Yeh et al., 2000) based on Nei,s Unweighted paired group of arithmetic mean average (UPGMA).
Results and Discussion
Microsatellites or Simple Sequence Repeats (SSRs) are co-dominant DNA markers and have a wide range of applications in the field of genetics. Marker based DNA fingerprinting is very powerful, effective and reliable technique to assess crop germplasm at genetic level and widely used to characterize and identify diverse genotypes among diverse gene pool (O’ Neill et al., 2003). Microsatellite loci have many alleles, genotypes within pedigrees are fully saturated, and in that the progenitor of particular allele can be identified. Microsatellite or SSRs are preferred for the estimation of paternity, recombination mapping and studies related to population genetics. In the present investigation, 24 genotypes were examined for the determination of genetic diversity using 24 SSR primers. The selected markers were reported to be highly polymorphic in nature. PCR conditions were optimized for all the 24 primers under our lab conditions at CABB, UAF. After PCR and gel electrophoresis, gels were examined under UV-light using gel doc Bio Rad (Universal II). The SSR bands were counted and aligned for the construction of similarity index and cluster analysis. List of selected markers and their sequences are described in the Table 1. Total of 76 loci produced by twenty four SSR markers among 24 rice cultivars/lines. All markers produced polymorphic fragments among all the genotypes. A total of 995 alleles were detected with an average of 13.87 alleles per locus. The relative results found by Pervaiz et al (2010) during the estimation of diversity at gene level using 35 microsatellite markers among 75 rice genotypes. Total of 142 alleles were developed by 32 polymorphic SSR while other three were monomorphic in nature. Effective allele ranged from 2 to 13 with size varied from 11 bp to 71 bp. Polymorphism information content ranged from 0.124 to 0.836. Average number of alleles were also similar to the finding of 14.6 and 13 average alleles per locus in rice crop using Simple Sequence Repeats as per Brondani et al (2006) and Thomson et al (2007) respectively.

 
Table 1 SSR primers used with related information

All the SSR primers produced polymorphic bands but RM-1 produced highest number of bands 120, followed by RM-154 developed 108 bands, RM-124 produced 65 bands and lowest number of bands (8) produced by primer RM-489 (Figure 1). The amplified bands range from 120 to 8. These results are comparable to that reported by Kanawapee et al (2011) whoevaluated the rice cultivars for polymorphisms after amplification with 20 SSR primer pairs. A total of 190 SSR alleles were produced which revealed 89.47% polymorphism. Mean genetic similarity coefficient was 0.70.

 
Figure 1 Amplification product of RM-124 showing different for genotypes bred in Pakistan and IRRI

Similarity Matrix of 24 Genotypes
To estimate genetic similarity and relatedness among the twenty four rice genotypes similarity matrix (genetic distance) analysis was conducted using popgene32 software (Yeh et al., 2000) based on Nei’ Unweighted Paired Group of Arithmetic Mean Average (UPGMA). The values from the Nei’s original measure of genetic distance and relatedness revealed that all genotypes are closely related to each other and relatively less dissimilar (Figure 2). Similarity matrix clearly revealed that most closely related genotypes were SRS-62 and SRS-505, securing the value of 0.9234. It means that these varieties are 92.34% similar, likewise genotypes Bas-370 and Bas-Pak, Bas-370 and S.S.Shaheen having the score 0.8966 and 0.8840 respectively. The most dissimilar genotypes are IRRI-12 and EF-1-2-51-2002, scoring the value of 0.5601; it means they are 43.85% dissimilar which is a huge difference considering that both the genotypes belong to the same species. Similarity coefficient had ranged from 0.54 to 0.9234. The average genetic similarity was calculated 0.73. Pervaiz et al (2010) measured the diversity at gene level using 35 microsatellite markers among 75 genotypes. Total of 142 alleles were develop by 32 polymorphic SSR while other three were monomorphic in nature with the ranged from 2~13 by each primers and average number of allele per locus was 4.4. Onaga et al (2013) evaluated genetic diversity on Ugandan accessions and results showed average gene diversity (H) value for all SSR loci for the 30 genotypes evaluated as 0.69.

 
Figure 2 Amplification product of RM-171, shows the same trend both in Pakistani and IRRI bred material
 
Cluster Analysis of 24 genotypes
To estimates the genetic distance between the twenty four rice genotypes, dendrogram was constructed using pop gene 32 software based on Nei’ UPGMA from collected SSRs data. Cluster analysis has the singular ability and efficacy to estimate crop genotypes with highest level of similarity using the dendrogram (Aliyu et al., 2000). Dendrogram divided twenty four rice genotypes into four main clusters as shown in the Figure 3. Cluster I and cluster II consists of two sub clusters. From the dendrogram it is clearly observed that all accessions are related but IRRI-12 distinct from all the accessions. Cluster I consists of 9 genotypes which include all basmati varieties like’ Bas2000, Bas-385, Bas198, Ba370, Super-Bas, Shaheen, S.S.Shaheen, Bas-Pak and EF-1-2-51-2002. In cluster I, out of nine accession Bas-370 and Bas-Pak, Bas-2000 and Shaheen are closely related. Genotypes Super-Bas and EF-1-2-51-2002 were distinct and may be used in breeding programs defining one’s own breeding objectives. It is clear from the dendrogram that aromatic (Basmati) and non-aromatic (coarse) distinguish from each other and may be used as distinct parents to develop hybrid rice. This observed finding was consistent with the results of Wang et al (2009) and Shah et al (2013).

Figure 3 Dendrogram of twenty four rice varieties obtained from similarity Matrix based on Nie’s UPGMA SSR

Second cluster further divided into 2 groups, 1st subgroup comprising of 4 genotypes, PB-95, IRRI
-I, IRRI-2, IRRI-3 and 2nd subgroup consist of IRRI-8, KSK-133, and DM-1-25-4-2002. Genotypes IRRI-2 and IRRI-3 and KSK-133 and IRRI-8 are closely related as shown in the Figure 3. In this cluster DM-1-25-4-2002 shown the distinct behavior from the other genotypes. Cluster III and cluster IV have five and three accessions respectively. Three accession of cluster III are distinct from each other, but SRS-62 and SRS are related. In cluster IV, IRRI-4 and IRRI-6 made a sub group which is closely related. IRRI-12 genotype is the most diversified among all the genotypes.
References
Anonymous, 2013, USDA National Nutrient Database for Standard Reference
Ahmad M., 2003, Pakistan lags behind in per hectare crop yield. An article published in Pakistan based daily newspaper, The News International on, March 27, 2012
Aliyu B., NQ N.G., and Fawole I., 2000, Inheritance of Pubescence in crosses between Vigna unguiculata and V. rhomboidea. Nig. J. Genet., 15: 9-14
Ashfaq M., Khan A.S., and Ahmad R., 2012, Association of various morphological traits with yield and genetic divergence in rice (Oryza sativa L.). Int. J. Agri. Biol. 14(1): 55-62
Brondani C., Borba T.C.O., Rangel P.H.N., and Brodani R.P.V., 2006, Determination of genetic variability of traditional varieties of Brazilian rice using microsatellite marker. Genet. Mol. Biol. 29: 676-684
http://dx.doi.org/10.1590/S1415-47572006000400017
Chakravarthi B.K., and Naravaneni R., 2006, SSR marker based DNA fingerprinting and diversity study in rice (Oryza sativa L.) Afric. J. Biotech., 5 (9): 684-688
Clarke J., 2009, Cetyltrimethyl Ammonium Bromide (CTAB) DNA miniprep for plant DNA isolation. Cold Spring Harb Protoc. doi:10.1101/pdb.prot5177
http://dx.doi.org/10.1101/pdb.prot5177
Kanawapee, N., Sanitchon J., Srihaban P., and Theerakulpisut P., 2011, Genetic diversity analysis of rice cultivars (Oryza sativa L.) differing in salinity tolerance based on RAPD and SSR markers. Elec J. Biotech., 14(6): 1-17
Khush G.S., 2005, What it will take to Feed 5.0 Billion Rice consumers in 2030. Plant Molecular Biology., 59 (1):1-6
http://dx.doi.org/10.1007/s11103-005-2159-5
McCouch S.R., Teytelman L., Xu Y., Lobos K.B., Clare K., Walton M., Fu B,, Maghirang R., Li Z., Xing Y., Zhang Q., Kono I., Yano M., Fjellstrom R., Declerck G., Schneider D., Cartinhour S., Ware D., and Stein L., 2002, Development of 2240 new SSR markers for rice (Oryza sativa L.), DNA Res., 9: 199-207
http://dx.doi.org/10.1093/dnares/9.6.199
http://dx.doi.org/10.1093/dnares/9.6.257
Nagaraju J., Kathirvel M., Ramesh K.R., Siddiq E.A., and Hasnain S.E., 2002, Genetic analysis of traditional and evolvedBasmati and non-Basmati rice varieties by using fluorescence-based ISSR-PCR and SSR markers. Proceedings of the National Academy of Sciences, 99: 5836-5841
http://dx.doi.org/10.1073/pnas.042099099
Onaga G., Egdane J., Edema R., and Abdelbagi I., 2013, Morphological and genetic diversity analysis of rice accessions (Oryza sativa L.) differing in iron toxicity tolerance, J. Crop Sci. Biotech., 16 (1): 53-62
http://dx.doi.org/10.1007/s12892-012-0104-0
O’Neill R., Snowdon R.J., and Kohler W., 2003, Population genetics aspects of biodiversity. Progress in Botany, 64(0): 115-137
http://dx.doi.org/10.1007/978-3-642-55819-1_7
Pervaiz Z.H., Rabbani M.A., Khaliq I., Pearce S.R., and Malik S.A., 2010, Genetic diversity associated with agronomic traits using microsatellite markers in Pakistani rice landraces. Electronic Journal of Biotechnology, 13: 4-5
Rangare N.R., Krupakar A., Ravichandra K., Shukla A.K., and Mishra A.K., 2012, Estimation of characters association and direct and indirect effet of yield contributing traits on grain yield in exotic and Indian rice (Oryza sativa L.) germplasm, Int. J. Agri. Sci., 2(1): 54-61
Rasheed M.S., Sadaqat H.A., and Babar M., 2002, Cause and effect relation of panicle traits in rice (Oryza sativa L.), Asian J. Plant Sci., 1(2): 123-125
http://dx.doi.org/10.3923/ajps.2002.123.125
Ray D.K., Mueller N.D., West P.C., and Foley J.A., 2013, Yield Trends Are Insufficient to Double Global Crop Production by 2050, PLoS ONE 8(6): e66428. doi:10.1371/journal.pone.0066428
http://dx.doi.org/10.1371/journal.pone.0066428
Shah S.M., Naveed S.A., and Arif M., 2013, Genetic diversity in basmati and non-basmati rice varieties based on microsatellite markers, Pak. J. Bot., 45(S1): 423-431
Thomson M.J., Septiningsih E.M., Suwardjo F., Santoso T.J., Silitonga T.S., and McCOUCH S.R., 2007, Genetic diversity analysis of traditional and improved Indonesian rice (Oryza sativa L.) germplasm using microsatellite markers, Theo. App. Gen.,114: 559-568
http://dx.doi.org/10.1007/s00122-006-0457-1
Wang Q., Sang X., Ling Y., Zhao F., Yang Z., Li Y., and He G., 2009, Genetic analysis and molecular mapping of a novel gene for zebra mutation in rice (Oryza sativa L.), J. Gen Geno., 36: 679-684
http://dx.doi.org/10.1016/S1673-8527(08)60160-5
Yeh F.C., Yang R., Boyle T.J., Ye Z., and Xiyan J.M., 2000, Popgene 32 microsoft windows based freeware for population genetic analysis, Molecular Biology and Biotechnology Center, University of Alberta, Edmonton.
Yepuri V., Surapaneni M, Kola V.S.R., Vemireddy L.R., Jyothi B, Deneshkumar V., Anuradha G., and Sidiq E.A., 2013, Assessment of genetic diversity in sesame (Sesamum indicum L.) Genotypes, using EST-derived SSR markers. J. Crop Sci. Biotech. 16 (2): 92-103
http://dx.doi.org/10.1007/s12892-012-0116-9


Zahid M.A., Akhter M., Sabar M., Manzoor M., and Awan T., 2006, Correlation and path analysis studies of yield and economic traits in Basmati rice (Oryza sativa L.), Asian J. Plant Sci. 5(4): 643-645
http://dx.doi.org/10.3923/ajps.2006.643.645

Molecular Plant Breeding
• Volume 4
View Options
. PDF(213KB)
. FPDF
. HTML
. Online fPDF
Associated material
. Readers' comments
Other articles by authors
pornliz suckporn porndick pornstereo . Muhammad Jamil
. Iqrar Ahmad Rana
. Zulfiqar Ali
. Faisal Saeed Awan
. Zaigham Shahzad
. Abdus Salam Khan
Related articles
. Genetics diversity
. Rice
. SSRs
. Polymorphism
Tools
. Email to a friend
. Post a comment