Research Article Identification and DUS Testing of Rice...

Research ArticleIdentification and DUS Testing of Rice Varieties throughMicrosatellite Markers

Ehsan Pourabed1 Mohammad Reza Jazayeri Noushabadi2 Seyed Hossein Jamali2

Naser Moheb Alipour1 Abbas Zareyan2 and Leila Sadeghi2

1Department of Agronomy and Plant Breeding College of Agriculture Miyaneh Branch Islamic Azad UniversityMiyaneh 53158-36511 Iran2Seed amp Plant Certification and Registration Institute (SPCRI) Collection Avenue Nabovvat Boulevard Karaj 31535-1516 Iran

Correspondence should be addressed to Ehsan Pourabed epourabedlivecom

Received 16 September 2014 Accepted 19 January 2015

Academic Editor Pierre Sourdille

Copyright copy 2015 Ehsan Pourabed et alThis is an open access article distributed under theCreativeCommonsAttribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Identification and registration of new rice varieties are very important to be free from environmental effects and using molecularmarkers that aremore reliableThe objectives of this studywere first the identification and distinction of 40 rice varieties consistingof local varieties of Iran improved varieties and IRRI varieties using PIC and discriminating power second cluster analysis basedon Dice similarity coefficient and UPGMA algorithm and third determining the ability of microsatellite markers to separatevarieties utilizing the best combination of markers For this research 12 microsatellite markers were used In total 83 polymorphicalleles (691 alleles per locus) were found In addition the variation of PIC was calculated from 052 to 09 The results of clusteranalysis showed the complete discrimination of varieties from each other except for IR58025A and IR58025B Moreover clusteranalysis could detect the most of the improved varieties from local varieties Based on the best combination of markers analysisfive pair primers together have shown the same results of all markers for detection among all varieties Considering the results ofthis research we can propose that microsatellite markers can be used as a complementary tool for morphological characteristics inDUS tests

1 Introduction

In order to introduce a new plant variety to the marketscommercially it is necessary to register a newly bred varietywhich relies upon the results of DUS (distinctness unifor-mity and stability) tests that is for a new genotype to beregistered as a commercial variety it needs to be distinct (D)from all other released varieties uniform (U) and stable (S)for morphological and other evaluated traits [1 2]ThereforeDUS test has been established to be the foundation of plantvariety protection and also to identify a new variety fromreference collection [3]

The new variety should pass legal examinations to becommercialized and receive the certificate for the plantbreederrsquos rights a part of which consists of DUS testsaccording to morphological characteristics

The current system of DUS testing has come acrossseveral significant shortcomings The varieties to be assessed

are increasing in number where their variability reduces andthe reference collections are expanding because of their inter-nationalization both of which result in the dramatic increasein expenses associated with these methods Moreover theexisting methods are time consuming which have altogetherled to more necessity for developing a substitutionary lesscostly system Thus the studies on the use of molecularmarkers in DUS testing proving the expected capability ofmolecular markers have encouraged International Unionfor the Protection of New Varieties of Plants (UPOV) tocontemplate the introduction of molecular markers to theDUS testing system Nevertheless before this decision couldbe made there are several issues to be resolved

DUS testing would benefit from the use as molecularmarkers that have been shown to be more rapid and costeffective in comparison with morphological traits In severalregistration processes such as cultivar identification molecu-lar markers have been utilized successfully [4]

Hindawi Publishing CorporationInternational Journal of Plant GenomicsVolume 2015 Article ID 965073 7 pageshttpdxdoiorg1011552015965073

2 International Journal of Plant Genomics

As a prominent concern molecular markers were notaccessible which have been gradually settled over timePrimarily studies were restricted to using the dominant typeof markers [5 6] where continuous development of simplesequence repeat (SSR) markers has recently resulted in theprevalence of mentioned markers [2 3 7]

Microsatellite markers have been characterized withmultiallelic nature codominance inheritance and relativeabundance as well as requiring small quantities of DNA foramplification [8] which have made these markers efficientlyapplicable in DUS test of rice varieties [9 10] UPOVhas confirmed the application of SSR markers as one ofthe commonly practical molecular marker systems for theidentification of plant varieties [11]

Thismarkerwas previously confirmed to be applied to thedistinction between plant varieties as well as complementaryfeatures in DUS tests where microsatellites were used in DUStests on pepper [3] canola [2] and corn [12]

In this study microsatellite markers was intended toidentify rice varieties The efficiencies of SSR markers wereevaluated as complementary tools for the distinction of thesevarieties

2 Materials and Methods

21 Plant Materials and DNA Extraction In this study 40rice varieties consist of 27 varieties created in Rice ResearchInstitute of Iran (RRII) and 10 local varieties from threeregions of Guilan Mazandaran and Isfahan in Iran andthree International Rice Research Institute (IRRI) varietieswere used (Table 1) All varieties used for this study werefrom indica subspecies Fifteen seeds of each variety wereselected and then 5 g of young and healthy leaves was usedfor DNA extractionThe DNAwas extracted using the CTABmethod with minor modifications (increasing extractionbuffer density in two times and replacing Mercaptoethanol(02 percent) with Dithiothreitol (30mM)) [13]

22 Microsatellites Reaction Twelve pairs of SSR primers (apair of primers of each chromosome) were selected fromthe panel of 50 from Gramene database (httpgrameneorgmarkersmicrosat50 ssrhtml) (Table 2)

It selected a pair of primers from a mitochondrial DNAsequence (drrcms marker) (Forward 51015840 ACCTTTGGGC-GATGGTT 31015840 Reverse 51015840 GGGTTTAGAGTCGCCAC 31015840) todetect the impurities in CMS line (IR58025A) from itscognate isogenic maintainer line (IR58025B) which is aprerequisite to obtain pure seeds of hybrid rice as well [14]

PCR reaction was carried out in a total volume of 15 120583Lcontaining 3 120583L (25 ng) of template DNA 1 120583L (066 120583molL)primers 15 120583L 10x PCR buffer 15 120583L dNTPs (02mmolL)12 120583L MgCl

2(twommolL) 02 120583L Taq DNA polymerase

(06U15 120583L) and 56 120583L ddH2O An initial denaturation

period of five min at 94∘C was followed by 35 cycles of60 s at 94∘C 30 s at 56ndash66∘C 120 s at 72∘C and then fivemin at 72∘C for final extension After amplification the PCRproducts were separated on 6 (wv) polyacrylamide gel andvisualized by silver staining [15]

Table 1 Forty rice varieties used for this study including name ofvariety and breeder

Number Variety name Breeder1 Parto RRII2 Jahesh RRII3 Nemat RRII4 Nemat-A RRII5 Neda RRII6 Neda-A RRII7 Tabesh RRII8 Sepid-Roud RRII9 Deylam RRII10 Sazandegi RRII11 Khazar RRII12 Danesh RRII13 Shafagh RRII14 Tarom-Jolodar RRII15 Saleh RRII16 Ghaem-1 RRII17 Ghaem-2 RRII18 Ghaem-3 RRII19 Zayande-Rood RRII20 Sang-e-Tarom RRII21 Pouya RRII22 Line-2 RRII23 Tarom-Milad RRII24 Dorfak RRII25 Sahel RRII26 Kadous RRII27 Fajr RRII28 Sang-e-Jo Local29 Shiroudi Local30 Hashemi Local31 Hassan-Saraie Local32 Ali-Kazemi Local33 Hassani Local34 Tarom-Mahali Local35 Lenjan Local36 Deylamani Local37 Binam Local38 IR58025A IRRI39 IR58025B IRRI40 IR42686R IRRI

23 Statistical Analysis The frequency of microsatellite poly-morphismwas calculated based onpresence (1) or absence (0)of common bands The genetic similarity between varietieswas calculated using the Dice coefficient [16] and a dendro-gram showing the genetic relationship of the 40 varieties wasconstructed using the unweighted pair group method with

International Journal of Plant Genomics 3

Table 2 Characteristics of polymorphic microsatellite markers used in this study including locus name number of chromosome primersequences number of alleles effective number of alleles polymorphic information content (PIC) and discriminating power (119863

119895)

Number Locus name Chromosome number Primer sequences Number ofalleles

Effectivenumber ofalleles

PIC 119863119895

1 RM11 1 F CAAATCCCGACTGCTGTCCR TGGGAAGAGAGCACTACAGC 7 415 073 078

2 RM44 2 F ACCCTCTCCGCCTCGCCTCCTCR CTCCTCCTCCTGCGACCGCTCC 8 555 08 08

3 RM55 3 F CCGTCGCCGTAGTAGAGAAGR TCCCGGTTATTTTAAGGCG 3 273 06 068

4 RM124 4 F ATCGTCTGCGTTGCGGCTGCTGR CATGGATCACCGAGCTCCCCCC 8 581 08 083

5 RM133 5 F TGCAGATGAGAAGCGGCGCCTCR TGTGTCATCAGACGGCGCTCCG 4 316 063 073

6 RM154 6 F TTGGATTGTTTTGCTGGCTCGCR GGAACACGGGGTCGGAAGCGAC 6 532 079 082

7 RM161 7 F TCTCCTCTTCCCCCGATCR ATAGCGGGCGAGGCTTAG 4 244 052 057

8 RM237 8 F ACGGGCAATCCGAACAACCR TCGGGAAAACCTACCCTACC 5 463 075 08

9 RM271 9 F CTAGTTGGGCATACGATGGCR ACGCTTATATGTTACGTCAAC 10 658 083 088

10 RM277 10 F TCAGATCTACAATTCCATCCR TCGGTGAGACCTAGAGAGCC 7 46 076 08

11 RM287 11 F TTCCCTGTTAAGAGAGAAATCR GTGTATTTGGTGAAAGCAAC 8 44 074 078

12 RM316 12 F CGGTCAAATCATCACCTGACR CAAGGCTTGCAAGGGAAG 13 952 09 093

Average 691 490 073 078

the arithmetic mean (UPGMA) [17] features of the NTSYSpcv202 statistical analysis package [18]

GDNL = [211987311

(211987311+ 11987310+ 11987301)] (1)

Accordingly11987311is the number of bands (alleles) in both

individuals 11987300

is the number of bands (alleles) absent inboth individuals 119873

10is the number of bands (alleles) in 119894

11987301is the number of bands (alleles) present in 119895 and119873 is the

total number of all bands (alleles)Effective number of alleles (119860

119890) [19] in each SSR locuswas

calculated by the following formula

119860119890=1

sum1198752119894

(2)

where 119875119894is the frequency of 119894th allele for each locus

The polymorphic information content (PIC) [20] ofmicrosatellite loci was calculated according to the followingformula

PIC = 1 minus119899

sum

119895=1

1198752

119894119895 (3)

where 119875119894119895is the frequency of the 119895th allele for 119894th marker and

the summation extends over 119899 alleles

Moreover the discriminative power ofmolecularmarkers(119863119895) and the best combination of microsatellite markers (119883

119896)

[21] were estimated using the following steps and formulasThere are 119873(119873 minus 1)2 different pairs in a set of 119873 indi-

viduals Based on this 119888119894is the 119894th pattern of the given 119895th

primer in which formula is

119888119894= 119901119894

(119873119901119894minus 1)

119873 minus 1 (4)

Moreover for the 119895th primer 119862119895is equivalent to the

summation of the different 119888119894for all 119868 patterns generated by

the primer

119862119895=

119868

sum

119894=1

119888119894=

119868

sum

119894=1

119901119894

(119873119901119894minus 1)

119873 minus 1 (5)

Therefore the discriminating power of the 119895th primer andthe best combination of 119896 primers are equal to

119863119895= 1 minus 119862

119895= 1 minus

119868

sum

119894=1

119875119894(119873119875119894minus 1

119873 minus 1)

119883119896=119873 (119873 minus 1)

2

119896

prod

119895=1

119862119895

(6)


M A B

150100

Figure 1 Detecting the impurities in CMS line from its cognateisogenic maintainer line Polymorphism between CMS (IR58025A)and maintainer line (IR58025B) of rice at mitochondrial drrcmsmarker

3 Results and Discussion

31 Evaluation ofMicrosatelliteMarkers The12microsatelliteprimers used for this study generated totally 83 polymorphicfragments with an average of 691 alleles per locus Amongthese markers RM316 with 13 alleles and RM55 with threealleles had the highest and lowest variation respectivelyEffective number of alleles was calculated from 244 (RM161)to 952 (RM316) with an average of 490 per locus Addi-tionally the PIC was estimated from 052 (RM161) to 09(RM316)with an average of 074 per locusThediscriminatingpower (119863

119895) ranged between 057 (RM161) and 093 (RM316)

with an average of 078 per locus (Table 2) The most ofmicrosatellite markers had a high PIC and discriminatingpower However a few markers had a low range of PIC anddiscriminating power such as RM55 and RM161 Our resultsagree partially with those of Hashemi et al [22] who utilized10 microsatellite markers to characterize the genetic diversityin a group of 16 Iranian rice hybrids PIC is regarded as oneof the important features of the molecular markers and canbe used to evaluate the differentiation ability of the markers[23]

32 Genetic Similarity andRelationships amongVarieties Therange of similarity among varieties was from 0 to 1 withan average of 026 and variance of 0063 for all microsatel-lite markers Similarity values in between varieties were 0for 66 pairs of varieties (supplementary file 1 see Sup-plementary Material available online at httpdxdoiorg1011552015965073) and similarity value had been 1 just forone pair of varieties (IR58025A versus IR58025B) IR58025Ais a CMS line and for detecting the CMS line from itscognate isogenic maintainer line (IR58025B) was used thepolymorphism of a mitochondrial DNA sequence (drrcmsmarker) between some of CMS population and their fertilelines is verified in both lines (Figure 1)

Dendrogram resulted from cluster analysis usingUPGMA algorithm based on the Dice similarity coefficientand could discriminate all varieties from each other except

Table 3 Unique identification keys achieved by specific marker forsome of the varieties

Locus name VarietiesRM44 Tarom-MahaliRM124 Ali-KazemiRM11 Danesh and SahelRM271 Hassan-Saraie and NedaRM316 Ghaem-1 Dorfak Nemat and HassaniRM287 Khazar IR42686R Ghaem-3 and Sazandegi

for two isonuclear lines (IR58025A and IR58025B) (Figure 2)As a result total microsatellite markers could detect most ofthe improved varieties (Group A) from local varieties (GroupB) However Sang-e-Jo and Hassan-Saraie as local varietiesstood with improved varieties in Group A because Sang-e-Joand Sepid-Rood were used as recipient parents for Ghaem-1variety As can be observed in the dendrogram Sang-e-JoSepid-Rood and Ghaem-1 have been in the same subclusterFive improved varieties stood beside local varieties inGroup B as well These varieties are Shafagh and Kadousthat improved from two IRRI lines as IR67015-94-2-3 andIR64669-153-2-3 respectively In addition Jahesh varietyis a mutation of a local variety named Tarom Sazandegipurified from Lenjan local mass and Shiroudi improvedby cross between Deylamani as a local variety and Khazaras an improved variety Consequently Jahesh stood withother varieties obtained from Tarom such as Tarom-JolodarSang-e-Tarom and Tarom-Milad in Group B and Sazandegiwith Lenjan and Shiroudi with Deylamani constructedidentical subcluster in Group B There is an interestingresult in the third group (Group C) in which three varietiesnamely Tabesh Pouya and Parto constructed the samesubcluster with Tarom-Mahali These varieties are mutant ofTarom-Mahali

Although the rice varieties in this study were fromdifferent rice breeding programs in Iran microsatellite mark-ers correctly grouped them depending on their respectivegroup local or improved varieties in the dendrogram Ourresults approve those of Garland et al [24] who analyzed43 rice samples using microsatellite markers and obtained asimilar classification of varieties according to their breedingprograms

In addition it obtained results from cluster analysis ofeach marker which have shown the ability of some of themarkers as unique identification key for some of the varieties(Table 3)

33 The Best Combination of Markers For finding the bestcombination of markers that result in the obtained resultof using all markers in discrimination of all varieties firstmarkers were chosen one after another in a way to minimize119883119896 that is the number of pairs of distinct varieties for each

primary compound in each step In the first step RM316was chosen which distinguished the highest pairs of varietiesfrom each other among 119873(119873 minus 1)2 pairs of varieties andmade the amount of 119863

119895maximum In the second step the


012 034 056 078 100

KhazarSalehDeylamLine-ALine-BLine-RFajrSepid-RoudSahelSang-e-JoGhaem-1Ghaem-2Ghaem-3DaneshDorfakHasan-SaraieShiroudiDeylamaniJaheshTarom-JolodarSang-e-TaromTarom-MiladBinamHashemiHasaniLenjanSazandegiZayande-RoudLine-2KadousShafaghAli-KazemiNedaNeda-ANematNemat-ATarom-MahaliTabeshPouyaParto

Coefficient

Group A

Group B

Group C

Figure 2 Dendrogram of 40 rice varieties based on UPGMA cluster analysis of Dice similarity matrix calculated from 12 microsatellitemarkers

compound of each 119899 minus 1 remaining marker with the chosenmarker of previous step was tested in order to determinethe most efficient compound that minimizes the amountof 119883119896 In this step compounding the RM271 with previous

marker left the lower number of variety pairs undeterminedIn subsequent steps the same method was used for keepingor omitting othermarkers Finally adding RM154 to previousmarkers could decrease expected number of undeterminedpairs of varieties calculated from 3927 to 003 which shouldpractically reach one pair of variety There were 41 pairs ofvarieties nondistinct from each other in calculating similaritycoefficient among varieties and then cluster analysis ofvarieties using RM316 that had the highest discriminatingpower (093) amongmarkers By adding consequent markersin accordance to distinguish the ability to this marker thenumber of undistinguished pairs of varieties was decreasedto one (Table 4)

Reliable identification of similar varieties is so difficult inplant species by morphological characteristics alone becausemorphological and physiological characteristics are limited[25 26] Accordingly using molecular markers as additionalinformation is inevitable in registering plant varieties consid-ering their benefits DNA markers can be utilized to simplyand rapidly detect varieties or approve the distinctiveness of avarietal impostor [27] For identification and characterizationof rice varieties and the testing of hybrid rice lines using STSand SSR markers was significantly easier than using typicalldquogrow-out testsrdquo that included growing plants to maturityand evaluating purity based onmorphological characteristics[28 29] Also microsatellite markers have been utilized in

Table 4 Comparison of combination of markers in the real andtheoretical states under the hypothesis of independence of markers

Locus nameNumber of indistinguishable pairs

Experimentallyobserved

Expected under theindependencehypothesis

RM316 41 3927RM316 + RM271 4 471RM316 + RM271 + RM124 3 080RM316 + RM271 +RM124 + RM154 2 014

RM316 + RM271 +RM124 + RM154 + RM44 1 003

the previous same study to distinguish traditional rice (Oryzasativa L) varieties from each other in Cuba [30] In thisresearch the results showed thatmicrosatellitemarkers easilycould be used for identification of rice varieties

Abbreviations

DUS Distinction uniformity and stabilityPVP Plant variety protectionPIC Polymorphic information contentPCR Polymerase chain reactionSSR Simple sequence repeat


UPGMA Unweighted pair groupmethod with arithmeticmean

UPOV International union for theprotection of new varietiesof plants

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by Seed and Plant Certificationand Registration Institute (SPCRI)The authors would like tothank Dr Zahra Tahernejad for their technical assistance ingenerating the SSR data

References

[1] V Lombard C P Baril P Dubreuil F Blouet and D ZhangldquoGenetic relationships and fingerprinting of rapeseed cultivarsby AFLP consequences for varietal registrationrdquo Crop Sciencevol 40 no 5 pp 1417ndash1425 2000

[2] L Tommasini J Batley G M Arnold et al ldquoThe developmentof multiplex simple sequence repeat (SSR) markers to com-plement distinctness uniformity and stability testing of rape(Brassica napus L) varietiesrdquo Theoretical and Applied Geneticsvol 106 no 6 pp 1091ndash1101 2003

[3] Y-S Kwon J-M Lee G-B Yi et al ldquoUse of SSR markers tocomplement tests of distinctiveness uniformity and stability(DUS) of pepper (Capsicum annuum L) varietiesrdquo Moleculesand Cells vol 19 no 3 pp 428ndash435 2005

[4] R J Mailer R Scarth and B Fristensky ldquoDiscriminationamong cultivars of rapeseed (Brassica napus L) using DNApolymorphisms amplified from arbitrary primersrdquo Theoreticaland Applied Genetics vol 87 no 6 pp 697ndash704 1994

[5] J de Riek E Calsyn I Everaert E van Bockstaele andM de Loose ldquoAFLP based alternatives for the assessment ofdistinctness uniformity and stability of sugar beet varietiesrdquoTheoretical and Applied Genetics vol 103 no 8 pp 1254ndash12652001

[6] J R Law P Donini R M D Koebner J C Reeves and R JCooke ldquoDNA profiling and plant variety registration III thestatistical assessment of distinctness in wheat using amplifiedfragment length polymorphismsrdquo Euphytica vol 102 no 3 pp335ndash342 1998

[7] R J Cooke G M M Bredemeijer M W Ganal et alldquoAssessment of the uniformity of wheat and tomato varieties atDNA microsatellite locirdquo Euphytica vol 132 no 3 pp 331ndash3412003

[8] S A Becher K Steinmetz K Weising et al ldquoMicrosatellites forcultivar identification in PelargoniumrdquoTheoretical and AppliedGenetics vol 101 no 4 pp 643ndash651 2000

[9] S Bonow E V R von Pinho M G C Vieira and B VosmanldquoMicrosatellite markers in and around rice genes applicationsin variety identifi cation and dus testingrdquo Crop Science vol 49no 3 pp 880ndash886 2009

[10] R K Singh R K Sharma A K Singh et al ldquoSuitabilityof mapped sequence tagged microsatellite site markers for

establishing distinctness uniformity and stability in aromaticricerdquo Euphytica vol 135 no 2 pp 135ndash143 2004

[11] Upov-Bmt MT3610 Progress Report of the 36th Session of theTechnical Committee the TechnicalWorking Parties andWorkingGroup on Biochemical and Molecular Techniques and DNA-Profiling in Particular Upov-Bmt Geneva Switzerland 2002

[12] J Gunjaca I Buhinicek M Jukic et al ldquoDiscriminating maizeinbred lines using molecular and DUS datardquo Euphytica vol 161no 1-2 pp 165ndash172 2008

[13] M A Saghai-Maroof K M Soliman R A Jorgensen andR W Allard ldquoRibosomal DNA spacer-length polymorphismsin barley mendelian inheritance chromosomal location andpopulation dynamicsrdquo Proceedings of the National Academy ofSciences of the United States of America vol 81 no 24 pp 8014ndash8018 1984

[14] P Rajendrakumar A K Biswal S M Balachandran MS Ramesha B C Viraktamath and R M Sundaram ldquoAmitochondrial repeat specific marker for distinguishing wildabortive type cytoplasmic male sterile rice lines from theircognate isogenic maintainer linesrdquo Crop Science vol 47 no 1pp 207ndash211 2007

[15] Promega SILVER SEQUENCE DNA Sequencing System Tech-nical Manual no 023 2004 httpwwwpromegacom

[16] M Nei andW H Li ldquoMathematical model for studying geneticvariation in terms of restriction endonucleasesrdquo Proceedings ofthe National Academy of Sciences of the United States of Americavol 76 no 10 pp 5269ndash5273 1979

[17] P H Sneath and R R Sokal Numerical Taxonomy ThePrinciples and Practice of Numerical Classification 1973

[18] F RohlfTaxonomy NTSYS-pc Numerical Multivariate AnalysisSystem Version 202 g Exeter Software Applied BiostatisticsInc New York NY USA 1998

[19] D L Hartl and A G Clark Principles of Population Geneticsvol 116 Sinauer Associates Sunderland Mass USA 1997

[20] W Powell M Morgante C Andre et al ldquoThe comparisonof RFLP RAPD AFLP and SSR (microsatellite) markers forgermplasm analysisrdquoMolecular Breeding vol 2 no 3 pp 225ndash238 1996

[21] C Tessier J David P This J M Boursiquot and A CharrierldquoOptimization of the choice of molecular markers for varietalidentification in Vitis vinifera Lrdquo Theoretical and AppliedGenetics vol 98 no 1 pp 171ndash177 1999

[22] S H Hashemi S A M Mirmohammadi-Maibody G ANematzadeh and A Arzani ldquoIdentification of rice hybridsusing microsatellite and RAPD markersrdquo African Journal ofBiotechnology vol 8 no 10 pp 2094ndash2101 2009

[23] J Ni P M Colowit and D J Mackill ldquoEvaluation of geneticdiversity in rice subspecies using microsatellite markersrdquo CropScience vol 42 no 2 pp 601ndash607 2002

[24] S H Garland L Lewin M Abedinia R Henry and ABlakeney ldquoThe use of microsatellite polymorphisms for theidentification of Australian breeding lines of rice (Oryza sativaL)rdquo Euphytica vol 108 no 1 pp 53ndash63 1999

[25] S Giancola S Marcucci Poltri P Lacaze and H E Hopp ldquoFea-sibility of integration of molecular markers and morphologicaldescriptors in a real case study of a plant variety protectionsystem for soybeanrdquo Euphytica vol 127 no 1 pp 95ndash113 2002

[26] J Rongwen M S Akkaya A A Bhagwat U Lavi and P BCregan ldquoThe use of microsatellite DNA markers for soybeangenotype identificationrdquo Theoretical and Applied Genetics vol90 no 1 pp 43ndash48 1995


[27] B C Y Collard C M Vera Cruz K L McNally P S Virkand D J Mackill ldquoRice molecular breeding laboratories inthe genomics era Current status and future considerationsrdquoInternational Journal of Plant Genomics vol 2008 Article ID524847 25 pages 2008

[28] Y Xu ldquoDeveloping marker-assisted selection strategies forbreeding hybrid ricerdquo Plant Breeding Reviews vol 23 pp 73ndash174 2004

[29] J Yashitolaa T Thirumuruganb R M Sundaramb et alldquoAssessment of purity of rice hybrids using microsatellite andSTS markersrdquo Crop Science vol 42 no 4 pp 1369ndash1373 2002

[30] AAlvarez J L Fuentes V Puldon et al ldquoGenetic diversity anal-ysis of Cuban traditional rice (Oryza sativa L) varieties basedon microsatellite markersrdquo Genetics and Molecular Biology vol30 no 4 pp 1109ndash1117 2007

Submit your manuscripts athttpwwwhindawicom

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Volume 2014

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Enzyme Research



Microbiology


As a prominent concern molecular markers were notaccessible which have been gradually settled over timePrimarily studies were restricted to using the dominant typeof markers [5 6] where continuous development of simplesequence repeat (SSR) markers has recently resulted in theprevalence of mentioned markers [2 3 7]

Microsatellite markers have been characterized withmultiallelic nature codominance inheritance and relativeabundance as well as requiring small quantities of DNA foramplification [8] which have made these markers efficientlyapplicable in DUS test of rice varieties [9 10] UPOVhas confirmed the application of SSR markers as one ofthe commonly practical molecular marker systems for theidentification of plant varieties [11]

Thismarkerwas previously confirmed to be applied to thedistinction between plant varieties as well as complementaryfeatures in DUS tests where microsatellites were used in DUStests on pepper [3] canola [2] and corn [12]

In this study microsatellite markers was intended toidentify rice varieties The efficiencies of SSR markers wereevaluated as complementary tools for the distinction of thesevarieties

2 Materials and Methods

21 Plant Materials and DNA Extraction In this study 40rice varieties consist of 27 varieties created in Rice ResearchInstitute of Iran (RRII) and 10 local varieties from threeregions of Guilan Mazandaran and Isfahan in Iran andthree International Rice Research Institute (IRRI) varietieswere used (Table 1) All varieties used for this study werefrom indica subspecies Fifteen seeds of each variety wereselected and then 5 g of young and healthy leaves was usedfor DNA extractionThe DNAwas extracted using the CTABmethod with minor modifications (increasing extractionbuffer density in two times and replacing Mercaptoethanol(02 percent) with Dithiothreitol (30mM)) [13]

22 Microsatellites Reaction Twelve pairs of SSR primers (apair of primers of each chromosome) were selected fromthe panel of 50 from Gramene database (httpgrameneorgmarkersmicrosat50 ssrhtml) (Table 2)

It selected a pair of primers from a mitochondrial DNAsequence (drrcms marker) (Forward 51015840 ACCTTTGGGC-GATGGTT 31015840 Reverse 51015840 GGGTTTAGAGTCGCCAC 31015840) todetect the impurities in CMS line (IR58025A) from itscognate isogenic maintainer line (IR58025B) which is aprerequisite to obtain pure seeds of hybrid rice as well [14]

PCR reaction was carried out in a total volume of 15 120583Lcontaining 3 120583L (25 ng) of template DNA 1 120583L (066 120583molL)primers 15 120583L 10x PCR buffer 15 120583L dNTPs (02mmolL)12 120583L MgCl

2(twommolL) 02 120583L Taq DNA polymerase

(06U15 120583L) and 56 120583L ddH2O An initial denaturation

period of five min at 94∘C was followed by 35 cycles of60 s at 94∘C 30 s at 56ndash66∘C 120 s at 72∘C and then fivemin at 72∘C for final extension After amplification the PCRproducts were separated on 6 (wv) polyacrylamide gel andvisualized by silver staining [15]

Table 1 Forty rice varieties used for this study including name ofvariety and breeder

Number Variety name Breeder1 Parto RRII2 Jahesh RRII3 Nemat RRII4 Nemat-A RRII5 Neda RRII6 Neda-A RRII7 Tabesh RRII8 Sepid-Roud RRII9 Deylam RRII10 Sazandegi RRII11 Khazar RRII12 Danesh RRII13 Shafagh RRII14 Tarom-Jolodar RRII15 Saleh RRII16 Ghaem-1 RRII17 Ghaem-2 RRII18 Ghaem-3 RRII19 Zayande-Rood RRII20 Sang-e-Tarom RRII21 Pouya RRII22 Line-2 RRII23 Tarom-Milad RRII24 Dorfak RRII25 Sahel RRII26 Kadous RRII27 Fajr RRII28 Sang-e-Jo Local29 Shiroudi Local30 Hashemi Local31 Hassan-Saraie Local32 Ali-Kazemi Local33 Hassani Local34 Tarom-Mahali Local35 Lenjan Local36 Deylamani Local37 Binam Local38 IR58025A IRRI39 IR58025B IRRI40 IR42686R IRRI

23 Statistical Analysis The frequency of microsatellite poly-morphismwas calculated based onpresence (1) or absence (0)of common bands The genetic similarity between varietieswas calculated using the Dice coefficient [16] and a dendro-gram showing the genetic relationship of the 40 varieties wasconstructed using the unweighted pair group method with



119895)



PIC 119863119895













Average 691 490 073 078


GDNL = [211987311

(211987311+ 11987310+ 11987301)] (1)









119860119890=1

sum1198752119894

(2)



PIC = 1 minus119899

sum

119895=1

1198752

119894119895 (3)




119896)




119888119894= 119901119894

(119873119901119894minus 1)

119873 minus 1 (4)



the primer

119862119895=

119868

sum

119894=1

119888119894=

119868

sum

119894=1

119901119894

(119873119901119894minus 1)

119873 minus 1 (5)


119863119895= 1 minus 119862

119895= 1 minus

119868

sum

119894=1

119875119894(119873119875119894minus 1

119873 minus 1)

119883119896=119873 (119873 minus 1)

2

119896

prod

119895=1

119862119895

(6)


M A B

150100

















012 034 056 078 100


Coefficient

Group A

Group B

Group C










RM316 + RM271 +RM124 + RM154 + RM44 1 003


Abbreviations







Acknowledgments


References








































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



119895)



PIC 119863119895













Average 691 490 073 078


GDNL = [211987311

(211987311+ 11987310+ 11987301)] (1)









119860119890=1

sum1198752119894

(2)



PIC = 1 minus119899

sum

119895=1

1198752

119894119895 (3)




119896)




119888119894= 119901119894

(119873119901119894minus 1)

119873 minus 1 (4)



the primer

119862119895=

119868

sum

119894=1

119888119894=

119868

sum

119894=1

119901119894

(119873119901119894minus 1)

119873 minus 1 (5)


119863119895= 1 minus 119862

119895= 1 minus

119868

sum

119894=1

119875119894(119873119875119894minus 1

119873 minus 1)

119883119896=119873 (119873 minus 1)

2

119896

prod

119895=1

119862119895

(6)


M A B

150100

















012 034 056 078 100


Coefficient

Group A

Group B

Group C










RM316 + RM271 +RM124 + RM154 + RM44 1 003


Abbreviations







Acknowledgments


References








































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


M A B

150100

















012 034 056 078 100


Coefficient

Group A

Group B

Group C










RM316 + RM271 +RM124 + RM154 + RM44 1 003


Abbreviations







Acknowledgments


References








































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


012 034 056 078 100


Coefficient

Group A

Group B

Group C










RM316 + RM271 +RM124 + RM154 + RM44 1 003


Abbreviations







Acknowledgments


References








































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






Acknowledgments


References








































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology













Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

Research Article Identification and DUS Testing of Rice...

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