Research Article
Genetic Polymorphism of Some BBRI Developed Rice Cultivars by SSR Markers
Syed MA*, Iftehkaruddaula KM, Biswas PS, Khaleque Mian MA, Rasul MG, Akter N and Mamunur Rahman M*
Corresponding Author: Mamunur Rahman M, Senior Scientific Officer, Farm Management Division, BRRI, Gazipur, Bangladesh and Syed MA, Plant Breeding Division, Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh
Received: November 20, 2019; Accepted: December 03, 2019 Available Online: January 07, 2020
Citation: Syed MA, Iftehkaruddaula KM, Biswas PS, Khaleque Mian MA, Rasul MG, et al. (2020) Genetic Polymorphism of Some BBRI Developed Rice Cultivars by SSR Markers. J Agric Forest Meteorol Res, 3(5): 390-401.
Copyrights: ©2020 Syed MA, Iftehkaruddaula KM, Biswas PS, Khaleque Mian MA, Rasul MG, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Share :
  • 293

    Views & Citations

Genetic diversity and relationships among seven modern rice cultivars were evaluated using 299 SSR markers that covered the rice genome. A total of 405 alleles were detected at 165 polymorphic SSR loci and the number of alleles per marker ranged from 2 to 5, averaging of 2.45 alleles per locus. The band size for a given microsatellite locus variation between 73 bp (RM9) to 335 bp (RM539). Polymorphism information content (PIC) values ranged from 0.21 to 0.70, with an average of 0.36. Total 43 markers were found as highly informative on 12 chromosomes. Among them, marker RM437 showed the highest polymorphism followed by RM9, RM472, RM85, RM548, RM334, RM336, RM464, RM222 and RM229 which might be effectively used for genetic diversity and relationship study of rice. Comparatively higher genetic distance was observed between BRRI dhan47 and BRRI dhan50 genotypes pair than the other combinations. Cluster analyses were used to group cultivars by constructing dendrogram based on SSR marker analysis which grouped the ice cultivars into three groups, where cluster 1 contained only one cultivar, and cluster 2 and cluster 3 consists of three cultivars each. The results of the genetic diversity will be useful for the selection of the parents for developing a rice breeding program and will construct a genetic fingerprint for varietal identification and background selection during breeding program.

 

Keywords: Genetic diversity, Cluster analysis, Microsatellite marker, Molecular diversity

INTRODUCTION

Rice (Oryza sativa L.) is one of the most important staple food crops supporting more than half of the world population [1]. South Asia, one of the major centers for rice domestication, has been described as the “food basket” and “food bowl” of Asia [2]. Compared with other crop species, the genetic diversity in the world rice germplasm is quite large. Three subspecies, i.e., indica, japonica and javanica, compose a large reservoir of rice germplasm including a variety of local landraces and cultivars [3-5]. Despite the richness of genetic resources, only a small proportion of the world rice germplasm collections have been used in breeding programs. As a consequence a high genetic similarity is found within several commercial rice germplasms around the world [6].

Genetic similarity and diversity could be assessed by plant morphology, physiology, isozymes, storage protein profiles, DNA markers, etc. [7]. Among different DNA markers, SSR markers have been extensively used as a powerful tool in variety protection [8], molecular diversity studies [9], QTL analysis, pedigree analysis and marker assisted breeding [10]. SSRs have been developed for many crop species, including wheat [11], maize [12], sorghum [13], tomato [14], etc. In rice, SSRs have been used to assess the genetic diversity of both wild and cultivated species [15-18]. SSR markers are particularly suitable for evaluating genetic diversity  and   relationships  among   closely   related   plant species, populations or individuals [19]. Recently a total of 18,828 Class I (di, tri, tetra nucleotide) SSRs, representing 47 distinctive motif families have been released after completion of the Nipponbare genome sequence [20]. This is the largest numbers of SSR markers publicly available for any crop species. SSR markers are more popular in rice because they are highly informative, mostly monolocus, co-dominant, easily analyzed and cost effective [12].

 

Using SSRs genetic diversity and polymorphisms of land races, local cultivars and diverse genetic stocks are frequently studied. The objectives of the present study were to use SSR markers: (1) to estimate the genetic diversity; (2) to evaluate of the degree of polymorphism; and (3) to reveal genetic relationships among seven modern rice cultivars released by Bangladesh Rice Research Institute (BRRI) between 1994 and 2011.

MATERIALS AND METHODS

The experiment was conducted at Marker Assisted Selection (MAS) laboratory of Plant Breeding Division of BRRI.

Plant materials and markers

Seven rice cultivars were used in this investigation (Table 1). These cultivars were used in this study to evaluate the use of SSR markers for identification of Arsenic (As) tolerant quantitative Trait Loci (QTL) and for the selection of As tolerant rice genotypes during breeding program. It was found that BRRI dhan47 showed high tolerance to As and BRRI dhan54 and BRRI dhan55 showed medium tolerance (data not published). On the contrary BRRI dhan45 is the most susceptible cultivars followed by the other three varieties used in the present study (unpublished data). A total of 299 SSR markers were used in this study. Markers were selected based on their location to cover all the chromosomes uniformly maintaining more or less similar distance considering distances in base pair (bp). 

Isolation of genomic DNA

Seeds were germinated in a germination chamber at 30°C temperature. Three days after germination, germinated seeds were sown in earthen pots. The pots were then kept in a net house. Genomic DNA was isolated from young leaves of 30 days old seedlings following modified Miniscale method as described by Rahman et al. [21]. DNA samples were evaluated both quantitatively and qualitatively using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA) and the concentration was determined using module Nucleic Acid, Software: 3.3.1.

Molecular analysis through SSR

Polymerase chain reaction (PCR) amplification was carried out in a 10 µl reaction mixture, each reaction containing 25 ng of template DNA, 0.5 μM of each forward and reverse primer, 3 mM MgCl2, 0.2 mM of dNTP mix, 1x PCR buffer and 1 unit of Taq DNA polymerase (Bio basic, Canada). Amplifications were performed at G-strom DNA Thermal Cycler (Gene Technology Ltd., England). The temperature cycles were programmed as initial denaturation at 94°C for 5 min, 35 cycles of 45 s denaturation at 94°C, 45 s annealing at 55°C, 1 min and 30 s extention at 72°C and additional temperature of 72°C for 7 min for final extension and finally cooled at 4°C temperature. 10x Loading dye was added to each well of the PCR plate and the amplified PCR products were subjected to electrophoresis in 8% polyacrylamide gel in 0.5x TBE buffer at 100 V for 1.30 to 2.15 h (depending on product size) along with a known DNA ladder. The gels were stained with ethidium bromide for 25-30 min and DNA fragments were visualised under UV light and gels were photographed using gel documentation system (BIO-RAD).

DATA ANALYSIS AND CLUSTERING

 

The molecular weights for each band were measured in base pairs using Alpha Ease FC 5.0 software (Alpha Innotech Corporation). 165 polymorphic SSR markers (Table 2) distributed across 12 chromosomes were used for diversity analysis. Data of non-amplified and monomorphic SSR markers were not used for diversity study. Summary statistics, including the number of alleles per locus, major allele frequency and Polymorphic information content (PIC) values were determined using Power Marker version 3.25 [22]. PIC values were calculated for each of the SSR loci with the following formula proposed by Anderson et al. [23].

PICi =  1 – n∑ j – 1 (Pij)2

Where, n is the number of marker alleles for marker i and Pij is the frequency of the jth allele for marker i. The genetic distance was calculated using the Nei distance [24]. The allele frequency data from Power Marker version 3.25 was used to export the data in binary format (allele presence=1; allele absence=0) for analysis with Numerical Taxonomy and Multivariate Analysis System (NTSYS-pc) version 2.2 [25]. The similarity matrix was calculated with the Simqual subprogram using the Dice coefficient [26] and subjected to cluster analysis by unweighted pair group method for arithmetic mean (UPGMA) and a dendrogram was generated using the programme NTSYS-pc.

 

RESULTS

Allelic diversity

The molecular marker is a useful tool for assessing genetic variations and resolving cultivar identities. Keeping this in mind, a total of 299 SSR markers was used across the tested cultivars. Among them, 27 markers did not amplify at all and 107 markers showed monomorphic nature. Therefore, these 134 markers do not have importance of diversity and genetic relationship study of the cultivars. The rest 165 markers showed polymorphism among the tested cultivars. A total of 405 alleles were detected at these SSR markers among the tested rice genotypes (Table 3). The average number of alleles per locus was 2.45, ranging from 2 to 5 across 165 SSR markers, with 2 alleles for 108 markers, 3 alleles for 40 markers, 4 alleles for 16 markers and 5 alleles for 1 marker. The overall size of PCR products amplified using 165 markers ranged from 73 bp (RM9) to 335 bp (RM539). The molecular size difference between the smallest and the largest allele for a given locus varied widely from 3 to 109 bp (Table 3). There was a considerable range in allele frequency (42.86-85.71%); on an average 68.46% of the rice varieties shared a common allele. No relationships were found among the number of alleles and nucleotide motif composition or the number of repetitions in the motifs.

Polymorphism in SSR markers

PIC values, a reflection of allele diversity and frequency among the varieties, were calculated for all the markers. The PIC values for the microsatellite loci varied from 0.21 to 0.70 with a mean of 0.36. Among the markers used in the present study, RM 437 on chromosome 5 is highly informative since it recorded high PIC value (0.70). Total 43 primers, 5 primers (RM493, RM9, RM212, RM472, RM519) on chromosome 1, 7 primers (RM154, RM279, RM423, RM263, RM526, RM250, RM208) on chromosome 2, 4 primers (RM232, RM570, RM442, RM85) on chromosome 3, 3 primer (RM252, RM470, RM567) on chromosome 4, 5 primers (RM548, RM437, RM538, RM334, RM31) on chromosome 5, 2 primer (RM589, RM549) on chromosome 6, 5 primers (RM481, RM180, RM336, RM18, RM248) on chromosome 7, 3 primer (RM337, RM152, RM210) on chromosome 8, 2 primer (RM464, RM23958) on chromosome 9, 4 primer (RM474, RM222, RM216, RM304) on chromosome 10, 3 primers (RM209, RM229, RM206) on chromosome 11, were found as highly informative because only PIC values 0.50 or more indicate high polymorphism. The Highest informative markers were found on chromosome 2. The second highest informative markers were obtained at chromosome 1, chromosome 5, chromosome 7 and no one found on chromosome 12.

Genetic distance-based analysis

The values of pair-wise comparisons of Nei’s genetic distances (D) between genotypes were computed from combined data for the 165 primers, ranged from 0.0571 to 0.7672, whereas average genetic distance was 0.51 (Table 3). Comparatively higher genetic distance was observed between BRRI dhan47 and BRRI dhan50 genotypes pair (0.7672) followed by BRRI dhan47 and BRRI dhan45 genotypes pair (0.7456) than the other combinations (Table 2). On the other hand, BRRI dhan54 and BRRI dhan55 cultivar pair showed the maximum similarity (0.0571).

 

The dendrogram based on Dice similarity index and UPGMA method (Figure 1) was obtained from the binary data that was deduced from the DNA profiles of the samples analyzed. Three distinct clusters were created from the analysis of the pooled SSR marker data at the similarity coefficient of 0.49. The cluster analysis showed high genetic variation among the rice cultivars studied, with similarity coefficient value ranging from 0.26 to 0.90. Cluster 1, Cluster 2 and Cluster 3 comprised one, three and three cultivars, respectively. Cluster 1 included only BRRI dhan47. While the second cluster was divided into two sub-clusters; the first one comprised two cultivars (BRRI dhan54 and BRRI dhan55) and the second sub-cluster included only BRRI dhan50. Cluster 3 was also divided into two sub-clusters; BRRI dhan29 alone grouped in a sub-cluster and remaining two cultivars (BRRI dhan28 and BRRI dhan45) had been grouped in another sub-cluster.

DISCUSSION

Polymorphism in SSR markers

SSRs were chosen for the analysis of genetic diversity of BRRI rice cultivars due to the fact that SSR markers are very powerful for differentiating individual germplasm accessions, particularly when they are closely related [21,27-29]. In this study, a microsatellite fingerprint database was generated for the tested rice genotypes using 299 SSRs. Among these markers, 27 markers did not amplify which might be due to the selection of these markers based on published japonica rice base sequences that might differ from indica type rice cultivars studied here [20]. These markers might be useful for japonica type rice genotypes rather than be indica rice. In addition, 107 marker showed monomorphic allele among the cultivars which means that these markers are not useful for the study of genetic diversity and genetic relationship of the studied cultivars. In case of closely related cultivars these phenomenon have been common and are reported in many previous studies [5,15,17].

In this study 165 SSR markers showed polymorphism among the tested modern rice cultivars that are commercially cultivated in Bangladesh. The variations of allele number per locus are within the range 2 to 5 with an average 2.58 which is quite comparable to the values reported by Prabakaran et al. [30] (range 2 to 3, mean 2.2) and Joshi [17] (average 2.2 alleles) but quite low compared with those reported Siwach et al. [31] (range 1-8, mean 4.58) and Matin et al. [32] (range 3-7, mean 4.4). Comparatively low allele per locus might be due to the use of lower number of genotypes in the present study. Close genetic make-up would be another cause of low allele number.

In our study, lower PIC value (0.40) indicated that the genotypes are not much diverged and are closely related. Besides that, identified highly informative 40 SSR markers may be useful tools in diversity studies as well as future genetic studies of rice germplasm, especially, in marker assisted breeding and QTL identification. The PIC value observed in our study are comparable to many of the previous reports, for example, 0.28 to 0.57 with a mean of 0.43 [30], 0.16 to 0.84 with an average 0.49 [21] and 0.20 to 0.90 with an average of 0.56 [33]. High PIC values can be attributed to the use of more informative markers [21]. PIC values indicated that RM437 (PIC value of 0.70) might be the best marker for diversity analysis of rice genotypes followed by RM9, RM472, RM85, RM548, RM334, RM336, RM464, RM222 and RM229 (PIC value of 0.64) (Table 3).

Genetic relationships among the rice cultivars

The UPGMA cluster analysis showed that all the seven rice cultivars could be easily distinguished based on the information generated by the 165 SSR markers. BRRI cultivars used in the present study have not been examined previously in terms of genetic relatedness using high number of SSR molecular markers covering all the 12 chromosomes of rice genome. In this study, three clusters were obtained with similarity coefficients of 0.49. Matin et al. [32] studied cluster analysis of the 12 deep water rice germplasms of Bangladesh and showed four major groups. Though the genetic divergence was not very high, but our studied cultivars showed considerable genetic diversity. Narrow genetic base of modern high yielding rice cultivars is available from several countries, including Latin America [7,16], Japan [34], USA [28], Korea [35] and Taiwan [36]. This might be due to the selection pressure and adaptation capability of the specific genotype in the specific climatic conditions. However, among the studied genotypes, the most As tolerant cultivar, BRRI dhan47 showed considerable high genetic distance with the most As susceptible cultivar BRRI dhan45. Therefore, the studied polymorphic SSRs could be effectively used for QTL identification for As tolerance in rice by crossing between these two cultivars.

CONCLUSION

 

In this study, a microsatellite fingerprint database was generated for 7 rice genotypes using 299 SSRs and genetic divergence was estimated. This study demonstrated that the tested samples possessed a considerable level of microsatellite variation. The markers used here were of value for DNA fingerprinting of rice varieties and constructing a database for breeding programs, especially in background selections during backcross breeding. In addition, the data could be used for As tolerant QTL identification.

 

1.       Ramkumar G, Biswal AK, Mohan KM, Sakthivel K, Sivaranjani AKP, et al. (2010) Identifying novel alleles of rice blast resistance genes Pikh and Pita through allele mining. Int Rice Res Notes 117: 4185.

2.       Pachauri V, Taneja N, Vikram P, Singh NK, Singh S (2013) Molecular and morphological characterization of Indian farmers rice varieties (Oryza sativa L.). Austr J Crop Sci 7: 923-932.

3.       Khush GS (1997) Origin, dispersal cultivation and variation of rice. Plant Mol Biol 35: 25-34.

4.       Lu HM, Redus A, Coburn JR, Rutger JN, Mccouch SR, et al. (2005) Population structure and breeding patterns of 145 US rice cultivars base don SSR marker analysis. Crop Sci 45: 66-76.

5.       Garris AJ, Tai TH, Coburn JR, Kresovich S, McCouch SR (2005) Genetic structure and diversity in Oryza sativa L. Genetics 169: 1631-1638.

6.       Herrera TG, Duque DP, Almeida IP, Núñez GT, Pieters AJ, et al. (2008) Assessment of genetic diversity in Venezuelan rice cultivars using simple sequence repeats markers. Electron J Biotechnol 11: 1-15.

7.       Fuentes JL, Escobar F, Alvarez A, Gallego G, Duque MC, et al. (1999) Analyses of genetic diversity in Cuban rice varieties using isozyme, RAPD and AFLP markers. Euphytica 109: 107-115.

8.       Rongwen J, Akakya MS, Bhagwat AA, Lavi U, Cregan PB (1995) The use of microsatellite DNA markers for soybean genotype identification. Theor Appl Genet 90: 43-48.

9.       Sebastian LS, Hipolito LR, Tabanao DA, Maramara GV, Caldo RA (1998) Molecular diversity of Philippines-based rice cultivars. SABRAO J Breed Genet 30: 83-90.

10.    Chen X, Temnykh S, Xu Y, Choand YG, McCouch SR (1997) Development of a microsatellite framework map providing genome wide coverage in rice (Oryza sativa L.). Theor Appl Genet 95: 553-567.

11.    Prasad M, Varshney RK, Roy JK, Balyan HS, Gupta PK (2000) The use of microsatellites for detecting DNA polymorphism, genotype identification and genetic diversity in wheat. Theor Appl Genet 100: 584-592.

12.    Gracia AAF, Benchimol LL, Antonica MM, Geraldi IO, Deuza AP (2004) Comparison of RAPD, RFLP, AFLP and SSR marker for diversity studies in tropical maize inbred lines. Euphytica 108: 53-63.

13.    Smith JSC, Kresovich S, Hopkins MS, Mitchell S, Dean RE, et al. (2000) Genetic diversity among elite sorghum inbred lines assessed with simple sequence repeats. Crop Science. 40: 226-232.

14.    Tanksley SD, Nelson JC (1996) Advanced backcross QTL analysis: A method for the simultaneous discovery and transfer of valuable QTLs from unadapted germplasm into elite breeding lines. Theor Appl Genet 92: 191-203.

15.    Cho YG, Ishii T, Temnykh S, Chen X, Lipovich L, et al. (2000) Diversity of microsatellites derived from genomic libraries and GenBank sequences in rice (Oryza sativa L.). Theor Appl Genet 100: 713-722.

16.    Cuevas-Perez EF, Guimaraes PE, Berrio LE, Gonzales DI (1992) Genetic base of the irrigated rice in Latin America and the Caribbean. Crop Sci 32: 1054-1059.

17.    Joshi RK, Behera L (2006) Identification and differentiation of indigenous non-Basmati aromatic rice genotypes of India using microsatellite markers. Afr J Biotechnol 6: 348-354.

18.    Tu M, Lu BR, Zhu Y, Wang Y (2007) Abundant within-varietal genetic diversity in rice germplasm from Yunnan province of China revealed by SSR fingerprints. Biochem Genet 45: 789-801.

19.    Kostova A, Todorovska E, Christov N, Hristov K, Atanassov A (2006) Assessment of genetic variability induced by chemical mutagenesis in elite maize germplasm via SSR markers. J Crop Improv 16: 37-48.

20.    IRGSP (International Rice Genome Sequencing Project); Matsumoto T (2005) The map-based sequence of the rice genome. Nature 436: 793-800.

21.    Rahman MM, Rasaul MG, Hossain MA, Iftekharuddaula KM, Hasegawa H (2012) Molecular characterization and genetic diversity analysis of rice (Oryza sativa L.) using SSR markers. J Crop Improv 26: 244-257.

22.    Liu K, Muse SV (2005) Power marker: An integrated analysis environment for genetic marker analysis. Bioinformatics 21: 2128-2129.

23.    Anderson JA, Churchill GA, Autique JE, Tanksley SD, Sorreils ME (1993) Optimizing parental selection for genetic-linkage maps. Genome 36: 181-186.

24.    Nei M (1973) The theory and estimation of genetic distance. In edited by Morton NE, Genetic structure of populations. Honolulu, HI: University of Hawaii Press, pp: 45-54.

25.    Rohlf FJ (2002) NTSYS-pc: Numerical taxonomy and multivariate analysis system. Setauket, NY: Exeter Software.

26.    Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26: 297-302.

27.    Bligh HFJ, Blackhall NW, Edwards KJ, McClung AM (1999) Using amplified length polymorphisms and simple sequence length polymorphisms to identify cultivars of brown and white milled rice. Crop Sci 39: 1715-1721.

28.    Xu Y, Beachell H, McCouch SR (2004) A marker-based approach to broadening the genetic base of rice in USA. Crop Sci 44: 1947-1959.

29.    Jeung JU, Hwang HG, Moon HP, Jena KK (2005) Fingerprinting temperate japonica and tropical indica rice genotypes by comparative analysis of DNA markers. Euphytica 146: 239-251.

30.    Prabakaran A, Paramasivam K, Rajesh T, Rajarajan D (2010) Molecular characterization of rice land races using SSR markers. Electron J Plant Breed 1: 512-516.

31.    Siwach P, Jain S, Saini N, Chowdhury VK, Jain RK (2004) Allelic diversity among Basmati and non-Basmati long-grain indica rice varieties using microsatellite markers. J Plant Biochem Biotechnol 13: 25-32.

32.    Matin S, Ashrafuzzaman M, Islam MM, Sikdar SU, Zobayer N (2012) Molecular marker based (SSR) genetic diversity analysis in deep water rice germplasms of Bangladesh. Int J Biosci 2: 64-72.

33.    Jain S, Mitchell SE, Jain RK, Kresovich S, McCouch SR (2003). DNA fingerprinting and phylogenetic analysis of Indian aromatic high quality rice germplasm using panels of fluorescent‐labeled microsatellite markers. In: Advance in Rice Genetics, Khush GS, Brar DS and Hardy B (Eds), IRRI, Philippine, pp: 162‐165.

34.    Hashimoto Z, Mori N, Kawamura M, Ishii T, Yoshida S, et al. (2004) Genetic diversity and phylogeny of Japanese sake-brewing rice as revealed by AFLP and nuclear and chloroplast SSR markers. Theor Appl Genet 109: 1586-1596.

35.    Song MT, Lee JH, Cho YS, Jeon YH, Lee SB, et al. (2002) Narrow genetic background of Korean rice germplasm as revealed by DNA fingerprinting with SSR markers and their pedigree information. Korean J Genet 24: 397-403.

36.    Lin MS (1991) Genetic base of japonica rice varieties released in Taiwan. Euphytica 56: 43-46.