Molecular marker based (SSR) genetic diversity analysis in deep water rice germplasms of Bangladesh

64 Matin et al. Int. J. Biosci. 2012 RESEARCH PAPER OPEN ACCESS Molecular marker based (SSR) genetic diversity analysis in deep water rice germplasms of Bangladesh Sadia Matin1, M. Ashrafuzzaman1*, Md. Monirul Islam2, Saif U. Sikdar1, Nayem Zobayer1 1Department of Genetic Engineering and Biotechmology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet-3114, Bangladesh 2Biotechnology Division, Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh Names and addresses of the institutions (where the work has been carried out): Biotechnology Division, Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh Received: 22 September 2012 Revised: 15 October 2012 Accepted: 16 October 2012 Key words: SSR markers, genetic diversity, deep water rice. Abstract The study was undertaken to assess the genetic diversity among deep water rice genotypes using Simple Sequence Repeat (SSR) markers through marker aided selection (MAS). Twelve deep water rice (Oryza sativa L.) germplasms of Bangladesh was selected for genetic diversity analysis using eighteen SSR markers. Upon PCR amplification the alleles were separated on Polyacrylamide Gel Electrophoresis (PAGE) system. Initial polymorphism detection was conducted using eighteen primer pairs distributed on twelve rice chromosomes. The chosen microsatellite marker panel consisted of RM1, RM452, RM130, RM252, RM13, RM204, RM11, RM25, RM205, RM244, RM206, and RM463 with one representative from each chromosome. A total of 79 alleles were detected with an average of 4.38 alleles per locus. The polymorphism information content (PIC) reflections of alleles diversity frequency among the varieties, which is ranged from 0.477 to 0.782, with an average of 0.634. RM 13 was found as the best marker for identification of genotypes as revealed by PIC values. The Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrogram revealed 2 major groups with 4 clusters and the wide range of dissimilarity values (0.14-0.89) which showed a high degree of diversity among the cultivars. The results of the genetic diversity will be useful for the selection of the parents for developing submergence tolerant and flash flood tolerant rice variety through molecular breeding program. *Corresponding Author: M. Ashrafuzzaman  [email protected] International Journal of Biosciences (IJB) ISSN: 2220-6655 (Print) 2222-5234 (Online) Vol. 2, No. 10(2), p. 64-72, 2012 http://www.innspub.net Introduction Rice (Oryza sativa L.) belonging to the family Graminae is the staple food for one third of the world’s population (Chakravarthi and Naraveni, 2006). Deepwater rice is grown in flooded conditions with water more than 50 cm (20 inch) deep. More than 100 million people in South and Southeast Asia rely on deepwater rice for their sustenance. Many districts of Bangladesh are flooded during the rice cultivation season every year and thus curtail the national rice yield by causing severe damage to the rice cultivated field. Therefore it is high time to select potential rice cultivars for breeding program to develop submergence tolerant as well as flash flood resistant rice variety. Molecular markers have proven to be powerful tools in the assessment of genetic variation and in the elucidation of genetic relationships within and among species. The recent development of DNA markers has provided new opportunities for the genetic improvement of rice cultivars (Causse et al., 1994). Satellite loci also known as simple sequence repeats (SSRs) are the most commonly used molecular markers. Microsatellites are PCR-based markers that are efficient and cost-effective to use. Compared with other markers, they are abundant, co-dominant, highly reproducible and interspersed throughout the genome (Panaud et al., 1996, Temnykh et al., 2000). In particular, microsatellite markers have been widely applied in rice genetic studies as they are able to detect high levels of allelic diversity (McCouch et al., 1997). These markers can detect a significantly higher degree of polymorphism in rice (Ni et al., 2002, Okoshi et al., 2004) which becomes ideal for studies on genetic diversity and intensive genetic mapping (Cho et al., 2000). They have been used for characterizing genetic diversity in several crop species including sorghum (Dean et al., 1999, Smith et al., 2000), maize (Senior et al., 1998), cotton (Liu et al., 2000) and wheat (Prasad et al., 2000). In rice, SSRs have been used to assess the genetic diversity of both wild and cultivated species (Siwach et al., 2004, Brondani et al., 2005, Neeraja et al., 2005). Rice microsatellites also have a demonstrated utility for gene-tagging and marker-assisted selection (Chen et al., 1997) and are polymorphic between (Akagi et al., 1996, Panaud et al., 1996) and within rice varieties (Olufowote et al., 1997).These studies showed that SSR markers are efficient in detecting genetic polymorphisms and discriminating among genotypes. The important advantages of microsatellites are that they are usually single locus and because of the high mutation rate, are often multi-allelic. They are very efficient tools that can be exchange between laboratories and their data are highly informative (Morgante and Olivicri, 1993). The present investigation was made to identify the suitable SSR primers for genetic analysis of deepwater rice and to measure the genetic diversity and relatedness among twelve deep-water rice genotypes using SSR markers. Materials and methods The whole experiment was conducted at Biotechnology Laboratory of BRRI (Bangladesh Rice Research Institute) during the period of July/2011December/2011. Plant materials Seeds of twelve deep water rice varieties were collected from BRRI (Table 1). Seeds were germinated at aseptic condition by incubating them at 300 C and grown in glass house. Isolation of genomic DNA Genomic DNA was isolated from young leaves of 21 days old plants following the mini preparation modified CTAB method (Zheng et al., 1995). DNA samples were evaluated both quantitatively and qualitatively using spectrophotometer and λ (lamda) DNA (concentration marker) respectively. SSR analysis PCR amplification of SSR markers was carried out using eighteen primer pairs listed in table 2. Each reaction tube contained 25 ng of template DNA, 1 x PCR buffer, 2 mM MgCl2, 0.4 mM of dNTPs, 0.4 μM each of forward and reverse primers and 1.6 66 Matin et al. Int. J. Biosci. 2012 units of Taq DNA polymerase. Amplification was performed using the following conditions: denaturation at 94ºC for 5 min; 35 cycles of 1 min denaturation at 94ºC, 1 min annealing at 55ºC, 1 min extension at 72ºC and a final extension at 72ºC for 5 min. The SSR amplification products were separated in a vertical denaturing 8% polyacrylamide. DNA fragments were revealed using the ethidium bromide staining procedure. The gels were stained for 30-35 minutes and were documented using UVPRO (Uvipro Platinum EU) gel documentation unit. Data analysis Polymorphic information content (PIC) values were calculated for each SSR locus based on Anderson et al. (1993). Major allele frequency, gene diversity, polymorphism information content (PIC) values were determined using Power Marker Version 3.25 (Liu and Muse, 2005). The amplified bands were scored for each SSR primer pairs based on the presence or absence of bands, generating a binary data matrix of 1 and 0 for each marker system. Both matrices were then analyzed using the NTSYS pc statistical package version 2.2. The data matrices were used to calculate genetic similarity based on Jaccard’s similarity coefficients, and two dendograms displaying relationships among 12 rice cultivars were constructed using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA). The Pearson’s correlation between similarity coefficients based on SSR markers was determined from data among all twelve rice cultivars. Results and discussion The level of polymorphism among rice cultivars was evaluated by calculating allelic number and PIC values for each of the eighteen SSR loci evaluated. A total of 79 alleles were detected at the loci of eighteen microsatellite markers across twelve rice germplasms. The results revealed that all the primers showed distinct polymorphisms among the cultivars studied indicating the robust nature of microsatellites in revealing polymorphism. Among the polymorphic markers, 3 produced three alleles each, 9 produced four alleles each, 3 generated five alleles each, 2 produced 6 alleles each and only one produced 7 alleles (table 3). The number of alleles per locus ranged from 3 (RM1, RM 211, and RM 134) to 7 alleles (RM 13) with an average of 4.4 alleles across the 18 loci .The landraces frequency of most common allele at each locus ranged from 25% (RM25) to 50% (RM1, RM211, RM130, RM413, RM134 and RM463). On an average, 42.13% of the 12 landraces shared a common major allele at any given locus. Similar number of microsatellite markers previously used as subset for genetic diversity analysis of Oryza sativa (Garris et al., 2005, Thomson et al., 2007). The Value is comparable to 1-8 allele per SSR locus with an average number of alleles of 4.58 per locus for various classes of microsatellite (Siwach et al., 2004). The amplicon size of all 12 genotypes for each marker alleles varied from 73-81 bp produced by RM130 and 267-288 bp produced by RM586. The landraces frequency of most common allele at each locus ranged from 25% (RM25) to 50% (RM1, RM211, RM130, RM413, RM134 and RM463). On an average, 42.13% of the 12 landraces shared a common major allele at any given locus. Of the 79 alleles scored all of 79 were found to be polymorphic. Maximum number of polymorphic alleles (7) was obtained with the marker RM 13, while the minimum numbers of polymorphic alleles (3) was obtained by using RM 1, RM 211, and RM 134. Polymorphism information content (PIC) value is a reflection of allele diversity and frequency among varieties. PIC values ranged from 0.477 to 0.782 with an average of 0.634 (table 3). The highest PIC value 0.7818 was obtained for RM13 followed by respectively RM25 (0.760), RM85 (0.73) and RM252 (0.72). PIC value revealed that RM13 was considered as best marker for 12 test genotypes. The PIC value observed, are comparable to three previous estimates of microsatellite analysis in rice via 0.34-0.88 (Thomson et al., 2009), 0.20-0.90 with an average of 0.56 (Jain et al., 2003). The PIC 67 Matin et al. Int. J. Biosci. 2012 value was higher than the earlier observations (Joshi and Behera, 2006) also. Figure 1 showed gel pictures of amplified fragment using primer designed for the SSR marker RM 252 and RM 13. Fig. 1. DNA profile of the twelve deepwater rice land races with SSR marker RM 252 and RM 13. Legend: 1= Kata Mukul; 2= Dula Bexh; 3= Mota Kartik Sail; 4= Laxmi Digha; 5= Dulai Aman; 6= Bichi Bazal; 7= Manik Gira; 8= Aguli Aman; 9= Dudhsor; 10= Kartik Jhul; 11= Kartik Sail and 12= Kartik Gurol. L= Ladder marker. Fig. 2. A UPGMA clustering dendogram showing the genetic relationships among 12 landraces on the alleles detected by 18 microsatellite markers. Legend: DWR1= Kata Mukul; DWR2= Dula Bech; DWR3= Mota Kartik Sail; DWR4= Laxmi Digha; DWR5= Dulai Aman; DWR6= Bichi Bazal; DWR7= Manik Gira; DWR8= Aguli Aman; DWR9= Dudhsor; DWR10= Kartik Jhul; TAL1= Kartik Sail and TAL2= Kartik Gurol. A cluster analysis using UPGMA based on similarity coefficients was done to resolve the phylogenetic relationships among the different deepwater rice genotypes considered for the present study. The UPGMA clustering system generated four genetic clusters with similarity coefficient 33% (fig. 2). Cluster 2 was the biggest group which contained four landraces viz. Kartik Jhul, Dudhsor, Manik Gira and Aguli Aman. The cluster analysis revealed that Kartik Jhul, Dudhsor and Manik Gira are closer than Anguli Aman while Kartik Jhul and Dudhsor are closer than Manik Gira. Cluster 1 and 4 contained three landraces in each cluster. The three genotypes, Kata Mukul, Mota Kartik sail and Dula Bech were clustered distinctly in the same group (cluster 1) but Kata Mukul and Mota Kartik are closer than the Dula Bech. Again, Laxmi Digha, Dulai Aman and Bhchi Bazal were clustered in same group (cluster 4) but Laxmi Digha and Dulai Aman are closer than the Bhchi Bazal. Cluster 3 was the smallest group which contains two T. Aman landraces viz. Kartik Sail and Kartik Gurol. Table 1. List of twelve test genotypes for diversity analysis. SI No. BRRI accession No. Germplasms G39 2047 Kata Mukul G40 2050 Dula Bech G41 6351 Mota kartik sail G42 6352 Laxmi Digha G43 6353 Dulai Aman G44 6355 Bichi Bazal G45 6356 Manik Gira G46 6357 Aguli Aman G47 6358 Dudhsor G48 6359 Kartik Jhul G49 6360 Katik Sail G50 6362 Kartik Gurol This cluster tree analysis agreed with the allelic diversity observed among Basmati and Nonbasmati long grain indica rice varieties using microsatellite markers (Siwach et al, 2004). DNA fingerprinting and phylogenic analysis of Indian aromatic high quality rice germplasms also showed similar trend (Jain et al, 2003). The pair wise genetic dissimilarity coefficient indicated that the highest (100%) genetic 68 Matin et al. Int. J. Biosci. 2012 dissimilarity was found between Kata Mukul with Kartik Sail and Kartik Gurol; Dula Bech with Aguli Aman, Kartik Sail & Kartik Gurol and Manik Gira with Kartik Gurol (table 4). Besides, 94% dissimilarity was found between Dula Bech with Bichi Bazal as well as Mota Kartik Sail with Bichi Bazal and Kartik Gurol. However, potential hybrid line can be produced by inter-varietal crossing based on the genetic dissimilarity value since the more the genetic dissimilarity value the more chance of getting vigorous heterosis in the progeny. Hence microsatellite marker based molecular fingerprinting could serve as a potential basis in the identification of genetically distance genotypes as well as in sorting of duplication for morphologically close accession. Table 2. List of eighteen SSR primers with position, and expected PCR product sizefor the study. There have been a number of studies that have reported on the assessment of genetic diversity in a relatively large set of cultivated germplasm. This include diversity analysis of high yielding cultivars (Ni et al., 2002), aromatic rice (Nagaraju et al., 2002), indigenous aromatic rice (Joshi and Behera, 2006) and even lowland rice (Bhuyan et al., 2007, Yu and Nguyen, 1994) using various molecular fingerprinting techniques like RFLP, RAPD, SSR, AFLP etc. Since the long term objective is to utilize rice germplasm for broadening the genetic base of the cultivated rice, the present study is an attempt at characterizing diversity at the molecular level in this set of lowland rice genotypes to broaden the genetic base of cultivated rice in the rain fed and lowland areas of Bangladesh such as Sunamganj, Hobigonj, Bogra and the Sirajgonj. Moreover, the cultivars with wide genetic distance can be crossed to widen the genetic base and exploit heterosis. The informative primers would prove useful in markerassisted selection, linkage mapping and gene tagging for specialty traits. Name Position Product size (bp) Forward primer (5/ - 3/) Reverse primer (3/- 5/) RM 1 4.63 113 gcgaaaacacaatgcaaaaa gcgttggttggacctgac RM452 9.50 105 Ctgatcgagagcgttaaggg gggatcaaaccacgtttctg RM 211 4.16 161 Ccgatctcatcaaccaactg cttcacgaggatctcaaagg RM 85 66.76 107 ccaaagatgaaacctggattg gcacaaggtgagcagtcc RM 130 33.33 85 tgttgcttgccctcacgcgaag ggtcgcgtgcttggtttggttc RM127 34.19 223 gtgggatagctgcgtcgcgtcg aggccagggtgttggcatgctg RM252 45.21 216 Ttcgctgacgtgataggttg atgacttgatcccgagaacg RM13 8.27 141 tccaacatggcaagagagag ggtggcattcgattccag RM413 2.19 79 Ggcgattcttggatgaagag tccccaccaatcttgtcttc RM204 3.17 169 Gtgactgacttggtcataggg gctagccatgctctcgtacc RM586 1.47 271 Acctcgcgttattaggtaccc gagatacgccaacgagatacc RM11 19.25 140 Tctcctcttcccccgatc atagcgggcgaggcttag RM134 26.63 93 acaaggccgcgagaggattccg gctctccggtggctccgattgg RM 25 2.59 146 ggaaagaatgatcttttcatgg ctaccatcaaaaccaatgttc RM205 22.72 122 Ctggttctgtatgggagcag ctggcccttcacgtttcagtg RM 244 4.34 163 Ccgactgttcgtccttatca ctgctctcgggtgaacgt RM 206 21.97 147 Cccatgcgtttaactattct cgttccatcgatccgtatgg RM 463 22.09 192 Ttcccctccttttatggtgc tgttctcctcagtcactgcg 69 Matin et al. Int. J. Biosci. 2012 Table 3. Number of alleles, highest frequency allele and Polymorphism Information Content (PIC) Values found among twelve rice germplasms for eighteen SSR markers. Primer names Chromosom e no. Allele no. Amplicon size range Major Allele Major Allele frequency PIC value Gene Diversity RM 1 1 3 76-111 82 0.5000 0.4768 0.5694 RM452 2 5 199-211 211 0.4167 0.6990 0.7361 RM 211 2 3 144-150 150 0.5000 0.5355 0.6111 RM 85 3 5 91-113 113 0.3333 0.7260 0.7639 RM 130 3 4 73-81 75 0.5000 0.5593 0.6250 RM127 4 4 218-226 224 0.4167 0.6218 0.6806 RM252 4 6 195-265 200 0.4167 0.7193 0.7500 RM13 5 7 136-152 138 0.3333 0.7818 0.8056 RM413 5 4 74-82 80 0.5000 0.6204 0.6667 RM204 6 4 117-124 117 0.4167 0.6218 0.6806 RM586 6 4 267-288 267 0.3333 0.6874 0.7361 RM11 7 5 127-149 127 0.4167 0.6437 0.6944 RM134 7 3 92-94 94 0.5000 0.5355 0.6111 RM 25 8 6 138-155 145 0.2500 0.7601 0.7917 RM205 9 4 123-134 134 0.4167 0.6218 0.6806 RM 244 10 4 162-168 165 0.4167 0.6218 0.6806 RM 206 11 4 137-166 137 0.4167 0.6218 0.6806 RM 463 12 4 195-205 205 0.5000 0.5593 0.6250 Mean 4.3889 0.4213 0.6341 0.6883 Table 4. Genetic dissimilarity pair (below diagonal)) values among studied twelve deep water rice genotypes. 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