EVALUATION OF GENETIC DIVERSITY IN WHEAT CULTIVARS AND BREEDING LINES USING INTER SIMPLE SEQUENCE REPEAT MARKERS

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1 Article DOI: /bbeq B&E EVALUATION OF GENETIC DIVERSITY IN WHEAT CULTIVARS AND BREEDING LINES USING INTER SIMPLE SEQUENCE REPEAT MARKERS Abdollah Najaphy 1, Reza Ashrafi Parchin 1,2 and Ezatollah Farshadfar 1 1 Department of Agronomy and Plant Breeding, Faculty of Agriculture, Razi University, Kermanshah, Iran 2 Present address: Young Researchers Club, Islamic Azad University, Ardabil Branch, Iran Correspondence to: Abdollah Najaphy nadjaphy@yahoo.com, anajaphy@razi.ac.ir ABSTRACT The genetic diversity of thirty wheat genotypes was evaluated using inter simple sequence repeat (ISSR) markers. The ten ISSR primers amplified a total of 86 bands in the set of thirty wheat accessions, of which 69 bands (80.2%) were polymorphic. The ISSR primers informativeness was assessed by calculating four marker parameters like polymorphism information content (PIC), effective multiplex ratio (EMR), marker index (MI) and resolving power (RP) were calculated. The majority of the primers showed PIC value close to the average ( ), while EMR feature varied from 1.8 to 12 with a mean value of The MI values ranged between 0.41 and The primers that showed higher polymorphism had higher EMR and MI values. The estimates of RP ranged from 7.2 to 16.5 with an average of 12 per primer. Three of the ISSR primers possessed high RP values (14.9, 15.7 and 16.5) and therefore seemed to be the most informative primers for distinguishing the genotypes. The genotyping data for all the ISSR markers were used to assess genetic variation in wheat accessions by CLINK- based dendrogram and principle coordinate analysis (PCoA). Both of the methods classified the 30 wheat accessions in five groups and presented similar grouping of the genotypes with some minor disagreements. The results showed that the studied ISSR markers provided sufficient polymorphism and reproducible fingerprinting profiles for evaluating genetic diversity of wheat genotypes. The molecular variation evaluated in the present study, in combination with agronomic and morphological characteristics of wheat, can be useful in traditional and molecular breeding programs. Biotechnol. & Biotechnol. Eq. 2011, 25(4), Keywords: genetic diversity, ISSR markers, marker parameters, polymorphism, wheat Abbreviations: PIC: polymorphism information content; EMR: effective multiplex ratio; MI: marker index; RP: resolving power; ISSR: inter simple sequence repeat Introduction Analysis of genetic relationships in crops is a prerequisite for crop breeding programs, as it serves to provide information about genetic variation (10). Lack of genetic diversity can potentially lower the resistance of cropping systems to unknown or evolving pests, pathogens, or adverse environmental conditions. Diversity studies based on morphological traits may not be completely reliable because the traits are influenced by environment. Molecular diversity evaluated by using molecular markers is independent of environmental influences and can be estimated by using DNA from any growth stage (21). Molecular characterization is now the favored means to quantify variation within germplasm samples (5). Molecular markers have elucidated the structure of genetic diversity in a wide range of plant species. Molecular diversity studies evaluate all levels of genetic structure, ranging from relationships between species complex components to the origin of particular genotypes (8). For these purposes, different marker systems such as AFLP (1, 21), ISSR (4, 20), RAPD (9) and SSR (26) have been used. The inter simple sequence repeat (ISSR) technique is a PCR based method that is highly effective in plant fingerprinting and phylogenetics studies (23). ISSR analysis involves amplification of regions between adjacent and inversely oriented microsatellites using di-, tri-, tetra- and pentanucleotide SSR primers, with the advantage the that knowledge of the DNA sequence of the target regions is not needed. This marker combines most of the benefits of AFLP and SSR analysis with the universality of RAPD (4, 16). Our objectives in the present study were: 1) to determine the genetic diversity in wheat genotypes using ISSR markers, and 2) to assess the informativeness of the markers for detecting molecular variation. Materials and Methods Plant materials Thirty wheat genotypes (Table 1) were planted in early November, growing season of , at the research farm of Razi University, Kermanshah, Iran. The seeds were provided by the Dryland Agricultural Research Institute, and Agricultural Research Center, Kermanshah, Iran. The wheat cultivars have been developed for cold, temperate and warm regions and the breeding lines have been selected for rain-fed conditions Biotechnol. & Biotechnol. Eq. 25/2011/4

2 List, pedigree and some characters of 30 wheat genotypes used in this study TABLE 1 Genotype code Name Pedigree Phenotypic character(s) 1 F103-L-1-12//PONY/OPATA Short awn 2 OR F1.158/FDL//BLO/3/SH14414/CROW/4/C ICWH AP-0AP-OMAR-6MAR High straw yield 3 PYN/BAU//VORONA/HD2402 Awn-less, short peduncle 4 KATILA-13 High thousand seed weight 5 SARDARI-HD35/5/DMN//SUT/AG(ES86-7)/3/ ICWH AP-0AP-OMAR-3MAR Drought resistance 6 Zarin High potential yield 7 CA8055//KS82W409/STEPHENS High straw yield 8 Bolani Rust susceptible 9 Shahriar Long awn 10 WS-82-9 Earliness, terminal drought resistance 11 SABALAN/4/VRZ/3/OR F1.148/TDL//BLO Terminal drought resistance 12 HAMAM-4 Tallness, low grain yield 13 Atila2/PBW65 Earliness, long awn 14 KAUZ S /MACHETE Earliness 15 M-79-7 High potential yield 16 Pishtase Terminal drought resistance 17 KAR-1//RMNF12-71/JUP S Lateness, high straw yield 18 QAFZAH-25 Long awn 19 Marvdasht High potential yield 20 Chamran Long awn, high potential yield 21 M Terminal drought resistance 22 TEVEE S //CROW/VEE S Terminal drought resistance 23 M Terminal drought resistance 24 M-83-6 Terminal drought resistance 25 M-82-6 Terminal drought resistance 26 Jcam/Emu s //dove S /3/Alvd/4/MV17/Attila Dwarfness, earliness, high potential yield 27 Shiraz Dense spike 28 STAR/SHUHA-4 Dwarfness 29 KATILA-1 KATILA-1 Short spike 30 Pishgam Bkt/Zhong Dwarfness, high potential yield, drought resistance DNA extraction and ISSR amplification Wheat young leaves were harvested from all genotypes and used for DNA isolation using the CTAB method described by Murray and Thompson (11). Fifteen ISSR markers were used for screening all the genotypes and revealing the genetic diversity. PCR amplification was conducted according to Williams et al. (25) with the exception that the reactions were performed in a volume of 25 μl in a CPRBETT Research thermocycler. Amplified PCR products were run in 1.2% agarose gels. Gels were stained with ethidium bromide and Biotechnol. & Biotechnol. Eq. 25/2011/4 visualized with a UV transilluminator. Ten out of 15 ISSR primers produced high resolution bands for all samples and were used for data analysis (Table 2). Statistical analysis ISSR markers were scored for the presence (1) or absence (0) of amplified bands for each of 30 samples. The ISSR binary data matrix was used to calculate the Jaccard similarity coefficient. Cluster analysis was performed via complete linkage method using NTSYS-pc software version 2.02 (18). 2635

3 Principle coordinate analysis (PCoA) was also carried out by this software. For each ISSR marker, total amplified bands, number of polymorphic bands, and percentage of polymorphic bands (PPB) were recorded. To measure the informativeness of the ISSR markers to differentiate between wheat genotypes, polymorphism information content (PIC), effective multiplex ratio (EMR), marker index (MI) and resolving power (RP) were calculated. PIC was calculated according to the formula of Anderson et al. (2), as PIC = 1 - p i2, where p i is the frequency of the ith allele of the locus in the set of thirty wheat genotypes. EMR is the product of the fraction of polymorphic bands and the number of polymorphic bands (9). MI was determined according to Powell et al. (15) as the product of PIC and EMR. RP was calculated using the formula RP= I b, where I b is band informativeness and I b = 1-[2 (0.5 - p)], where p is the proportion of genotypes containing the band (1). TABLE 2 ISSR markers used for analysis of genetic diversity of wheat genotypes Primer Annealing temperature Sequence (3-5 ) names ( C) UBC-811 (GA) 8 C 50 UBC-814 (CT) 8 A 54 UBC-815 (CT) 8 G 52 UBC-822 (TC) 8 A 55 UBC-826 (AC) 8 C 55 UBC-834 (AG) 8 TT 54 UBC-840 (GA) 8 TT 46 UBC-845 (CT) 8 TT 46 UBC-852 (TC) 8 AA 48 UBC-876 (GATA) 2 (GACA) 2 49 Results and Discussion Fifteen ISSR primers were initially screened for their ability to produce polymorphic patterns across the thirty wheat genotypes. Ten primers which were repeatable and produced high resolution bands for all the genotypes were selected for evaluation of genetic diversity in the accessions (Table 3). ISSR polymorphism The ten ISSR primers amplified a total of 86 bands in the set of thirty wheat accessions, of which 69 bands were polymorphic. The number of bands varied from five (UBC- 822) to twelve (UBC-834 and UBC 840). The percentage of polymorphic bands (PPB) ranged between 60 and 100 with an average of 80.2% (Table 3). The mean numbers of bands and polymorphic bands per primer were 8.6 and 6.9, respectively. Variable efficiencies of different marker systems for detecting DNA polymorphism in wheat have been reported. Joshi and Nguyen (6) observed 1.8 polymorphic bands per RAPD primer among 15 wheat cultivars, while SSRs with 6.2 alleles/ bands were more polymorphic (14). The number of RFLP polymorphic bands per probe/enzyme combination in 124 bread wheat cultivars was 3.3 (13). Altintas et al. (1) observed 47% polymorphism among 22 bread wheat cultivars using five AFLP and three SAMPL primer pairs with an average of 20.4 polymorphic loci per primer pair. Nagaoka and Ogihara (12) detected 3.7 polymorphisms per ISSR primer, while Carvalho et al. (4) reported 12.9 polymorphic bands per primer using 18 ISSR primers in 48 wheat accessions. We detected a high level of polymorphism among the wheat genotypes using ISSRs, indicating high efficiency of the marker technique to reveal genetic diversity in the case of wheat. The lowest polymorphism value (57.1%) was obtained with the UBC-876 primer [(GATA) 2 (GACA) 2 ] (Table 3). Primers based on more infrequent tetranucleotide SSRs amplified few bands in rice (3), while they detected more polymorphism in Dent and Popcorn (7). The ISSR primers with dinucleotide motifs (GA)n, (CT)n and (AG)n produced a high level of polymorphism (Table 3). These results are in agreement with those of Carvalho et al. (4) who reported that dinucleotide primers were more suitable for amplifying ISSRs in bread and durum wheat. SSRs seems to be randomly distributed in the genome, and (GA) n dinucleotide repeats are most abundant in plant species (19, 24). Polymorphism information content (PIC) The PIC values for the ten primers varied from 0.13 to 0.42 with an average of The lowest and highest PIC indices were recorded for primer UBC-811 and UBC-815, respectively. More than half of the primers (6) showed PIC values between 0.21 and 0.23 (Table 3). The moderate values of PIC for the ISSR primers could be attributed to the diverse nature of the wheat accessions and/or highly informative ISSR markers used in this study. The PIC index has been used extensively in many genetic diversity studies (20, 21, 22). Marker index (MI) and effective multiplex ratio (EMR) MI is a feature of a marker and was calculated for all the primers. The MI values ranged between 0.41 and The maximum MI (3.36) was observed for the primer UBC-815 and the minimum MI (0.41) was obtained with ISSR primers UBC-822 and UBC-876. The primers that showed higher polymorphism had higher EMR values. This feature varied from 1.8 to 12 with a mean value of MI was positively correlated with PIC (r = 0.79, P < 0.01). A positive correlation was found between EMR and PPB (r = 0.896, P < 0.001). EMR is the product of the fraction of polymorphic bands and the number of polymorphic bands and MI is the product of PIC and EMR, therefore the higher polymorphism provides higher EMR. These two features have been used to evaluate the discriminatory power of molecular marker systems in some plant species e.g. apricot (ISSR, EMR = 4.8, MI = 3.74) (9), Jatropha (AFLP, EMR = 97, MI = 25.13) (21), Pongamia (AFLP, EMR = 77.2, MI = 16.83) (22). In our assessment, EMR varied from 1.8 to 12 (average 5.69) and MI was reported in the range of (average 1.34) Biotechnol. & Biotechnol. Eq. 25/2011/4

4 Parameters of genetic variation generated by ISSR markers TABLE 3 Primer Total amplified No. of polymorphic bands bands PPB a PIC b EMR c MI d RP e UBC UBC UBC UBC UBC UBC UBC UBC UBC UBC Total Minimum Maximum Mean a Percentage of polymorphic bands; b Polymorphism information content; c Effective multiplex ratio; d Marker index; e Resolving power Resolving power (RP) The estimates of RP ranged from 7.2 to 16.5 with an average of 12 per primer. The highest RP was recorded for the primer UBC-845 (16.5) followed by UBC-834 (15.7) and the lowest value was scored with the primers UBC-815 (7.2) and UBC- 822 (7.3). RP was positively correlated with the total amplified bands (r = 0.732, P < 0.05). Prevost and Wilkinson (17) introduced the RP (resolving power) index that provides a moderately accurate estimate of the number of genotypes identified by a primer. Three of the ISSR primers (834, 840 and 845) possessed high RP values (15.7, 14.9 and 16.5, respectively) and therefore seem to be the most informative primers for distinguishing the genotypes. RP had no significant correlation with the other parameters e.g. PPB, PIC, EMR and MI in this study. The resolving power provides no information on the ability of a primer to reflect the genetic or taxonomic relationships of a group of genotypes under study (17). Genetic relationships among wheat genotypes Jaccard similarity matrix based on ISSR binary data was used to group the wheat accessions using the complete linkage (CLINK) method. The dendrogram obtained from the method, in comparison with the UPGMA method had higher cophenetic correlation and no chaining. Genetic similarity ranged between 0.48 and 0.91 (data not shown). The dendrogram classified the thirty wheat genotypes into five clusters (Fig. 1). Cluster 1 included genotypes 1 and 23. Accessions 7, 19, 26, 25, 10, 30 and 29 were grouped in the second cluster. Cluster 3 contained Biotechnol. & Biotechnol. Eq. 25/2011/4 five genotypes (8, 22, 28, 9 and 24). Cluster 4 contained a total of 13 genotypes (2, 3, 5, 15, 18, 6, 14, 12, 20, 16, 17, 21 and 4). Genotypes 11, 27 and 13 were grouped in cluster 5. Fig. 1. CLINK dendrogram of 30 wheat genotypes based on ISSR marker data. The principle coordinate analysis results are illustrated in Fig. 2. The thirty genotypes were grouped into five groups based on two-dimensional graph. Group 1 contained genotypes 1 and 23. Group 2 included 10 genotypes (7, 19, 20, 22, 28, 8, 26, 10, 30 and 29). The third group contained accessions 11, 27, 28, 25, 9 and 24. Group 4 contained a total of 12 genotypes 2637

5 (2, 3, 5, 15, 18, 6, 14, 12, 16, 17, 21 and 4). Group 5 included only genotype 13. Fig. 2. Scatter plot of wheat genotypes using principle coordinate analysis based on ISSR data. The results of the two methods (Cluster analysis and principle coordinate analysis) were comparable. Both of them classified the 30 wheat accessions in 5 groups and presented similar grouping of the genotypes with some minor disagreements. The obtained clusters/groups were not in accordance with the known geographical location. Conclusions The present study showed that ISSR analysis is quick and reliable. The marker system provided sufficient polymorphism and reproducible fingerprinting profiles for evaluating genetic diversity of wheat genotypes. MI and RP are proposed as marker parameters for selecting informative primers. Molecular variation assessed in this study in combination with agronomic and morphological characters of wheat can be useful in traditional and molecular breeding programs. REFERENCES 1. Altıntas S., Toklu F., Kafkas S., Kilian B., Brandolini A., Ozkan H. (2008) Plant Breed., 127, Anderson J.A., Churchill G.A., Autrique J.E., Tanksley S.D., Sorrells M.E. (1993) Genome, 36, Blair M.W., Panaud O., Mccoush S.R. (1999) Theor. Appl. Genet., 98, Carvalho A., Lima-Brito J., Macas B., Guedes-Pinto H. (2009) Biochem. Genet., 47, Glaszmann J.C., Kilian B., Upadhyaya H.D., Varshney R.K. (2010) Curr. Opin. Plant Biol., 13, Joshi C.P. and Nguyen H.T. (1993) Plant Sci., 93, Kantety R.V., Zeng X.P., Bennetzen J.L., Zehr B.E. (1995) Mol. Breed., 1, Kilian B., Ozkan H., Walther A., Kohl J., Dagan T., Salamini F., Martin W. (2007) Mol. Biol. Evol., 24, Kumar M., Mishra G.P., Singh R., Kumar J., Naik P.K., Singh S.B. (2009) Physiol. Mol. Biol. Plants, 15, Mohammadi S.A. and Prasanna B.M. (2003) Crop Sci., 43, Murray M.G. and Thompson W.F. (1980) Nucl. Acids Res., 8, Nagaoka T. and Ogihara Y. (1997) Theor. Appl. Genet., 94, Paul J.G., Chalmers K.J., Karakousis A., Kretschmer J.M., Manning S., Langridge P. (1998) Theor. Appl. Genet., 96, Plaschke J., Ganal M.W., Roder M.S. (1995). Theor. Appl. Genet., 91, Powell W., Morgante M., Andre C., Hanafey M., Vogel J., Tingey S., Rafalsky A. (1996) Mol. Breed., 2, Pradeep Reddy M., Sarla N., Siddiq E.A. (2002) Euphytica, 28, Prevost A. and Wilkinson M.J. (1999) Theor. Appl. Genet., 98, Rohlf F.J. (2000) NTYSYS-pc ver Numerical taxonomy and multivariate analysis system. Exeter software, Setauket, NY. 19. Steinkellner H., Lexer C., Turetscheck E., Glossl J. (1997) Mol. Ecol., 6, Talebi R., Haghnazari A., Tabatabaei I. (2010) Biharean Biol., 4, Tatikonda L., Wani S.P., Kannan S., Beerelli N., Sreedevi T.K., Hoisington D.A., Devi P., Varshney R.A. (2009) Plant Sci., 176, Thudi M., Manthena R., Wani S.P., Tatikonda L., Hoisington D.A., Varshney R.A. (2010) J. Plant Biochem. Biotech., 19, Vaillancourt A., Nkongolo K.K., Michael P., Mehes M. (2008) Euphytica, 159, Wang Z., Weber J.L., Zhong G., Tanksley S.D. (1994) Theor. Appl. Genet., 88, Williams J.G., Kubelik A.R., Livak K.J., Rafalski J.A., Tingey S.V. (1990) Nucl. Acids Res., 18, Zarkti H., Ouabbou H., Hilali A., Udupa S.M. (2010) Afr. J. Agric. Res., 5, Biotechnol. & Biotechnol. Eq. 25/2011/4