Table of Contents
International Journal of Plant Genomics
Volume 2015 (2015), Article ID 965073, 7 pages
Research Article

Identification and DUS Testing of Rice Varieties through Microsatellite Markers

1Department of Agronomy and Plant Breeding, College of Agriculture, Miyaneh Branch, Islamic Azad University, Miyaneh 53158-36511, Iran
2Seed & Plant Certification and Registration Institute (SPCRI), Collection Avenue, Nabovvat Boulevard, Karaj 31535-1516, Iran

Received 16 September 2014; Accepted 19 January 2015

Academic Editor: Pierre Sourdille

Copyright © 2015 Ehsan Pourabed et al. This is an open access article distributed under the Creative Commons Attribution License, which 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 molecular markers that are more reliable. The objectives of this study were, first, the identification and distinction of 40 rice varieties consisting of local varieties of Iran, improved varieties, and IRRI varieties using PIC, and discriminating power, second, cluster analysis based on Dice similarity coefficient and UPGMA algorithm, and, third, determining the ability of microsatellite markers to separate varieties utilizing the best combination of markers. For this research, 12 microsatellite markers were used. In total, 83 polymorphic alleles (6.91 alleles per locus) were found. In addition, the variation of PIC was calculated from 0.52 to 0.9. The results of cluster analysis showed the complete discrimination of varieties from each other except for IR58025A and IR58025B. Moreover, cluster analysis could detect the most of the improved varieties from local varieties. Based on the best combination of markers analysis, five pair primers together have shown the same results of all markers for detection among all varieties. Considering the results of this research, we can propose that microsatellite markers can be used as a complementary tool for morphological characteristics in DUS tests.

1. Introduction

In order to introduce a new plant variety to the markets commercially, it is necessary to register a newly bred variety, which relies upon the results of DUS (distinctness, uniformity, and stability) tests; that is, for a new genotype to be registered 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]. Therefore, DUS test has been established to be the foundation of plant variety protection and also to identify a new variety from reference collection [3].

The new variety should pass legal examinations to be commercialized and receive the certificate for the plant breeder’s rights, a part of which consists of DUS tests according to morphological characteristics.

The current system of DUS testing has come across several significant shortcomings. The varieties to be assessed are increasing in number where their variability reduces, and the reference collections are expanding because of their internationalization, both of which result in the dramatic increase in expenses associated with these methods. Moreover, the existing methods are time consuming, which have altogether led to more necessity for developing a substitutionary, less costly system. Thus, the studies on the use of molecular markers in DUS testing proving the expected capability of molecular markers have encouraged International Union for the Protection of New Varieties of Plants (UPOV) to contemplate the introduction of molecular markers to the DUS testing system. Nevertheless, before this decision could be made, there are several issues to be resolved.

DUS testing would benefit from the use as molecular markers that have been shown to be more rapid and cost effective in comparison with morphological traits. In several registration processes such as cultivar identification, molecular markers have been utilized successfully [4].

As a prominent concern, molecular markers were not accessible, which have been gradually settled over time. Primarily, studies were restricted to using the dominant type of markers [5, 6] where continuous development of simple sequence repeat (SSR) markers has recently resulted in the prevalence of mentioned markers [2, 3, 7].

Microsatellite markers have been characterized with multiallelic nature, codominance inheritance, and relative abundance as well as requiring small quantities of DNA for amplification [8] which have made these markers efficiently applicable in DUS test of rice varieties [9, 10]. UPOV has confirmed the application of SSR markers as one of the commonly practical molecular marker systems for the identification of plant varieties [11].

This marker was previously confirmed to be applied to the distinction between plant varieties as well as complementary features in DUS tests where microsatellites were used in DUS tests on pepper [3], canola [2], and corn [12].

In this study, microsatellite markers was intended to identify rice varieties. The efficiencies of SSR markers were evaluated as complementary tools for the distinction of these varieties.

2. Materials and Methods

2.1. Plant Materials and DNA Extraction

In this study, 40 rice varieties consist of 27 varieties created in Rice Research Institute of Iran (RRII) and 10 local varieties from three regions of Guilan, Mazandaran, and Isfahan in Iran, and three International Rice Research Institute (IRRI) varieties were used (Table 1). All varieties used for this study were from indica subspecies. Fifteen seeds of each variety were selected and then 5 g of young and healthy leaves was used for DNA extraction. The DNA was extracted using the CTAB method with minor modifications (increasing extraction buffer density in two times and replacing Mercaptoethanol (0.2 percent) with Dithiothreitol (30 mM)) [13].

Table 1: Forty rice varieties used for this study, including name of variety and breeder.

2.2. Microsatellites Reaction

Twelve pairs of SSR primers (a pair of primers of each chromosome) were selected from the panel of 50 from Gramene database ( (Table 2).

Table 2: Characteristics of polymorphic microsatellite markers used in this study, including locus name, number of chromosome, primer sequences, number of alleles, effective number of alleles, polymorphic information content (PIC), and discriminating power ().

It selected a pair of primers from a mitochondrial DNA sequence (drrcms marker) (Forward: 5′ ACCTTTGGGCGATGGTT 3′; Reverse: 5′ GGGTTTAGAGTCGCCAC 3′) to detect the impurities in CMS line (IR58025A) from its cognate isogenic maintainer line (IR58025B) which is a prerequisite to obtain pure seeds of hybrid rice as well [14].

PCR reaction was carried out in a total volume of 15 μL containing 3 µL (25 ng) of template DNA, 1 µL (0.66 µmol/L) primers, 1.5 µL 10x PCR buffer, 1.5 µL dNTPs (0.2 mmol/L), 1.2 µL MgCl2 (two mmol/L), 0.2 µL Taq DNA polymerase (0.6 U/15 µL), and 5.6 µL ddH2O. An initial denaturation period of five min at 94°C was followed by 35 cycles of 60 s at 94°C, 30 s at 56–66°C, 120 s at 72°C, and then five min at 72°C for final extension. After amplification, the PCR products were separated on 6% (w/v) polyacrylamide gel and visualized by silver staining [15].

2.3. Statistical Analysis

The frequency of microsatellite polymorphism was calculated based on presence (1) or absence (0) of common bands. The genetic similarity between varieties was calculated using the Dice coefficient [16], and a dendrogram showing the genetic relationship of the 40 varieties was constructed using the unweighted pair group method with the arithmetic mean (UPGMA) [17] features of the NTSYSpc v2.02 statistical analysis package [18]:

Accordingly, is the number of bands (alleles) in both individuals; is the number of bands (alleles) absent in both individuals; is the number of bands (alleles) in , is the number of bands (alleles) present in , and is the total number of all bands (alleles).

Effective number of alleles [19] in each SSR locus was calculated by the following formula: where is the frequency of th allele for each locus.

The polymorphic information content (PIC) [20] of microsatellite loci was calculated according to the following formula: where is the frequency of the th allele for th marker and the summation extends over alleles.

Moreover, the discriminative power of molecular markers and the best combination of microsatellite markers [21] were estimated using the following steps and formulas.

There are different pairs in a set of individuals. Based on this, is the th pattern of the given th primer in which formula is

Moreover, for the th primer, is equivalent to the summation of the different for all patterns generated by the primer:

Therefore, the discriminating power of the th primer and the best combination of primers are equal to

3. Results and Discussion

3.1. Evaluation of Microsatellite Markers

The 12 microsatellite primers used for this study generated totally 83 polymorphic fragments with an average of 6.91 alleles per locus. Among these markers, RM316 with 13 alleles and RM55 with three alleles had the highest and lowest variation, respectively. Effective number of alleles was calculated from 2.44 (RM161) to 9.52 (RM316) with an average of 4.90 per locus. Additionally, the PIC was estimated from 0.52 (RM161) to 0.9 (RM316) with an average of 0.74 per locus. The discriminating power ranged between 0.57 (RM161) and 0.93 (RM316) with an average of 0.78 per locus (Table 2). The most of microsatellite markers had a high PIC and discriminating power. However, a few markers had a low range of PIC and discriminating power such as RM55 and RM161. Our results agree partially with those of Hashemi et al. [22], who utilized 10 microsatellite markers to characterize the genetic diversity in a group of 16 Iranian rice hybrids. PIC is regarded as one of the important features of the molecular markers and can be used to evaluate the differentiation ability of the markers [23].

3.2. Genetic Similarity and Relationships among Varieties

The range of similarity among varieties was from 0 to 1 with an average of 0.26 and variance of 0.063 for all microsatellite markers. Similarity values in between varieties were 0 for 66 pairs of varieties (supplementary file 1; see Supplementary Material available online at and similarity value had been 1 just for one pair of varieties (IR58025A versus IR58025B). IR58025A is a CMS line, and, for detecting the CMS line from its cognate, isogenic maintainer line (IR58025B) was used; the polymorphism of a mitochondrial DNA sequence (drrcms marker) between some of CMS population and their fertile lines is verified in both lines (Figure 1).

Figure 1: Detecting the impurities in CMS line from its cognate isogenic maintainer line. Polymorphism between CMS (IR58025A) and maintainer line (IR58025B) of rice at mitochondrial drrcms marker.

Dendrogram resulted from cluster analysis using UPGMA algorithm based on the Dice similarity coefficient and could discriminate all varieties from each other except for two isonuclear lines (IR58025A and IR58025B) (Figure 2). As a result, total microsatellite markers could detect most of the improved varieties (Group A) from local varieties (Group B). However, Sang-e-Jo and Hassan-Saraie as local varieties stood with improved varieties in Group A, because Sang-e-Jo and Sepid-Rood were used as recipient parents for Ghaem-1 variety. As can be observed in the dendrogram, Sang-e-Jo, Sepid-Rood, and Ghaem-1 have been in the same subcluster. Five improved varieties stood beside local varieties in Group B as well. These varieties are Shafagh and Kadous that improved from two IRRI lines as IR67015-94-2-3 and IR64669-153-2-3, respectively. In addition, Jahesh variety is a mutation of a local variety named Tarom. Sazandegi, purified from Lenjan local mass, and Shiroudi improved by cross between Deylamani as a local variety and Khazar as an improved variety. Consequently, Jahesh stood with other varieties obtained from Tarom such as Tarom-Jolodar, Sang-e-Tarom, and Tarom-Milad in Group B, and Sazandegi with Lenjan and Shiroudi with Deylamani constructed identical subcluster in Group B. There is an interesting result in the third group (Group C), in which three varieties, namely, Tabesh, Pouya, and Parto, constructed the same subcluster with Tarom-Mahali. These varieties are mutant of Tarom-Mahali.

Figure 2: Dendrogram of 40 rice varieties based on UPGMA cluster analysis of Dice similarity matrix calculated from 12 microsatellite markers.

Although the rice varieties in this study were from different rice breeding programs in Iran, microsatellite markers correctly grouped them depending on their respective group, local or improved varieties, in the dendrogram. Our results approve those of Garland et al. [24], who analyzed 43 rice samples using microsatellite markers and obtained a similar classification of varieties according to their breeding programs.

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

Table 3: Unique identification keys achieved by specific marker for some of the varieties.
3.3. The Best Combination of Markers

For finding the best combination of markers that result in the obtained result of using all markers in discrimination of all varieties, first, markers were chosen one after another in a way to minimize , that is, the number of pairs of distinct varieties for each primary compound in each step. In the first step, RM316 was chosen which distinguished the highest pairs of varieties from each other among pairs of varieties and made the amount of maximum. In the second step, the compound of each remaining marker with the chosen marker of previous step was tested in order to determine the most efficient compound that minimizes the amount of . In this step, compounding the RM271 with previous marker left the lower number of variety pairs undetermined. In subsequent steps, the same method was used for keeping or omitting other markers. Finally, adding RM154 to previous markers could decrease expected number of undetermined pairs of varieties calculated from 39.27 to 0.03 which should practically reach one pair of variety. There were 41 pairs of varieties nondistinct from each other in calculating similarity coefficient among varieties and then cluster analysis of varieties using RM316 that had the highest discriminating power (0.93) among markers. By adding consequent markers in accordance to distinguish the ability to this marker, the number of undistinguished pairs of varieties was decreased to one (Table 4).

Table 4: Comparison of combination of markers in the real and theoretical states under the hypothesis of independence of markers.

Reliable identification of similar varieties is so difficult in plant species by morphological characteristics alone, because morphological and physiological characteristics are limited [25, 26]. Accordingly, using molecular markers as additional information is inevitable in registering plant varieties considering their benefits. DNA markers can be utilized to simply and rapidly detect varieties or approve the distinctiveness of a varietal impostor [27]. For identification and characterization of rice varieties and the testing of hybrid rice lines, using STS and SSR markers was significantly easier than using typical “grow-out tests” that included growing plants to maturity and evaluating purity based on morphological characteristics [28, 29]. Also, microsatellite markers have been utilized in the previous same study to distinguish traditional rice (Oryza sativa L.) varieties from each other in Cuba [30]. In this research, the results showed that microsatellite markers easily could be used for identification of rice varieties.


DUS:Distinction, uniformity, and stability
PVP:Plant variety protection
PIC:Polymorphic information content
PCR:Polymerase chain reaction
SSR:Simple sequence repeat
UPGMA:Unweighted pair group method with arithmetic mean
UPOV:International union for the protection of new varieties of plants.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.


This study was supported by Seed and Plant Certification and Registration Institute (SPCRI). The authors would like to thank Dr. Zahra Tahernejad for their technical assistance in generating the SSR data.


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