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© 2017 Conscientia Beam. All Rights Reserved. 

 

 

 

MOLECULAR CHARACTERIZATION OF PARENTAL LINES OF RICE AIMING TO 
ADDRESS HIGH YIELD AND NUTRITIONAL QUALITY UNDER DROUGHT AND 
COLD STRESS CONDITION 

 

 

Nomita Das1  
Partha S. Biswas2+ 

 

1Jahangirnagar University, Savar, Dhaka, Bangladesh 
2Bangladesh Rice Research Institute, Gazipur, Bangladesh 
 

 
(+ Corresponding author) 

 ABSTRACT 
 
Article History 
Received: 2 March 2017 
Revised: 30 March 2017 
Accepted: 3 May 2017 
Published: 7 June 2017 
 
 
 

Keywords 
Drought 
Cold 
Grain zinc content 
Genetic diversity 
SSR 
Rice. 

 

 
Abiotic stresses limit crop growth at different growth stages resulting low yield of rice. 
Molecular characterization of parental materials gives precise information on the extent 
of genetic diversity exists between them. A set of 60 SSRs randomly distributed over 12 
chromosomes were used to analyze eight cultivars intend to be used as parent in 
breeding programs to address cold and drought tolerance, and nutritional quality of 
rice. A total of 300 alleles were detected across the cultivars for 51 polymophic markers 
with 5.88 alleles per loci. On average 30.6% of the genotypes shared a common allele at 
any given locus. UPGMA cluster analysis showed that 67% common alleles were 
shared by the cultivars. The cultivars were clearly grouped into two distinct clusters at 
67.0% genetic similarity. Hbj. BVI showing high tolerance to cold stress at seedling 
stage differed from cold susceptible BR1, BRRI dhan28 and BRRI dhan29 by 33% 
alleles while BR18 that showed moderately cold tolerance differed by 29.0 - 29.9% 
alleles which indicated that only 3-4% alleles difference caused higher cold tolerance in 
Hbj. BVI. The moderate genetic distance between cold tolerant Hbj. BVI and high 
yielding BRRI dhan28 and BRRI dhan29 indicated that there is higher possibility of 
obtaining high yielding cold tolerant segregants from the crosses between them. On the 
other hand,  Kalobokri, which had 31.7 mg zinc a kilogram of polished rice differed by 
33.0% alleles from the drought tolerant rainfed low varieties BRRI dhan56 and BRRI 
dhan57 having moderate level of zinc (~20 mg/kg), which also indicated that crosses 
between them might produce progenies with higher nutritional quality under drought 
environment.  

 

Contribution/ Originality: This study is one of the few studies which have investigated the genetic distance 

between the parental lines that are intended to be used in the breeding program to address abiotic stresses, like 

drought and cold tolerance with high yield potential and enhanced nutritional quality, particularly zinc content in 

rice.    

 

1. INTRODUCTION 

Rice (Oryza sativa L.) production in Bangladesh is affected by various abiotic stresses. The rainfed lowland rice 

is greatly affected by drought stress accounting for 46%, 37% and 73% yield loss if it occurs during flowering, 

maturity and both flowering and maturity, respectively [1]. On the other hand, Boro rice is suffered from critical 

Current Research in Agricultural Sciences 
2017 Vol. 4, No. 2, 51-60. 
ISSN(e): 2312-6418 
ISSN(p): 2313-3716 
DOI:  10.18488/journal.68.2017.42.51.60 
© 2017 Conscientia Beam. All Rights Reserved. 

 

 
 

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low temperature at different stages of growth from germination to maturity that in turn results into low yield. 

However, low temperature stress at booting stage produces sterile spikelets causing direct yield loss of short 

duration varieties up to 100% in some years in the low lying haor areas of Bangladesh. Approximately 22.4% and 

20% of total rice areas in the country are affected by moderate to severe level of drought and cold stress, 

respectively. Landraces play very important role as genetic resource for genetic improvement of rice [2] 

particularly to address abiotic stresses like drought and cold. Modern plant breeding techniques can effectively use 

landraces having different economic traits including enhanced resistance to certain stress, better grain and 

nutritional quality and of course the yield contributing traits. Knowledge on genetic divergence between parents is 

necessary to design a breeding program in order to have transgressive segregation. Genetic diversity determines 

the inherent potential of a cross for heterosis and frequency of desirable recombinants in advanced generations. 

Several workers have emphasized the importance of genetic divergence for the selection of desirable parents [3, 4]. 

The estimation of genetic diversity between different genotypes is the first and foremost process in plant 

breeding [5]. Among various techniques available for assessing genetic variability and relatedness among crop 

germplasm, DNA based markers provide very effective and reliable means for measuring genetic diversity and 

studying evolutionary relationships. Molecular markers can reveal abundant difference among genotypes at the 

DNA level, providing a more direct, reliable and efficient tool for germplasm characterization, conservation and 

management avoiding environmental influence in contrast to morphological traits. Simple sequence repeat (SSR) 

markers which are abundant in rice genome and cost effective in discriminating small number of 

varieties/germplasm have been effectively used to identify genetic variation among rice cultivars [3, 6-8]. In this 

study, we used eight rice varieties/cultivars to dissect genetic variation among them using SSR markers to measure 

the extent of genotypic differences, genetic relationship and to assist in broadening the germplasm base of future 

drought and cold tolerance rice breeding programs. 

 

2. MATERIALS AND METHODS 

A total of eight rice cultivars having diverged trait benefits, like drought tolerance, cold tolerance, enhanced 

grain zinc content, short duration and high yielding (Table 1) were analyzed using 60 SSR markers randomly 

distributed over the rice genome. Among the eight cultivars, BR1, BR18, BRRI dhan28, BRRI dhan29 and Hbj.B.VI 

were evaluated against artificial cold treatment for seedling stage cold tolerance following protocol described in 

Khatun, et al. [9]. Cold tolerance was estimated based on leaf discoloration (LD) score following SES of IRRI, % 

Survival plants at day of scoring and % Recovery of plants after seven days of cold stress withdrawal. BRRI dhan56 

and BRRI dhan57, two drought tolerant varieties showing no significant yield losses even when ground water table 

remained 70-80 cm below the surface at reproductive stage [10] were included in this study. All the genotypes 

except Hbj.BVI were analyzed for polished grain zinc content using X-Ray Fluorescence method in X-Supreme 

8000. 

For SSR analysis, DNA was extracted from young and actively growing fresh leaves using miniprep modified 

CTAB method as described by Virk, et al. [11]. Polymerase chain reaction (PCR) was performed in 10µl volume 

containing 2µl of genomic DNA, 5.3µl of DDH2O, 1µl of 1X PCR buffer, 1µl of 0.1mM dNTP mix, 0.5µl of 0.25uM 

of each primer, 2µl of Taq polymerase of 1U. The temperature cycles were programmed at 94oC for 5 min(initial 

denaturation), 94oC for 30 sec (denaturation), 55oC for 30sec (primer annealing), 72oC for 60sec (extension), 72oC 

for 5 min (final extension) and 10oC forever (storage). The PCR products were detected using 6% polyacrylamide 

gel electrophoresis. DNA bands were visualized in a UV transilluminator with ethidium bromide staining. 

Molecular weight of clearly resolved and unambiguous band was determined comparing with the known sized 

marker DNA using Alpha Ease FC 4.0. Polymorphic Information Content (PIC) values were calculated for each 

SSR loci using Power Marker V3.25 [12] based on the formula developed by Anderson, et al. [13]. 



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53 

© 2017 Conscientia Beam. All Rights Reserved. 

PICi= 1 –  

where,  Pij is the frequency of the jth allele for the ith marker and is summed over n alleles. 

The similarity matrices deduced from simple matching coefficient in PowerMarker analysis were subjected to 

unweighed pair-group arithmetic average (UPGMA) clustering analysis and represented in dendrogram form using 

NTSYS-pc program [14]. 

 

3. RESULTS 

3.1. Evaluation for Seedling Stage Cold Tolerance 

All the genotypes varied significantly in all three cold related traits. LD scores ranged from 2.0 to 8.7 among 

the genotypes. The highest LD was obtained with BR1 (8.7) followed by BRRI dhan28 (7.3) and BRRI dhan29 (7.0), 

while the lowest LD was observed with Hbj.BVI followed by BR18. Per cent survivability and % recovery values 

were also highest with Hbj.BVI followed by BR18, while BR1 had the lowest values for these traits. BR18 and 

Hbj.BVI were significantly different from the rest of the three genotypes in LD score. In % survivability and % 

recovery BR18 and Hbj.B.VI were also significantly different from other three genotypes and they were 

significantly different from each other as well (Table 2). 

 

3.2. Evaluation of Polished Grain Zinc Content 

The genotypes showed wide variation in grain zinc content ranging from 14.5 to 31.7 mg/kg. The highest zinc 

content was observed with the landrace Kalobokri, while all the cultivated varieties had zinc value close to 15 

mg/kg except BRRI dha57, which had 20.8 mg zinc a kilogram of polished rice grain (Table 3). BRRI dhan28 and 

BRRI dhan29 had 15.6 and 16.9 mg zinc in a kilogram of polished rice, respectively. 

 

3.3. Polymorphic Information Content and SSR Diversity 

Sixty SSR markers were analyzed for assessing genetic divergence among eight rice cultivars. Among them, 

seven SSRs did not amplify at all and two markers showed monomorphism. Table 4 summarizes the results 

obtained from the analysis of 51 SSR loci across the test cultivars. Polymorphic information content which evident 

the extent of polymorphism among the cultivars varied from 0.511 to 0.861 with an average of 0.758. The highest 

PIC value was observed with RM 6024 and the lowest with RM193, RM 7193, RM335 and RM 1282.  Out of 51 

markers, 7 markers on chromosome 1, 6 markers on each of chromosome 2 and 6, 5 markers on each of chromosome 

3 and 7, 4 markers on each of chromosome 12, 3 markers on each of chromosome 4, 5 and 11 and, 2 markers on each 

of chromosome 8, 9 and 10 were found polymorphic. A total of 300 alleles across the cultivars were detected at 51 

SSR loci. The average number of alleles per locus was 5.88 with a range from 3 (RM6024 and RM6370) to 8 

(RM193, RM335, RM1282 andRM7193). Among 51 SSRs, 3 alleles were detected for 2 markers, 4 alleles for 5 

markers, 5 alleles for 7 markers, 6 alleles for 24 markers, 7 alleles for 9 markers and 8 alleles for 4 markers. The 

frequency of occurrence of an allele at each locus ranged from 12.5 % (RM193, RM335, RM1282 andRM7193) to 

50% (RM329, RM497, RM6023, RM6024, RM6370 and RM7341) with a mean of 30.6%. The occurrence of number 

of alleles per loci was found variable irrespective of the numbers of repeat motifs and their base composition. 

Among 51 SSRs, 26 makers anchoring dinucleotide motif produced 154 alleles, 15 markers anchoring trinucleotide 

produced 82 alleles, 5 markers having tetranucleotide motifs produced 32 alleles and mixture of di- and 

trinucleotide motifs produced 32 alleles.  

The UPGMA cluster analysis based on Shanon indices obtained from binary data that were deduced from DNA 

profiles of the test genotypes for the SSR markers showed high genetic variation among the cultivars with 

similarity coefficient values ranging from 0.67 to 0.73 (Figure 1). The cultivars were clearly grouped into two 



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distinct clusters at 67.0 % genetic similarity. Local variety, Kalobokri and local improved variety, Hbj.BVI were 

grouped into cluster I, while BRRI varieties constellated into cluster II, which was further subdivided into  two sub-

clusters at 70.5% similarity. BR1 and BRRI dhan56 were grouped into one sub-cluster and rest all the varieties into 

other sub-cluster.  BRRI dhan28 and BRRI dhan29 shared 73% common alleles between them, while both of them 

had 71% common alleles with BR18. 

 

4. DISCUSSION 

Genetic diversity among the progenitors is crucial for any successful breeding program. Strong genetic 

diversity means diverse morphological traits and potentially valuable genetic information. Rice varieties with high 

level of genetic variation are extremely beneficial resources for broadening genetic base of the germplasm and 

therefore, play a good foundation for rice breeding [15]. The genotypes of this study are highly diverged in 

different traits of benefits. Some of them are very high yielding but they lack some specific traits of interest. These 

traits are present in other specific genotypes of this study (Table 1). Among eight genotypes in this study, BRRI 

dhan28 and BRRI dhan29 are the two most popular and high yielding rice varieties of Bangladesh with yield 

potential up to 7.5 and 9.1 t/ha, respectively [16]. Since release in 1994 these two varieties are still giving the 

highest average yield in the farmers field in the Boro rice ecosystem (November to May) [17] although there a 

series of new high yielding modern rice varieties have been released recently in Bangladesh but they are not being 

cultivated widely by the farmer due to adoption lags [18]. However, both BRRI dhan28 and BRRI dhan29 lack cold 

tolerance which is very important to uphold the potential yield in cold prone environment. In this present study we 

also observed cold susceptibility of these two varieties in different cold related traits, viz. LD score, % survivability 

and % recovery (Table 2). Khatun, et al. [9] also reported cold sensitivity of these two varieties. Among the five 

genotypes tested for seedling stage cold tolerance, BR18 and Hbj.BVI showed moderate to high tolerance to cold 

stress at seedling stage, respectively. Importantly, BR18 was released for cold prone low lying haor areas for it long 

stature and Hbj.BVI is a local improved Boro rice of haor areas of Bangladesh. On the other hand, Kalobokri which 

showed higher zinc content (Table 3) is a landrace of upland Aus ecosystem. This genotype might significantly 

contribute in enhancement of nutritional quality in the high yielding segregating progenies when crossed with high 

yielding varieties like BRRI dhan28 and BRRI dhan29. Mohiuddin [19] mapped QTLs for grain zinc content from 

a F2:3 population of BRRI dhan28×Kalobokri. BRRI dhan56 and BRRI dha57, the two drought tolerant rainfed low 

varieties having early maturing traits also showed moderate level of zinc (~20 mg/kg) in polished grains. Thus, 

possibility of obtaining progenies with higher nutritional quality under drought environment would be higher from 

the crosses of Kalobokri with BRRI dhan56 and BRRI dhan57. 

SSR markers are powerful tool for analyzing genetic variability among the germplasm accessions, particularly 

when they are closely related [20-22]. In this study, 60 microsatellite markers were analyzed to reveal genetic 

distance among eight rice varieties aiming to use in a breeding program targeting high yield and nutritional quality 

under drought and cold prone environments. Seven markers out of 60 did not amplify and two markers showed 

monomorphic amplification. The non-amplification of SSRs might be due to their japonica based sequence [23] of 

which complementary sequence may not be present in the indica type rice in this study. On the other hand, 

monomorphism of the markers reflected genetic closeness of the cultivars. In closely related cultivars, these 

phenomena are frequent and are reported in many previous studies [22, 24-26]. However, comparatively high 

average PIC value (0.758) with a range from 0.511 to 0.861 that was calculated based on 51 polymorphic makers 

indicated that the cultivars are much diverged. In fact, the local varieties (Kalobikri and Hbj.BVI) had diverse 

morphological traits and specific adaption under certain stress condition than the other cultivars (Table 1). In this 

study, Kalobokri showed to have 31.7±0.7 mg zinc in a kilogram of polished rice and Hbj.BVI showed strong cold 

tolerance at seedling stage. Although, all 51 SSRs markers had higher PIC values than 0.50 (an arbitrary value 

which is considered as threshold value for determining informative markers), RM193, RM7193, RM335 and 



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RM1282 had the highest PIC values (0.861), which indicated them to be the best markers for diversity analysis 

(Table 4). The level of PIC values of our study is comparatively higher than the reported PIC values in previous 

works [27-31].  

The occurrence of alleles per SSR locus ranged from 3 (RM6024 and RM6370) to 8 (RM193, RM335, RM1282 

andRM7193) alleles across 51 SSRs accounting a total of 300 alleles for eight cultivars. The average number of 

alleles (5.88) obtained in this study was bit higher than the mean allele values reported by Etemad, et al. [32] 

(3.57),  Hossain, et al. [31] (3.8), Matin, et al. [27] (4.4), however it was much lower than the mean allele number 

reported by Yasmin, et al. [33]; Xu, et al. [34]; Jain, et al. [35]; Jayamani, et al. [36]; Zeng, et al. [37] and 

Prathepha [38] who reported an average of 13, 11.9, 7.8, 14.6, 7.7 and 11.85  alleles per locus using US rice genetic 

resources, Indian quality rice germplasm, a diverse collection of Portuguses rice, rice landraces from China and 

Wild rice (Oryza rufipogon) from Northeastern Thailand and Laos, respectively. The reason behind the detection of 

higher number of alleles in those studies was the wider variability among the germplasm. In our study, the wide 

genetic distance of Kalobokri and Hbj.BVI from other varieties might contribute a lot in occurrence of higher mean 

allele per SSR locus.  Another cause might be the low resolution of markers density covering the whole genome and 

less number of germplasm used in the diversity analysis. However, on average 30.6% of the total genotypes shared 

at least a common major allele at any given locus ranging from 12.5% (RM335 and RM1282) to 50% (RM6024 and 

RM6370) common alleles at each locus. The gene diversity across the SSR loci ranged from 0.594 (RM6024) to 

0.875 (RM193, RM335, RM1282 andRM7193) with an average of 0.788. This higher gene diversity indicated that 

there was wide range genetic variation among the genotypes and this was due to inclusion of local varieties in the 

study. The highest number of alleles (8) at each locus for RM193, RM335, RM1282 and RM7193 also confirmed 

this finding. However, detection of higher number of alleles at each locus was not found correlated with the 

anchoring motifs of the SSR loci. Majority of the SSR markers had dinucleotide repeat motifs (GA/AG, CT/TC and 

TA). Di-ncleootide repeat motifs are thought to be perfect repeat motif for discerning high level of variation among 

the genotypes [39]. In this study, the loci with perfect dinucleotide repeat motifs detected almost similar level of 

alleles per locus (on average 5.9, n = 26) to those with tri-, tetra- and mixture of compound di- and tri- and tetra 

nucleotide motifs.   

UPGMA cluster analysis also revealed the existence of genetic distance among the genotypes using Shahon 

similarity index. The cultivars varied among themselves in a range between 67% and 73% in common allele sharing. 

The cultivars were clearly grouped into two distinct clusters at 67.0% genetic similarity. Kalobokri and Hbj.BVI 

which shared around 67.8% common alleles were grouped into one cluster and all the HYVs constellated into a 

second cluster. However, the second cluster was further grouped at 70.4% genetic similarity discriminating 

cultivars of Bangladesh origin from Philippines origin except BR18, which was introduced from Indonesia. Cold 

tolerant Hbj.BVI differed from cold susceptible varieties BR1, BRRI dhan28 and BRRI dhan29 by 33% while 

moderately cold tolerant variety BR18 differed by 29.0 - 29.9% alleles, which indicated that 3-4% allelic difference 

between Hbj.BVI and BR18 was responsible genetic factors for higher cold tolerance in Hbj.BVI. Generally, 

modern HYVs of rice share a relatively narrow genetic background as because they are mostly derived from 

common progenitors [40]. The high level of genetic similarity might be due to predominance of the HYV (6 out of 

8 cultivars) in this study. The moderate level of genetic distance between Hbj.BVI and the high yielding BRRI 

dhan28 and BRRI dhan29 indicates that there is possibility to obtained high yielding and cold tolerant segregating 

progenies if crosses are made between them.  On the other hand, the drought tolerant BRRI dhan56 and BRRI 

dhan57 differed from BRRI dhan28 and BRRI dhan29 by only 29.6% and 28.85% alleles and from Kalobokri by 33% 

alleles. These moderate level of allele difference might produce better recombinant if crosses are made between 

them, particularly to address zinc nutritional quality of rice under drought prone environments, as Kaloborkri 

possesses high zinc content and drought tolerant BRRI dhan56 and BRRI dhan57 have moderate level of zinc in 

their grains.  



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5. CONCLUSION 

The larger range of similarity values for cultivars revealed by the SSR markers provides greater confidence for 

assessment of genetic diversity and relationships, which can be used in future breeding programs. With the aid of 

the SSR makers used in this study, beneficial traits (cold or drought tolerance and nutritional quality) from 

Hbj.BVI, BRRI dhan56 or Kalobokri could be combined with modern HYVs by intercrossing. Furthermore, genetic 

mapping, population structure or kinships could be explored by increasing marker resolution.  

 

Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no competing interests. 
Contributors/Acknowledgement: We acknowledge Bangladesh Rice Research Institute for providing 
laboratory support for this study. We are also grateful to the scientists and staffs working in the molecular 
laboratory of Plant Breeding Division in BRRI. We express our gratitude to BRRI Gene bank for providing 
germplasm for this study.   

 

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Table-1. Basic features of the genotypes used in the diversity analysis. 

Rice variety Origin Characteristics  

BR1  IRRI,  Medium duration HYV for Boro ecosystem, cold susceptible, short statured 
plant, SB grain 

BR18 Indonesia HYV for Boro ecosystem, cold tolerant at seedling stage, tall statured plant, 
long growth duration, MB grain 

BRRI dhan28 Bangladesh Medium duration HYV for Boro ecosystem, cold susceptible, LS grain, 
BRRI dhan29 Bangladesh Long duration HYV for Boro ecosystem, cold susceptible, MS grain, 
BRRI dhan56 IRRI Short duration HYV for RLR, drought tolerant,  MS grain, 

BRRI dhan57 Bangladesh Short duration HYV for RLR ecosystem, drought tolerant 
Hbj.BVI Bangladesh Local improved short duration variety suitable for irrigated condition and has 

strong cold tolerance at both vegetative and reproductive stage, bold grain 
Kalobokri Bangladesh Landrace of upland ecosystem, black husk  color, high grain zinc content, tall 

statured plant, medium duration , bold grain 

Note: SB, short bold; HYV, High yielding variety; MB, Medium bold; MS, Medium slender; LS, Long slender; RLR, Rainfed lowland; Boro, Irrigated dry season rice 

 

Table-2. Cold response of five genotypes at seedling stage under artificial cold stress of 13⁰C 

Genotype Leaf discoloration score % Survivability % Recovery 

BR1 8.7a 17.0c 3.3c 
BR18 4.0b 62.3b 47.9b 
BRRI dhan28 7.3a 31.0c 10.7c 
BRRI dhan29 7.0a 31.0c 12.7c 
Hbj.B.VI 2.0b 94.9a 80.3a 

 

 

 

 

https://scholar.google.com/scholar?hl=en&q=Determination%20of%20genetic%20relatedness%20among%20selected%20rice%20(Oryza%20Sativa,%20L.)%20cultivars%20using%20microsatellite%20markers
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https://scholar.google.com/scholar?hl=en&q=Evaluation%20of%20genetic%20diversity%20of%20rice%20landraces%20(Oryza%20Sativa%20L.),%20in%20Yunnan
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Table-3. Zinc content in polished rice grain 

Genotype Grain zinc content (mg/kg) 

BR1 14.5±0.6 
BR18 17.3±0.9 
BRRI dhan28 15.6±0.3 
BRRI dhan29 16.9±0.6 
BRRI dhan56 18.3±0.2 
BRRI dhan57 20.8±0.3 
Kalobokri 31.7±0.7 

 

Table-4. Diversity analysis of 51 SSR markers across 8 rice cultivars 

SN Marker Chromosome Repeat motif No, of allele  Major allele 
frequency 

Gene 
Diversity 

PIC 

1 RM259 1 (CT)17 6 0.375 0.781 0.754 

2 RM329 1 (CAT)7 5 0.500 0.688 0.653 

3 RM431 1 (AG)16 6 0.250 0.813 0.786 

4 RM495 1 (CTG)7 5 0.250 0.781 0.746 

5 RM1282 1 (AG)17 8 0.125 0.875 0.861 

6 RM6840 1 (TCT)17 6 0.375 0.781 0.754 

7 RM7341 1 (CATT)6 5 0.500 0.688 0.653 

8 RM174 2 (AGG)7(GA)10 6 0.250 0.813 0.786 

9 RM207 2 (CT)25 7 0.250 0.844 0.825 

10 RM497 2 (CAC)11 4 0.500 0.656 0.605 

11 RM4499 2 (TA)20 5 0.250 0.781 0.746 

12 RM6023 2 (CCG)8 4 0.500 0.656 0.605 

13 RM7451 2 (TAAT)8 7 0.250 0.844 0.825 

14 RM168 3 T15(GT)14 6 0.250 0.813 0.786 

15 RM545 3 (GA)30 6 0.250 0.813 0.786 

16 RM1230 3 (AG)15 7 0.250 0.844 0.825 

17 RM3586 3 (GA)12 6 0.250 0.813 0.786 

18 RM6349 3 (GAA)9 6 0.250 0.813 0.786 

19 RM241 4 (CT)31 6 0.250 0.813 0.786 

20 RM335 4 (CTT)25 8 0.125 0.875 0.861 

21 RM3333 4 (CT)15 5 0.375 0.75 0.712 

22 RM1248 5 (AG)15 6 0.375 0.781 0.754 
23 RM3170 5 (CT)12 5 0.375 0.75 0.712 

24 RM3328 5 (CT)14 6 0.250 0.813 0.786 

25 RM6024 5 (CCG)8 3 0.500 0.594 0.511 

26 RM30 6 (AG)9A(GA)12 7 0.250 0.844 0.825 

27 RM193 6 (GCT)5 8 0.125 0.875 0.861 

28 RM276 6 (AG)8A3(GA)33 7 0.250 0.844 0.825 

29 RM469 6 (AG)15 6 0.250 0.813 0.786 

30 RM5405 6 (TC)14 6 0.375 0.781 0.754 

31 RM7193 6 (ATAG)7 8 0.125 0.875 0.861 

32 RM18 7 (GA)4AA(GA)(AG)16 6 0.375 0.781 0.754 

33 RM436 7 (TAA)6 6 0.375 0.781 0.754 

34 RM1243 7 (AG)15 4 0.375 0.719 0.668 

35 RM1362 7 (AG)25 6 0.250 0.813 0.786 

36 RM6872 7 (TGG)8 5 0.375 0.75 0.712 

37 RM284 8 (GA)8 6 0.250 0.813 0.786 

38 RM407 8 (AG)13 6 0.375 0.781 0.754 

39 RM3120 8 (CA)12 7 0.250 0.844 0.825 

40 RM205 9 (CT)25 7 0.250 0.844 0.825 

41 RM7048 9 (AATA)8 6 0.375 0.781 0.754 

42 RM590 10 (TCT)10 7 0.250 0.844 0.825 

43 RM3451 10 (CT)19 7 0.250 0.844 0.825 

44 RM6370 10 (GAA)14 3 0.500 0.625 0.555 

45 RM441 11 (AG)13 6 0.250 0.813 0.786 

46 RM5349 11 (TC)13 4 0.375 0.719 0.668 

47 RM6094 11 (CCT)13 6 0.250 0.813 0.786 

48 RM1246 12 (AG)15 4 0.375 0.719 0.668 

49 RM1261 12 (AG)16 6 0.250 0.813 0.786 

50 RM7619 12 (TGTA)13 6 0.375 0.781 0.754 

51 RM8216 12 (TAA)25 6 0.250 0.813 0.786 

Total - - - 5.88 0.306 0.788 0.758 

 



Current Research in Agricultural Sciences, 2017, 4(2): 51-60 
 

 
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© 2017 Conscientia Beam. All Rights Reserved. 

 
Figure-1. Dendogram derived from UPGMA cluster analysis using Shanon indices based on DNA profiling with 51 SSR markers across eight 
rice cultivars.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
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