Genetic Diversity Analysis in Rice (Oryza sativa L.) Accessions using SSR Markers

Abstract

Sixty-five rice accessions were analyzed to evaluated the genetic polymorphism and identification of diverse parents using simple sequences repeat (SSR) markers. These accessions showed significant phenotypic variation for all the characters studied. A total of 52 alleles were detected by 19 polymorphic markers showing highly polymorphic across all accessions with an average of 2.7 alleles per polymorphic marker. The marker RM-84 and RM-481 produced maximum 4 alleles. The PIC value ranged from 0.032 to 0.588 and marker RM231 was found to be the most appropriate marker to discriminate among the rice genotypes owing to the highest PIC value of 0.588. The cluster analysis showed that these accessions grouped in to nine clusters in which cluster IB-1a had maximum thirty-one genotypes followed by cluster IB-1b and cluster V. While highest dissimilarity coefficient value was observed between the cultivar LC-4 and IR-82635-B-B-47-and between OR-1946-2-1 and UPLRI-7 showing highly diverse genotypes. These accessions were showing wide genetic divergence among the constituent in it and may be directly utilized in hybridization programme for improvement of yield related traits. Highlights • 52 alleles were detected with an average of 2.7 alleles per polymorphic marker. • PIC value ranged from 0.032 to 0.588, indicates that markers were highly informative and capable of distinguishing between genotypes. • Cultivar LC-4 and IR-82635-B-B-47-1 (0.0429) identified as highly diverse genotypes on the basis of dissimilarity coefficient.

Full text

International Journal of Agriculture, Environment and Biotechnology
Citation: AEB: 10(4): 1-11, August 2017
DOI:
©2017 New Delhi Publishers. All rights reserved
Genetic Diversity Analysis in Rice (Oryza sativa L.) Accessions
using SSR Markers
Deep Rashmi1, Prashant Bisen1, Shoumik Saha1, Bapsila Loitongbam1, Sakshi Singh2,
Pallavi2 and P.K. Singh1*
1Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India
2Centre of Advance Study Botany, Institute of Sciences, Banaras Hindu University, Varanasi, India
*Corresponding author: pksbhu@gmail.com
Paper No. Received: Accepted:
ABSTRACT
Sixty-ve rice accessions were analyzed to evaluated the genetic polymorphism and identication of
diverse parents using simple sequences repeat (SSR) markers. These accessions showed signicant
phenotypic variation for all the characters studied. A total of 52 alleles were detected by 19 polymorphic
markers showing highly polymorphic across all accessions with an average of 2.7 alleles per polymorphic
marker. The marker RM-84 and RM- 481 produced maximum 4 alleles. The PIC value ranged from 0.032
to 0.588 and marker RM231 was found to be the most appropriate marker to discriminate among the rice
genotypes owing to the highest PIC value of 0.588. The cluster analysis showed that these accessions
grouped in to nine clusters in which cluster IB-1a had maximum thirty-one genotypes followed by cluster
IB-1b and cluster V. While highest dissimilarity coecient value was observed between the cultivar LC-4
and IR-82635-B-B-47- and between OR-1946-2-1 and UPLRI-7 showing highly diverse genotypes. These
accessions were showing wide genetic divergence among the constituent in it and may be directly utilized
in hybridization programme for improvement of yield related traits.
Highlights
• 52 alleles were detected with an average of 2.7 alleles per polymorphic marker.
• PIC value ranged from 0.032 to 0.588, indicates that markers were highly informative and capable of
distinguishing between genotypes.
• Cultivar LC-4 and IR-82635-B-B-47-1 (0.0429) identied as highly diverse genotypes on the basis of
dissimilarity coecient.
Keywords: Oryza sativa accessions, Molecular diversity, SSR, PIC
Rice an important food for about half of the world’s
population and 90% of it is being produced and
consumed in Asia (Rao et al., 2016) and share
maximum in grain production. India is one of the
centers for rice diversity (Singh et al., 2016). The
rice accessions are a rich reservoir of useful genes
that rice breeder can harness for rice improvement
programme and the genetic variability exists among
rice accessions leaving a wide scope for crop
improvements (Singh et al., 2015). Genetic diversity
is necessary for any crop improvement program
as it helps in analyzing and establishing genetic
relationship in accessions collection, its monitoring,
identication of diverse parental combinations to
create segregating progenies with high genetic
variability and to obtain potential recombinations
for further selection and introgression of desirable
genes from these diverse accessions (Ramadan et al.,
2015; Thompson et al., 1998 and Islam et al., 2012).
Since been a long time a major goal in evolutionary
biology is to Characterize and quantify the genetic
diversity. Determination of genetic diversity can be

Rashmi et al.
2
Print ISSN : 1974-1712 Online ISSN : 2230-732X
done by assessing morphological or molecular data.
The use of advanced molecular technologies is one
possible approach to understand their diversity.
Evaluation of genetic diversity using DNA marker
technology is non-destructive, not affected by
environmental factors, requires small number of
samples, and does not require large experimental
setup and equipments for measuring physiological
parameters (Kanawapee et al., 2011).
Simple sequence repeat (SSR) is an important tool for
genetic variation identication of accessions (Sajib
et al., 2012, Ma et al., 2011). SSR marker are highly
informative, mostly monolocus, codominant, easily
analyzed and cost eective (Gracia et al., 2004) and
able to detect high level of allelic diversity (Ni et al.,
2002), thus being widely applied in genetic diversity
analysis, molecular map construction and gene
mapping (Zhang et al., 2007, Ma et al., 2011), and
analysis of germplasm diversity (Zhou et al., 2003,
Jin et al., 2010, Ma et al., 2011). SSR markers even
in less number can give a beer genetic diversity
spectrum due to their multi-allelic and highly
polymorphic nature (Singh et al., 2016). Reports
suggest that genetic diversity in crop varieties
released over the years uctuates in successive time
periods (Upadhyay et al., 2012). Over the last few
centuries, rice has faced diversity loss (Choudhary
et al., 2013) especially, aer the green revolution due
to replacement of native varieties with high yielding
varieties (Heal et al., 2004).
Therefore the present study was undertaken with
the aim to assess the trend in genetic diversity in
sixty five accessions of rice using SSR markers.
The generated information will enable maximized
selection of diverse parents and selecting appropriate
parental genotypes in breeding programme.
MATERIALS AND METHODS
The experiment was carried out during kharif season
2015 at Agricultural Research Farm, Institute of
Agricultural Sciences, Banaras Hindu University,
Varanasi. The experimental seed material comprises
of sixty five rice accessions provided by DBT
Networking Project (Table 1). The nursery was sown
on on 12th June, 2015 on uniform raised beds applied
with a fertilizer dose of 1.0 Kg N, 1.0 Kg P2O5 and
0.5 Kg K2O per 50 m2 area. 21 days old seedling was
transplanted in a randomized block design (RBD)
with three replications by maintaining row to row
and plant to plant spacing 20 × 15 cm, respectively.
Table 1: List of rice accessions used under study
Sl. No. Accessions Sl. No. Accessions Sl. No. Accessions
1 OR 1946-2-1 2 IR 83142-76 3 Vandana
4 IR 82635-B-B-47-1 5 IR 82589-B-B-7-2 6 IR 82635-B-B-23-1
7 CRR 660-2 8 CRR 428-237-1-3-1 9 Rewa 1208-15
10 IR 83399-B-B-52-1 11 PAU 3832-79-4-3-1 12 IR 83182-6-4
13 IR 78755-190-B-1-3 14 IR 82635-B-B-25-4 15 RP 5345-9-6-3
16 RRF-48 17 IR 55423-01 18 CB 10-504
19 GK 5022 20 Anjali 21 IR 10L-105
22 B 11576F-MR-18-2 23 CR 3631-1-3 24 BAU 411-05
25 IR 82921-B-B-1 26 BD 104 27 IR 77298-14-1-2-13
28 CR 422-63-51-B-2-1-1-1-B 29 IR 1718-59-1-2-3 30 IR 82635-B-B-145-1
31 NDR 1140 32 CR 3633-1-2 33 IR 368B-TB-25-MP-2
34 UPLRI – 7 35 IR 87694-28-7-2-1 36 RP 5330-63-5-2-1-B
37 MGD 1206 38 IR83867-B-B-250-CRA-1-1 39 IR 83926-B-B-71-4
40 IR 60080-46A 41 BD 108 42 BVS 1
43 BVD 111 44 BVD 203 45 BAU 389-02
46 BAU/IRRI 497 47 LC -1 48 LC -2
49 LC -3 50 LC -4 51 LC -5
52 LC – 6 53 LC – 7 54 LC – 8
55 LC – 9 56 LC – 10 57 LC – 11
58 LC – 12 59 LC – 13 60 LC – 14
61 LC – 15 62 LC – 16 63 LC – 17
64 LC – 18 65 LC – 19

Genetic Diversity Analysis in Rice (Oryza sativa L.) Accessions using SSR Markers
3
Print ISSN : 1974-1712 Online ISSN : 2230-732X
Morphological assessment
Observations were recorded on eleven quantitative
traits viz., days to 50% flowering (DF), days to
maturity (DM), number of eective tillers per plant
(ET), plant height (PH), panicle length (PH), panicle
weight (PW), lled grains per panicle (FG), total
grains per panicle (TG), spikelet fertility percent
(SFP), test weight (TW) and grain yield per plant
(GY).
Genomic DNA extraction
Young leaves of 15-20 days old seedlings from
sixty ve rice genotypes were clipped and stored
in ice-box to carry it to the lab which is then stored
in -800C till DNA extraction. Genomic DNA was
then extracted using CTAB method (Doyle and
Doyle 1987). DNA samples were diluted to 10 ng/
μl. The DNA was quantied spectrophotometrically
(PerkinElmer, Singapore) by measuring A260/A280
and DNA quality was checked by electrophoresis
in 0.8% agarose gel.
SSR Markers and PCR amplication
A total of twenty rice SSR markers were used for
molecular diversity (Table 2). The PCR amplication
was carried out in 15μl of reaction mixture containing
30 ng genomic DNA, 1.5 mM PCR buffer (MBI
Fermentas, USA), 400 μMdNTPs (MBI Fermentas),
1 U Taq DNA polymerase (MBI Fermentas) and 0.4
μM primer using a thermal cycler (Master cycler
gradient, Eppendorf). Thermal cycling program
involved an initial denaturation at 94° for 45 sec,
annealing at 2° bellow Tm of respective primers for
30 sec, primer extension at 72° for 30 sec, followed
by a nal extension at 72° for 7 min. Electrophoresis
separation and visualization of amplied products.
The amplied PCR products along with a 50 bp
DNA marker ladder (MBI Fermentas) were size
fractioned by electrophoresis in 2.5% agarose
gel prepared in TAE buffer and visualized by
staining with ethidium bromide (0.5 μg/ml) in a
gel documentation system (BIO-RAD, USA). The
reproducibility of amplification products was
compared twice for each primer.
SSR data analysis
Standardization of quantitative data
The eects of dierent scales of measurement for
different quantitative traits were minimized by
standardizing the data for each trait separately prior
to cluster analysis. Standardization was done by
dividing the deviation of mean for a line from the
mean for sixty ve lines with the standard deviation
for the given trait; the STAND module of NTSYS
(Rohlf 1997) soware was used to furnish the same.
Genetic dissimilarity and Cluster analysis
based on UPGMA
The binary data matrix generated by polymorphic
SSR markers were subjected to further analysis
using NTSYS-pc version 2.11W (Rohlf 1997). The
SIMQUAL program was used to calculate the
Jackard’ dissimilarity coecient. The dissimilarity
matrix was used as an input for analysis of clusters.
UPGMA-based clustering was done using SAHN
module of NTSYSpc for dendrogram construction.
In Unweighted pair-group average (UPGMA)
clusters are joined based on the average distance
between all members in the two groups.
Polymorphic information content (PIC) and
Principal component analysis (PCA)
PIC for SSR markers was calculated as per the
formula:
PIC = 1 – Pij2
where, PICi is the polymorphic information content
of a marker i and the summation extends over n
paerns. PCA was also done to check the result
of UPGMA base clustering using EIGEN module
of NTSYSpc. In principal component analysis
(PCA), the total variance of original characters is
divided into a limited number of uncorrelated new
variables known as principal components (PCs).
The rst step in PCA is to calculate eigen values,
which dene the amount of total variation that is
displayed on PC axes. The first PC summarizes
most of the variability not summarized by, and
uncorrelated with, the rst PC, and so on. PCs were
used for 2-dimentional (2-D) and 3-dimentional (3-
D) ploing, respectively, against each other using
module PROJ and MXPLOT of NTSYSpc.
RESULTS AND DISCUSSION
Polymorphism and marker eciency
Sixty five rice genotypes were subjected to SSR

Rashmi et al.
4
Print ISSN : 1974-1712 Online ISSN : 2230-732X
Table 2: Mean performance of sixty ve rice accessions for various characters
Sl.
No.
Character Days
to 50%
Flowering
Days to
Maturity
No. of
Tillers
Plant
Height
Panicle
Length
(cm)
Panicle
Weight
(gm)
Filled
Grains
Per
Panicle
Grains
Per
Panicle
Spikelet
Fertility
(%)
Test
Weight
(gm)
Grain
Yield
Per
Plant
(gm)
1 OR 1946-2-1 99.67 135.67 12.93 107.69 28.43 3.78 151.67 180.20 83.54 23.83 32.51
2 IR 83142-76 130.67 164.33 7.73 188.54 26.03 4.98 193.93 212.93 91.12 23.88 29.36
3 Vandana 102.67 137.00 6.27 126.15 26.19 2.98 104.33 135.40 76.82 24.97 16.66
4 IR 82635-B-B-47-1 131.33 165.33 14.00 172.51 27.28 3.01 147.87 165.27 89.40 17.72 19.97
5 IR 82589-B-B-7-2 127.33 156.67 8.13 85.79 22.28 2.22 115.33 131.91 86.95 18.83 14.53
6 IR 82635-B-B-23-1 155.33 180.33 10.33 177.21 25.47 2.78 80.73 112.40 72.00 24.89 16.24
7 CRR 660-2 91.00 121.00 9.67 110.89 22.98 2.90 122.33 133.27 88.67 21.94 18.41
8 CRR 428-237-1-3-1 83.67 115.67 11.53 110.48 20.28 2.29 102.47 113.87 89.83 19.70 20.36
9 Rewa 1208-15 130.33 164.33 10.73 170.48 22.77 2.46 152.67 191.53 78.77 15.63 18.07
10 IR 83399-B-B-52-1 126.33 159.67 8.60 91.23 27.43 3.94 139.07 166.80 84.30 25.41 25.76
11 PAU 3832-79-4-3-1 110.67 140.33 9.33 81.14 23.24 2.22 113.00 136.00 79.00 16.75 20.36
12 IR 83182-6-4 126.00 161.00 10.07 146.77 26.87 2.91 123.27 143.33 90.05 21.49 26.59
13 IR 78755-190-B-1-3 89.67 120.00 9.27 131.28 22.36 2.30 87.27 90.87 93.12 24.69 15.43
14 IR 82635-B-B-25-4 84.33 115.00 9.73 84.73 23.50 1.45 61.00 83.73 71.03 20.21 16.95
15 RP 5345-9-6-3 87.67 116.67 15.00 120.05 20.95 2.30 104.53 132.80 79.42 19.85 22.21
16 R RF-48 96.67 125.00 11.07 91.01 23.42 1.85 81.13 98.93 84.49 19.09 13.48
17 IR 55423-01 95.00 128.00 8.00 112.93 26.59 3.41 143.73 169.40 78.93 22.39 21.98
18 CB 10-504 90.67 122.67 11.60 97.63 24.11 2.20 102.80 124.07 82.68 20.12 19.33
19 GK 5022 111.67 140.67 7.47 125.45 27.25 5.77 198.00 220.73 82.74 28.68 32.36
20 Anjali 107.33 138.33 9.60 123.09 31.49 2.46 137.80 177.07 77.69 16.72 16.86
21 IR 10L-105 97.67 137.00 11.13 108.24 27.01 2.45 86.53 118.40 72.93 24.91 23.15
22 B 11576F-MR-18-2 138.00 163.33 10.27 137.22 27.57 2.15 177.60 234.20 84.76 10.02 16.04
23 CR 3631-1-3 144.33 178.00 10.47 177.50 23.48 3.25 122.80 171.67 71.65 20.19 24.42
24 BAU 411-05 83.67 115.67 11.93 92.03 23.50 1.82 75.73 104.00 72.46 23.71 16.52
25 IR 82921-B-B-1 95.00 122.33 8.27 142.71 24.95 5.41 210.07 251.33 89.20 22.14 30.81
26 BD 104 85.00 119.33 10.93 136.74 25.68 3.51 102.60 119.33 86.12 32.30 29.61
27 IR 77298-14-1-2-13 131.33 164.33 7.73 123.29 25.46 3.29 140.13 169.80 82.51 21.03 23.25
28 CR 422-63-51-B-2-
1-1-1-B
96.00 131.00 9.53 109.72 26.09 2.83 97.07 111.80 87.15 28.24 21.64
29 IR 1718-59-1-2-3 95.00 130.33 6.60 116.47 25.05 3.18 126.00 167.40 70.44 22.39 22.12
30 IR 82635-B-B-145-1 112.33 142.00 11.80 117.81 22.85 3.14 166.33 183.33 90.26 17.74 30.35
31 NDR 1140 103.33 137.00 9.80 98.11 23.41 3.00 199.87 226.13 85.74 14.89 22.25
32 CR 3633-1-2 101.00 137.67 7.00 109.51 27.60 3.50 105.93 175.60 60.38 26.41 17.60
33 IR 368B-TB-25-MP-2 99.33 137.00 7.47 99.71 24.35 2.12 111.40 131.53 82.90 20.86 18.33
34 UPLRI - 7 121.67 157.67 14.20 136.75 23.15 2.44 87.73 107.67 81.73 24.46 22.84
35 IR 87694-28-7-2-1 123.33 157.00 7.67 90.20 21.97 2.82 135.67 147.47 91.83 20.57 19.11
36 RP 5330-63-5-2-1-B 91.00 122.33 14.87 133.97 26.57 2.93 114.27 122.13 93.63 24.71 33.90
37 MGD 1206 82.33 111.67 10.00 89.89 23.47 1.98 90.40 114.67 78.55 21.84 18.50
38 IR 83867-B-B-250-
CRA-1-1
95.33 123.67 10.07 95.23 26.26 3.02 140.60 203.47 69.26 19.01 24.95
39 IR 83926-B-B-71-4 87.33 116.00 10.40 63.42 15.79 0.91 43.93 52.00 84.30 19.84 5.22
40 IR 60080-46A 82.33 114.67 13.00 91.07 24.78 2.32 93.67 107.20 87.44 22.12 23.87
41 BD 108 125.33 157.00 10.80 128.57 22.13 3.20 163.47 200.20 81.77 17.37 26.47

Genetic Diversity Analysis in Rice (Oryza sativa L.) Accessions using SSR Markers
5
Print ISSN : 1974-1712 Online ISSN : 2230-732X
marker assay to assess the molecular diversity. Out
of twenty primers used, 19 produced reproducible
and polymorphic paern while one primer (RM39)
was monomorphic. The 19 polymorphic primers
yielded a total of 52 fragments (amplied products).
The size of fragments varied from 50bp (marker
RM84) to 290bp (marker RM424). Maximum
fragments were produced by primers RM84 and
RM481 which yielded four fragments each and an
average of 2.7 fragments was produced per primer
which showed polymorphic amplification. Gel
image showing SSR banding prole obtained by
primer RM17 is presented in Fig. 1.
The polymorphic information content (PIC) was
employed for each locus to assess the information
of each marker and its discriminatory ability. PIC
value refers to the value of a marker for detecting
polymorphism within a population, depending on
the number of detectable alleles and the distribution
of their frequency; thus, it provides an estimate of
the discriminating power of the marker (Nagy et
al., 2012). The PIC value is an evidence of allele
diversity and frequency among varieties. The PIC
value of SSR markers ranged from 0.032 to 0.588
with a mean PIC of 0.366. Markers RM231, RM17,
RM481 and RM174 were the most informative
primers on the basis of highest PIC of 0.588, 0.585,
0.497 and 0.467 respectively. SSR marker RM270
showed least PIC value of 0.032 (Table 3).
Dendrogram analysis
A dendrogram (Fig. 2) based on Jackard’s
dissimilarity coefficient was constructed using
UPGMA. The genetic divergence was studied based
on D2 statistics. The sixty ve rice accessions were
grouped into two main clusters (Table 4) i.e. cluster
I and cluster II with dissimilarity coecient (0.15).
Cluster I was further sub-divided into two minor
sub-groups IA and IB with dissimilarity coecient
(0.28). Cluster IA and IB were further sub-divided
into two subgroups i.e. IA-1 and IA-2 (0.31) and IB-1
and IB-2 (0.32) respectively. The second main cluster
was also sub-divided into two minor subgroups i.e.
IIA and IIB with dissimilarity coecient 0.16. This
indicated presence of considerable diversity in the
accessions studied. The most diverse genotype is
therefore, important in order to select desirable
genotypes for utilizing in breeding programmes.
42 BVS 1 92.00 124.67 7.40 112.69 25.07 3.25 125.80 157.67 79.82 21.75 21.66
43 BVD 111 132.67 167.00 10.47 166.08 28.88 2.81 107.53 142.53 74.86 21.29 27.92
44 BVD 203 100.33 132.67 6.27 135.36 28.04 3.50 137.87 180.80 77.93 23.03 20.89
45 BAU 389-02 93.00 124.67 8.93 125.00 28.43 3.96 146.13 193.67 75.67 24.45 30.50
46 BAU/IRRI 497 136.67 167.67 7.60 139.69 30.25 2.78 152.87 187.13 78.55 17.26 17.93
47 LC - 1 132.67 165.33 10.20 156.79 24.86 2.26 155.73 220.13 70.84 12.64 21.47
48 LC - 2 102.67 138.67 9.13 122.88 24.81 3.13 114.13 137.73 80.74 26.46 24.08
49 LC - 3 94.67 130.00 8.73 105.38 28.72 3.01 108.67 136.33 79.66 23.41 21.66
50 LC - 4 104.67 138.33 8.07 100.20 25.15 3.09 138.20 149.00 92.71 21.63 18.93
51 LC - 5 103.00 141.33 9.13 149.80 27.11 4.88 186.93 199.80 91.73 25.13 33.09
52 LC - 6 101.67 139.00 9.33 113.02 27.67 4.01 148.87 187.47 79.73 25.93 23.95
53 LC - 7 97.33 137.00 6.87 109.09 26.77 3.26 113.47 179.07 63.18 23.04 19.70
54 LC - 8 114.67 141.00 9.27 112.67 26.15 3.50 132.53 142.13 90.83 25.77 24.56
55 LC - 9 96.00 137.00 10.67 95.29 23.28 1.59 73.07 101.80 71.71 19.78 13.27
56 LC - 10 109.33 142.33 8.07 155.49 33.38 4.32 142.87 166.93 89.07 29.72 28.63
57 LC - 11 99.67 134.67 9.33 117.81 27.87 4.11 162.13 187.73 86.06 25.30 25.98
58 LC - 12 83.00 116.00 12.60 95.16 23.51 1.85 73.47 103.13 66.72 21.32 21.69
59 LC - 13 96.00 127.67 8.40 117.84 26.20 2.84 129.73 170.33 76.35 20.97 22.40
60 LC - 14 101.67 131.67 9.87 147.23 27.87 4.31 168.00 182.53 91.91 23.93 31.00
61 LC - 15 148.33 176.00 9.60 119.09 21.37 2.88 118.87 155.40 71.46 20.54 21.50
62 LC - 16 96.67 131.00 7.47 115.12 26.01 3.62 151.47 199.87 75.59 23.00 22.22
63 LC - 17 98.67 137.00 7.67 105.34 26.51 3.17 132.87 157.33 84.50 21.93 17.62
64 LC - 18 96.67 128.67 8.13 111.93 28.29 3.65 160.30 187.57 85.47 24.09 23.91

Rashmi et al.
6
Print ISSN : 1974-1712 Online ISSN : 2230-732X
On the basis of dendrogram, the highest similarity
was observed between Rewa 1208-15 and PAU 3832-
79-4-3-1 followed by LC-15 and LC-16, BAU 411-05
and IR 82635-B-B-145-1 and BVD 111 and LC-11. The
most diverse cultivar was IR 82635-B-B-47-1 and OR
1946-2-1. These accessions were grouped into nine
clusters. Cluster indicated that 31 germplasm out
of sixty-ve belong to the cluster IB-1a followed by
cluster IB-1b which has 24 accessions and cluster
IB-1c with 3 accessions. Clusters IA-1, IA-2, IIA-1,
IIA-2 and IIB were monogenic in nature containing
single accession each i.e. IR 82635-B-B-47-1, LC-10,
IR 83926-B-B-71-4 and OR 1946-2-1, respectively.
Jackard dissimilarity coecient
The dissimilarity coefficient varies from one to
zero, close to one shows high similarity while close
to zero shows high dissimilarity. The average of
dissimilarity coecient varies from 0.77 to 0.51.
The total average of dissimilarity coecient of all
Fig. 1: SSR banding prole obtained by marker RM17. Lane 1-65 represents rice cultivar used in the present study; M = 50bp DNA
size markers

Genetic Diversity Analysis in Rice (Oryza sativa L.) Accessions using SSR Markers
7
Print ISSN : 1974-1712 Online ISSN : 2230-732X
sixty ve cultivar is 0.61. the dissimilarity coecient
varied from the largest value 0.94 between the
cultivar LC-4 and IR 82635-B-B-47-1 followed by
cultivar value 0.93 between the cultivar OR 1946-2-1
and UPLRI-7 which shows high similarity between
them and it may be expected that both of them may
have arouse from the same parents. The lowest
value 0.15 was found between REWA 1208-15 and
PAU 3832-79-4-3-1 followed by 0.19 between the
cultivar BAU 411-05 and IR 82635-B-B-145-1, LC-
15 and LC-16, and BVD 111 and LC-11 showing
that they are highly dissimilar from each other.
Cultivar IR 82635-B-B-47-1 shows highest similarity
with cultivar LC-4 (0.94) and highest dissimilarity
with CRR 428-237-1-3-1 (0.58). LC-15 has highest
similarity with IR 82635-B-B-47-1 (0.85) and
Fig. 2: Jackard IJ Distance

Rashmi et al.
8
Print ISSN : 1974-1712 Online ISSN : 2230-732X
Table 3: Details of the SSR primers used in present study, Allele size (bp) and polymorphism information content
(PIC)
Sl. No. SSR Primer Sequence Chr.
No.
Tm(ºC) No. of
Alleles
amplied
Approx. size of
amplied product
(bp)
PIC
1 RM1 F GCGAAAACACAATGCAAAAA 1 54.7 2 80-110 0.294
RM1 R GCGTTGGTTGGACCTGAC
2 RM17 F TGCCCTGTTATTTTCTTCTCTC - 58.55 3 160-190 0.585
RM17 R GGTGATCCTTTCCCATTTCA
3 RM424 F TTTGTGGCTCACCAGTTGAG 2 55.5 3 230-290 0.458
RM424 R TGGCGCATTCATGTCATC
4 RM16874 F TAGCAAGCTTGGAGAAGTGATGG 4 61.8 2 190-210 0.084
RM16874 R CAGAAGAAGTCAGCTCTATGCTTGG
5 RM190 F CTTTGTCTATCTCAAGACAC 6 54.5 3 135-180 0.382
RM190 R TTGCAGATGTTCTTCCTGATG
6 RM481 F TAGCTAGCCGATTGAATGGC 7 57.3 4 160-245 0.497
RM481 R CTCCACCTCCTATGTTGTTG
7 RM434 F GCCTCATCCCTCTAACCCTC 9 59.35 2 150-170 0.231
RM434 R CAAGAAAGATCAGTGCGTGG
8 RM216 F GCATGGCCGATGGTAAAG 10 55.65 3 110-145 0.418
RM216 R TGTATAAAACCACACGGCCA
9 RM202 F CAGATTGGAGATGAAGTCCTCC 11 57.6 2 150-190 0.281
RM202 R CCAGCAAGCATGTCAATGTA
10 RM174 F AGCGACGCCAAGACAAGTCGGG 2 65.8 3 210-250 0.467
RM174 R TCCACGTCGATCGACACGACGG
11 RM 231 F CCAGATTATTTCCTGAGGTC 3 55.6 3 175-205 0.588
RM231 R CACTTGCATAGTTCTGCATTG
12 RM232 F CCGGTATCCTTCGATATTGC 3 58.05 3 140-175 0.397
RM232 R CCGACTTTTCCTCCTGACG
13 RM263 F CCCAGGCTAGCTCATGAACC 2 60.4 2 160-195 0.364
RM 263 R GCTACGTTTGAGCTACCACG
14 RM270 F GGCCGTTGGTTCTAAAATC 12 57.95 2 125-140 0.032
RM270 R TGCGCAGTATCATCGGCGAG
15 RM 39 F GCCTCTCTCGTCTCCTTCCT 5 57.55 1 120 (monomorphic) 0.0
RM39 R AATTCAAACTGCGGTGGC
16 RM335 F GTACACACCCACATCGAGAAG 4 59.8 2 110-165 0.368
RM335 R GCTCTATGCGAGTATCCATGG
17 RM528 F GGCATCCAATTTTACCCCTC 6 57.3 3 180-220 0.447
RM528 R AAATGGAGCATGGAGGTCAC
18 RM551 F AGCCCAGACTAGCATGATTG 4 58.35 2 195-230 0.226
RM551 R GAAGGCGAGAAGGATCACAG
19 RM84 F TAAGGGTCCATCCACAAGATG 1 56.9 4 50-170 0.432
RM84 R TTGCAAATGCAGCTAGAGTAC
20 RM87 F CCTCTCCGATACACCGTATG 5 58.35 3 75-150 0.401
RM87 R GCGAAGGTACGAAAGGAAAG

Genetic Diversity Analysis in Rice (Oryza sativa L.) Accessions using SSR Markers
9
Print ISSN : 1974-1712 Online ISSN : 2230-732X
dissimilarity with genotype LC-16 (0.19). Similarly,
GK 5022 has highest similarity (0.88) with BAU/
IRRI 497 and highest dissimilarity (0.32) with Anjali.
The level of polymorphism among rice genotypes
was evaluated by calculating allelic number and
PIC values for each of the nineteen polymorphic
SSR markers. A total of 52 alleles were detected
by 19 polymorphic markers across sixty five
rice accessions with an average of 2.7 alleles per
polymorphic marker. Among the polymorphic
markers, 8 produced 2 alleles each, 9 markers had
produced 3 alleles each and 2 of them produced a
maximum of 4 alleles each. The amplicon size varied
from 50 bp produced by RM84 to 290 bp produced
by marker RM424. PIC value is a reection of allele
diversity and frequency among genotypes. The
PIC value observed in the present investigation
ranged from 0.032 to 0.588 with a mean PIC of
0.366; comparable to three previous estimates of
microsatellite analysis in rice viz., 0.26 to 0.65 with
an average of 0.47 (Singh et al., 2015), 0.28-0.50 with
a mean of 0.45 (Umadevi et al., 2014) and 0.239 to
0.765 with an average of 0.508 (Hossain et al., 2012).
The higher the PIC value of a locus, the higher the
number of alleles detected. RM231 was found to be
the most appropriate marker to discriminate among
the rice genotypes owing to the highest PIC value
of 0.588.
The dendrogram showed a total nine clusters on
the basis of dissimilarity coecient values. In the
dendrogram cluster IB-1a had maximum thirty-one
genotypes followed by cluster IB-1b and cluster IB-
1c. Clusters IA-1, IA-2, IIA-1, IIA-2 and IIB have one
germplasm each. On the basis of dendrogram the
highest similarity observed between cultivar Rewa
1208-15 and PAU 3832-79-4-3-1 followed by LC-15
and LC-16, BAU 411-05 and IR 82635-B-B-145-1
and BVD 111 and LC-11. The most diverse cultivar
was IR 82635-B-B-47-1 and OR 1946-2-1. Similar
result was found also by Singh et al. (2015) where
the accessions were grouped in two major groups
and 14 sub-groups. The dissimilarity coefficient
was calculated by Jackard  distance analysis, and
result showed value ranged from 0 to 1. According
to this dissimilarity coecient we can understand
the dendrogram and their relatedness. So, highest
diverse genotypes can be used as parents in
breeding programme. The average of dissimilarity
coecient varies from 0.7689 to 0.5079. The total
average of dissimilarity coecient of all sixty ve
Table 4: Grouping of sixty-ve rice accession into dierent clusters based on Jaccard’s  coecient
Cluster Number of
Genotypes
Name of the Genotypes
IA-1 1 IR 82635-B-B-47-1
IA-2 1 LC-10
IB-1a 31 CRR 660-2, IR 82589-B-B-7-2, IR 83182-6-4, RP 5345-9-6-3, IR 55423-01, B 11576F-MR-
18-2, IR 77298-14-1-2-13, IR 1718-59-1-2-3, IR 83142-76, Vandana, IR 82635-B-B-23-1,
CRR 428-237-1-3-1, Rewa 1208-15, IR 83399-B-B-52-1, PAU 3832-79-4-3-1, IR 78755-
190-B-1-3, IR 82635-B-B-25-4, R RF-48, CB 10-504, NDR 1140, CR 3633-1-2, IR 60080-
46A, LC – 5, LC – 6, LC – 7, LC – 8, LC-14, LC-15, LC-16, LC-17, LC-18
IB-1b 24 IR 10L-105, BAU 411-05, BD 104, CR 422-63-51-B-2-1-1-1-B, IR 82635-B-B-145-1, IR
368B-TB-25-MP-2, UPLRI – 7, RP 5330-63-5-2-1-B, MGD 1206, IR 83867-B-B-250-
CRA-1-1, IR 87694-28-7-2-1, IR 60080-46A, BVS 1, BVD 111, BVD 203, BAU 389-02,
LC-2, LC-3, LC-4, LC-9, LC-11, LC-12, LC-13, LC-19
IB-1c 3 CR 3631-1-3
IR 82921-B-B-1
BAU 389-02
IB-2 2 GK 5022
Anjali
IIA-1 1 IR 83926-B-B-71-4
IIA-2 1 LC-1
IIB 1 OR 1946-2-1

Rashmi et al.
10
Print ISSN : 1974-1712 Online ISSN : 2230-732X
cultivar is 0.6119. The dissimilarity coecient varied
from the largest value 0.9429 between the cultivar
LC-4 and IR 82635-B-B-47-1 followed by cultivar
value 0.9286 between the cultivar OR 1946-2-1 and
UPLRI-7 which shows high similarity between them
and it may be expected that both of them may have
arouse from the same parents. The lowest value
0.150 was found between REWA 1208-15 and PAU
3832-79-4-3-1. Similar result was repoted by Lapitan
et al. (2007) and Siva et al. (2010).
CONCLUSION
Twenty random SSR markers were used out of
which nineteen were found polymorphic. A total of
y two alleles were detected by 19 polymorphic
markers across sixty ve rice genotypes with an
average of 2.7 alleles per polymorphic marker. The
amplicon size varied from 50bp produced by RM84
to 290bp produced by marker RM424. PIC value
ranged from 0.032 to 0.588 and marker RM231
was found to be the most appropriate marker to
discriminate among the rice genotypes owing to the
highest PIC value of 0.588. The genetic divergence
study grouped sixty ve rice genotypes into nine
clusters in which cluster IB-1a had maximum
thirty-one genotypes followed by cluster IB-1b and
cluster V. On the basis of dendrogram the highest
similarity observed between cultivar Rewa 1208-
15 and PAU 3832-79-4-3-1 followed by LC-15 and
LC-16, BAU 411-05 and IR 82635-B-B-145-1 and
BVD 111 and LC-11. The most diverse cultivar was
IR 82635-B-B-47-1 and OR 1946-2-1. The highest
dissimilarity coecient value was observed between
the cultivar LC-4 and IR 82635-B-B-47-1 (0.0429) and
between OR 1946-2-1 and UPLRI-7 (0.9286) whereas
lowest value was seen between REWA 1208-15
and PAU 3832-79-4-3-1 (0.150) showing highly
diverse genotypes. Thus, these accessions were
genetically diverse and could be directly utilized
in hybridization programme for improvement of
yield related traits or to execute ecient selection
in highly segregating generations.
ACKNOWLEDGEMENTS
Authors thankfully acknowledge the Department
Networking Project, Banaras Hindu University for
providing the requisite germplasm and nancial
support to get this work accomplished.
REFERENCES
Doyle, J.J. and Doyle, J.L. 1987. A rapid DNA isolation
procedure for small quantities of fresh leaf tissue.
Phytochemical Bulletin, 19: 11-15.
Hossain, M.M., Islam, M.M., Hossain, H., Ali, M.S., Jamie, A.,
Komamine, A. and Prodhan, S.H. 2012. Genetic diversity
analysis of aromatic landraces of rice (Oryza sativa L.) by
microsatellite markers. Genes, Genomes and Genomics, 6:
42-47.
Lapitan, V.C., Brar, D.S., Abe, T. and Redoa, E.D. 2007.
Assessment of genetic diversity of Philippine Rice
cultivars carrying good quality traits using SSR markers.
Breeding Science, 57: 263-270.
Ma, H., Yin, Y., Guo, Z.F., Cheng, L.J., Zhang, L., Zhong, M.
and Shao, G.J. 2011. Establishment of DNA ngerprinting
of Liaojing series of japonica rice. Middle-East Journal of
Scientic Research, 8: 384-392.
Nagy, S., Poczai, P., Cernak, I., Gorji, A.M., Hegedus, G. and
Taller, J. 2012. PICcalc: An Online Program to Calculate
Polymorphic Information Content for Molecular Genetic
Studies. Biochemical Genetics, 50: 670–672.
Ni, J., Colowit, P.M. and Mackill, D.J. 2002. Evaluation of
genetic diversity in rice subspecies using microsatellite
markers. Crop Science, 42: 601-607.
Ramadan, E.A., Elmoghazy, A.M. and El-Mowa, H.F. 2015.
Molecular markers based genetic diversity analysis for
drought tolerance in rice (Oryza Sativa L.) Using SSR
Markers. Int. J. Sci. Res. Agric. Sci., 2: 137-146.
Rohlf, F.J. 1997. NTSYS-pc (Numerical Taxonomy and
Multivariate Analysis System), Version 2.02i, Exeter,
Setauket, NY.
Sajib, A.M., Hossain, M.M., Mosnaz, A.T.M., Hossain, H.,
Islam, M.M., Ali, M.S. and Prodhan, S.H. 2012. SSR
marker-based molecular characterization and genetic
diversity analysis of aromatic landreces of rice (Oryza
sativa L.). J. BioSci. Biotech., 1: 107-116.
Singh, N., Choudhury, D.R., Tiwari, G., Singh, A.K., Kumar, S.,
Srinivasan, K. and Singh, R. 2016. Genetic diversity trend
in Indian rice varieties: an analysis using SSR markers.
BMC Genetics, 17: 127.
Siva, R., Kumar, K. and Rajasekaran, C. 2010. Genetic
diversity study of important Indian rice genotypes using
biochemical and molecular markers. African Journal of
Biotechnology, 12: 1004-1009.
Thompson, J.A., Nelson, R.L. and Vodkin, L.O. 1998.
Identication of diverse soybean germplasm using RAPD
markers. Crop Science, 38: 1348-1355.
Umadevi, M., Veerabadhiran, P. and Manomani, S. 2014.
Assessment of genetic diversity of rice (Oryza sativa)
cultivars using simple sequence repeat (SSR) markers.
African Journal of Biotechnology, 13: 3547-3552.
Upadhyay, P., Neeraja, C.N., Kole, C. and Singh, V.K. 2012.
Population structure and genetic diversity in popular
rice varieties of India as evidenced from SSR analysis.
Biochemical Genetics, 50: 770–83.

Genetic Diversity Analysis in Rice (Oryza sativa L.) Accessions using SSR Markers
11
Print ISSN : 1974-1712 Online ISSN : 2230-732X
Zhang, D., Zhang, H., Wei, X., Qi, Y., Wang, M. and Sun, J., et
al. 2007. Genetic structure and diversity of Oryza sativa L.
in Guizhou, China. Chinese Sci. Bull., 52: 343–51.
Zhou, H.F., Xie, Z.W. and Ge, S. 2003. Microsatellite analysis
of genetic diversity and population genetic structure of
a wild rice (Oryza rupogon Gri) in China. Theor. Appl.
Genet., 107: 332-339.
Journal with DOI
Choudhary, G., Ranjitkumar, N., Surapaneni, M., Deborah,
D.A., Vipparla, A. and Anuradha, G. et al. 2013. Molecular
genetic diversity of major Indian rice cultivars over
decadal period. PLoS One, 8(6)- e66197. doi.org/10.1371/
journal.pone.0066197
Gracia, A.A.F., Benchimol, L.L., Antonica, M.M., Geraldi,
I.O. and Deuza, A.P. 2004. Comparison of RAPD, RFLP,
AFLP and SSR marker for diversity studies in tropical
maize inbred lines. Euphytica, 108: 53-63. hp://dx.doi.
org/10.1590/S1415-47572004000400019
Heal, G., Walker, B., Levin, S., Arrow, K., Dasgupta, P. and
Dailyd, G. et al. 2004. Genetic diversity and interdependent
crop choices in agriculture. Resour. Energy Econ., 26:175–84.
hps://doi.org/10.1016/j.reseneeco.2003.11.006
Islam, M.R., Gregorio, G.B., Salam, M.A., Collard, B.C.Y.,
Singh, R.K. and Hassan, L. 2012. Validation of SalTol
linked markers and haplotype diversity on chromosome
1 of rice. Molecular Plant Breeding, 3: 103-114. doi: 10.5376/
mpb.2012.03.0010
Jin, L., Lu, Y., Xiao, P., Sun, M., Corke, H. and Bao, J. 2010.
Genetic diversity and population structure of a diverse
set of rice germplasm for association mapping Theor Appl
Genet., 121: 475-487. doi:10.1007/s00122-010-1324-7
Kanawapee, N., Sanitchon, J., Srihaban, P. and Theerakulpisut,
P. 2011. Genetic diversity analysis of rice cultivars (Oryza
sativa L.) diering in salinity tolerance based on RAPD
and SSR markers. Electronic Journal of Biotechnology, 14:
2-2. doi 10.2225/vol14-issue6-fulltext-4
Rao, M., Grithlahre, S., Bisen, P., Singh, N.K., Dar, M.H., Singh,
U.S. and Singh, P.K. 2016. Genetics of marker assisted
backcross progenies of the cross HUR-105 X Swarna-
SUB1. International Journal of Agriculture, Environment &
Biotechnology, 9: 499-505. doi: 0.5958/2230732X.2016.00066.8.
Singh, A., Saini, R., Singh, J., Arya, M., Ram, M. and Singh,
P.K. 2015.. Genetic diversity studies in rice (Oryza Sativa
L.) using microsatellite markers. International Journal of
Agriculture, Environment & Biotechnology, 8: 143-152. doi:
10.5958/2230-732X.2015.00019.