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A set of 36 microsatellite markers distributed over 12 chromosomes of rice were used to assess genetic diversity in 33 medicinal rice genotypes. A total of 169 alleles were amplified, of which 166 were polymorphic. The number of alleles detected per locus ranged from 2 to 9 with an average of 4.69 alleles per locus. The polymorphism information content (PIC) ranged between 0.24 and 0.956 with an average of 0.811 per locus, indicating the suitability of the microsatellite markers for detecting genetic diversity among these rice genotypes. All the rice genotypes showed the presence of multiple alleles. Genetic similarities among genotypes varied from 0.239 to 0.827 with an average of 0.5. The cluster analysis grouped all the genotypes into two major clusters at 0.43 level of genetic similarity. The first three principal coordinates explained more than 0.63 of total genetic variation. All the genotypes included in the study could be uniquely distinguished from each other. The data provides basic information on medicinal rice genotypes of India which will be useful for future reference and to protect these unique rices under Intellectual Property Rights (IPR) regime.
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AJCS 6(9):1369-1376 (2012) ISSN:1835-2707
Assessment of genetic diversity in medicinal rices using microsatellite markers
L. Behera1*, B.C. Patra 1, R.K. Sahu 1, A. Nanda 1, S.C. Sahu 1, A. Patnaik 1, G.J.N. Rao1, O.N.
1Central Rice Research Institute, Cuttack-753 006, Odisha, India
*Corresponding author:
A set of 36 microsatellite markers distributed over 12 chromosomes of rice were used to assess genetic diversity in 33 medicinal
rice genotypes. A total of 169 alleles were amplified, of which 166 were polymorphic. The number of alleles detected per locus
ranged from 2 to 9 with an average of 4.69 alleles per locus. The polymorphism information content (PIC) ranged between 0.24
and 0.956 with an average of 0.811 per locus, indicating the suitability of the microsatellite markers for detecting genetic
diversity among these rice genotypes. All the rice genotypes showed the presence of multiple alleles. Genetic similarities among
genotypes varied from 0.239 to 0.827 with an average of 0.5. The cluster analysis grouped all the genotypes into two major
clusters at 0.43 level of genetic similarity. The first three principal coordinates explained more than 0.63 of total genetic
variation. All the genotypes included in the study could be uniquely distinguished from each other. The data provides basic
information on medicinal rice genotypes of India which will be useful for future reference and to protect these unique rices under
Intellectual Property Rights (IPR) regime.
Keywords: DNA fingerprinting; genetic diversity; medicinal rice; microsatellite marker; molecular marker.
Abbreviations: AFLP-Amplified Fragment Length Polymorphism, CTAB- Cetyl Trimethyl Ammonium Bromide, PCA-
Principal Component Analysis, SSR- Simple Sequence Repeat.
Rice is the staple food of more than 50% of the world's
population. Among the rice growing countries in the world,
India has the largest area under rice crop and ranks second
in production next to China. The cultivated rice (Oryza
sativa L.) is rich in genetic diversity apart from highly
diverse 21 wild progenitors and the African cultivated rice,
Oryza glaberrima Steud. In addition to staple food, rice has
extensive protective and curative properties against human
ailments like epilepsy, chronic headache, rheumatism,
paralysis, skin diseases, diabetes, arthritis, indigestion,
blood pressure, colon cancer, internal rejuvenation of
tissues and overcoming postnatal weaknesses (Kirtikar and
Basu, 1935). Medicinal uses of rice are well documented in
Ayurvedic system of medicine in the ancient Indian
Ayurvedic books viz., Susrutha Samhita, Charak Samhita
and Astanga Hridayam. Medicinal rices appear to be
confined to specific pockets of Central India, Southern
India (Kerala, Tamil Nadu and Karnataka) and North
eastern hilly regions. Most of these varieties are
traditionally cultivated in small scale and in restricted
pockets. Rice surveys in Chhattisgarh have led to the
identification of many traditional rice varieties like Alcha,
Baissor, Gathuhan, Laicha, Bhejri, Karhani and Kalimooch
possessing medicinal properties (Das and Oudhia, 2001).
The variety, Alcha is used for treatment of pimples while
Baissor is used for chronic headache. The traditional
varieties, Alcha and Kuzhiyadichan (Kulikulichan) in
Tamil Nadu (Arumugasamy et al., 2001), Karibhatta and
Atikaya in Karnataka (Balchandran et al., 2004), and
Njavara (Navara), Chennellu, Erumakkari, Raktasali and
Kunjinellu in Kerala (Sujith Kumar, 1999) are used for
treatment of various diseases. The Njavara is the most
widely used medicinal rice in the world. This rice can also
be prompted as a health food. The promotion of medicinal
rices can open up greater awareness, benefit a broader
public and result in increased income for the poor farming
community of India. Unambiguous, reliable, fast and cost-
effective identification, assessment of genetic diversity and
relationships within and among crop species and their wild
relatives is essential for the effective utilization and
protection of plant genetic resources (Paterson et al., 1991;
Barcaccia, 2009). Traditionally used morphological and
biochemical markers are not discriminative enough,
warranting more precise techniques. Further, these markers
are not reliable because many characters of interest have
low heritability and genetically complex in nature.
Molecular marker technology provides powerful tool for
assessment of genetic diversity among cultivars,
identification of cultivars and thus add to management and
protection of plant genetic resources (Virk et al., 2000). Of
the wide array of DNA markers available, microsatellite or
simple sequence repeat (SSR) markers are considered to be
appropriate for assessment of genetic diversity and variety
identification because of their ability to detect large
numbers of discrete alleles repeatedly, accurately and
efficiently (Smith et al., 1996; Varshney et al., 2005; Ijaz,
2011). Microsatellite markers have been ideal for
identification and purity checking of rice varieties
(Nandakumar et al., 2004; Singh et al., 2004),
characterization of genetic diversity in cultivated (Nagaraju
et al., 2002; Ni et al., 2002; Yu et al., 2003; Jain et al.,
2004; Kibria et al., 2009; Zhang et al., 2009) and wild rices
(Ren et al., 2003; Juneja et al., 2006) and also more
distantly related grass species (Ishii and McCouch, 2000).
A random set of microsatellite markers should facilitate an
unbiased assay of genetic diversity and unambiguous
molecular description of rice genotypes. The medicinal
properties of different rice cultivars are yet to be supported
by sound scientific data. No serious efforts have been made
at national level for genetic improvement except for reports
on collection, characterization and evaluation of the
medicinal rice, Njavara (Menon and Potty, 1998; Sreejayan
et al., 2003; Leena Kumary, 2004, Thomas et al., 2006;
Deepa et al., 2008). The valuable genetic wealth of
medicinal rices is being eroded because of their poor yield
and introduction of high yielding varieties. Thus, it is
highly essential that these valuable rice germplasm are to
be collected, conserved, properly characterized, genetically
enhanced and documented in view of their importance in
IPR regime. The present study was undertaken to assess the
extent of genetic diversity present among the medicinal rice
genotypes using microsatellite markers.
Results and discussion
Allelic diversity of microsatellite markers
All the 33 microsatellite primers revealed polymorphism
between genotypes. A total of 169 bands/alleles were
amplified, of which 166 (98.22%) were polymorphic. The
number of alleles per locus ranged from 2 to 9 with an
average of 4.69 (Supplementary Table 1). Two loci, RM
203 and RM333 amplified highest number alleles (9 in
each case). The lowest number of alleles was observed in
RM432 loci. The number of alleles detected in the present
study corresponded well with earlier reports (Nagaraju et
al., 2002; Jain et al., 2006; Juneja et al., 2006; Ram et al.,
2007). However, Jain et al. (2004) obtained higher number
of alleles (3 to 22) as compared to present study, because of
inclusion of Basmati as well as non-Basmati varieties and
the use of fluorescent techniques in their study. The number
of alleles detected by a single SSR locus varied from 1 to
31 depending upon the fingerprinting techniques and
materials used in the studies (Ni et al., 2002; Blair et al.,
2002; Lu et al., 2005; Jayamani et al., 2007; Thompson et
al., 2009; Kaushik et al., 2011). Microsatellite markers with
simple tri-nucleotide repeat motifs tended to detect a
greater number of alleles (average: 5.71, n=7) than those
with di-nucleotide repeat motifs (average: 4.48, n=21)
(Supplementary Table 2). Non significant correlation (r =
0.079, P > 0.10) was found between number of alleles
amplified per locus and number of repeats in simple motif
of a SSR locus (Supplementary Table 4). Similar to our
observation, Nagaraju et al. (2002) and Juneja et al. (2006)
found no direct correlation between number of repeats and
number of alleles detected in aromatic and wild rice
materials, respectively. However, Ni et al. (2002) and Yu et
al. (2003) found positive correlation between number of
alleles amplified and number of repeats within a
microsatellite marker. Cho et al. (2000) and Jain et al.
(2004) observed that SSR loci with di-nucleotide repeats
detected greater number of alleles than those with tri-
nucleotide repeats. Kaushik et al. (2011) found SSR loci
with complex mixed repeats detected highest number of
alleles followed by those with simple tri-nucleotide, di-
nucleotide and tetra-nucleotide repeat motifs. Eleven
unique alleles (6.51%) were observed at 8 SSR loci. An
allele that was observed only in one of the 36 rice
genotypes was considered to be a unique allele. Unique
alleles are important because they may be diagnostic of a
particular genotype and useful for breeding purpose.
Number of unique alleles per locus varied from 1 to 2 (RM
307, RM 333 and RM 470) (Table 1). All the markers
amplifying unique alleles showed high level polymorphism
information content (PIC) values (> 0.75) (Supplementary
Table 1).Three medicinal rice genotypes, Ratanchudi,
Gudamatia and Kandul which are used as tonic for general
weakness, treatment of diabetes and paralysis, respectively
amplified two unique alleles each (Table 1). Two high
yielding varieties (Kalinga III and Swarna) and three
medicinal genotypes (Bangali, Kalamali and Gotia)
amplified an unique allele each. The occurrence relatively
higher number of unique alleles in the medicinal rice
genotypes indicates its potentiality as a reservoir of novel
alleles for crop improvement. Saini et al. (2004) identified
58 unique alleles (36.2%) among Basmati and non-Basmati
rice varieties. These unique alleles were observed at 25 of
30 SSR loci. Davierwala et al. (2000) identified many
alleles specific to elite cultivars of India using
microsatellite markers. Similarly, others also detected
unique alleles both in cultivated and wild rices (Giarrocco
et al., 2007; Wong et al., 2009). Sixty eight (40.24%) high
frequency and 90 (53.25%) of low frequency alleles were
identified among 36 medicinal rice genotypes. On an
average, 56.9% of the 36 rice genotypes produce any low
frequency alleles. The presence of high proportion of low
frequency alleles in medicinal rice genotypes indicated that
they make a greater contribution to overall genetic diversity
of the collection. Jain et al. (2004) observed that 53.6% of
69 rice genotypes shared common alleles at any locus.
Thompson et al. (2009) indicated that on an average, 62%
of the 190 rice accessions of Indonesia shared a common
major allele at any given of SSR locus. Similar results were
also observed by others (Saini et al., 2004; Lu et al., 2005;
Jayamani et al., 2007). All the rice genotypes including
high yielding varieties, Kalinga III, Vandana and Swarna
showed the presence of multiple alleles. Thirty four of 36
microsatellite loci amplified one to five multiple alleles per
locus (Supplementary Table 1). For any given marker,
multiple alleles were detected in an average of 37.42%
genotypes per locus, indicating presence of high level of
heterogeneity in the genotypes. Number of genotypes with
multiple alleles vary from one (2.78 %) at RM 30 locus to
36 (100%) at RM 20, RM 189 and RM269 loci
(Supplementary Table 1). Figure 1 shows the amplification
of multiple alleles in all the genotypes by RM 20 locus. A
positive correlation (r = 0.665, P < 0.0001) was found
between total number of alleles and number of multiple
alleles amplified per SSR locus (Supplementary Table 4).
Rice is a self pollinated, diploid crop species. The
microsatellites usually reveal single copy, homozygous loci
and allelic heterogeneity is rare in respect of pure line
varieties. Hence, the presence of multiple alleles in
cultivars is generally an indication of seed mixtures,
mixture of pure lines or residual heterozygosity (Jain et
al., 2004). This phenomenon is quite common in landraces
that contribute to their broad genetic plasticity to adapt
themselves to different agro-climatic conditions in
traditional farming systems (Olufowate et al., 1997). In the
present study, the heterozygosity could not be differentiated
from heterogeneity because the DNA samples were
extracted from bulked leaf samples. Analysis of 43
Australian cultivars with microsatellite markers indicated
that 42% of cultivars were heterogeneous (Garland et al.,
1999). They observed the high level of heterogeneity at
microsatellite locus, OSR6. Jain et al. (2004) observed
multiple alleles in 62% of Indian aromatic and Basmati rice
accessions and even in modern varieties such as IR 36 and
Nipponbare. Similar results were also obtained both in
cultivated and wild rices (Yu et al., 2003; Saini et al., 2004;
Lu et al., 2005; Jain et al., 2006; Juneja et al., 2006;
Giarrocco et al., 2007; Jayamani et al., 2007).
Polymorphism information content
The Polymorphism information content (PIC) value
provides an estimate of discriminating power of a marker
based on the number of alleles at a locus and relative
frequencies of these alleles. The PIC values for 36 SSR
loci in our study varied from 0.24 (RM 189) to 0.956 (RM
333) with an average of 0.811 (Supplementary Table 1).
All the loci except the three (RM 259, RM 189 and RM
269) showed high PIC values (>0.60). The estimated
average PIC values are relatively higher than the average
PIC values as reported by others (Lu et al., 2005; Juneja et
al., 2006; Joshi et al., 2010) and thus might be due to higher
genetic diversity present in selected medicinal rice
genotypes. Moreover, the SSR markers used in the study
were selected on the basis of their high PIC values reported
earlier. Higher PIC values for some SSRs similar to our
findings were also reported in the literature (Garland et al.,
1999; Giarrocco et al., 2003; Juneja et al., 2006; Jayamani
et al., 2007; Ram et al., 2007). Microsatellite markers with
simple tri-nucleotide repeat motifs detected higher
polymorphism (Mean: 0.853; n = 7) than those with di-
nucleotide repeat motifs (Mean: 0.788 n = 21)
(Supplementary Table 2). No correlation (r = 0.147, P >
0.10) was observed between number of repeats of simple
motif and PIC value of a SSR locus. However, a positive
correlation (r = 0.4, P < 0.01) was evident between number
of alleles amplified and PIC values (Supplementary Table
4). Jain et al. (2006) had also observed that PIC values
showed a positive correlation with total number of alleles at
SSR locus (P = 0.01). Microsatellite loci with (CTT)n and
AT-rich di- and tri-nucleotide repeat motifs amplified a
greater number of alleles and revealed greater
polymorphism(Supplementary Table 3). Two of the
microsatellite loci, RM 203 and RM 333 having (AT)n and
(TAT)n(CTT)n repeats, respectively amplified nine alleles
each and had PIC values of 0.914 and 0.956, respectively
(Supplementary Table 3). Similar results are also reported
by Temnykh et al. (2000) who revealed that (CTT)n and
AT-rich tri-nucleotide repeats amplified with higher
efficiency and revealed greater polymorphism. Juneja et al.
(2006) indicated that the markers, RM 340 and RM 333
having (CTT)n repeat yielded 6-7 alleles and were most
informative with PIC value of 0.8.
Genetic diversity and relationship among medicinal rice
Genetic similarity coefficients of pair-wise comparisons
estimated on the basis of all the 36 microsatellite loci
ranged from 0.239 to 0.827 with an average of 0.5,
indicating a wide range of genetic variation present in the
medicinal rice genotypes. The genotype Gadaguta showed
the highest similarity with Gudamatia (i.e. 0.827) while
Meher showed the least similarity with Kande (i.e. 0.239).
High level of similarity was found among Bogasoli and
Ratanchudi (0.824) as well as between Bedro and Mancha
(0.808). Similar to our observations, other studies using
SSR markers revealed varying degrees of genetic similarity
among the accessions of cultivated and wild species of rice
(Ren et al., 2003; Juneja et al., 2006; Jayamani et al., 2007;
Joshi et al., 2010). Juneja et al. (2006) analyzed genetic
variation among 127 O. nivara accessions and two cultivars
using 33 SSR markers. Genetic similarities varied from
0.22 to 0.9, indicating a wide range of variability present in
wild germplasm. Jayamani et al. (2007) detected a
significant genetic variation among the 176 rice accessions
originating from 19 countries in the Portuguese working
germplasm collection and two standard rice varieties, IR36
and Nipponbare with a genetic similarity coefficient
varying between 0.09 and 1.00 using 24 SSR loci. Joshi et
al. (2010) analyzed 21 low land and 3 shallow low land rice
using 45 SSR markers. Genetic similarity varied from
0.041-0.728. Cluster analysis based on genetic similarity
values provided a clear resolution of relationships among
all the 36 rice genotypes. The Cophenetic correlation
coefficients (r = 0.803) revealed the reliability and stability
of clustering. Two major clusters were observed at 0.43 of
genetic similarity coefficient index (Fig. 2). First major
cluster contained 27 medicinal rice genotypes with an
average similarity index of 0.568. Further, it was sub-
grouped into two clusters, IA and IB having 22 and 5
genotypes, respectively. The similarity coefficients
between any two rice genotypes in the sub-cluster, IA
ranged from 0.396 (Sindursingha and Chinabhuchi) to
0.827 (Gadaguta and Gudamatia) with an average of 0.574.
Second sub cluster, IB was less diverse than first sub-
cluster, IA. Interestingly, all the 3 high yielding varieties
were included in this sub-cluster along with two medicinal
rice genotypes, Kande and Sambalpuri. The similarity
coefficients between any two genotypes varied from 0.429
(Kalinga III and Sambalpuri) to 0.626 (Sambalpuri and
Vandana) with an average of 0.540. The second major
cluster included 9 medicinal rice genotypes with similarity
indices from 0.457 (Marangdhan and Mehera) to 0.785
(Limchudi and Katak) with an average of 0.608. The
classification of rice genotypes based on 36 SSR primers
Table 1. Microsatellite loci that amplified unique alleles in different rice genotypes.
Microsatellite locus (RM)
Unique allele (bp)
Name of rice genotype
Kalinga III
M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 M
M 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 M
Fig 1. DNA profiles of 33 medicinal and 3 high yielding rice genotypes (A and B) obtained with microsatellite locus, RM 20.
The lane number corresponds to a rice genotype as given in the Table1, M-50bp DNA ladder. All the rice genotypes show the
presence of multiple alleles.
was highly similar with that based on fifteen and ten of the
most informative primers with matrix correlation ( r ) of
(+) 0.80 and (+) 0.68, respectively. Thomas et al. (2006)
analyzed the genetic diversity of different collections of
medicinal rice, Njavara using morphological and molecular
(AFLP and SSR) markers. Six groups in Njavara
germplasm were identified that could be clubbed under four
distinct varietal types. The PCA is one of the multivariate
approaches of grouping based on similarity coefficients or
variance-covariance values. It is expected to be more
informative about differentiation of major groups. The
groupings identified by PCA (Fig. 3A, B) were comparable
to those identified by UPGMA cluster analysis. More than
0.63 of genetic variation was explained by the first three
coordinates, indicating suitability microsatellite markers for
genetic clustering. The first, second and third principal
coordinates explained 0.517, 0.07 and 0.043 , respectively
of total genetic variation in SSR data. Two major clusters
were revealed by both two (Fig. 3A) and three principal
components (Fig. 3B). Unlike UPGMA analysis, where all
genotypes were included in a group, KalingaIII (No.19) and
Meher (No. 2) were out of the major clusters, possibly
indicating genetic differentiation. The first major cluster
included 26 genotypes while second major cluster had 8
genotypes. Jayamani et al. (2007) obtained comparable
groupings of Portuguese rice accessions by PCA and
UPGMA cluster analysis with some deviations. The
groupings identified by PCA were very similar to those
identified by the UPGMA tree cluster analysis of the 52
Indian aromatic/ quality rice genotypes (Jain et al., 2004).
Aggarwal et al. (2002) observed six clusters in PCA while
3 clusters in UPGMA analysis by characterizing Indian
Basmati and other elite genotypes using AFLP markers.
Differentiation of medicinal rice genotypes
A number of microsatellite markers were identified that
distinguished between different medicinal and high
yielding rice genotypes. All the rice genotypes used in the
present study could be distinguished precisely from each
other at the level of 18 to 68 polymorphic alleles between
individuals in pair wise comparison. However, none of the
microsatellite loci could differentiate all the genotypes. The
discriminating power of microsatellite loci vary from 0.243
(RM 189) to 0.952(RM 169) with an average of 0.812
(Supplementary Table 1). On the basis of discriminating
power, the minimum number of microsatellite loci required
to differentiate between genotypes in the present study was
found to be three (i.e. RM 169, RM 335 and RM 333).
These three SSR loci amplified a total of 23 alleles, all
being polymorphic. The frequency of these alleles ranged
from 1/36 to 17/36. Similar results were obtained by others
Fig 2. UPGMA dendrogram showing genetic relationship among 33 medicinal and 3 high yielding rice genotypes based on Dice
similarity matrix derived from 169 alleles at 36 microsatellite loci. The major clusters and sub-clusters are indicated on right
(Olufowote et al., 1997; Saini et al., 2004). Six well chosen
SSLPs were found to be sufficient to discriminate between
71 related lines of rice (Olufowote et al., 1997). Saini et al.
(2004) evaluated the genetic diversity among the 18 rice
genotypes representatives of the traditional Basmati, cross-
bred Basmati and non-Basmati rice varieties using AFLP,
ISSR and SSR markers. The minimum number of markers
needed to distinguish between all the cultivars was one for
AFLP, two for ISSR and five for SSR. Some researchers
were able to unambiguously identify and discriminate
twenty eight rice varieties which included thirteen high
yielding, fourteen local and a wild rice cultivars using only
three microsatellite markers. The combination of all the
polymorphic and non-polymorphic alleles obtained with all
the 36 SSR loci enabled development of DNA profile data
set for 33 medicinal rice genotypes along with three high
yielding rice varieties as check (data not shown), which can
serve as a guide for easy visual comparison of any
additional genotype as and when become available.
Materials and methods
Plant materials
The experimental materials comprised of a set of 33
landraces having different therapeutic values and a set of
three high yielding rice varieties as check (Supplementary
Table 5). Based on the earlier reports of medicinal rices
from Bastar region of Chhattisgarh, India (Das and Oudhia,
2001), these 33 medicinal rice genotypes were collected
from farmers field. During collection, interactions were
made with the farmers to record the medicinal uses of rice
genotypes along with the prescribed passport data.
Genomic DNA isolation
Twenty seeds per genotype were germinated in a Biological
Oxygen Demand (BOD) incubator under aseptic condition
at 300C. Then young seedlings (five days after germination)
were transplanted in individual pots in green house. After
20 days of transplanting, young leaves were harvested from
10-15 plants and bulked for each genotype. Genomic DNA
was isolated from 3-4 gm of bulked leaf samples following
CTAB method (Murray and Thompson, 1990). The
quantity was estimated by spectrometrically and by
ethidium bromide staining after agarose gel electrophoresis
using known concentration of Lambda DNA. The samples
were diluted in T10E1 buffer (10 mM Tris-HCl, 1 mM
EDTA, pH 8.0) to get final concentration of 15 ng/ µl for
PCR amplification and electrophoresis
A set of 36 microsatellite markers distributed over 12
chromosomes of rice were used for DNA profiling
Fig 3. Two-dimensional (A) and three-dimensional (B) plot
of 33 medicinal and 3 high yielding rice genotypes
resulting from principal component analysis (PCA) based
on the 169 alleles amplified by 36 microsatellite loci. The
numbers plotted represent individual rice genotypes that are
listed in Supplementary Table 5. Circles in the figures A
and B indicate the major clusters. The first three
coordinates explained more than 63% of total genetic
(Supplementary Table 1). The primer sequences for these
markers can be found in the Gramene website
( The amplification was carried
out in a 20 µl reaction mixture volume containing 30 ng of
genomic DNA, 1X PCR buffer {75 mM Tris-HCl (pH 9.0),
50 mM KCl, 20 mM (NH4)2SO4}, 200 µM dNTP mix
(MBI Fermantas, Lithuania, USA), 4 picomole of each of
forward and reverse primers, 2 mM of MgCl2 and 1U of
Taq (Thermus aquaticus) DNA polymerase (Biotools,
Spain). The PCR was performed in a thermal cycler
(Thermal Cycler, Perkin Elmer, Cetus) as per following
cycling parameters: initial denaturation at 940C for 3 min
followed by 36 cycles of denaturation at 940C for 1 min,
annealing at 55-670C (depending upon primer) for 1 min
and extension at 720C for 1.5 min and final extension at
720C for 5 min. The amplified products were separated on
2.5% agarose gel containing ethidium bromide (0.5 µg/ml)
using 1X TBE buffer. The gels were visualized under UV
radiation and photographed using a gel documentation
system (Fluor ChemTM 5500, Alpha Innotech, USA) to
detect polymorphism. The size of amplified bands was
determined by using size standards (50 bp DNA ladder,
MBI Fermentas, Lithuania).
Data analysis
The amplified bands/alleles were scored as present (1) or
absent (0) for each genotype and primer combination. The
data were entered into a binary matrix and subsequently
analyzed using the computer package, NTSYS-pc (Version
2.02)(Rolf, 1998). The number of high frequency alleles
per locus (HFA), number of low frequency alleles (LFA),
number of multiple alleles (MA), number of polymorphic
alleles (PA), polymorphism information content (PIC),
total number alleles (TA) and number of unique alleles
(UA) were calculated to assess diversity of alleles of
marker locus. An allele that was observed in > 30% of the
36 rice genotypes was considered to be a high frequency/
abundant/ common allele while an allele having frequency
between 5% and 30% is called as a low frequency/
intermediate allele. The polymorphism information content
(PIC) was calculated using the formula, PIC = 1-Pij2,
where Pij is the frequency of ith allele for the jth locus and
summation extends over n alleles (Anderson et al., 1993).
In order to find the efficiency of SSR markers for
differentiation of genotypes, the discriminating power (D)
of each marker loci was calculated following formula, Dj =
1-Cj = 1- Pi (NPi-1)/(N-2), where Dj is discriminating
power of jth locus, Pi is frequency of ith allele, Cj confusion
probability of jth locus (Tessier et al., 1999). Further, in
order to know minimum number of marker loci required to
identify and differentiate genotypes from each other, total
number of non-differentiated pairs( Xj) of genotypes were
calculated for the jth locus using formula, Xj = {N(N-
1)/2}Cj. Dice genetic similarity coefficients were
calculated and used to assess the genetic relationship
among 48 low land rice genotypes (Nie and Li,1979) and
then were used to construct dendrogram using unweighted
pair group method using arithmetic averages (UPGMA)
and sequential agglomerative hierarchal nested (SHAN)
cluster analysis. The Cophenetic correlation coefficient
(Lapointe and Legendre, 1992) was calculated to measure
the goodness of fit of clusters. Principal component
analysis (PCA) was performed to high light the resolving
power of the ordination.
The present study clearly indicated that microsatellite
markers are useful in assessing genetic diversity in
medicinal rice genotypes. All the genotypes analyzed could
be distinguished from each other. A basic molecular data
set was created for the medicinal rice genotypes which can
be used for variety registration, preventing
misappropriation and protecting the plant breeders as well
as farmers’ rights. We suggest a wider survey and
collection of medicinal rice genotypes from different
geographical regions of India in order to conserve
maximum diversity, and utilize this unique and valuable
resource in breeding programs benefit of both farmers and
We gratefully acknowledge the help and financial support
provided by the Director, Central Rice Research Institute,
Cuttack, India.
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... The high PIC value found in these local Indonesian varieties was much higher than those of rice landraces observed by Kumbhar et al. (2015) and Das et al. (2013), even compared to other landraces/accessions from Malaysia, India, and Pakistan (Aljumaili et al. 2018;Behera et al. 2012;Wang 2005). Notably, all markers used in this study were highly informative (Botstein et al. 1980), suggesting their suitability to differentiate among rice varieties/accessions that would be potentially used to explore the genetic diversity of other local rice varieties in Indonesia. ...
... The high PIC value found in these local Indonesian varieties was much higher than those of rice landraces observed by Kumbhar et al. (2015) and Das et al. (2013), even compared to other landraces/accessions from Malaysia, India, and Pakistan (Aljumaili et al. 2018;Behera et al. 2012;Wang 2005). Notably, all markers used in this study were highly informative (Botstein et al. 1980), suggesting their suitability to differentiate among rice varieties/accessions that would be potentially used to explore the genetic diversity of other local rice varieties in Indonesia. ...
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Indonesia's local rice varieties (Oryza sativa L.) have a wide range of diversity that can be valuable sources for crop improvement with molecular markers. This study investigated local rice varieties' genetic diversity and population structure using simple sequence repeat (SSR) markers. The SSR markers demonstrated their informativeness for genotypic characterization as represented by the gene diversity indices and polymorphic information content. The UPGMA dendrogram divided 63 varieties into two distinct clusters with different levels of sub-grouping and the tendency according to their origins, as supported by PCoA. In contrast, PCA of these varieties according to agro-morphological traits was scattered in all quadrants. Thus, DNA level variation analyzed by SSR seems to complement the phenotypic traits, which were not well structured and revealed significant genetic diversity among varieties, within, and among populations (P<0.01). The pattern of grouping structure analysis of total varieties into two subpopulations is similar to the dendrogram according to SSR markers but better resembles the pedigree information of the set local varieties. These findings have implied their importance in rice breeding for genetic improvement, maintenance, and management of local genetic resources in Indonesia.
... Genomic tools in this regard established as impartial and appropriate alternate for GOT. Several co-dominant DNA-based markers are developed and utilized for genetic purity analysis in hybrids/parents (Behera et al., 2012;Verma et al., 2017). Besides, genomics-assisted selection/MABB/genome editing tools is providing strong utensils for improvement of trait of interest in crop plants. ...
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Around three fourth of total rice production, comes from irrigated rice which is about 55% of total rice land area in World and India as well. Rice production in India has made tremendous progress over the years. However, it is facing unprecedented challenges of environmental degradation and climate change in recent years. At the current level of per capita availability rice production is required to go up by 40 and 70%, by 2030 and 2050, hence productivity has to enhance further. Genetic enhancement is one of the most potential approaches of higher productivity. Early work on rice basically focused on improving native varieties of highly heterogeneous native farmers varieties with resistance to different biotic and abiotic stress situations. Systematic efforts by national and international agencies with collaborative approach focused on improvement of yield and other traits mostly through breeding and selection utilizing diverse germplasm. Breakthrough in yield potential was achieved through modification of plant type, first by introducing dwarfing gene 'sd1'from the dwarf plant type Dee-Geo-Woo-Gen, which ushered green revolution in mid-sixties. Subsequent development focussed more on stability of the varieties incorporating more disease, pest resistance as well as earliness, however yield remains static. Then research on New Plant Type for improvement of dry matter and harvest index for enhancing yield potential was targeted. Similarly, hybrid rice also played a significant role for upgrading the productivity pattern. In the meantime many QTL for yield and yield attributing traits and genes for major diseases and pests have been identified which paves the way for molecular breeding. Similarly, transgenic approach of yield enhancement was also tried with limited success. The present strategy 'the modernization of breeding initiative includes both modification of classical breeding along with utilization modern breeding schemes for incorporating high precision tools and genomic selection on the basis of necessary demands. To support this pre-breeding of wild ancestors and physiological manipulation are also necessary without compromising diversity.
... The number of alleles ranged from 2 to 5 with an average of 3.43. The number of alleles observed in this study are higher than those reported by Gowda et al. (2012), Shah et al. (2013), Anandan et al. (2016) and Rashmi et al. (2017) ranging from 2.70 to 3.28 alleles per locus, whereas few studies reported an average of 4.09 to 4.69 (Behera et al. 2012;Aljumaili et al. 2018) with higher average allelic number than the present study. The PIC value of each marker, which can be evaluated on the basis of its alleles, varied greatly for all tested SSR loci (0.14 to 0.99) with an average of 0.52. ...
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Genetic purity is conventionally performed through grow-out-test (GOT) with morphological characters. Simple sequence repeat (SSR) markers are independent of G X E interaction. Herewith, 16 high yielding varieties of rice were analysed using 55 SSR markers for DNA fingerprinting and identification of genetic impurities: 14 were found to be polymorphic and amplified 48 alleles with an average of 3.43 alleles per each primer pair. The number of alleles amplified ranged from 2 to 6 and the size of the PCR products amplified from these 14 primer pairs ranged from 80 to 450 bp with polymorphic information content (PIC) from 0.14 (RM 346) to 0.99 (RM 5900). PICs at 0.5 or higher are highly informative SSR markers for genetic studies and the study reported (7/14) markers to be highly informative based on PIC values. Besides, the values of effective multiplex ratio, marker index and resolving power for the selected polymorphic primer pairs disclosed that the SSR markers used in the study are highly informative and could be potentially used for distinctness, uniformity and stability (DUS) testing, genetic purity analysis and DNA fingerprinting of rice varieties. Economic analysis of grow-out-test and SSR marker based genetic purity showed that the GOT process incurs Rs. 5.8 (INR) per seed (USD 0.07), while DNA-based marker analysis incurs Rs. 53.75 (INR) or $ 0.71 (USD). Graphical Abstract
... Molecular markers in this context found to be a suitable alternative, provide an unbiased means of identifying crop varieties. Among available DNA-based markers, sequence-tagged microsatellites (STMSs), which are co-dominant in nature, are widely used for speedy genetic purity assessment of the hybrids and parental lines [27,28]. Besides, ICAR-NRRI has developed another set of nine signature markers which can distinguish parents CRMS 31A, CRMS 32A; and hybrids Ajay, Rajalaxmi and CR Dhan 701, unambiguously. ...
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Heterosis is a solitary means of exploiting hybrid vigor in crop plants. Given its yield advantage and economic importance, several hybrids in rice have been commercialized in more than 40 countries, which has created a huge seed industry worldwide. India has made commendable progress and commercialized 117 three-line indica hybrids for different ecology and duration (115-150 days), which accounted for 6.8% of total rice area in the country. Besides, several indigenous CMS lines developed in diversified genetic and cytoplasmic backgrounds are being utilized in hybrid rice breeding. NRRI, which has been pioneering to start with the technology, has developed three popular rice hybrids, viz., Ajay, Rajalaxmi, and CR Dhan 701 for irrigated-shallow lowland ecosystem. Biotechnological intervention has supplemented immensely in excavating desirable genomic regions and their deployment for further genetic enhancement and sustainability in rice hybrids. Besides, hybrid seed production creates additional job opportunity (100-105 more-man days) and comparatively more net income (70% more than production cost) than HYVs. Hence, this technology has great scope for further enhancement in per se rice productivity and livelihood of the nation.
... Maytinee et al., [22] used InDel (Insertion/Deletion), ISSR and SSR markers to detect genetic diversity among 126 Thai rice accessions. Behera et al., [23] used 36 microsatellite markers to assess genetic diversity in a set of 33 medicinal rice genotypes and detected 166 polymorphic loci. The PIC values ranged between 0.24 and 0.956 with an average of 0.811 per locus. ...
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Adaptations to different habitats across the globe and consequent genetic variation within rice have resulted in more than 120,000 diverse accessions including landraces, which are vital genetic resources for agronomic and quality traits. In India the rice landraces of the states West Bengal, Assam, Mizoram, Manipur and Nagaland are worthy candidates for genetic assessment. Keeping the above in view, the present study was conducted with the aim to (i) calculate the genetic distances among the accessions of 83 landraces collected from these states along with 8 check accessions (total 91 accessions) using 23 previously mapped SSR markers and (ii) examine the population structure among the accessions using model-based clustering approach. Among the 91 accessions, 182 alleles were identified which included 51 rare and 27 null alleles. The average PIC value was 0.7467/marker. The non-aromatic landraces from West Bengal was most diverse with 154 alleles and an average PIC value of 0.8005/marker, followed by the aromatic landraces from West Bengal with 118 alleles and an average PIC value of 0.6524/marker, while the landraces from North East ranked third with 113 alleles and an average PIC value of 0.5745/marker. In the dendrogram distinct clusters consisting of predominantly aromatic landraces and predominantly North East Indian landraces were observed. The non-aromatic landraces from West Bengal were interspersed within these two clusters. The accessions were moderately structured, showing four sub-populations (A-D) with an Fst value of 0.398, 0.364, 0.206 and 0.281, respectively. The assigned clustering of accessions was well in agreement in both distance-based and model-based approaches. Each of the accessions could be identified unequivocally by the SSR profiles. Genetically the non aromatic landraces from West Bengal were most diverse followed by the aromatic landraces from the same state. The North Eastern accessions ranked third. Further, grouping of accessions based on their agronomic traits may serve as a resource for future studies, leading to the improvement of rice. Moreover in-situ preservation of the landraces is also a means of protection of biodiversity and cultural heritage.
... Similar results for average PIC were reported by Das et al. 66 in the landraces of northeast India. Behera et al. 67 reported a higher average PIC of 0.811 per locus, this might be due to the use of a more diverse set of rice accession in their study or due to the use of highly polymorphic markers. Shah et al. 68 and ...
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The studies on genetic variation, diversity and population structure of rice germplasm of North East India could be an important step for improvements of abiotic and biotic stress tolerance in rice. Genetic diversity and genetic relatedness among 114 rice genotypes of North East India were assessed using genotypic data of 65 SSR markers and phenotypic data. The phenotypic diversity analysis showed the considerable variation across genotypes for root, shoot and drought tolerance traits. The principal component analysis (PCA) revealed the fresh shoot weight, root volume, dry shoot weight, fresh root weight and drought score as a major contributor to diversity. Genotyping of 114 rice genotypes using 65 SSR markers detected 147 alleles with the average polymorphic information content (PIC) value of 0.51. Population structure analysis using the Bayesian clustering model approach, distance-based neighbor-joining cluster and principal coordinate analysis using genotypic data grouped the accession into three sub-populations. Population structure analysis revealed that rice accession was moderately structured based on FST value estimates. Analysis of molecular variance (AMOVA) and pairwise FST values showed significant differentiation among all the pairs of sub-population ranging from 0.152 to 0.222 suggesting that all the three subpopulations were significantly different from each other. AMOVA revealed that most of the variation in rice accession mainly occurred among individuals. The present study suggests that diverse germplasm of NE India could be used for the improvement of root and drought tolerance in rice breeding programmes.
Traditional rice varieties grown by the farmers serve as valuable genetic resources for future rice improvement. These varieties are highly adapted to varied agro-ecological conditions. However, they are rapidly lost because of the adoption of high-yielding varieties. The extent of allelic and genetic diversity present in the germplasm is a prerequisite for the improvement of any crop and conservation strategies under adverse impacts of climate. Farmers' rice varieties are usually poor yielders but are allelic treasurer for different traits, especially biotic and abiotic stresses, grain qualities, early seedling vigor, input use efficiency, etc. Therefore, the present study was aimed for a detailed understanding of allelic and genetic diversity, and population structure of 607 farmers' rice varieties using 36 fluorescently labeled microsatellite markers and 53 morphological traits. A total of 363 alleles was detected with an average of 10.33 alleles per locus and moderately high Nei's allelic/gene diversity (0.502) was detected. Polymorphic information content ranged from 0.685 to 0.987 with an average of 0.901. 34 unique, 236 rare, 84 low-frequency and 44 high-frequency alleles were detected. 53 morphological traits harbored a total of 195 variables with an average of 4.217 variables per trait. 50 out of 53 morphological traits showed polymorphism and highly significant differences among varieties. High genetic diversity was observed among 607 farmers' rice varieties both at molecular (0.653) and phenotypic (0.656) levels. The dendrogram based on both microsatellite markers and morphological traits grouped the 607 farmers' rice varieties into three major groups. A moderate population structure was observed with two independent subpopulations SP1 and SP2, which have membership percentages of 82.6 % and 17.4 %, fixation index values of 0.19 and 0.194, respectively. The AMOVA could explain 63 % of the total variation among varieties and 34 % within varieties. Our results showed that the farmers' rice varieties of Odisha harbored higher levels of both allelic and genetic diversity. Hence, these varieties would be useful for the identification of novel and elite alleles, and serve as a source of donors for the development of climate-smart varieties with improved grain yield and qualities, and input use efficiency, which would be sustainable in changing climate scenario conditions and improve farmers' income.
Purpose - Kala Jirga and Ajara Ghansal are the non-basmati aromatic rice landraces having small grains and good cooking qualities. In spite of huge demand these landraces are cultivated in a few pockets of Kolhapur district of India due to micro-climate required for the development of aroma and grain quality. Both the varieties are late maturing (> 160 days), tall (> 140 cm) and highly susceptible to lodging which resulted into low productivity. To overcome these constraints, induced mutation was attempted to improve the traits in these important rice landraces. Material and methods – Seeds of two landraces were treated with three concentrations/doses of Ethyl methanesulphonate (EMS), Sodium Azide (SA) and Gamma (γ) rays. Putative mutants were identified and isolated in M2 generation for desirable traits by comparing with adjacent untreated control. Putative mutants were grown in three replications to test their breeding behavior and other economical traits in M3 generation. Results – In the present study, differential response of landraces towards mutagenic treatments was observed which resulted into greater number of putative mutants in Ajara Ghansal (56 putative mutants) as compared to Kala Jirga (24 putative mutants). EMS induced the highest mutation frequency followed by Gamma rays and SA. In M3 generation, fifteen and eighteen mutants of Kala Jirga and Ajara Ghansal respectively exhibited true breeding for mutant traits, while rest of the putative mutants had very poor agronomic traits or reverted back to their normal trait. Desirable mutants of both the landraces viz., dwarf, early, non-lodging and more tillers with high yield were very promising and can be released for commercial cultivation after multi-location testing or used in crossing program as a donor for desirable traits. Conclusions – Induced mutations were found to be very useful to improve the specific defect present in both landraces. The desirable mutants with early maturity and high number of tillers may prove useful in improvement of aromatic rice.
Fifty SSR markers were used to assess genetic diversity among 40 drought-tolerant, five moderately drought-tolerant, and three susceptible genotypes to identify new donors for drought tolerance in rice. Out of 50 SSR markers, 17 markers are reported to be linked to major grain yield QTLs under reproductive stage drought stress. 163 (97.7%) out of 167 alleles were found to be polymorphic. The PIC value ranged from 0 to 0.963 with an average of 0.795 per locus. The minor allele frequency varied from 0.312 (RM336) to 0.895 (RM530) with an average of 0.60. The genetic diversity ranged from 0.187 (RM530) to 0.739 (RM336) with an average of 0.51. The cluster analysis grouped all the genotypes into three major clusters at 50% level of genetic dissimilarity. Structure analysis identified two subpopulations among 48 rice genotypes. 81% and 19% of molecular variances were revealed within and among the subpopulations, respectively. The first three principal components explained over 88% of total genetic variation. Two genotypes, Kalakeri and RR-2-6 were identified as new drought-tolerant donors. Kalakeri contributes drought tolerance QTL, qDTY1.1, while RR-2-6 contributes QTLs, qDTY2.2, and qDTY2.3.
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Simple sequence repeat (SSR) markers detect a signifi cantly high degree of polymorphism in rice (Oryza sativa L.) and are particularly suitable for evaluating genetic diversity among closely related cultivars. A total of 176 rice accessions originating from 19 countries in the Portuguese working germplasm collection and two standard rice varieties (IR36-indica and Nipponbarejaponica) were analyzed for DNA profi le using 24 SSR loci covering two loci per chromosome. A total of 184 alleles were detected. The number of alleles per locus ranged from 3 to 16, with an average of 7.7, and the PIC value ranged from 0.179 to 0.894 with an average of 0.667. All the loci were polymorphic among the accessions and clearly distinguished the indica and japonica subspecies. At 20% similarity, cluster analysis of the 178 accessions revealed three major groups, japonica, basmati, and indica (Groups I, II, and III, respectively). The japonica group contained 87% of the accessions and showed a wide range of similarity values (0.21–0.92), revealing a high degree of diversity among the accessions. Many of the accessions included in this study are morphologically similar and lack pedigree information. Hence, identifi cation of genetic distances among the accessions should improve their use in breeding programs. As a result of this study, genetically diverse parents can be identifi ed, increasing the usefulness of germplasm collections by broadening the genetic base of rice varieties.
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Rice yield seems to have reached a plateau, probably as a consequence of narrow genetic base of modern varieties that are being used in the breeding programmes. Wild species, especially the closely related ones, could be a potential source for broadening the genetic base both for qualitative and quantitative traits, including productivity-related traits. Oryza nivara, the immediate progenitor of cultivated indica rice, is one such species that grows widely in South and South East Asia. A large number of accessions of this species are being preserved in vitro. However, for effective utilization of this germplasm a core collection needs to be identified for use in hybridization. A set of 127 O. nivara accessions and two cultivars were analysed for genetic variation using SSR markers. DNA of each of the 129 lines was amplified in vitro using 33 SSR markers and resolved in 2.5% high-resolution agarose gels. A total of 132 alleles, with an average of 3-7 alleles per accession, were amplified. PIC value of the primers varied from 0.41 to 0.80. The similarity coefficients based on Nei and Li's similarity coefficient ranged from 0.2 to 1.0, showing strikingly high level of genetic variation in the candidate germplasm. At 0.5% similarity, the 127 accessions formed 42 independent lineages. Based on genetic diversity, resistance to bacterial blight and other morphological traits in these accessions, a set of 13 accessions has been identified that are now being used for detection and transfer of novel QTL alleles for productivity traits into the O. sativa background.
A method is presented for the rapid isolation of high molecular weight plant DNA (50,000 base pairs or more in length) which is free of contaminants which interfere with complete digestion by restriction endonucleases. The procedure yields total cellular DNA (i.e. nuclear, chloroplast, and mitochondrial DNA). The technique is ideal for the rapid isolation of small amounts of DNA from many different species and is also useful for large scale isolations.
Molecular markers provide novel tools for varietal identification, diversity analysis and assessing phylogenetic relationships among various rice groups in genus Oryza. A DNA fingerprint database has been developed for 50 rice genotypes representative of the traditional Basmati (TB), cross-bred Basmati, indica, japonica and wild rice groups using fifty SSR and thirty transposable element (TE) based markers. The salient features of marker data analyzed using various clustering algorithms, principal component analysis and Mantel test are as given below: (i) SSR generated higher levels of polymorphism (mean PIC value = 0.698) than TE based markers (PIC = 0.258), (ii) a total of 341 alleles were generated with an average of 6.8 allele per locus using SSR markers of which 40 were rare/unique alleles being present in only one of 50 genotypes with 17 unique alleles in the nine wild rice genotypes, (Mi) analysis of SSR database clearly exhibited the formation of four distinct groups of Basmati, indica, japonica and wild rice genotypes, (iv) traditional Basmati rice varieties except Basmati 217 were genetically distinct from indica, japonica and wild rice types and invariably formed a separate cluster, (v) twelve Basmati rice varieties developed from indica x Basmati crosses/backcrosses were scattered between the traditional Basmati and indica rice groups with CSR30, Super, Kasturi and Pusa Basmati 1 being closer to theTB, (vi) genetic relationships assessed using TE based markers (mPing and Dasheng) were essentially the same as obtained using SSRs except that it also differentiated between the temperate and tropical japonica rice genotypes into separate clusters, and (vii) SSR and TE based marker data-set showed high levels of positive correlation (Mantel test, r= 0.655). The study demonstrate that SSRs are best for differentiating between the closely related Basmati, indica or japonica rice varieties, while TE based markers may provide vital clues about evolution/ speciation in rice.
Background: Rainfed lowland rice genotypes are often defined ambiguously for its historic lack of attention from the rice researchers. With little scope for expansion of the irrigated rice lands, it is necessary to exploit the diversity of the rainfed lowland and deep water rice genotypes to improve the productivity and to develop new cultivars showing tolerance to submergence and other abiotic stresses. Results: Genetic variation of 21 lowland rice cultivars and 3 shallow water rice cultivars were investigated at the DNA level using SSR marker method using PCR. 45 SSR markers were used to amplify DNA fragments and 146 PCR products were obtained. The result of PAGE electrophoretic analysis confirmed 131 bands (89.72%) to be polymorphic. All the 45 primers amplified generating two to eight major bands. Polymorphism information content ranged from 0.041 to 0.728. Two primers showed monomorphism. Cluster analysis grouped the rice genotypes into 5 classes. In general, higher polymorphism was found between rainfed lowland and deepwater varieties. A dendrogram that shows the genetic distance of 24 rice cultivars was constructed based on their DNA polymorphisms. Conclusion: This diversity analysis of lowland rice analysis was found to be better and more accurate than the previous classification made by RAPD and other SSRs. This micro satellite information can be efficiently used to assess the diversity of various rice genotypes that are potentially useful in further breeding programs and can act as a landmark for variety registration authority.
ABSTRACT Kibria K., Nur F., Begum S. N., Islam M. M., PaulS. K., Rahman K. S., and Azam S. M. M. 2009. Molecular Marker based Genetic Diversity Analysis in Aromatic Rice Genotypes Using SSR and RAPD Markers.Int. J. Sustain. Crop Prod. 4(1):23-34 The study was undertaken to assess ,the genetic diversity among ,aromatic rice genotypes ,using simple
Genetic diversity of rice (Oryza sativa L.) cultivars that are historically significance to rice breeding and production in Argentina were evaluated at the DNA level. Sixty-nine accessions were surveyed with 26 simple sequence repeat (SSR) markers revealing the genomic relationship among cultivars. A total of 219 polymorphic bands were detected. Cluster analysis based on pair-wise comparisons of cultivar genetic similarities resolved the O. sativa accessions into two major O. sativa groups, indica and japonica, and the japonica group into the subgroups, tropical and temperate. These clusters agree with the pedigree information available on the accessions and almost all Argentina-released cultivars grouped within the japonica cluster. Application of DNA polymorphism analysis revealed genomic relationships in Argentine rice germplasm, generating a database useful for cultivar identification, local germplasm conservation, and breeding programs.