Molecular marker based (SSR) genetic diversity analysis in deep water rice germplasms of Bangladesh
ABSTRACT The study was undertaken to assess the genetic diversity among deep water rice genotypes using Simple Sequence Repeat (SSR) markers through marker aided selection (MAS). Twelve deep water rice (Oryza sativa L.) germplasms of Bangladesh was selected for genetic diversity analysis using eighteen SSR markers. Upon PCR amplification the alleles were separated on Polyacrylamide Gel Electrophoresis (PAGE) system. Initial polymorphism detection was conducted using eighteen primer pairs distributed on twelve rice chromosomes. The chosen microsatellite marker panel consisted of RM1, RM452, RM130, RM252, RM13, RM204, RM11, RM25, RM205, RM244, RM206, and RM463 with one representative from each chromosome. A total of 79 alleles were detected with an average of 4.38 alleles per locus. The polymorphism information content (PIC) reflections of alleles diversity frequency among the varieties, which is ranged from 0.477 to 0.782, with an average of 0.634. RM 13 was found as the best marker for identification of genotypes as revealed by PIC values. The Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrogram revealed 2 major groups with 4 clusters and the wide range of dissimilarity values (0.14-0.89) which showed a high degree of diversity among the cultivars. The results of the genetic diversity will be useful for the selection of the parents for developing submergence tolerant and flash flood tolerant rice variety through molecular breeding program.
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ABSTRACT: The growing number of rice microsatellite markers warrants a comprehensive comparison of allelic variability between the markers developed using different methods, with various sequence repeat motifs, and from coding and non-coding portions of the genome. We have performed such a comparison over a set of 323 microsatellite markers; 194 were derived from genomic library screening and 129 were derived from the analysis of rice-expressed sequence tags (ESTs) available in public DNA databases. We have evaluated the frequency of polymorphism between parental pairs of six inter- subspecific crosses and one inter-specific cross widely used for mapping in rice. Microsatellites derived from genomic libraries detected a higher level of polymorphism than those derived from ESTs contained in the GenBank database (83.8% versus 54.0%). Similarly, the other measures of genetic variability [the number of alleles per locus, polymorphism information content (PIC), and allele size ranges] were all higher in genomic library-derived microsatellites than in their EST-database counterparts. The highest overall degree of genetic diversity was seen in GA-containing microsatellites of genomic library origin, while the most conserved markers contained CCG- or CAG-trinucleotide motifs and were developed from GenBank sequences. Preferential location of specific motifs in coding versus non-coding regions of known genes was related to observed levels of microsatellite diversity. A strong positive correlation was observed between the maximum length of a microsatellite motif and the standard deviation of the molecular-weight of amplified fragments. The reliability of molecular weight standard deviation (SDmw) as an indicator of genetic variability of microsatellite loci is discussed.Theoretical and Applied Genetics 02/2000; 100(5):713-722. · 3.51 Impact Factor
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ABSTRACT: The existence of Oryza glumaepatula is threatened by devastation and, thus, the implementation of conservation strategies is extremely relevant. This study aimed to characterize the genetic variability and estimate population parameters of 30 O. glumaepatula populations from three Brazilian biomes using 10 microsatellite markers. The levels of allelic variability for the SSR loci presented a mean of 10.3 alleles per locus and a value of 0.10 for the average allelic frequency value. The expected total heterozygosity (H(e)) ranged from 0.63 to 0.86. For the 30 populations tested, the mean observed (H(o)) and expected heterozygosities (H(e)) were 0.03 and 0.11 within population, respectively, indicating an excess of homozygotes resulting from the preferentially self-pollinating reproduction habit. The estimated fixation index ( (IS) ) was 0.79 that differed significantly from zero, indicating high inbreeding within each O. glumaepatula population. The total inbreeding of the species ((IT) ) was 0.98 and the genetic diversity indexes among populations, (ST) and (ST), were 0.85 and 0.90, respectively, indicating high genetic variability among them. Thus, especially for populations located in regions threatened with devastation, it is urgent that in situ preservation conditions should be created or that collections be made for ex situ preservation to prevent loss of the species genetic variability.Genetica 12/2005; 125(2-3):115-23. · 1.75 Impact Factor
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ABSTRACT: Ninety-four newly developed microsatellite markers were integrated into existing RFLP framework maps of four rice populations, including two doubled haploid, a recombinant inbred, and an interspecific backcross population. These simple sequence repeats (SSR) were predominantly poly(GA) motifs, targetted because of their abundance in rice. They were isolated from a previously described sheared library and a newly constructed enzyme-digested library. Differences in the average length of poly(GA) tracts were observed for clones isolated from the two libraries. The length of GA motifs averaged 21 repeat units for clones isolated from the Tsp-509-digested library, while motifs averaged 17 units for clones from the sheared library. There was no evidence of clustering of microsatellite markers near centromeres or telomeres. Mapping of the 94 newly developed markers as well as of 27 previously reported microsatellites provided genome-wide coverage of the 12 chromosomes, with an average distance of 1 SSLP (simple sequence repeat polymorphism) per 16–20 cM.Theoretical and Applied Genetics 08/1997; 95(4):553-567. · 3.51 Impact Factor
64 Matin et al.
Int. J. Biosci. 2012
RESEARCH PAPER OPEN ACCESSOPEN ACCESS
Molecular marker based (SSR) genetic diversity analysis in deep
water rice germplasms of Bangladesh
Sadia Matin1, M. Ashrafuzzaman1*, Md. Monirul Islam2, Saif U. Sikdar1,
1Department of Genetic Engineering and Biotechmology, School of Life Sciences, Shahjalal
University of Science and Technology, Sylhet-3114, Bangladesh
2Biotechnology Division, Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh
Names and addresses of the institutions (where the work has been carried out): Biotechnology
Division, Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh
Received: 22 September 2012
Revised: 15 October 2012
Accepted: 16 October 2012
Key words: SSR markers, genetic diversity, deep water rice.
The study was undertaken to assess the genetic diversity among deep water rice genotypes using Simple Sequence
Repeat (SSR) markers through marker aided selection (MAS). Twelve deep water rice (Oryza sativa L.)
germplasms of Bangladesh was selected for genetic diversity analysis using eighteen SSR markers. Upon PCR
amplification the alleles were separated on Polyacrylamide Gel Electrophoresis (PAGE) system. Initial
polymorphism detection was conducted using eighteen primer pairs distributed on twelve rice chromosomes. The
chosen microsatellite marker panel consisted of RM1, RM452, RM130, RM252, RM13, RM204, RM11, RM25,
RM205, RM244, RM206, and RM463 with one representative from each chromosome. A total of 79 alleles were
detected with an average of 4.38 alleles per locus. The polymorphism information content (PIC) reflections of
alleles diversity frequency among the varieties, which is ranged from 0.477 to 0.782, with an average of 0.634.
RM 13 was found as the best marker for identification of genotypes as revealed by PIC values. The Unweighted
Pair Group Method with Arithmetic Mean (UPGMA) dendrogram revealed 2 major groups with 4 clusters and the
wide range of dissimilarity values (0.14-0.89) which showed a high degree of diversity among the cultivars. The
results of the genetic diversity will be useful for the selection of the parents for developing submergence tolerant
and flash flood tolerant rice variety through molecular breeding program.
*Corresponding Author: M. Ashrafuzzaman firstname.lastname@example.org
International Journal of Biosciences (IJB)
ISSN: 2220-6655 (Print) 2222-5234 (Online)
Vol. 2, No. 10(2), p. 64-72, 2012
Rice (Oryza sativa L.) belonging to the family
Graminae is the staple food for one third of the
world’s population (Chakravarthi and Naraveni,
2006). Deepwater rice is grown in flooded
conditions with water more than 50 cm (20 inch)
deep. More than 100 million people in South and
Southeast Asia rely on deepwater rice for their
sustenance. Many districts of Bangladesh are
flooded during the rice cultivation season every year
and thus curtail the national rice yield by causing
severe damage to the rice cultivated field. Therefore
it is high time to select potential rice cultivars for
breeding program to develop submergence tolerant
as well as flash flood resistant rice variety.
Molecular markers have proven to be powerful tools
in the assessment of genetic variation and in the
elucidation of genetic relationships within and
among species. The recent development of DNA
markers has provided new opportunities for the
genetic improvement of rice cultivars (Causse et al.,
1994). Satellite loci also known as simple sequence
repeats (SSRs) are the most commonly used
molecular markers. Microsatellites are PCR-based
markers that are efficient and cost-effective to use.
Compared with other markers, they are abundant,
co-dominant, highly reproducible and interspersed
throughout the genome (Panaud et al., 1996,
Temnykh et al., 2000). In particular, microsatellite
markers have been widely applied in rice genetic
studies as they are able to detect high levels of
allelic diversity (McCouch et al., 1997). These
markers can detect a significantly higher degree of
polymorphism in rice (Ni et al., 2002, Okoshi et al.,
2004) which becomes ideal for studies on genetic
diversity and intensive genetic mapping (Cho et al.,
2000). They have been used for characterizing
genetic diversity in several crop species including
sorghum (Dean et al., 1999, Smith et al., 2000),
maize (Senior et al., 1998), cotton (Liu et al., 2000)
and wheat (Prasad et al., 2000). In rice, SSRs have
been used to assess the genetic diversity of both
wild and cultivated species (Siwach et al., 2004,
Brondani et al., 2005, Neeraja et al., 2005). Rice
microsatellites also have a demonstrated utility for
gene-tagging and marker-assisted selection (Chen et
al., 1997) and are polymorphic between (Akagi et
al., 1996, Panaud et al., 1996) and within rice
varieties (Olufowote et al., 1997).These studies
showed that SSR markers are efficient in detecting
genetic polymorphisms and discriminating among
genotypes. The important advantages of
microsatellites are that they are usually single locus
and because of the high mutation rate, are often
multi-allelic. They are very efficient tools that can
be exchange between laboratories and their data are
highly informative (Morgante and Olivicri, 1993).
The present investigation was made to identify the
suitable SSR primers for genetic analysis of deep-
water rice and to measure the genetic diversity and
relatedness among twelve deep-water rice
genotypes using SSR markers.
Materials and methods
The whole experiment was conducted at
Biotechnology Laboratory of BRRI (Bangladesh
Rice Research Institute) during the period of
Seeds of twelve deep water rice varieties were
collected from BRRI (Table 1). Seeds were
germinated at aseptic condition by incubating them
at 300 C and grown in glass house.
Isolation of genomic DNA
Genomic DNA was isolated from young leaves of 21
days old plants following the mini preparation
modified CTAB method (Zheng et al., 1995). DNA
samples were evaluated both quantitatively and
qualitatively using spectrophotometer and λ
(lamda) DNA (concentration marker) respectively.
PCR amplification of SSR markers was carried out
using eighteen primer pairs listed in table 2. Each
reaction tube contained 25 ng of template DNA, 1 x
PCR buffer, 2 mM MgCl2, 0.4 mM of dNTPs, 0.4
μM each of forward and reverse primers and 1.6
66 Matin et al.
Int. J. Biosci. 2012
units of Taq DNA polymerase. Amplification was
performed using the following conditions:
denaturation at 94ºC for 5 min; 35 cycles of 1 min
denaturation at 94ºC, 1 min annealing at 55ºC, 1
min extension at 72ºC and a final extension at 72ºC
for 5 min. The SSR amplification products were
separated in a vertical denaturing 8%
polyacrylamide. DNA fragments were revealed
using the ethidium bromide staining procedure. The
gels were stained for 30-35 minutes and were
documented using UVPRO (Uvipro Platinum EU)
gel documentation unit.
Polymorphic information content (PIC) values were
calculated for each SSR locus based on Anderson et
al. (1993). Major allele frequency, gene diversity,
polymorphism information content (PIC) values
were determined using Power Marker Version 3.25
(Liu and Muse, 2005). The amplified bands were
scored for each SSR primer pairs based on the
presence or absence of bands, generating a binary
data matrix of 1 and 0 for each marker system. Both
matrices were then analyzed using the NTSYS pc
statistical package version 2.2. The data matrices
were used to calculate genetic similarity based on
Jaccard’s similarity coefficients, and two
dendograms displaying relationships among 12 rice
cultivars were constructed using the Unweighted
Pair Group Method with Arithmetic Mean
(UPGMA). The Pearson’s correlation between
similarity coefficients based on SSR markers was
determined from data among all twelve rice
Results and discussion
The level of polymorphism among rice cultivars was
evaluated by calculating allelic number and PIC
values for each of the eighteen SSR loci evaluated. A
total of 79 alleles were detected at the loci of
eighteen microsatellite markers across twelve rice
germplasms. The results revealed that all the
primers showed distinct polymorphisms among the
cultivars studied indicating the robust nature of
microsatellites in revealing polymorphism. Among
the polymorphic markers, 3 produced three alleles
each, 9 produced four alleles each, 3 generated five
alleles each, 2 produced 6 alleles each and only one
produced 7 alleles (table 3). The number of alleles
per locus ranged from 3 (RM1, RM 211, and RM
134) to 7 alleles (RM 13) with an average of 4.4
alleles across the 18 loci .The landraces frequency of
most common allele at each locus ranged from 25%
(RM25) to 50% (RM1, RM211, RM130, RM413,
RM134 and RM463). On an average, 42.13% of the
12 landraces shared a common major allele at any
given locus. Similar number of microsatellite
markers previously used as subset for genetic
diversity analysis of Oryza sativa (Garris et al.,
2005, Thomson et al., 2007). The Value is
comparable to 1-8 allele per SSR locus with an
average number of alleles of 4.58 per locus for
various classes of microsatellite (Siwach et al.,
2004). The amplicon size of all 12 genotypes for
each marker alleles varied from 73-81 bp produced
by RM130 and 267-288 bp produced by RM586.
The landraces frequency of most common allele at
each locus ranged from 25% (RM25) to 50% (RM1,
RM211, RM130, RM413, RM134 and RM463). On
an average, 42.13% of the 12 landraces shared a
common major allele at any given locus. Of the 79
alleles scored all of 79 were found to be
polymorphic. Maximum number of polymorphic
alleles (7) was obtained with the marker RM 13,
while the minimum numbers of polymorphic alleles
(3) was obtained by using RM 1, RM 211, and RM
Polymorphism information content (PIC) value is a
reflection of allele diversity and frequency among
varieties. PIC values ranged from 0.477 to 0.782
with an average of 0.634 (table 3). The highest PIC
value 0.7818 was obtained for RM13 followed by
respectively RM25 (0.760), RM85 (0.73) and
RM252 (0.72). PIC value revealed that RM13 was
considered as best marker for 12 test genotypes. The
PIC value observed, are comparable to three
previous estimates of microsatellite analysis in rice
via 0.34-0.88 (Thomson et al., 2009), 0.20-0.90
with an average of 0.56 (Jain et al., 2003). The PIC
67 Matin et al.
Int. J. Biosci. 2012
value was higher than the earlier observations
(Joshi and Behera, 2006) also. Figure 1 showed gel
pictures of amplified fragment using primer
designed for the SSR marker RM 252 and RM 13.
Fig. 1. DNA profile of the twelve deepwater rice
land races with SSR marker RM 252 and RM 13.
Legend: 1= Kata Mukul; 2= Dula Bexh; 3= Mota
Kartik Sail; 4= Laxmi Digha; 5= Dulai Aman; 6=
Bichi Bazal; 7= Manik Gira; 8= Aguli Aman; 9=
Dudhsor; 10= Kartik Jhul; 11= Kartik Sail and 12=
Kartik Gurol. L= Ladder marker.
Fig. 2. A UPGMA clustering dendogram showing
the genetic relationships among 12 landraces on the
alleles detected by 18 microsatellite markers.
Legend: DWR1= Kata Mukul; DWR2= Dula Bech;
DWR3= Mota Kartik Sail; DWR4= Laxmi Digha;
DWR5= Dulai Aman; DWR6= Bichi Bazal; DWR7=
Manik Gira; DWR8= Aguli Aman; DWR9=
Dudhsor; DWR10= Kartik Jhul; TAL1= Kartik Sail
and TAL2= Kartik Gurol.
A cluster analysis using UPGMA based on similarity
coefficients was done to resolve the phylogenetic
relationships among the different deepwater rice
genotypes considered for the present study. The
UPGMA clustering system generated four genetic
clusters with similarity coefficient 33% (fig. 2).
Cluster 2 was the biggest group which contained
four landraces viz. Kartik Jhul, Dudhsor, Manik
Gira and Aguli Aman. The cluster analysis revealed
that Kartik Jhul, Dudhsor and Manik Gira are closer
than Anguli Aman while Kartik Jhul and Dudhsor
are closer than Manik Gira. Cluster 1 and 4
contained three landraces in each cluster. The three
genotypes, Kata Mukul, Mota Kartik sail and Dula
Bech were clustered distinctly in the same group
(cluster 1) but Kata Mukul and Mota Kartik are
closer than the Dula Bech. Again, Laxmi Digha,
Dulai Aman and Bhchi Bazal were clustered in same
group (cluster 4) but Laxmi Digha and Dulai Aman
are closer than the Bhchi Bazal. Cluster 3 was the
smallest group which contains two T. Aman
landraces viz. Kartik Sail and Kartik Gurol.
Table 1. List of twelve test genotypes for diversity
SI No. BRRI
Mota kartik sail
This cluster tree analysis agreed with the allelic
diversity observed among Basmati and Non-
basmati long grain indica rice varieties using
microsatellite markers (Siwach et al, 2004). DNA
fingerprinting and phylogenic analysis of Indian
aromatic high quality rice germplasms also showed
similar trend (Jain et al, 2003).
The pair wise genetic dissimilarity coefficient
indicated that the highest (100%) genetic
68 Matin et al.
Int. J. Biosci. 2012
dissimilarity was found between Kata Mukul with
Kartik Sail and Kartik Gurol; Dula Bech with Aguli
Aman, Kartik Sail & Kartik Gurol and Manik Gira
with Kartik Gurol (table 4). Besides, 94%
dissimilarity was found between Dula Bech with
Bichi Bazal as well as Mota Kartik Sail with Bichi
Bazal and Kartik Gurol. However, potential hybrid
line can be produced by inter-varietal crossing
based on the genetic dissimilarity value since the
more the genetic dissimilarity value the more
chance of getting vigorous heterosis in the progeny.
Hence microsatellite marker based molecular
fingerprinting could serve as a potential basis in the
identification of genetically distance genotypes as
well as in sorting of duplication for morphologically
Table 2. List of eighteen SSR primers with position, and expected PCR product sizefor the study.
There have been a number of studies that have
reported on the assessment of genetic diversity in a
relatively large set of cultivated germplasm. This
include diversity analysis of high yielding cultivars
(Ni et al., 2002), aromatic rice (Nagaraju et al.,
2002), indigenous aromatic rice (Joshi and Behera,
2006) and even lowland rice (Bhuyan et al., 2007,
Yu and Nguyen, 1994) using various molecular
fingerprinting techniques like RFLP, RAPD, SSR,
AFLP etc. Since the long term objective is to utilize
rice germplasm for broadening the genetic base of
the cultivated rice, the present study is an attempt
at characterizing diversity at the molecular level in
this set of lowland rice genotypes to broaden the
genetic base of cultivated rice in the rain fed and
lowland areas of Bangladesh such as Sunamganj,
Hobigonj, Bogra and the Sirajgonj. Moreover, the
cultivars with wide genetic distance can be crossed
to widen the genetic base and exploit heterosis. The
informative primers would prove useful in marker-
assisted selection, linkage mapping and gene
tagging for specialty traits.
Name Position Product size
Forward primer (5/ - 3/) Reverse primer (3/- 5/)
RM 1 4.63 gcgaaaacacaatgcaaaaa gcgttggttggacctgac
RM452 9.50 105 Ctgatcgagagcgttaaggg gggatcaaaccacgtttctg
RM 211 4.16 161 Ccgatctcatcaaccaactg cttcacgaggatctcaaagg
RM 85 66.76 107 ccaaagatgaaacctggattg gcacaaggtgagcagtcc
RM 130 33.33 85 tgttgcttgccctcacgcgaag ggtcgcgtgcttggtttggttc
RM127 34.19 223 gtgggatagctgcgtcgcgtcg aggccagggtgttggcatgctg
RM413 2.19 79 Ggcgattcttggatgaagag tccccaccaatcttgtcttc
RM204 3.17 169 Gtgactgacttggtcataggg gctagccatgctctcgtacc
RM586 1.47 271 Acctcgcgttattaggtaccc gagatacgccaacgagatacc
RM11 19.25 140 Tctcctcttcccccgatc atagcgggcgaggcttag
RM134 26.63 93 acaaggccgcgagaggattccg gctctccggtggctccgattgg
RM 25 2.59 146 ggaaagaatgatcttttcatgg ctaccatcaaaaccaatgttc
RM205 22.72 122 Ctggttctgtatgggagcag ctggcccttcacgtttcagtg
RM 244 4.34 163 Ccgactgttcgtccttatca ctgctctcgggtgaacgt
RM 206 21.97 147 Cccatgcgtttaactattct cgttccatcgatccgtatgg
RM 463 22.09 192 Ttcccctccttttatggtgc tgttctcctcagtcactgcg
69 Matin et al.
Int. J. Biosci. 2012
Table 3. Number of alleles, highest frequency allele and Polymorphism Information Content (PIC) Values found
among twelve rice germplasms for eighteen SSR markers.
Table 4. Genetic dissimilarity pair (below diagonal)) values among studied twelve deep water rice genotypes.
The results revealed that highest genetic distance
for one pair of landraces can be utilized as potential
parents for improvement of varieties. The SSR
markers based molecular fingerprinting could serve
as a sound basis in the identification of genetically
distant accessions as well as sorting of duplicate
germplasm of morphologically close accessions.
The authors gratefully acknowledged to the
authority of Biotechnology Division, Bangladesh
Rice Research Institute (BRRI), Gazipur,
Bangladesh for providing the facilities to carry out
this research work.
Akagi H, Yokozek Y, Fujimura T. 1996.
Microsatellite DNA markers for rice
chromosomes.Theoretical and Applied Genetics 93,
Bhuyan N, Borah BK, Sarma RN. 2007.
Genetic diversity analysis in traditional lowland rice
(Oryzae sativa L.) of Assam using RAPD and SSR
markers. Current Science 93, 967-972.
Brondani RPV, Zucchi MI, Brondani C,
Rangel PHN, Borba TCDO, Rangel PN,
Magalhaes MR, Vencovsky R. 2005. Genetic
structure of wild rice Oryza glumaepatula
populations in three Brazilian biomes using
microsatellite markers. Genetica, 125(2-3), 115-
Causse MA, Fulton TM, Cho YG, Ahn SN,
Chunwongse J. 1994. Saturated molecular map
of the rice genome based on an interspecific
backcross population. Genetics 138, 1251–1274.
Chakravarthi BK, Naravaneni R. 2006. SSR
marker based DNA finger printing and diversity
study in rice (Oryza sativa L). African Journal of
Biotechnology 5 (9), 684-688.
Chen X, Temnykh S, Xu Y, Cho YG, McCouch
SR. 1997. Development of a microsatellite frame
work map providing genome wide coverage in rice
(Oryza sativa L.).Theoretical and Applied Genetics
Cho YG, Ishii T, Temnykh S, Chen X,
Lipovich L, Park WD, Ayres N, Cartinhour
S, McCouch SR. 2000. Diversity of
microsatellites derived from genomic libraries and
GenBank sequences in rice (Oryza sativa L.).
Theoretical and Applied Genetics 100, 713–722.
Dean RE, Dahlberg JA, Hopkins MS,
Mitchell SE, Kresovich S. 1999. Genetic
redundancy and diversity among ‘Orange’
accessions in the U.S. national Sorghum collection
as assessed with simple sequence repeat (SSR)
markers. Crop Science 39(4), 1215-1221.
Garris AJ, Thomas HT, Coburn J, Krcsovich
S, McCouch S. 2005. Genetic structure and
diversity in (Oryza sativa L.). Genetics 169, 1631-
Jain S, Mitchell SE, Jain RK, Kresovich S,
McCouch SR. 2003. DNA fingerprinting and
phylogenetic analysis of Indian aromatic high
quality rice germplasm using panels of
fluorescent‐labeled microsatellite markers. In:
Advance in Rice Genetics, Khush GS, Brar DS and
Hardy B (Eds), IRRI, Philippine, 162‐165.
Joshi RK, Behera L. 2006. Identification and
differentiation of indigenous non-Basmati aromatic
rice genotypes of India using microsatellite
markers. African Journal of Biotechnology 6(4),
Liu S, Cantrell RG, McCarty JC, Stewart
JMD. 2000. Simple sequence repeat-based
assessment of genetic diversity in cotton race stock
accessions. Crop Science 40(5),1459-1469.
71 Matin et al.
Int. J. Biosci. 2012
Liu K, Muse SV. 2005. PowerMarker: an
integrated analysis environment for genetic marker
analysis. Bioinformatics 21, 2128-2129.
Morgante M, Olivieri AM. 1993. PCR-
amplified microsatellites as marker in plant
genetics. The Plant Journal 3, 175-182.
McCouch SR, Chen X, Panaud O, Temnykh S,
Xu Y, Cho YG, Huang N, Ishii T, Blair M.
1997. Microsatellite marker development, mapping
and applications in rice genetics and breeding. Plant
Molecular Biology 35, 89–99.
Nagaraju J, Kathirvel M, Ramesh KR, Siddiq
EA and Hasnain SE. 2002. Genetic analysis of
traditional and evolved Basmati and non-Basmati
rice varieties by using fluorescence-based ISSR-PCR
and SSR markers. Proceedings of the National
Academy of Sciences 99, 5836-5841
Neeraja CN, Hariprasad AS, Malathi S,
Siddiq EA. 2005. Characterization of tall
landraces of rice (Oryza sativa L.) using gene
derived simple sequence repeats. Current Science
Ni J, Colowitand PM, Mackill DJ. 2002.
Evaluation of genetic diversity in rice subspecies
using microsatellite markers. Crop Science 42, 601-
Olufowote JO, Xu Y, Chen X, Goto M,
McCouch SR, Park WD, Beachell HM, Dilday
RH. 1997. Comparative evaluation of within-
cultivar variation of rice (Oryza sativa L.) using
microsatellite and RFLP markers. Genome 40(3),
Okoshi M, Hu J, Ishikawa R, Fujimura T.
2004. Polymorphoic analysis of landraces of
Japanese rice using microsatellite markers.
Breeding Research 6, 125-133.
Panaud O, Chenayd X, McCouch SR. 1996.
Development of microsatellite markers and
characterization of simple sequence length
polymorphism (SSLP) in rice (Oryza sativa L.).
Molecular and General Genetics 252, 597-607.
Prasad M, Varshney RK, Roy JK, Balyan HS,
Gupta PK. 2000. The use of microsatellites for
detecting DNA polymorphism, genotype
identification and genetic diversity in wheat.
Theoretical and Applied Genetics 100(3-4), 584-
Senior ML, Murphy JP, Goodman MM,
Stuber CW. 1998. Utility of SSRs for determining
genetic similarities and relationships in maize using
an agarose gel system. Crop Science 38(4), 1088-
Siwach P, Jain S, Saini N, Chowdhury VK,
Jain RK. 2004. Allelic diversity among Basmati
and non-Basmati long-grain indica rice varieties
using microsatellite markers. Journal of Plant
Biochemistry and Biotechnology 13, 25-32.
Smith JSC, Kresovich S, Hopkins MS,
Mitchell S, Dean RE, Woodman WL, Lee M,
Porter K. 2000. Genetic diversity among elite
sorghum inbred lines assessed with simple
sequence repeats. Crop Science 40(1), 226-232.
Temnykh S, Park WD, Ayers N, Cartinhour
S, Hauck N, Lipovich L, Cho YG, Ishii T,
McCouch SR. 2000. Mapping and genome
organization of microsatellite sequences in rice
(Oryza sativa L.). Theoretical and Applied Genetics
Thomson MJ, Septiningsihn, Endang M,
Suwardjo, Patimah, Santoso, Tiur S,
McCouch SR. 2007. Genetic diversity analysis of
traditional improved Indonesian rice (Oryza sativa
L.) germplasm using microsatellite markers.
Theoretical and Applied Genetics 114, 559-568.
72 Matin et al.
Int. J. Biosci. 2012
Thomson MJ, Polato NR, Prasetiyono J,
Trijatmiko KR, Silitonga TS, McCouch SR.
2009. Genetic diversity of isolated populations of
Indonesian landraces of rice (Oryza sativa L.)
collected in East Kalimantan on the island of
Borneo. Rice 2, 80-92.
Yu LX, Nguyen HT. 1994. Genetic variation
detected with RAPD markers among upland and
lowland rice cultivars (Oryza sativa L.). Theoretical
and Applied Genetics 87, 668–672.
Zheng K, Huang N, Bennett J, Khush GS.
1995. PCR assisted marker based selection in rice
breeding. IRRI Discussion Paper Series No. 12.
IRRI. Manila. Philipines, 5-6.