Molecular diversity assessment in a general cross population of sugarcane using SSR markers

Abstract

Fourteen selected genotypes from a population derived from a general cross of the sugarcane variety 'CoLk 8102' were subjected to genetic diversity analysis using simple sequence repeat markers. Thirty nine SSR primers used for amplification yielded 997 amplicons. Pair-wise genetic similarity coefficients of these fourteen genotypes ranged from 0.39 to 0.89. UPGMA analysis of similarity coefficients separated the genotypes into different clusters and helped in the identification of most distant progenies. Two highly polymorphic SSR primers were identified that would be useful for rapid genotyping and molecular diversity studies in sugarcane.

Full text

Molecular diversity assessment in a general cross population of sugarcane using
SSR markers
P KUMAR S KHARATE1, B B JOSHI2, R KUMAR2, A D PATHAK2, ZENU JHA1 and S SRIVASTAVA2
1Indira Gandhi Krishi Vishwavidyalaya, Raipur-492012, C.G., India
2ICAR-Indian Institute of Sugarcane Research, Lucknow- 226006, U.P., India
ABSTRACT
Fourteen selected genotypes from a population derived from a general cross of the sugarcane variety ‘CoLk 8102’ were
subjected to genetic diversity analysis using simple sequence repeat markers. Thirty nine SSR primers used for amplification
yielded 997 amplicons. Pair-wise genetic similarity coefficients of these fourteen genotypes ranged from 0.39 to 0.89.
UPGMA analysis of similarity coefficients separated the genotypes into different clusters and helped in the identification
of most distant progenies. Two highly polymorphic SSR primers were identified that would be useful for rapid genotyping
and molecular diversity studies in sugarcane.
Keywords: Cluster analysis, Genetic Diversity, Genetic similarity, microsatellites, PIC.
Sugarcane is an important crop of almost 100 countries of
the world for producing sugar and fuel, as well as for molasses
and paper as by products and supplies an estimated 75% of
the world’s sugar (Dillon et al. 2007; Waclawovsky et al. 2010).
India is the second largest producer of sugarcane in the world
and sugarcane occupies a commanding position as an agro-
industrial crop of the country, covering around 5.0 million
hectare area.Sugarcane belongs to the genus Saccharum which
is the complex of six species: S. officinarum,S. barberi,S.
sinense,S. edule,S. spontaneum and S. robustum. Out of
these S. spontaneum and S. robustum are wild in nature while
other four are cultivated species. Sugarcane varieties are man-
made hybrid clones involving S. officinar um and S.
spontaneum with a few genes incorporated from, S. sinense,
S. barberi and to a limited extent from S. robustum (Daniels et
al. 1987). Crossing is done to improve the economically
important traits for development of commercial sugarcane
varieties, and superior progenies are selected from segregating
populations. The genetic base of cultivated sugarcane being
quite narrow, it becomes difficult sometimes to distinguish the
progeny on the basis of morphological attributes (Srivastava
and Gupta 2006). Molecular markers are powerful tools to
estimate genetic variability in vivo and in vitro, as they are
accurate, abundant and are not affected by the environment.
A number of PCR based markers such as Random Amplified
Polymorphic DNA (RAPD), Inter Simple Sequence Repeats
(ISSR), Amplified Fragment Length Polymorphism (AFLP) and
Simple Sequence Repeats (SSR), also known as microsatellites
have become available for molecular characterization of
sugarcane (Srivastava et al. 2005; Srivastava and Gupta 2008;
Swapna and Srivastava 2012; Srivastava and Pathak 2017).
Screening and evaluating the available genetic variability with
molecular markers will help to understand the molecular-based
genetic relationship of sugarcane genotypes for exploitation
of new gene resources of sugarcane, to help broaden the
genetic base of sugarcane. Therefore, the objective of the
present study was to evaluate the molecular diversity of subset
of progeny clones of a general cross using SSR markers.
MATERIALSAND METHODS
Plant material
The experimental material comprised of progeny clones from
a population of a general cross (GC) of the sugarcane variety
‘CoLk 8102’. The cross was effected at National Hybridization
Garden, Sugarcane Breeding Institute, Coimbatore, India. Fluff
was collected and sown in mist chamber, and seedlings
obtained were transplanted in field. The progeny genotypes
were later evaluated for various economic traits in ratoon crop
(data not presented here) and fourteen genotypes showing
good performance with respect to economic attributes were
selected for molecular diversity analysis using SSR markers.
DNA isolation, PCR amplification and electrophoresis
Genomic DNA was extracted from young fresh leaf tissues
of the selected genotypes of sugarcane using modified CTAB
method (Srivastava and Gupta 2001), purified, quantified and
stored at -20oC. A set of 39 SSR primers was used to amplify
the DNA from all the samples. PCR amplification was performed
on thermal cycler PTC 200 (Peltier thermal cycler, MJ research
Pvt. Ltd., USA). The reactions were carried out in 20µl final
volume of the reaction mix, containing 20 ng template DNA,
0.5 Unit Taq polymerase, 2 µl 10 X PCR buffer, 2 µl 25 mM 10
mM MgCl2, 1.6 µl dNTPs and 4.0 pmoles each of the forward
and reverse primers. The PCR conditions were as follows: An
initial step of denaturation at 94°C for 3 minutes, thirty five
cycles of denaturation for 45 sec at 94°C, annealing for 30
seconds at 48-55°C (depending upon annealing temperature
of the primers) followed by 30 sec at 72°C and a final elongation
Indian Journal of Sugarcane Technology 31(02): 1-5, December 2016

2 KUMAR ET AL. Indian Journal of Sugarcane Technology 31 (02)
step at 72°C for 10 minutes. The PCR products were stored at
4°C before loading.
The PCR products were separated on 3% agarose gels in
1X TAE buffer containing 0.5 ìg/ml of ethidium bromide (EtBr)
at 3 V/cm in SubCell GT electrophoresis unit of BioRad. A 50
bp DNA ladder (Fermentas, Gene Ruler) was used as molecular
weight marker. The gels were photographed under UV light,
using an AlphaImagerTM 1220 Gel Documentation System.
Scoring of gels and data analysis
The size of the amplified fragments was calculated by
comparison with a 50 bp ladder (Fermentas, Gene Ruler) using
the software Alpha Imager EC. The reproducible SSR bands
from the agarose gels were scored as present (1) or absent (0)
in each sample. The bands were arranged in decreasing order
of molecular weight for each primer. Each DNA fragment
generated was treated as a separate character and scored as a
discrete variable. Accordingly, rectangular binary data matrix
was obtained which was used for further analysis.
Th e polymor phism informatio n co ntent (PIC) was
calculated for each locus according to Anderson et al. (1993)
as PIC = 1”xi2
,where, xi is the relative frequency of the ith
allele of the SSR loci.PIC pr ovides an estimate of the
discriminating power of a locus by taking into account the
number of alleles generated by each reaction unit and their
frequency distribution in the population. Markers were
classified as informative when PIC was e” 0.5.
Effective Multiplex Ratio (EMR) for an individual primer
was obtained by the formula; EMR = nâ where â = percent of
polymorphic markers and n = number of bands per reaction
unit. The marker index (MI) was calculated to characterize the
ability of each primer to detect polymorphic loci among the
genotypes was calculated for all the primers as the product of
two functions that is PIC and EMR, as described by Prevost
and Wilkinson (1999).
Pair-wise similarity coefficient matrix was computed for all
the markers by simple matching similarity algorithm using
NTSYS-pc version 2.1 (Rohlf 2000). Mean similarity coefficients
of individual progeny were calculated by taking average of
SM coefficients of one genotype with respect to rest of the
genotypes. Phylogenetic dendrogram was constructed using
the UPGMA (Unweighted Pair Group Method with Arithmetic
Mean clustering) method (Sneath and Sokal 1973) following
the SAHN (Sequential Agglomerative Hierarchical Nested)
cluster analysis module of software NTSYS-pc.
RESULTSAND DISCUSSION
Molecular variation in progeny genotypes
Among the various molecular markers, SSRs have evolved
as a useful marker system for breeders due to their suitability
for assessment of genetic diversity (Hoxha et al. 2004; Yepuri
et al. 2013). Their use in sugarcane is a potential cost effective
method for molecular diversity analysis.SSR markers have
provided significant information about genetic diversity of
sugarcane genotypes which is essential to establish breeding
strategies. In the present study, molecular diversity was
analyzed in 14 selected progeny genotypes of sugarcane
derived from ‘CoLk 8102’ GC using 39 microsatellite (SSR) primer
pairs. Electrophoretic analyses of amplicons using these SSR
primers on 3% agarose gel provided reliable distinct multiple
band profiles for these sugarcane genotypes (Fig 1). Eleven
out of these thirty-nine primers showing complete parsimony
were very useful for diversity analysis and the rest twenty
eight primers showed monomorphism. A total of 997 bands
Fig 1. SSR amplification profile of sugarcane population
‘CoLk 8102’ GC using primer SS08-17. M= 50bp Gene Ruler
ladder, 1-14= progeny genotypes of ‘CoLk 8102’ GC
were produced across all genotypes, of which, approximately
26.98 % bands were polymorphic. An average number of 71.21
fragments/ genotype were amplified. Total number of bands
amplified for each primer ranged from 13-70, with an average
of 25.56 fragments/primer. Only the polymorphic primers were
considered for diversity analysis (Table 1). Thus, a total of 255
amplicons from these eleven polymorphic primers with an
average of 23.18 amplicons/ pri mer wer e taken int o
consideration. The molecular weight of these 255 amplicons
ranged from 85 to 750bp, based on which they were grouped
into 41alleles of distinct molecular weight, ranging from 2 to 9
alleles with an average of 3.72 alleles/ primer.
Primer efficiency based on PIC, EMR and MI values
The average number of polymorphic bands/genotype
produced by 11 polymorphic primers ranged from 0.92-3.28,
with a mean value of 1.65 (Table 1). The Polymorphic
Information Content (PIC) index indicating the extent of
polymorphic bands generated by a primer ranged from 0.26-
0.86, with a mean value of 0.53. Five out of eleven primers (SS-
53, SS-64, SS-65, SS-67 and SS-08-17) showed PIC value of 0.5
or more (Table 1). Effective Multiplex Ratio (EMR) of the primers
ranged from 0.71-2.33 with a mean value of 1.33 (Table 1). Marker
Index (MI) ranged from 0.24 to 2.02 with a mean value of 0.78.
Overall, in the ‘CoLk 8102’ GC progeny, the highest values
of average number of polymorphic bands/genotype (3.28), PIC
index (0.86) EMR (2.33) and MI (2.02) were obtained for primer
SS-08-17 (Table 1). High PIC index was also obtained for the
primer SS-67 (0.72), along with EMR of 1.57, MI of 1.14 and n
of 1.57, thus proving the suitability of these two primers for
the study of molecular polymorphism and genetic diversity.

December 2016] MOLECULAR DIVERSITYASSESSMENT INA GENERAL CROSS POPULATION 3
The other three primers with PIC indices more than 0.50
exhibited EMR values ranging from 1.02–2.50, MI from 0.56-
1.70 and n from 1.28-2.50. Earlier sugarcane researchers (Pinto
et al. 2006; Cordeiro et al. 2003) have also suggested the
suitability of SSR markers for diversity analysis in sugarcane,
on the basis of their high PIC values. Using EST-SSRs markers
in sugarcane, Liu et al. (2011) obtained PIC value as high as
0.90. In another study using SSR markers in commercial
sugarcane cultivars, the PIC values ranged from 0.34 to 0.78
(Duarte Filho et al. 2010). Similarly, the features MI and EMR
have been used to evaluate the discriminatory power of
molecular marker systems in some plant species like wheat
(ISSR, EMR = 12, MI = 3.36) and apricot (ISSR, EMR = 4.8, MI
= 3.74), (Abdollah et al. 2015).
Although, the average number of polymorphic bands/
primer is only 1.65 in the present study, the level of
polymorphisms among the genotypes tested indicates that
distinction between any two genotypes should be possible
with appropriate primers. Comparative values in some other
plants range from 3.8 polymorphic bands/primer in rapeseed
(Mailer et al.1994), and 3.9 in rice (Song et al. 1992).
Genetic similarity and Cluster analysis
The data of SSR markers scored in each genotype was
analyzed using simple matching similarity algorithm (Sneath
and Sokal 1973) of the software NTSYS-pc version 2.1 (Rohlf
2000).The pair-wise SM similarity coefficients (Table 2) ranged
from 0.39 (between the progeny 7 and 14) to 0.89 (between the
progeny 3 and 4; 8 and 9) with the mean value of 0.64. This
genetic similarity matrix was used to obtain dendrogram
through UPGMA based cluster analysis (Fig. 2). The
dendrogram showed two clusters; the Cluster A contained the
genotypes 5, 7, 8 and 9, and the Cluster B contained the
genotypes 1, 2, 3, 4, 6, 10, 11, 12, 13 and 14. The Cluster B
consisted of two sub-clusters viz. BI and BII with 6 (1, 2, 3, 4,
6 and 13) and 4 genotypes each (10, 11, 12 1nd 14).
In general the genetic similarity estimated for the selected
genotypes from ‘CoLk 8102’ GC population is quite high (0.39
to 0.89) which indicates that genetic distance amongst the
alleles using SSR markers in this sub-set of the population is
not much. It also indicates that the genetic diversity among
the genotypes studied is very less. The narrow genetic base
of cultivated sugarcane and the low genetic diversity
docume nted among the cultivated genotypes has be en
supported by Pan (2010); Srivastava and Gupta (2006, 2008);
Srivastava et al. (2005, 2011); Zhang et al. (2008), and there is
a need to identify diverse genotypes for future breeding
programmes of sugarcane.
Identification of diverse progenies in the cross population
The result of cluster analysis of cross populations may be
used to design a strategy for developing more genetic
variability in order to obtain improved sugarcane variety by
using distant genotypes in crossing programmes. On the basis
S. No
Name of
primer
Total number of
bands
Number of
alleles
Range of product size
(bp)
PIC*
EMR*
MI*
N*
1
SS-43
20
2
152-244
0.42
0.71
0.29
1.42
2
SS-44
23
2
150-230
0.47
0.82
0.39
1.64
3
SS-45
13
2
145-161
0.26
0.92
0.24
0.92
4
SS-53
18
4
200-600
0.54
1.02
0.56
1.28
5
SS-58
21
4
295-556
0.49
0.75
0.37
1.50
6
SS-62
20
3
195-290
0.45
1.42
0.65
1.42
7
SS-64
23
3
280-457
0.52
1.64
0.86
1.64
8
SS-65
35
5
155-750
0.68
2.50
1.70
2.50
9
SS-67
22
5
85-275
0.72
1.57
1.14
1.57
10
SS-70
14
2
236-258
0.40
1.00
0.40
1.00
11
SS08-17
46
9
189-529
0.86
2.33
2.02
3.28
Table 1 SSR markers used in the study and their parameters in sugarcane progeny population of ‘CoLk 8102’ GC
*PIC = Polymorphic Information Content, EMR = Effective Multiplex Ratio, MI = Marker
Index and n = Average number of polymorphic bands/genotype
Fig 2. UPGMA based clustering of progeny population of
‘CoLk 8102’ GC using genetic similarity matrix for SSR
markers

4 KUMAR ET AL. Indian Journal of Sugarcane Technology 31 (02)
of SSR marker-based similarity coefficient analysis, the lowest
genetic similarity coefficient of 0.39 was found between
progenies 7 and 14 which indicated that these two progenies
were genetically more distant from each other. Mean similarity
coefficients of individual progeny genotypes of ‘CoLk 8102’
GC, with respect to rest of the genotypes are given in Fig 3.
The highest mean similarities coefficients (MSC) were
observed for progenies 4 and 6 (0.694 and 0.681 respectively),
whereas, the lowest mean similarities coefficients (MSC) were
observed for progeny 7 followed by 12 (0.54 and 0.592
respectively). Thus, the progeny 7 of ‘CoLk 8102’ GC
(MSC=0.54) was genetically least similar to rest of the 13
progenies of the cross sub-population studied. Interestingly,
when seen in context with pairwise similarity coefficients,
progeny 7 was the most dissimilar one showing SM similarity
coefficients of 0.50 or less with 6 other progeny genotypes,
followed by progeny 12 showing SM similarity coefficients of
0.53 or less with 5 other progeny genotypes (Table 2).
Overall, at least two progeny genotypes could be identified
among the 14 selected progenies of ‘CoLk 8102’ GC population,
which were quite distant from rest of the genotypes. The
usefulness of SSRs was once again ascertained as a tool for
molecular diversity analysis in general and identification of
genetically diverse genotypes from the progeny of a cross
population in particular. Moreover, two highly efficient SSR
primers were also identified which could be utilized to facilitate
molecular diversity and polymorphism analysis of other
cultivars and wild species of sugarcane. The genetic diversity
assessed in the cross population in this study can be used to
select the better progenies taking into account their qualitative
and quantitative attributes.
ACKNOWLEDGEMENTS
The authors are grateful to the Director, ICAR-IISR,
Lucknow for providing the facilities to carry out this research
work. The first author (P.S. Kharate) gratefully acknowledges
the DBT, Govt. of India for fellowship during the period of
research.
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