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Genetic diversity assessment in clusterbean(Cyamopsis tetragonoloba (L.) Taub)) by RAPD
markers
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1 AUTHOR:
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International Rice Research Institute
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Indian Society of Pulses Research and DevelopmenIndian Institute of Pulses Research
Kanpur, India
Volume 27
of
Journal
Food Legumes
June 2014
P
98
ISSN
0970-6380
Online ISSN
0976-2434
www.isprd.in
Number 2
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EXECUTIVE COUNCIL : 2013-2015
Zone I : Dr Brij Nandan
(SKUAST) Sambha (J&K)
Zone II : Dr C Bharadwaj, IARI, New Delhi
Zone III : Dr Rajib Nath, BCKV, Kalyani
Zone IV : Dr OP Khedar
Durgapura, Jaipur, Rajasthan
Councillors
Dr HC Sharma, ICRISAT, Hyderabad
Dr Shiv Kumar, ICARDA, Morroco
Dr Harsh Nayyar, Chandigarh
Dr NB Singh, YSPUHF, Solan
Dr KP Vishwanath, UAS, Raichur
Dr KS Reddy, BARC, Mumbai
Chief PatronDr S Ayyappan
PatronDr SK Datta
Co-patronDr NP Singh
Zone V : Dr DK Patil
Badnapur
Zone VI : Dr V Jayalakshmi, Nandyal
Zone VII : Dr P Jayamani, TNAU
Zone VIII : Dr Devraj Mishra
IIPR, Kanpur, U.P.
PresidentDr NP Singh
SecretaryDr GP DixitJoint Secretary
Dr. KK Singh
TreasurerDr Devraj Mishra (Acting)
Vice PresidentDr Guriqbal Singh
Editors
Dr A Amrendra Reddy, IARI, New Delhi
Dr SS Dudeja, Hisar
Dr CS Praharaj, IIPR, Kanpur
Dr Subhojit Datta, IIPR, Kanpur
Dr Mohd. Akram, IIPR, Kanpur
Dr Aditya Pratap, IIPR, Kanpur
Editor-in-Chief
Dr Jagdish Singh
The Indian Society of Pulses Research and
Development (ISPRD) was founded in April 1987 with the
following objectives:
To advance the cause of pulses research
To promote research and development, teaching and
extension activities in pulses
To facilitate close association among pulse workers
in India and abroad
To publish Journal of Food Legumes which is the
official publication of the Society, published four times
a year.
Membership :Any person in India and abroad interestedin pulses research and development shall be eligible for
membership of the Society by becoming ordinary, life or
corporate member by paying respective membership fee.
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The contribution to the Journal, except in case of
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Renewal of subscription should be done in January
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Indian Society of Pulses Research and Development,
through M.O./D.D. may be sent to the Treasurer,
Indian Society of Pulses Research and Development,
Indian Institute of Pulses Research, Kanpur 208 024,India. In case of outstation cheques, an extra amount of
Rs. 40/-may be paid as clearance charges.
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Journal of Food Legumes
(Formerly Indian Journal of Pulses Research)
Vol. 27 (2) June 2014
CONTENTS
RESEARCH PAPERS
1. Estimation of genetic diversity in fieldpea (Pisum sativum L.) based on analysis of hyper-variable regions 85
of the genome
Subhojit Datta, Pallavi Singh, Sahil Mahfooz and G.P. Dixit
2. Genetic diversity assessment in clusterbean (Cyamopsis tetragonoloba (L.) Taub.) by RAPD markers 92
S.R. Kalaskar, S. Acharya, J.B. Patel, W.A. Sheikh, A.H. Rathod and A.S. Shinde
3. Environmental influence on heritability and selection response of some important quantitative traits in 95
greengram [Vigna radiata (L.) Wilczek]
Chandra Mohan Singh, S.B. Mishra, Anil Pandey and Madhuri Arya
4. Genetic diversity study for grain yield and its components in urdbean (Vigna mungo L. Hepper) using 99
different clustering methods
Basudeb Sarkar
5. Studies on genetic variability and inter-relationship among yield contributing characters in pigeonpea 104
grown under rainfed lowland of eastern region of India
Santosh Kumar, Sanjeev Kumar, S.S. Singh, R. Elanchezhian and Shivani
6. Response of frenchbean (Phaseolus vulgaris L.) to various sowing methods, irrigation levels and 108
nutrient substitution in relation to its growth, seed yield and nutrient uptake
Binod Kumar and G.R. Singh
7. Effect of planting method, irrigation schedule and weed management practice on the performance of 112
fieldpea (Pisum sativum L. arvense)
Brij Bhooshan and V.K. Singh
8. Effect of pre- and post-emergence herbicides on weed dynamics, seed yield, and nutrient uptake in 117
dwarf fieldpea
Shalini and V.K. Singh
9. Impact of biochemicals on the developmental stages of pulse beetle, Callosobruchus maculatus 121
infesting green gram
Litty Lazar, Bindu Panickar and P.S. Patel
10. Screening Indole acetic-acid over-producing rhizobacteria for improving growth of lentil 126
under axenic conditions
Sukhjinder Kaur and Veena Khanna
11. Optimization of operational parameters of multi-crop spikes tooth thresher for threshing black gram 130
Baldev Dogra, Ritu Dogra, Ranjit Kaur, Dinesh Kuamr and Manes G.S.
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12. Use of ARIMA modeling for forecasting green gram prices for Maharashtra 136
D.J. Chaudhari and A.S. Tingre
13. Analysis of pulse production in major states of India 140
S. Chatterjee, R. Nath, Jui Ray, M. Ray, S.K. Gunri and P. Bandopadhyay
14. Adoption gap as the determinant of instability in Indian legume production 146
M.S. Nain, S.K. Dubey, N.V. Kumbhare and Ram Bahal
SHORT COMMUNICATIONS
15. Genetic association and path coefficient analysis in green gram [Vigna radiata (L.) Wilczek] 151
U.A. Garje, M.S. Bhailume, Deepak R. Nagawade and Sachin D. Parhe
16. Genetic variability and correlation studies in advance inter-specific and inter-varietal lines and 155
cultivars of mungbean (Vigna radiata)
Niharika Bisht, D.P. Singh and R.K. Khulbe
17. Hierarchical clustering, genetic variability, correlation and path analysis studies in cowpea 158
(Vigna unguiculata L. Walp.)
P.K. Pandey, H. Lal and Vishwa Nath
18. Seasonal incidence of gram pod borer,Helicoverpa armigera (Hub.) in chickpea under Jabalpur condition 161
Y.A. Shinde, B.R. Patel and V.G. Mulekar
19. Screening ofLathyrus genotypes for resistance against downy mildew and leaf blight diseases 163
V.Y. Zhimo, J. Saha, B.N. Panja and R. Nath
20. Isolation of root exuded allelochemicals of marigold (Tagetes erecta) and their effect on the mortality 166
and egg hatching of root knot nematode (Meloidogyne javanica)
Lalit Kumar, Usha Devi, Bansa Singh and G.K. Srivastava
21. Adoption level of Integrated pest management technology in chickpea 170
R.P. Singh, Dinesh Singh, A.P. Dwivedi and Mamta Singh
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Journal of Food Legumes 27(2): 85-91, 2014
Abstract
The genetic diversity present in the widely adapted Indian
fieldpea varieties, many of which are from exotic background,
has rarely been studied with DNA based markers. Forty-five
microsatellite markers were used to assess the genetic variation
within twenty four elite pea cultivars grown extensively in
India. Out of total 45 markers, 39 markers amplified a total of
55 alleles with an average of 1.4 alleles per marker. Maximum
diversity was recorded between cultivars KPMR 44-1 and
Ambika. The average similarity coefficient value was found to
be 0.84. Cluster analysis based on Dice similarity coefficient
using UPGMA, grouped tall type and dwarf type varieties into
two different clusters based upon their pedigree. Very low
polymorphism within the studied genotypes indicates an urgent
need to include diverse parents in fieldpea breeding
programmes. The present study also generated valuable
information about the comparative usefulness of genic and
genomic microsatellite markers. Genomic microsatellite
markers showed higher degree of polymorphism compared to
the genic microsatellite markers.
Key words: Genetic diversity, Markers, Microsatellite, Pisum
sativum.
The development of cultivated species and breeding of
new varieties have always relied on the availability of
biological diversity, issuing from the long term evolution of
species. Estimates of genetic relations among parental lines
may be useful for determining which material should be
combined in crosses to maximize genetic gain. In a study with
soybean, Manjarrez-Sandovel et al. (1997) found that genetic
variance for yield was positively associated with parental
genetic distance and that genetic variance declined to near
zero when the coefficient of parentage was above 0.27. Other
studies with oat (Kisha et al. 1997) and wheat (Souza and
Sorrells 1991, Cox and Murphy 1990) showed the relationbetween genetic distance and variance varied among traits
and populations.
The development of PCR based markers has opened
new avenues for molecular differentiation of closely related
strains in a species. Simple Sequence Repeats (SSR) marker
system revealed higher genetic diversity level than Random
Amplified Polymorphic DNA (RAPD) marker system
(Zietkiewiczet al. 1994). The successful development of locus-
specific SSR markers in pea (Burstin et al. 2001, Loridon et al.
2005) allow us using pea SSR marker system for systematic
Estimation of genetic diversity in fieldpea (Pisum sativumL.) based on analysis of
hyper-variable regions of the genome
SUBHOJIT DATTA, PALLAVI SINGH, SAHIL MAHFOOZ and G.P. DIXIT
Indian Institute of Pulses Research, Kanpur 208 024, India; E-mail: [email protected]
(Received: January 3, 2014; Accepted: June 7, 2014)
studies of genetic diversity, population structure and genetic
relationship withinPisum genus. Recent diversity studies in
pea have focused on assessment the genetic diversity within
Pisum using, different molecular markers (Posvee and Griga
2000, Burstin et al. 2001, Simioniuc et al. 2002, Taran et al.
2005, Choudhary et al. 2007, Zong et al. 2008, Nasiri et al.
2009, Gowhar et al. 2010), DNA transposable elements
(Vershininet al. 2003), and numerical taxonomy (Muhammad
et al. 2009). Despite being one of the most important winter
pulse crop, with the exception of few reports (Choudhary etal. 2007, Yadav et al. 2007, Gowhar et al. 2010) the genetic
diversity in elite cultivars of field pea (Pisum sativum L.) in
India has rarely been studied with genome wide molecular
markers. This has necessitated in depth characterization of
molecular diversity in the leading pea cultivars with markers
derived from both expressed and unexpressed parts of the
genome. The availability of highly polymorphic, locus specific,
easily transferable and cost effective molecular markers
distributed throughout the genome is of great value.
Microsatellite markers have been developed from plant
genomes from both coding and non coding sequences
containing simple repeats. Microsatellite loci that are found
in gene coding sequences are referred to as genicmicrosatellites. Sequence data obtained from several crop
plants indicate sufficient homology existing between genomes
in the region flanking the SSR loci. This allows primer pairs
designed on the basis of the sequence obtained from one
crop to detect SSRs in related crop species. Such homology
in the flanking region of SSRs loci has extended the utility of
these markers to related species or genera where no and/or
very little information on SSR is available. This phenomenon
is sometimes described as transferability of microsatellite
primers across species/genera and Datta et al. (2010a, b, 2011,
2012) analyzed the transferability of microsatellite markers
across different legume taxa and reported marker transferabilityfrom 36-95%.
The purpose of the present study was to investigate
and quantify the magnitude of genetic diversity at molecular
level between 24 pea cultivars and will help in selecting better
parents for future breeding programs.
MATERIALS AND METHODS
Plant materials and DNA isolation
Twenty-four fieldpea cultivars used in the present study
were developed and released in India over the past 50 years
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8 6 Journal of Food Legumes 27(2), 2014
using different breeding methods. Total genomic DNA was
extracted from the leaves of three-week old plants of each
genotype, grown in the net house, following the modified
CTAB method (Abdelnoor et al. 1995). The extracted DNA
was purified with RNase treatment (10 mg/ml) for 1 hour at
37C followed by treatment with phenol: chloroform: isoamyl
alcohol (25:24:1). The pellet was dissolved in appropriateamount of T
10E
1(Tris10mM, EDTA 1mM) buffer. DNA from
different samples was quantified both by visual quantification
and UV spectrophotometer (Smart Spec Plus, BioRad,
Hercules, USA) and finally diluted to a concentration of 25
ng/ml.
Microsatellite markers and PCR amplification
A total of 45 pea specific microsatellite markers were
used for PCR amplification to study genetic diversity within
the cultivars. Microsatellite markers were based on the
sequences published by Burstin et al. (2001) and details are
provided in Table 2. Length of the primers varied from 18 to 24
nucleotides. Markers were custom synthesized from IntegratedDNA Technologies, USA. Amplification of SSR motif was
conducted in 200 ml thin-wall PCR tubes using a touch down
progr amme (Don et al.1991) in a PTC-200 gradient
thermocycler (MJ Research, USA). PCR amplifications were
carried out in total volume of 5 l containing 5 ng genomic
DNA, 1X PCR buffer, 0.1mM dNTPs (Bangalore Genei,
Bengaluru), 0.1 unit of TaqDNA polymerase (Bangalore Genei)
and 2.5 pM of each primer. An initial denaturation was given
for 3 min at 95C. Subsequently, five touch-down PCR cycles
comprising of 94C for 20 s, 60-56C (depending on the marker
as given in Table 1) for 20 s, and 72C for 30 s were performed.
These cycles were followed by 40 cycles of 94C for 20 s withconstant annealing temperature (depending on marker) for 20
s, and 72C for 20 s, and a final extension was carried out at
72C for 20 min. PCR products were checked by agarose gel
(3%) electrophoresis and were separated in 1X TBE buffer.
Digital images of gels were made using gel documentation
system (Alpha Digi Doc, Alpha Innotech Corporation) (Fig.1).
The sizes of alleles were determined by comparing with Gene
Ruler 100 bp ladder (MBI Fermentas).
Statistical analysis
Markers were scored based on the band pattern
generated from the gel imaging system for the presence or
absence of the corresponding band among the genotypes.Using the binary coding system 1 indicating the presence of
clear and unambiguous bands and 0 indicating the absence
of bands. Polymorphism Information Content (PIC) (Anderson
et al. 1993) was calculated for each marker using the following
equation:
Polymorphism information content
n
1j
2ijiP1)(PIC
Where, Pij is the frequency of the jthallele for ithmarker
and summation extends over n alleles. The 0/1matrix was
used to calculate genetic similarity as Dice coefficient (Dice
1945, Sorensen 1948) using SIMQUAL subprogram and the
resultant similarity matrix was employed to construct
dendrogram using Sequential Agglomerative Hierarchical
Nesting (SAHN) based Unweighted Pair Group Method ofArithmetic Means (UPGMA) as implemented in NTSYS-PC
version 2.1 (Rohlf 1998) to infer genetic relationships and
phylogeny.
In order to estimate the congruence among
dendrograms, product moment correlation (r) was computed
and compared using Mantel statistics (t) in MXCOMP
program (Mantel 1967).
RESULTS AND DISCUSSION
Microsatellite polymorphism
A set of 45 microsatellite markers (27 genic and 18
genomic) were used to amplify 24 genotypes of pea. Of the 45
markers, 18 contained loci for di-nucleotide repeats and 25
amplified tri- nucleotide repeats, whereas, motifs for two
markers was unknown. Allelic differences were determined by
relative mobility in 3% agarose gel and the size of alleles was
estimated by reference to a 100 base pair DNA ladder. Where
a PCR product was not obtained, data for the relevant sample
were treated as null allele. Out of total 45 markers, 39 markers
amplified easily scorable alleles ranging from 110 to 1100 bp
size in all the cultivars. Out of 39 markers, 20 (51%) were
polymorphic and 19 (49%) were monomorphic (Table 2). Thirty-
nine markers amplified a total of 55 alleles with an average of1.4 alleles per marker. Among the total alleles amplified, 33
(61%) alleles showed polymorphism whereas, 21 (39%) alleles
were found to be monomorphic. The highest number of alleles
(6) was amplified by marker PEACPLHPPS, followed by
PSBLOX13.1 and PSGDPP which produced three alleles each.
Two alleles each were amplified by PEARHOGTPP,
PEARHOGTPP, PSAJ3318, PSCAB66, PSBT2AGEN and
PEAOM14A. Rest of the markers amplified single alleles.
Eighteen markers PSBLOX13.1, PEACPLHPPS, AF016458,
PEAATPASE, PEARHOGTPP, PSGDPP, PSP4OSG, AA430902,
PSAS, PSGSRI, PEAPHTAP, PATRG31A, PSBLOX13.2,
PSY14558, PSAJ3318, PSCAB66, PSLEGJP, PSLEGKL showed100% polymorphism whereas 50% polymorphism was shown
by PSBT2AGEN and PEAOM14A (Table 2). Among the
polymorphic markers, maximum PIC value of 0.99 was shown
by PSY14558 and minimum (0.12) with PSGDPP and PSLEGJP
with the average value being 0.48. Earlier, Burstin et al. (2001)
used the same set of markers in their study on 12 pea genotypes
and they found 31 markers to be polymorphic. The higher
number of polymorphic markers obtained by them may be due
to the reason they selected more diverse genotype in their
study which comprised of wide range of cultivated as well as
exotic types.
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Datta et al. : Estimation of genetic diversity in fieldpea (Pisum sativum L.) based on analysis of hyper 8 7
In order to quantify the level of polymorphism, Dice
estimate of similarity coefficients was used to generate a
similarity matrix which is based on the probability that an
amplified fragment from one plant will also be found in another.
Similarity coefficient among pea genotypes varied from 0.75
to 0.96, the average being 0.84. Similarity coefficient values
were highest (0.96) between the two pair of genotypes JM 6and Jayanti and KPM 400 and HFP 4, followed by (0.94)
among VL-3 and Subrita and DDR 44 and HFP 8909. Minimum
values of similarity coefficients were observed between
KPMR44-1 and Ambika (0.75) followed by KPF 103 and KPMR
44-1(0.76). Rachna and HFP 8909, and IPF 99-25 and KPMR
522 had coefficient values of 0.77. Taran et al. (2004) studied
diversity within 65 pea varieties and 21 accessions from wild
Pisumsubspecies using RAPD and SSR markers. The pair
wise genetic similarity value among the 65 varieties ranged
from 0.34 to 1.0 in their study. Earlier, Yadav et al. (2007)
conducted similar study in fifteen germplasm line ofPisum
sativumwith 12 RAPD markers. They observed a similarity
coefficient value ranges from 0.263 to 0.793.
Polymorphism with genomic microsatellites
Out of the 45 microsatellite markers used in the present
study, 18 (PSBLOX13.1, PEACHLROPH, PSADH1,
CHPSTZPP, PEALCTN, PSRBCS3C, PEAATPASE,
PSJ000640A, PSP4OSG, PEAEGL1, PSGSRI, PATRG31A,
PSBLOX13.2, PSCAB66, PSLEGJL, PSLEGJP, PSLEGKL,
PSLEGKP) were genomic. No amplification could be observed
with the marker PEACHLROPH and PEAEGL1. These 16
markers amplified alleles of size range 110-800 bp. All the
markers showed the polymorphic alleles except PSADH1,
CHPSTZPP, PEALCTN, PSRBCS3C, PSJ000640A, PSLEGJL,
and PSLEGKP. A total of 21 alleles were amplified by the 16
markers with an average of 1.31 alleles per marker. The highest
number of allele (3) was amplified by the marker PSBLOX13.1,
two alleles each was amplified by PSP4OSG, PSCAB66 andPSLEGJP; rest all the markers amplified one allele each.
Maximum PIC (0.95) was obtained with PSGSRI whereas
PSLEGJP showed the minimum PIC value i.e. 0.12 with the
average value being 0.53. The genetic similarity coefficient
value with the genomic microsatellite markers ranged from
0.70 to 0.97 and the average value was found to be 0.86 (Table
3). Genomic microsatellites are found to be more polymorphic
as they are mostly developed from non transcribed regions of
genome, thus, they are ideal for mapping and diversity studies.
The utility and effectiveness of genomic microsatellites have
been proven in many legumes like pigeonpea (Odeny et al.
2009, Saxena et al. 2009), chickpea (Winter et al. 1995,
Buhariwalla et al. 2005) and common bean (Blair et al. 2003).
Polymorphism with genic microsatellite markers
A total of 27 genic microsatellite markers were used, out
of which four markers (PEADRR230B, PSU81288, PEALEGBC,
and AF029243) did not amplify any scorable bands. The 23
markers amplified scorable bands of the size range 110-1100
bp. Markers PEACPLHPPS, AF016458, PEARHOGTPP,
PSGDPP, AA430902, PSAS, PEAPHTAP, PSY14558, PSAJ3318,
PSBT2AGEN and PEAOM14A were found to be polymorphic
Table 1. Pedigree and morphological descriptions of 24 pea genotypes used in the present study.
S. No. Variety Parentage No. of seeds /
Pod
Plant type Yield per
plant( g)
Days to
flower
Days to
maturity
100 seed
weight (g)
1 HFP-4 T 163x EC 109196 6.0 dwarf 21.7 66 123 21.5
2 KPMR 144-1 Rachna x HFP 4 5.0 dwarf 23.6 62 121 18.9
3 HFP 8909 EC 109185 x HFP 4 6.0 dwarf 17.7 68 115 16.3
4 HUDP-15 (PG 3 x S143) x FC 1 6.0 dwarf 24.3 66 122 22.3
5 KPMR 400 Rachna x HFP 4 6.0 dwarf 25.3 62 120 23.06 KPMR 522 KPMR 156 x HFP 4 6.0 dwarf 24.7 62 128 19.2
7 IPFD 99-13 HFP 4 x LFP 80 5.0 dwarf 35.6 58 110 22.4
8 DDR 44 HFP 4 x KPMR 157 6.0 dwarf 20.7 64 122 20.8
9 SWATI Flavanda x HFP 4 5.0 dwarf 15.3 62 119 21.4
10 JAYANTI HFP 4 x PG 3 5.0 dwarf 27.0 64 124 20.0
11 RACHNA T 163 x T 10 7.0 tall 24.3 68 126 23.3
12 HUP 2 (Alfaknud x C 5064) x S143 5.0 tall 39.3 66 126 17.6
13 KFP 103 KPMR 83 x KPMR 9 5.0 tall 39.7 68 127 20.1
14 JP 885 (T 163 x 6588-1) x 46C 4.0 tall 37.6 64 129 19.7
15 DMR 7 6587 x L 116 5.0 tall 28.6 68 125 22.0
16 PANT P5 T 10 x T 163 4.0 tall 28.3 68 128 25.2
17 VL 1 Selection from Miller 6.0 tall 24.6 66 126 17.4
18 Ambika DMR 22 x HUP 7 4.0 tall 40.0 64 126 17.5
19 B 22 Selection of local material from
Berhampore (W.B.)
5.0 tall 12.3 72 126 16.0
20 IPF 99-25 PDPD 8 x Pant P5 4.0 tall 32.3 60 118 20.0
21 Subrita Rachna x JP 885 4.0 tall 36.4 63 125 18
22 PG 3 T 163 x Bonnevilla 6.0 dwarf 19.8 60 121 19.5
23 VL 3 Old Sugar x Wrinkled Dwarf 5.0 dwarf 20.1 61 126 17.8
24 JM 6 Local yellow Botri x (6588-1x 46C) 5.0 tall 18.9 67 125 17.3
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8 8 Journal of Food Legumes 27(2), 2014
whereas rest markers showed monomorphic bands. Total 34
alleles were amplified by the 23 markers with an average of
1.48 alleles per marker. The marker PEACPLHPPS amplified
the highest number of six alleles. Three alleles were amplified
with PSGDPP, whereas two alleles each were amplified with
the markers PEARHOGTPP, PSAJ3318, PSBT2AGEN,
PEAOM14A. Rest of the markers amplified only one allele
each. Maximum PIC (0.99) was obtained with PSY14558 and
minimum with PSGDPP (0.124). Dice similarity coefficient value
for genic microsatellites ranged from 0.71 to 0.98 and theaverage coefficient being the 0.84 quite close to genomic
microsatellites (Table 3). Evaluation of germplasm with
microsatellite markers derived from genes or ESTs might
enhance the role of genetic markers by assaying the variation
in transcribed and known-function genes, although there is a
higher probability of bias owing to selection. Expansion and
contraction of microsatellite repeats in genes of known
function can be tested for association with phenotypic
variation or, more desirably, biological function (Varsheneyet
al. 2005). The microsatellite markers derived from EST are
considered less powerful in the discrimination of genotypes
than other sources. Eujayl et al. (2001) compared genic and
genomic SSR markers to investigate genotypic variation of 64
durum wheat lines, land races, and varieties obtaining 255
polymorphic loci among 137 EST microsatellite markers and
505 among 108 genomic microsatellite markers, with an average
of 4.1 and 5 alleles per locus, respectively. Earlier studies by
many researchers also reported that genic markers are less
polymorphic (Scott et al. 2000, Rungis et al. 2004) because of
greater sequence conservation in transcribed region however,several studies have found that genic SSRs are useful for
estimating genetic relationship (Hempel et al. 2007) and at the
same time provide opportunities to examine functional
diversity in relation to adaptive variations (Eujayl et al. 2001).
The low level of polymorphism detected with genic
microsatellites may be compensated by their higher potential
for cross species/genus transferability.
Our study find that genomic microsatellites were more
efficient in detecting polymorphism between the 24 pea
Table 2. Details of the properties of different primers used to evaluate genetic diversity and summary of their amplification inpea genotypes
Primer Name/ locus Forward primer (5'-3') Reverse primer (5'-3') Mean
Tm (0C)used
Nature Motif No. of
alleles
Allele size PIC
PEAATPSYND CTCCAGCCCATCATAGTCGAAG TCACAACCGAAGTCACAACC 58 Genic (AC)6 1 200 -
AA427337 GCTAGCTAGACTAGTCTTTACAG CTGTTCATAACTAAAAAACATCTC 50 Genic (AC)5 1 200 -
PSBLOX13.1 GAACTAGAGCTGATAGCATGT GCATGCAAAAGAACGAAACAGG 54 Genomic (AT)17 3 270-300 0.94
PSGAPA1 GACATTGTTGCCAATAACTGG GGTTCTGTTCTCAATACAAG 51 Genic (AT)17 1 200 -
PSADH1 GATGTGATAGGCCTAGAACAAGC CAGTCACACACTACAAGAGATC 54 Genomic (AT)10 1 400 -
PEACPLHPPS GTGGCTGATCCTGTCAACAA CAACAACCAAGAGCAAAGAAAA 58 Genic (AT)6 6 260-1100 0.17
CHPSTZPP TGAATAAAGGGCAGAGTTAATACA GAATCACGGGACCAAAACC 55 Genomic (AT)6 1 350 -
PEALCTN TATGCTTCCTCCTCGCGTTA TTTTGCCCCTATTTCACTATTTA 50 Genomic (AT)6 1 210 -
PSRBCS3C CCCAGTGAAGAAGGTCAACA CAATGGTGGCAAATAGGAAA 58 Genomic (AT)6 1 210 -
PSY14273 AATTCGGCACGAGGAGAGA TGCAGCCTTGAGCTGGTTAT 50 Genic (TC)18 1 300 -
AF016458 CACTCATAACATCAACTATCTTTC CGAATCTTGGCATGAGAGTTGC 54 Genic (TC)9 1 170 0.94
PSU58830 CACACTCCATTTTCACCACCT AGCATTGAAGAACAAAAGCACT 55 Genic (TC)8 1 220 -
AF004843 CCATTTCTGGTTATGAAACCG CTGTTCCTCATTTTCAGTGGG 54 Genic (TC)7 1 220 -
PSARGDECA CTGTTCCTCTTTCAAGCACTCC GGGAAAGCAAAGCATGCGGATC 58 Genic (TC)6 1 250 -
PEAATPASE TGCAACATTCTATCTCTCTCTTT AGTAGCCACATCGGTGGAGA 55 Genomic (TC)6 1 200 0.39
PEARHOGTPP ACGCTTCAACGGCAAAAT AGGACCCCAATCACTCTCAC 58 Genic (TC)5 2 200,300 0.24
PSJ000640A GTCCACCTCCCGGGTTCGAA CGGCTAGAAGAACCACCCCCAT 60 Genomic (AAC)7 1 200 -
PSZINCFIN CGCGGAGTTTACATCAGGTC CTGGCCTAATAATGGCAACC 60 Genic (AAC)5 1 200 -
PSGDPP AAACCGTGCAACTCTGAAGC AAGAAACCCACCAACACGTC 60 Genic (AAC)5 3 200-500 0.12
PSP4OSG CAACCAGCCATTATACACAAACA GGCAATAAAGCAAAAGCAGA 58 Genomic (AAT)36 2 250,350 0.49AA430902 CTGGAATTCTTGCGGTTTAAC CGTTTTGGTTACGTCGAGCTA 54 Genic (AAT)7 1 200 0.44
PSAS GGTGATAACTATTTGGCTCATC GTAGATTTCTCCATTCACCTG 54 Genic (AAT)6 1 250 0.5
PSGSRI TGGATTGGATTGGATGATGA TGGAGCCCTTAGTCCACAAC 60 Genomic (AAT)14 1 200 0.95
PEAPHTAP TGAAACCACCATTCTCTGGA AAGACCCCACTTGAAAATTACTTC 58 Genic (AAT)5 1 200 0.16
PATRG31A CATGAAATGGAATAATCTTATG CAGTCTAGTTGGCATATACC 48 Genomic (AAT)4 1 500 0.39
PSBLOX13.2 CTGCTATGCTATGTTTCACATC CTTTGCTTGCAACTT AGTAACAG 54 Genomic (CAT)8 1 110 0.8
PSY14558 ACATGTCTCTGTTAGTGTG GCCAATATCTTCTTTGTTGAAG 48 Genic (CAT)7 1 150 0.99
PSAJ3318 CAGTGGTGACAGCAGGGCCAAG CCTACATGGTGTACGTAGACAC 58 Genic (CAT)6 2 180,650 0.66
PSCAB66 CACACGATAAGAGC ATCTGC GCTTGAGTTGCTTGCCAGCC 55 Genomic (CAT)5 2 300,800 0.28
PSBT2AGEN GCAGCAGAGCTTGTCTTTGAG GGAATCAGAAACAGCCTTGGG 58 Genic (CCT)5 2 110,290 0.37
PEAOM14A GGTGCCCTAGCATTTGCTG TAGTAACAACCGCGCTCAAA 60 Genic (CCT)5 2 200,500 0.15
PSLEGJL GGTTCGTCGATTCAGAAAAGG CACATTAGTTTAATAGTTACC 49 Genomic (GAA)8 1 200 -
PSLEGJP GCGAGTTGAGGGAGGTCTCCGC GTCGGCACGTGCAGCGTCCGC 61 _ _ 2 250, 280 0.12
PSLEGKL CCATTCATACAGTATGCTCT ATAGTTAGTACTATACACACC 50 Genomic (GAA)8 1 700 0.44
PSLEGKP GCGAGTTGAGGGAGGTCTCCGC CTGATACGACCAGCACGTGGG 61 _ _ 1 250 -
PSU51918 GTCGTAACAGATCAATATGGC CGATAGTGAGAGTGGCGGTTG 54 Genic (GAA)6 1 150 -
PSY17134 GAGGCAATCCTTCGTTTCTC CGAGTAAAGCCGCATAGAGC 58 Genic (TGG)5 1 380 -
PSU81287 AGAGACACCGGAAGATCGAG CATCCCCATAGCCACCAC 58 Genic (TGG)7 1 280 -
PS11824 ACCACCACCACCGAGAAGAT TTTGTGGCAATGGAGAAACA 60 Genic (TGG)5 1 200 -
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Datta et al. : Estimation of genetic diversity in fieldpea (Pisum sativum L.) based on analysis of hyper 8 9
genotypes as compared to their genic counterparts, however,
genic microsatellites amplified more alleles. The average PIC
value of genomic microsatellite markers was higher (0.53) than
that of genic one (0.43). The Mantel matrix correspondence
test used to compare the similarity matrices and the correlation
coefficient was found to be 0.819. The test indicated that
clusters produced based on genic and genomic microsatellite
markers were conserved since the minimum required matrixcorrelation value was 0.80. The finding of this study showed
that genic microsatellite are equally good for polymorphism
studies along with genomic SSRs. Hanai et al. (2007) observed
similar results while comparing genic and genomic
microsatellites in common bean.
In the present investigation three DNM (di-nucleotide
motif) and six TNM (tri-nucleotide motif) of varied repeats
length were used to survey the level of polymorphism (Table
4). Most of the markers used (21) were from TNM whereas
rest had DNM. The maximum no. of alleles (28) was amplified
by TNM which ranged from 3-10 with an average of 4.66 alleles/
tri-nucleotide motif. Among the TNMs, CAT repeat was foundto be most informative in observing polymorphism inPisum
genome which is followed by AAT. Similarly, 24 alleles were
amplified by DNM with an average of eight alleles/dinucleotide
motif. The average PIC value of DNM was 0.127 whereas TNM
revealed a higher average PIC value of 0.26. In this study no
correlation was observed between number of repeats with
either the alleles amplified or with the PIC value however, it
was found that TNMs were more useful in detecting
polymorphism when compared with DNMs. This result
contrasts the earlier findings of Cupic et al. (2009) where a
significant correlation between number of alleles and PIC
values inPisumgenome was found.
Cluster analysis
Determining the relatedness among potential parents
forms the basis for choosing genetically distant parents in a
breeding programme. Cluster analysis indicated the abilityand usefulness of SSR markers for studying the differentiation
and relatedness among pea genotype. The genetic
relationship among the 24 pea genotypes has been
investigated using SSR profiles. It is evident from the cluster
analysis that the field pea cultivars can be broadly grouped
into two clusters (A and B) (Fig 2.). The cluster A includes all
the tall type cultivars except HUDP 15 (a dwarf cultivar
generated from an exotic line S 143). Three tall cultivars viz.
PG 3, Rachna and DMR 7 positioned themselves away from
any core cluster because of wide geographical distribution of
their parents. Most of these tall cultivars have T 163 as a
parent directly or indirectly in their pedigree. Cluster B is
constituted by all the dwarf cultivars except HUP 2 (a tall
cultivar generated from an exotic line Alfakund) and Subrita (a
cultivar generated from diverse background). HFP 4 has been
involved as one of the parent in the pedigree of most of the
dwarf type cultivars. Again, if the pedigree analysis of HFP 4
is done, an obsolete cultivar T 163 is one the parent.
In a recent study, the pedigree analysis of released
cultivars in India has been traced back to 26 ancestors (Dixit
and Katiyar 2006). Out of these 26 ancestors, three ancestors
contributed 49% of the genetic base. T 163 was the most
Table 3. Comparison between genomic and genic SSRs interms of their ability to reveal polymorphism
Genic
markers
Genomic
markersTotal
Markers used 27 18 45
Marker amplified 23(85%) 16(89%) 39 (87%)
No of monomorphic markers 12 (52%) 7 (44%) 19 (49%)
No of polymorphic markers 11 (48%) 9 (56%) 20 (51%)
Average PIC value 0.43 0.53 0.48
No. of alleles amplified 34 21 54
Similarity coefficient value (Avg) 0.84 0.86 0.85
Size range (bp) 110-1100 110-800 110-1100
Table 4. The efficiency of different microsatellite repeat motifin detecting polymorphism in pea
Repeat
Motif
No. of
repeats
No. of
markers
No. of
alleles
Average
PIC
(AC) 5-6 2 2 0
(AT) 6-17 7 14 0.158
(TC) 5-18 7 8 0.224
(AAC) 5-7 3 5 0.04(AAT) 4-36 6 7 0.48
(CAT) 5-8 4 6 0.682
(CCT) 5 2 4 0.245
(GAA) 6-8 3 3 0.146
(TGG) 5-7 3 3 0
Fig. 1. Amplification profile of 24 pea cultivars obtained with microsatellite markers PSLEGJP and PSBT2AGEN.
Lanes M; Molecular weight marker, 100 bp DNA ladder, Lanes 1-24; 24 pea genotypes as per the serial in Table 1.
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9 0 Journal of Food Legumes 27(2), 2014
frequently used parent followed by EC 109196 and T 10. T 163
was mostly used for its wide adaptability whereas T 10 was
used as donor parent for powdery mildew resistance. EC
109196 was used as a source of afilagene and dwarf plant
type. T 163 contributed maximum to the genetic base of field
pea with occurrence more than 51%. In other words, at least
51% cultivars of field pea released so far in India are more or
less related due to involvement of T 163 in their pedigree.
This has led towards genetic erosion and the narrowing of
genetic base in this crop. So, it is desirable to have more
diverse and usable genetic backgrounds in future varieties to
provide protection again st biot ic and abiotic stresses.
Furthermore this study highlighted the importance of genic
microsatellite markers for use in resolving diversity.
ACKNOWLEDGEMENTS
We thank the Indian Council of Agricultural Research
for generous funding through NPTC- Genomics and Indo-US
AKI - PGI Projects which helped to carry out this work.
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Journal of Food Legumes 27(2): 92-94, 2014
Genetic diversity assessment in clusterbean (Cyamopsis tetragonoloba (L.) Taub.) by
RAPD markers
S.R. KALASKAR, S. ACHARYA, J.B. PATEL, W.A. SHEIKH, A.H. RATHOD and A.S. SHINDE
Department of Plant Molecular Biology and Biotechnology, Centre of Excellence for Research on Pulses,
Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar, Gujarat, India ;Email: [email protected]
(Received: November 25, 2013; Accepted: March 10, 1014)
ABSTRACT
Genetic diversity analysis of 12 clusterbean ( Cyamopsis
tetragonoloba (L.) Taub.) genotypes were carried out using
Random Amplified Polymorphic DNA (RAPD) markers. The 19
RAPD primers amplified a total of 212 bands, out of which 151
were polymorphic. The size of amplified DNA fragment varied
from 146 to 2995 bp. The polymorphic bands varied from 22.22
percent in OPA-11 to 88.88 percent in OPA-12. Dendrogrambased Jaccards similarity coefficient grouped the 12 genotypes
into four major clusters encompassing five subclusters. The
first cluster comprised three subclusters with subcluster A1
contained three genotypes viz; GG-2, HG-75 and HG-365.
Subcluster B1 had only one genotype GG-1 while, subcluster
C1 contained two genotypes viz; RGC-471 and HVG-2-30.
Cluster 2 entailed two subclusters viz; B1and B2 having two
genotypes each namely PRT-15 and GAUG-0013 grouped in
subcluster B1 and FS-277 and PNB in subcluster B2. The third
and fourth cluster contained single genotype GAUG-0522 and
GAUG-9404, respectively. The similarity index values ranged
from 0.52 to 0.87 indicating the presence of enormous genetic
diversity at molecular level. Therefore, RAPD analysis could
be used as tool for detecting genetic diversity and can be
precisely used for grouping and selection of diverse parents.
GG-2, HG-75 and HG-365 (Sub group A1) and GAUG-0522
(Group C) may be utilized for breeding good genotypes with
high yield and resistance to bacterial blight in clusterbean.
Key words: C. tetragonoloba, Clusterbean, Genetic diversity,
RAPD markers.
Clusterbean is an important arid legume known for its
adaptation to rugged environments. It has multi facet uses as
vegetable, food, fodder and feed. However, its economic
importance reflects in having a rubber-like substance called
galactomannan in its endosperm that has conspicuous widearrays of industrial utilities. Lately, the crop assumed
enhanced importance due to uses of galactomannan in
fracking process of oil exploration (Kyawet al.,2012; Narayan,
2012).
The genetic variability is the backbone of any breeding
programme. More genetically wider is the involvement of
parents, better is the chance of recovering high yielding
genotypes with appropriate quality and resistance to biotic
stresses. The overall expression of different characters is the
function of juxtaposition of environment with genotype and
this interaction is never consistent making it difficult to have
precise selection of the parents. Therefore, it would be
advantageous to study polymorphism through molecular
markers. Among the various molecular markers, RAPD
(Williams et al., 1990), are not sequence based and can detect
genome wide variation in both coding as well as non-coding
region besides being dominant, cheaper and allows a largenumber of marker to be assayed in short time.
MATERIALS AND METHODS
Plant material and DNA extraction
Experimental material comprised of twelve genotypes
of clusterbean obtained from Centre of Excellence for Research
on Pulses, Sardarkrushinagar Dantiwada Agricultural
University, Sardarkrushinagar. The genotypes were selected
based on different morphological characters and reaction to
bacterial leaf blight in particular. The salient features of the
selected genotypes included in present study are given inTable 1.
The genotypes were grown in pots. Genomic DNA was
isolated from the young leaves as per modified Cetyl Trimethyl
Ammonium Bromide (CTAB) method of Murray and
Thompson (1980). The quality and quantity of DNA was
determined by nano spectrophotometer.
PCR and RAPD analysis
PCR amplification was performed with random decamer
primers obtained from Operon Technologies (Almeda, Calif.,
USA). Amplification was performed in a 25-l reaction volume
containing Taq Buffer B (10X Tris without MgCl2), 25 mMMgCl
2, 20 pmol RAPD primer, 50 ng genomic DNA, and 1 U
Taq DNA polymerase (Bangalore Genei, Bangalore, India).
Amplification was performed in an Eppendorf Master Cycler
Gradient (Eppendorf Netheler-Hinz, Hamburg, Germany).
Amplification conditions comprised initial denaturation at 94C
for 4 min, 41 cycles at 94C for 1 min (denaturation), 1.5 min
annealing (depending on Tm), 72C for 2 min (extension) and
followed by final extension of 4 min at 72C. Amplified products
were separated on 1.5% agarose gel in 1 TBE buffer (100 mM
Tris-HCl, pH 8.3, 83 mM boric acid, 1 mM EDTA) at 50 V. The
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Kalaskar et al. : Genetic diversity assessment in clusterbean (Cyamopsis tetragonoloba (l.) Taub.) by RAPD markers 9 3
gels were stained with 0.5 g/ ml ethidium bromide solution
and visualized by illumination under UV light.
Statistical analyses
Amplified products obtained from random primers were
used to estimate genetic distances among the accessions.
The entire fingerprint data was converted into a binary matrix
based on the presence (1) or absence (0) of individual bands
for each genotype. The cluster analysis was performed by
using Unweighted Pair Group Methods of Arithmetic
Averages (UPGMA) using NTSYS-pc version 2.1 (Numerical
Taxonomy and Multivariate Analysis System for Personal
Computers, Exeter Software) developed by Rohlf (2000) and
analyzed by the SIMQUAL (similarity for qualitative data)
program with Jaccards Similarity Coefficient.
RESULTS AND DISCUSSION
The role of specific primers as a valuable resource can
not be over emphasised in precise assessment of genetic
diversity bereft of vague impact of environmental factors. This
can be used for efficient selection of diverse parents for
efficient crop improvement programme (Virket al., 1995).
The extracted DNA of each genotype was amplified with
19 random decamer primers. A total of 212 bands were obtained
with an average of 11.15 bands per primer. Out of these, 151
fragments were found polymorphic. The mean number of
polymorphic bands per primer among 12 clusterbean
genotypes was 7.94. The size of PCR amplified DNA fragment
varied from 146 to 2995 bp. Among the primers, OPA-12 evinced
the maximum polymorphism (88.88%), while the lowest
polymorphism (22.22 %) was exhibited with OPA-11. The
average polymorphism detected was 71.22 % (Table 2) that
was good enough for efficient genetic analysis. In consonance
to the present findings, Punia et al. (2009) have also reported
amplification of maximum number of 20 bands by primer OPB-
15 and minimum of 4 bands by primer OPB-1. The average
fragments amplified per primer were 11.15 that were also in
consonance to the findings of Punia et al.(2009), who had
also reported average 10.29 fragments per primer in their study
on 18 genotypes of clusterbean.
UPGMA cluster analysis based on Jaccards Similarity
Coefficient grouped the 12 genotypes into four major clusters
and five subclusters. The first Cluster A comprised three
subclusters with subcluster A1 containing three genotypes
viz.,GG-2, HG-75 and HG-365. Subcluster A2 had only one
genotype GG-1, while subcluster A3 contained two genotypes
viz.,RGC-471 and HVG-2-30. Second cluster B comprised twosubclusters viz., B1 and B2 encompassing two genotypes
each i.e. PRT-15 and GAUG-0013 grouped in subcluster B1
and FS-277 and PNB in subcluster B2. The third cluster C and
fourth cluster D contained single genotype GAUG-0522 and
GAUG-9404, respectively (Fig. 1).
Based on the simple matching coefficient, a genetic
similarity matrix was constructed using the RAPD data to
assess the genetic relatedness among the 12 accessions. The
similarity coefficients ranged from 0.52 to 0.87 for all accessions
with the minimum genetic similarity between GAUG-0522 and
GAUG-9404 and the maximum similarity between GG-2 and
HG-75 (Figure 4). Higher the dissimilarity or diversity betweenthe genotypes, better is the scope to include them in
hybridization. Bacterial leaf blight is the major yield limiting
factor in clusterbean. Out of the different genotypes studied,
GAUG-0522 was resistant to bacterial leaf blight while, GAUG-
9404 was susceptible and thereby expected to throw better
segregants for resistance to bacterial leaf blight. Sub group
A1 contained all the three genotypes viz.,GG-2, HG-75 and
HG-365 as resistant to bacterial leaf blight. The genetically
distant genotype along with resistance to bacterial leaf blight
was GAUG-0522 (Group C). Therefore, crosses between three
genotypes in Sub group 1A (GG-2, HG-75 and HG-365) and
Group C (GAUG-522) could be exploited for enhancingproductivity along with resistance to bacterial leaf blight.
The suitability of individual primers for genetic diversity
study was determined from the number of polymorphic
fragments produced by the different genotypes and the
number of them that can be utilized for fingerprinting of
individual genotype. This is similar to the method used by
and Russell et al. (1997) and Rajora and Rahman (2003). Out
of the 19 primers used, all but OPA-11 were consistently
repeatable and were useful in detecting polymorphism among
Table 1. List of clusterbean genotypes used for RAPD analysis
Genotype Salient feature PedigreeGG-2 Resistant to bacterial leaf blight disease HG 7-4/P2-1 RGC 137
GAUG-0522 Resistant to bacterial leaf blight disease GAUG 90005 HGS 844
HG-75 Resistant to bacterial leaf blight disease Selection from germplasm
HG-365 Resistant to bacterial leaf blight disease Durgajay x Hisar Local
RGC-471 Resistant to bacterial leaf blight disease Selection from Nagaur district of Rajasthan
PRT-15 Resistant to bacterial leaf blight disease Not available
GG-1 Susceptible to bacterial leaf blight disease Mutant of Kutch-8 (10 K r alpha rays)
GAUG- 0013 Susceptible to bacterial leaf blight disease HGS 844 GAUG 9003
GAUG-9404 Susceptible to bacterial leaf blight disease Selection from germplasm
HVG-2-30 Susceptible to bacterial leaf blight disease Pusa Sadabahar x HGS 296
FS-277 Susceptible to bacterial leaf blight disease Selection from germplasm
PNB Susceptible to bacterial leaf blight disease Pusa Mausami Pusa Sadabahar
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9 4 Journal of Food Legumes 27(2), 2014
the genotypes studied. Further, OPA-12, OPH-8, OPB-15, OPB-
14 and OPB-3 were the best primers evincing more than 80
percen t polymorphism and encompassed 59 of the 151
polymorphic bands
Table 2. List of pr imers along with se quences andamplification details
Primer Primer
sequence
5 ? 3
Total number
of
bands
Number of
Polymorphic
bands
Per cent
Polymorphism
OPA-11 CAATCGCCGT 9 2 22.22
OPA-12 TCGGCGATAG 9 8 88.88
OPB-1 GTTTCGCTCC 4 3 75.00
OPB-3 CATCCCCCTG 15 12 80.00
OPB-4 GGACTGGAGT 6 4 66.66
OPB-5 TGCGCCCTTC 6 3 50.00
OPB-7 GGTGACGCAG 11 8 72.72
OPB-13 TTCCCCCGCT 13 10 76.92
OPB-14 TCCGCTCTGG 12 10 83.33
OPB-15 GGAGGGTGTT 20 17 85.00
OPB-16 TTTGCCCGGA 14 9 64.28
OPB-19 ACCCCCGAAG 9 7 77.77
OPH-1 GGTCGGAGAA 7 5 71.42
OPH-8 GAAACACCCC 13 10 76.92
OPH-13 GACGCCACAC 15 9 60.00
OPH-14 ACCAGGTTGG 15 9 60.00
OPH-17 CACTCTCCTC 7 5 71.42OPH-18 GAATCGGCCA 14 12 85.71
OPH-20 GGGAGACATC 13 8 61.53
TOTAL 212 151 71.22
Figure 1. Dendrogram showing clustering pattern of clusterbean
genotypes based on genetic similarity values obtainedfrom the RAPD data
RAPDs are among the most-widely used markers for
economically important traits in cultivated plants. Earlier
studies also reported that RAPD technique generates large
number of polymorphisms in clusterbean (Pathak et al. 2011).
The phylogenetic relationship exhibited among different
genotypes of clusterbean in the study was congruent with
earlier studies conducted by Punia et al. (2009) and Pathak etal. (2010) Therefore, RAPD analysis could be used as a good
tool for detecting genetic diversity and can be precisely used
for grouping and selection of diverse parents. From the
present study genotypes like GG-2, HG-75 and HG-365 (Sub
group 1A) and GAUG-0522 (Group C) may be utilized for
breeding good genotypes with high yield and resistance to
bacterial blight in clusterbean.
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Pathak R, Singh SK and Singh M. 2011. Assessment of genetic diversity
in clusterbean using nuclear rDNA and RAPD markers. Journal of
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of genetic diversity in cluster bean (Cyamopsis tetragonoloba)
genotypes. Journal of Genetics 89:243-246.
Punia A, Yadav R, Arora P and Chaudhury A. 2009. Molecular andmorphophysiological characterization of superior cluster bean
(Cymopsis tetragonoloba) varieties. Journal of Crop Science and
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RFLPs, AFLPs, SSRs and RAPDs. Theoretical and Applied Genetics
95 :714-722.
Virk PS, Newbury HJ, Jackson MT and Ford-Llyod BV. 1995. The
identification of duplicate accessions within a rice germplasm
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Journal of Food Legumes 27(2): 95-98, 2014
Environmental influence on heritability and selection response of some important
quantitative traits in greengram [Vigna radiata (L.) Wilczek]
CHANDRA MOHAN SINGH1, S.B. MISHRA2, ANIL PANDEY2 and MADHURI ARYA2
1Department of Plant Breeding and Genetics, Rajendra Agricultural University, Bihar, Pusa (Samastipur)-
848 125, India;2
Department of Plant Breeding and Genetics, Tirhut College of Agriculture, Dholi 843 121,Muzaffarpur, Bihar, India; E-mail: [email protected]
(Received: July 2, 2013 ; Accepted: May 22, 2014)
ABSTRACT
The phenotypic performance, heritability and selection
response of quantitative traits vary due to genotypic differences,
environmental factors and genotype by environment
interaction. The present investigation was conducted with 36
greengram genotypes under three varying environments.
Results of study indicated the significant differences among
genotypes for almost all the traits studied under different
environments. This study also revealed that heritability is
affected by the environment. Some important traits viz., plant
height, number of primary branches per plant, number of
secondary branches per plant, pod mass, seed mass, biological
yield per plant, harvest index and seed yield per plant showed
low environmental influence comprising high heritability
coupled with high proportion of selection response. Due to
preponderance of additive gene action simple plant selection
may be rewarding to improve yield and yield components.
Key words: Environmental influence, Greengram, GCV,
Heritability, PCV, Selection response, Yield
contributing traits
Greengram [Vigna radiata (L.) Wilczek], belonging to
family leguminoseae, is a tropical and sub-tropical grain
legume, adapted to different types of soil conditions and
environments (kharif,spring, summer). It ranks third in India
after chickpea and pigeonpea. It has strong root system and
capacity to fix the atmospheric nitrogen into the soil and
improves soil health and contributes significantly to enhancing
the yield of subsequent crops (Jat et al. 2012). However the
production and productivity is very low in greengram mainly
due to its cultivation in resource poor lands with minimum
inputs, non-synchronous maturity and indeterminate growth
habit. Greengram yield is also affected by insect-pests and
diseases, especially by mungbean yellow mosaic virus
(MYMV) and Cercosporaleaf spot (CLS). There is a strong
need to develop the lines/varieties which give outstanding
and consistent performance in kharif season over diverse
environment. Development of varieties with high yield and
stable performance is a prime target of all mungbean
improvement programmes.
Yield is a very complex trait and depends on several
components highly influenced by environment. For any crop
improvement programme selection of superior parents/ lines
is essential that possess high heritability and genetic advance
for various traits (Khan et al. 2005). Knowledge of genetic
variability on different yield parameters is also an important
criterion for yield enhancement. However, in greengram natural
variation is narrow due to its self pollinating nature (Siddique
et al. 2006), resulting in limited selection opportunity. The
efficacy of selection depends upon the magnitude of genetic
variability for yield and yield contributing traits in the breeding
material. The knowledge of heritability and selection response
(R) can provide useful information to select the trait for
improvement and to select superior parents for hybridization
and to choose appropriate breeding procedure for genetic
improvement. Several plant researchers have emphasized upon
the use of heritability and genetic advance to identify desirable
populations in legumes (Ghafooret al.2000, Ullah et al.2010,
Ullah et al.2011). However, yield and growth of greengram is
highly influenced by environment (Ullah et al. 2011), thus
screening of genotypes over environments can give good
results for its improvement. Change in environmental factorsaffects the performance of genotypes; hence, the present
experiment was conducted to find out the nature and extent of
heritability and selection response of yield and its related
traits under three environments.
MATERIALS AND METHODS
The present experiment was conducted with 36
genotypes of greengram received from Pulse Breeding
Section, Department of Plant Breeding and Genetics, Tirhut
College of Agriculture (TCA), Dholi, Muzaffarpur, Bihar, India.
The experiment was conducted at Crop Research Farm of TCA,
Dholi (25.50
N, 35.40
E, 52.12 m MSL) in district Muzaffarpur ofNorth Bihar, India in Randomized Block Design (RBD) with 3
replications in three environments by adjusting the sowing
dates at 15 days intervals viz., 10 July 2012 (early sown E1), 25
July 2012 (timely sown E2) and 11 August 2012 (late sown E
3).
Each genotype was sown in six rows of 4 m length each with
30 cm inter-row and 10 cm intra-row spacing. Five random
plants were tagged from each plot to record the data for all the
yield and agro-morphological traits (except days to 50%
flowering) viz.,plant height (PH), number of primary branches
per plant (NPBP), number of secondary branches per plant
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9 6 Journal of Food Legumes 27(2), 2014
(NSBP), number of clusters per plant (NCP), number of pods
per cluster (NPC), pod length (PL), number of seeds per pod
(NSP), shelling percentage (SP), seed index (SI), biological
yield per plant (BYP), harvest index (HI) and seed yield per
plant (SYP). Days to 50% flowering (DFF) was recorded on
plot basis. Pod mass (PM) and seed mass (SM) were recorded
by weighing the 10 pods and seeds from these 10 pods fromfive randomly selected plants and averaged. Pod wall mass
(PWM) was obtained by subtracting the seed mass from pod
mass. Pod wall proportion (PWP) is an index obtained by
dividing the weight of pod wall by weight of whole pod. The
data were subjected to analysis of variance and genetic
parameters i.e. genotypic coefficient of variation (GCV),
phenotypic coefficient of variation (PCV), heritability in broad
sense (h2bs), selection response (R) and proportion of
selection response (pR) by using online statistical package
OPSTAT.
RESULTS AND DISCUSSION
The analysis of variance (ANOVA) showed significant
differences among the genotypes for all the traits studied in
E2 and E3, whereas in E1 most of the traits showed significant
differences except PWM, PWP & SP (data not shown). The
range, mean, standard error (SE) and coefficient of variation
(CV) have been presented in Table 1. DFF ranged from 29.00
to 49.00 days in E1, 29.00 to 41.00 days in E2 and 24.00 to 41.00
days in E3. The gradual reduction in variability for PH was
also observed with extending the showing dates. It exhibited
34.08 to 72.46, 30.46 to 62.40 and 27.82 to 56.74 cm minimum
and maximum limit under E1, E2 and E3, respectively. A
significant reduction in variability for NPBP, NSBP, NPC, PL
and PM were also recorded by extending the sowing dates.
The range of PWP was recorded as 12.52 to 49.30 in E1, 16.23
to 47.95 in E2 and 34.30 to 58.80% in E3 which clearly reflected
that proportion of pod wall increases with an extension in the
showing dates and affects the seed yield. The good magnitude
of variability for SP was recorded in E2 (52.5 83.77%) ascompared to E1 (50.70 87.31) and E3 (33.62 65.70). The
maximum variability for BYP was recorded in E3 as compared
to other two environments. The maximum HI was recorded in
E2, whereas maximum limit for SYP was recorded in E1.
The meanperformance for various traitsviz.,DFF, NPBP,
NSBP, NPC, PL and SI showed gradual decrease with extended
sowing dates. The maximum PH was observed in E1, whereas
it was almost the same in E2 and E3, indicating the stability of
this trait under E2 and E3. The tall PH in E1 might be due to
prolonged vegetative period. Yimram et al. (2009) suggested
that tall plant structure in greengram is beneficial for both
manual and mechanical harvesting. NCP, NSP, PM, SM, SP, HIand SYP showed high magnitude in E2 as compared to E1 and
E3. PWP and BYP was highest recorded in E3. The coefficient
of variation (CV) was categorized in three groups viz., high
(>50%), moderate (20-50%) and low (
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Singh et al. : Environmental influence on heritability and selection response of some important quantitative 9 7
of variability. Therefore, it is expected to be more useful for
the assessment of real variability. Success of any breeding
programme is dependent on genetic variation present in
breeding materials. The magnitude and extent of genetic
variability existing in genotypes is very important. The more
variability gives more chance to incorporate the traits/ genes
from one genotype to another one, for effective utilizationand improvement of crops. In the present study, the variability
for all the traits was estimated on the basis of phenotypic and
genotypic coefficient of variation. The phenotypic coefficient
of variation (PCV) was higher than the corresponding GCV
for all the traits studied over environment. This difference
indicated that the traits were influenced by environmental
factors. High magnitude of GCV and PCV were recorded for
NPBP, NSBP, BYP, HI and SYP in all three environments. NCP,
NPC exhibited high GCV and PCV only in E1. SM exhibited
high extent of GCV and PCV in E1 and E3, whereas high PCV
and moderate GCV in E3 indicated the influence of
environment on this trait in E2. Among the pod traits, highPCV values were recorded for PWM and PWP in E1 and E2.
High GCV and PCV have been reported earlier for HI, SYP
(Suresh et al. 2010), PH & SYP (Rahim et al. 2010), PH & SYP
(Baisakh et al. 2013), SYP & NCP (Narasimhan et al. 2013).
Low magnitude of GCV and PCV were recorded for DFF,
whereas rest of the traits exhibited moderate extent of GCV
and PCV. Low magnitude of GCV and PCV indicated the lack
of sufficient variability in the tested breeding material. Similar
findings have also earlier been reported for DFF by
Venkateshwarlu (2001), Biradar et al. (2007), Reddy et al. (2013).
The moderate GCV and PCV values for PH, NBP, NCP, NPC, SI
and low for PL, NSP were recorded earlier by Suresh et al.
(2010).
Heritability (h2) estimates give the best picture of the
extent of advance to be expected by selection. In the present
study, High h2bswere recorded for all the traits over different
environments studied except for PWM, SP, DFF, NPC, NSP
and NCP. PWM showed low, moderate and high h2bsfor E1,
E2 and E3, respectively. DFF and NSP exhibited high h2bsfor
E1 and E3, whereas moderate h2bsfor E2. High h2for various
yield contributing traits viz., NPBP, NCP, NPC and PL
(Veeramaniet al. 2005), PH, TW (Makeenet al.2007), DFF, PH
and SI (Begume et al. 2013), SI (Verma et al. 2001), PH, NCP &
PL (Narasimhan et al. 2013) have been reported earlier also.
The variation in h2of these traits clearly reflected that h2is
affected by changing the environments. Shimelis and
Shiringani (2010) also suggested that h2of traits are
environment specific and selection done on the basis of
variance components and h2estimates alone may mislead.
The selection response (R) was low to moderate for all thetraits studied in all environments but the nature of R was
almost similar in all the environments. Thus,Rmay be used as
selection criteria for selection of traits. The maximumRwas
recorded for HI in E1 and E2, whereasRof PH was predominant
in E3. High h2 coupled with high genetic gain (GG) or
proportion of selection response (pR) were found for PH,
NPBP, NSBP, PM, SM, BYP, HI and SYP in all the environments.
The pre-dominance of additive gene action to govern these
important yield contributing traits in all three environments,
indicated that these could be effectively utilized for improving
the seed yield in greengram by simple plant selection method.
Table 2. Genetic Parameters for yield and yield contributing traits in greengram
GCV= Genotypic coefficient of variation, PCV= Phenotypic coefficient of variation, h2bs= Heri tabilit y in broa d sense, R= Selection response,
GG= Genetic gain, pR= proportion of selection response, DFF= Days to 50% flowering (Days), PH= Plant height (cm), NPBP= Number of primary
branches per plant, NSBP= Number of secondary branches per plant, NCP= Number of clusters per plant, NPC= Number of pods per cluster, PL= Pod
length (cm), PM= Pod mass (g), PWM= Pod wall mass (g), PWP= Pod wall proportion (%), SM= Seed mass (g), SP= Selling percentage, SI= Seed index
(%), BYP= Biological yield per plant (g), HI= Harvest index (%), SYP= Seed yield per plant (g), E1= Environment 1 (Early sown condition),
E2= Environment 2 (Timely sown condition), E3= Environment 3 (Late sown condition).
DFF PH NPBP NSBP NCP NPC PL NSP PM PWM PWP SM SP SI BYP HI SYPTraits
Genetic
parametersE1
GCV 9.27 18.72 26.19 42.10 17.32 24.35 8.44 14.19 15.21 11.02 14.74 20.71 8.14 9.92 30.94 33.80 37.67
PCV 10.85 19.80 28.77 44.55 21.15 27.65 10.20 18.31 16.79 27.46 24.68 22.12 15.59 11.38 32.33 38.52 39.98
h2bs 72.93 89.41 82.91 89.30 67.12 77.51 68.37 60.01 82.05 16.11 35.69 87.67 27.24 75.98 91.60 77.00 88.77
R 5.68 19.68 1.81 3.15 3.37 2.15 1.00 2.40 0.12 0.01 6.46 0.11 5.64 0.63 11.86 15.94 3.48
GG (pR) 16.30 36.46 49.13 81.96 29.24 44.15 14.37 22.64 28.38 9.12 18.14 39.95 8.75 17.81 61.00 61.10 73.11
E2
GCV 5.95 15.87 32.75 35.86 15.64 10.25 9 .69 11.638 18.14 27.61 19.42 18.83 10.31 10.03 27.71 33.00 33.61
PCV 8.95 20.29 36.88 38.00 19.57 17.53 11.19 16.24 18.59 34.15 27.25 20.69 14.57 11.12 29.34 38.66 36.87
h2bs 44.10 61.18 78.87 89.06 63.86 34.17 74.99 51.36 95.23 65.39 50.79 82.80 50.06 81.34 89.17 72.83 83.07
R 2.73 11.53 1.81 2.25 3.09 0.55 1.11 1.93 0.15 0.07 9.88 0.09 9.82 0.62 9.31 17.84 3.21
GG (pR) 8.13 25.57 59.91 69.71 25.74 12.34 17.29 17.18 36.47 46.00 28.51 35.29 15.02 18.63 53.90 58.01 63.10E3
GCV 8.62 17.22 30.41 38.03 7.79 18.02 6.82 12.92 15.89 16.28 12.35 24.92 12.85 11.39 28.95 42.93 28.68
PCV 10.71 18.79 35.06 42.45 62.18 23.84 10.36 16.26 16.39 19.77 15.30 26.45 15.92 14.53 30.06 51.29 36.57
h2bs 64.68 84.03 75.21 80.29 1.57 57.18 43.26 63.08 94.02 67.79 65.15 88.73 65.12 61.38 92.76 70.04 61.52
R 4.19 14.64 1.44 1.62 0.14 0.95 0 .51 1.37 0.12 0.05 10.46 0.09 10.48 0.44 13.28 10.00 1.35
GG (pR) 14.27 32.52 54.32 70.20 2.01 28.08 9.24 21.13 31.75 27.61 20.54 48.35 21.36 18.37 57.43 74.00 46.34
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9 8 Journal of Food Legumes 27(2), 2014
Singh et al. (2009) has also observed highpRfor HI, SYP,
BYP, SI, NSP and PH. Similar findings for SYP, NSP and PH
were reported earlier by Singh and Kumar (2009). The maximum
pRalong with high h2 was recorded for NSBP followed by
SYP, HI and BYP under E2, whereas in E1 it was recorded for
SYP followed by NSBP, HI and BYP. This finding indicated
the stability under varied environmental conditions (E1 andE2), as environment is less influential on highly heritable traits
and could easily be improved by applying selection pressure
and these traits showed greater importance for improvement
of greengram. The maximumpRalong with highh2under E3
was recorded for HI followed by NSBP. DFF and SI exhibited
high heritability but low to moderate magnitude of GG, indicated
the preponderance of non additive gene action governing
these traits and improvement can be done by recombination
breeding. NCP showed high h2coupled with high GG (pR) in
E1 and E2, indicated that improvement of this trait could be
done by single plant selection for E1 and E2 (timely and late
sown conditions) although there is a need to identify thesuperior parents for trait manipulation by recombination
breeding for improvement of NCP for very late sown (E3)
condition.
Among all the 17 quantitative traits, some important
traits viz., PH, NPBP, NSBP, PM, SM, BYP, HI and SYP were
found consistent for various genetic parameters (GCV, PCV,
h2,R, pR). Nevertheless, a perusal of additive gene action
involved in governing these traits indicated that the simple
selection method might give better response, while
recombination breeding could be used for improving other
traits of greengram.
ACKNOWLEDGEMENT
The authors are highly thankful toCoordinator,Pulse
Breeding Section, Department of Plant Breeding and Genetics,
TCA, Dholi, Muzaffarpur, Bihar (RAU, Pusa, Bihar) for
providing seeds of greengram genotypes developed by
different centres and also other facilities for conducting the
present experiment.
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Journal of Food Legumes 27(2): 99-103, 2014
Genetic diversity study for grain yield and its components in urdbean (Vigna mungo
L. Hepper) using different clustering methods
BASUDEB SARKAR*
Indian Institute of Pulses of Research (IIPR), Kanpur - 208 024, India; E-mail: [email protected]
*Present Address: Central Research Institute for Dryland Agriculture (CRIDA), Santosh Nagar,Hyderabad 500059
(Received: April 17, 2014 ; Accepted: June 26, 2014)
ABSTRACT
In the present study, 66 urdbean genotypes were evaluated for
various agro-morphological traits during rainy season (kharif)
2012 at IIPR, Kanpur to assess the level of genetic diversity
among the genotypes. Based on hierarchical average linkage
clustering method and D2 statistic the genotypes were grouped
into seven clusters having significant inter-cluster distances.
Shannon-Weavers diversity index (H) and Simpsons index
(1/D) was used to assess the phenotypic diversity for all eight
yield attributes. The H index revealed moderate diversity for
most of the traits. The average Shannon Weavers diversity
index for all traits in whole population was 0.54 with the lowest
value of 0.49 for biological yield per plant to highest value for
grain yield per plant and pods per plant (0.58). The simple
correlation coefficients showed significant positive correlation
of grain yield per plant with plant height (0.44**), clusters per
plant (