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Genetic diversity assessment in clusterbean(Cyamopsis tetragonoloba (L.) Taub)) by RAPD

markers

ARTICLE JUNE 2014

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250

1 AUTHOR:

Waseem Sheikh

International Rice Research Institute

18PUBLICATIONS 21CITATIONS

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Available from: Waseem Sheikh

Retrieved on: 15 December 2015
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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.

Membership Fee Indian (Rs.) Foreign (US $)Ordinary (Annual) 350 25

Life Member 3500 200Admission Fee 20 10Library/ Institution 3000 100Corporate Member 5000 -

INDIAN SOCIETY OF PULSES RESEARCH AND DEVELOPMENT(Regn. No.877)

The contribution to the Journal, except in case of

invited articles, is open to the members of the Society

only. Any non-member submitting a manuscript will be

required to become annual member. Members will be

entitled to receive the Journal and other communications

issued by the Society.

Renewal of subscription should be done in January

each year. If the subscription is not received by February

15, the membership would stand cancelled. The

membership can be revived by paying readmission fee of

Rs. 10/-. Membership fee drawn in favour of Treasurer,

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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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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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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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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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.


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.


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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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.

REFERENCES

Kyaw A, Azahar BSBN and Tunio SQ. 2012. Fracturing fluid (guar

polymer gel ) degradat ion stu dy by usi ng oxidat ive and enzyme

Breaker. Research Journal of Applied Sciences, Engineering andTechnology 4:1667-1671.

Murray MG and Thompson WF. 1980. Rapid isolation of high

molecular- weight plant DNA. Nucleic Acids Research 8:4321-

4325.

Narayan R. 201 2. From food to fracking: Guar gum and international

regulation. RegBlog. University of Pennsylvania Law School.

Pathak R, Singh SK and Singh M. 2011. Assessment of genetic diversity

in clusterbean using nuclear rDNA and RAPD markers. Journal of

Food Legumes 24:180-183.

Pathak R, Singh SK, Singh M and Henry A. 2010. Molecular assessment

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

Biotechnology 12:143-148.

Rajora OP and Rahman MH. 2003. Microsatellite DNA and RAPD

fingerprinting, identification and genetic relationships of hybrid

poplar (Populus x canadiensis) culti vars. Theoretica l and Applied

Genetics 106:470-477.

Rohlf FJ. 2000. NTSYSpc: numerical taxonomy and multivariate

analysis system, version 2.02. Exeter Software, Setauket, New York.

Russell JR, Fuller JD and Macaulay M. 1997. Direct comparison of

levels of genetic variation among barley accessions de tec ted by

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

collection using RAPD markers. Theoretical and Applied Genetics

90 :1049-1055.

Williams JGK, Kubelik AR, Livak KJ, Rafalaski JA and Tingey SV.

1990. DNA polymorphisms amplified by arbitrary primers are useful

as genetic markers. Nucleic Acids Research 18:6531-6535.


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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.


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


17/99


(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.


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 (


18/99

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


19/99


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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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 (

1) Dr. Sampat, Clusterbean-RAPD.pdf

Documents

Transcript of 1) Dr. Sampat, Clusterbean-RAPD.pdf