Genetic analysis of Pongamia pinnata (L.) Pierre populations using AFLP markers

ORIGINAL PAPER

Genetic analysis of Pongamia pinnata (L.) Pierre populationsusing AFLP markers

H. R. Pavithra & M. B. Shivanna & K. Chandrika &

K. T. Prasanna & Balakrishna Gowda

Received: 17 August 2012 /Revised: 30 September 2013 /Accepted: 7 October 2013 /Published online: 24 October 2013# Springer-Verlag Berlin Heidelberg 2013

Abstract A study of genetic diversity in Pongamia pinnatarepresenting 33 candidate plus trees (CPT) was undertaken infive agro-ecological zones of Southern Peninsular India.Eleven primer combinations generated 532 bands with anaverage of 48 bands per primer combination. E-ACG/M-GCC showed the highest polymorphism of 98.80 %. E-ACG/M-GCC, E-CAA/M-CTG, and E-CAG/M-GTG withhigher polymorphic information, marker index, and resolvingpower were found to be effective in detecting unique bands.Nei's gene diversity and Shannon's indices of 0.159 and 0.247,respectively, indicated the presence of high gene diversity insouthern dry and transition zones of Karnataka. The inter-population differentiation between zones (GST) was 0.296.The level of gene flow (Nm) estimated was 1.34. Analysisof molecular variance indicated that 44.78 % of varianceoccurred within the provenance when compared to variationsamong agro-ecological zones (22.22 %) and amongprovenances within zones (33 %). Based on un-weighted pairgroup method using arithmetic averages method, thepongamia accessions were grouped into three major clustersand four sub-groups depending on their geographicallocations. The diverse CPT identified in this study could beselected and used for tree breeding, germplasm conservation,and biofuel program in India and other tropical countries.

Keywords AFLP . Genetic diversity . Markerpolymorphism . Pongamia pinnata . Population analysis

Introduction

Pongamia pinnata (Family: Fabaceae; Synon. Pongamiaglabra Vent., Derris indica Lam. Bennett and Millettiapinnata L. Panigrahi) is one of the most suitable tree speciesfor the production of biofuel, a sustainable substitute for fossilfuel (Naik et al. 2008). Pongamia is indigenous to Indian sub-continent and South East Asia and is grown in other regions ofthe world like Australia, USA and China (Anonymous 1969).It can grow up to an altitude of 1,200 m, with temperatureranging from 27 to 38 °C and is tolerant to water logging,saline soil and can withstand other harsh climatic conditions.The plant has a number of applications in the field of medicine(Elanchezhiyan et al. 1993; Shivanna and Rajakumar 2010),agriculture, and construction. The plant starts its productivephase at 6–7 years and fruits with high oil content areharvested during February to April (Pavithra et al. 2012).The seeds yield 27–40 % oil.

The presence of a large number of pongamia trees innatural habitats (wastelands, bunds, roadside, canal side, farmbund, and backyards in villages) located in different districtsof Karnataka, Andhra Pradesh, and Tamil Nadu gives widescope for selection of elite planting stocks. The morphologicaltraits of a plant species and seed protein profiling methods(Ghafoor et al. 2003) have been used conventionally forstudying genetic diversity. Pongamia accessions from differentprovenance of Karnataka have indicated region-specific bands,when tested for seed protein profiling (Pavithra et al. 2010).This report as well as that of Pavithra et al. (unpublished)based on morphometric diversity clearly indicated theexistence of diversity in pongamia population across theSouthern Peninsular India. The major area of concern in the

Communicated by J. Wegrzyn

H. R. Pavithra :K. Chandrika :K. T. Prasanna :B. Gowda (*)Biofuel Park, Department of Forestry and Environmental Sciences,University of Agricultural Sciences, GKVK, Bengaluru-560065,Karnataka, Indiae-mail: [email protected]

H. R. Pavithra :M. B. ShivannaDepartment of Applied Botany, School of Biosciences, KuvempuUniversity, Shankaraghatta, Jnana Sahyadri, Shimoga-577451,Karnataka, India

Tree Genetics & Genomes (2014) 10:173–188DOI 10.1007/s11295-013-0674-0

identification of improved and potential planting materialbased on phenotypic characters is that they are largelyinfluenced by the environmental variables. Hence, diversitystudy by genetic markers that gives unbiased results has beenfollowed. Recently, the PCR-based DNA markers have beenused in marker-assisted selection of genotypes for treebreeding as these are unaffected by growth conditions,developmental stages of the plant and source of tissue usedfor genome isolation. The DNA markers such as restrictionfragment length polymorphism (RFLP; Lu et al. 1996),randomly amplified polymorphic DNA (RAPD; Williamset al. 1990), amplified fragment length polymorphism(AFLP; Vos et al. 1995), inter-simple sequence repeats(ISSR), and simple sequence repeat polymorphism (SSR;Zietkiewicz et al. 1994) have been developed with widerapplication, particularly the genetic diversity. The techniqueof AFLP has been recommended over other marker systemsfor genetic diversity studies due to its efficiency, highpolymorphic information, and reproducibility (Jones et al.1997). Reports in the literature indicate that the AFLPmarkershave been widely used to identify genetic diversity in variousoil yielding species like jatropha (Tatikonda et al. 2009), neem(Singh et al. 1999), almond (Sorkheh et al. 2007), and brassica(Srivastava et al. 2001). The informativeness of AFLP primercombination could be studied by assessing the markerattributes like polymorphic information, marker index (MI),and resolving power (RP). Based on the above, it was plannedto utilize the informative AFLP primers to identify theprevalence of polymorphism in pongamia genotype acrossthe different agro-ecological zones of Southern peninsularIndia.

In our previous study, distribution of P. pinnata across thedifferent agro-ecological zones of Southern Peninsular Indiaand the existence of phenotypic and genotypic variations hasbeen documented (Pavithra et al., unpublished). Thepolymorphic marker identification based on AFLP could beused for genetic improvement, conservation, and utilization ofpongamia genetic material. The choice of primer pairs inAFLP is critical for obtaining high polymorphism. Therefore,it would be helpful in identifying informative primers forstudies on genetic diversity in pongamia. The study also helpsto assess the pattern of genetic variation among plantpopulations and differentiate accessions within the elitegermplasm collections. There are a few reports on thedetermination of diversity of pongamia population in northGuwahati (Kesari et al. 2010), Delhi (Sharma et al. 2010),Karnataka and Andhra Pradesh (Thudi et al. 2010) in India.These studies were confined to narrow geographical areaswith less diverse population. On the other hand, the pongamiapopulation growing in the Southern Peninsular India is diversewith respect to land area and agro-ecological conditions(Thudi et al. 2010; Pavithra et al. 2009). In view of the above,the present study was designed to assess the genetic diversity

using AFLP markers among representative set of 33individual candidate plus trees (CPT) growing across selectedagro-ecological zones of Southern Peninsular India.Discriminatory power or the informativeness of the AFLPprimer combinations as revealed by marker attributes wasstudied. The level of gene flow, genetic variations amongthe pongamia populations was studied. The data obtainedwould help in determining the existence of the geneticallydiverse population of pongamia by employing cluster analysismethods.

Materials and methods

Selection of germplasm

A total of 232 CPT growing in five different agro-ecologicalzones of Karnataka, Andhra Pradesh, and Tamil Nadurepresenting the Southern Peninsular India were identifiedand labeled for the study. The morphometric traits includingseed oil content in all the CPTwere studied during February–March 2008 to 2010 (Pavithra et al. unpublished). In thisstudy, a representative set of 33 individual CPT (Table 1;Fig. 1) based on the data of seed oil content stability for threeconsecutive years (data not shown) were randomly selectedfor studying the genetic diversity using AFLP markers.Among the 232 CPT, 7 CPT growing in central dry zone(Zone 1), 9 CPT growing in eastern dry zone (Zone 2), 7CPT growing in southern dry and transition zone (Zone 3),and 5 CPT each in transition zones between Karnataka andAndhra Pradesh (Zone 4), and Karnataka and Tamil Nadu(Zone 5) were selected.

Genomic DNA extraction

Leaf samples were collected and frozen in liquid nitrogen andstored until use at −80 °C. The genomic DNAwas extractedand purified from one gram of frozen tender leaf material fromindividual CPT using cetyl-tri-methyl ammonium bromide(CTAB) method (Doyle and Doyle 1990) with minormodifications. The purified DNA samples were dissolved in200 μl of Tris ethylene di-amine tetra acetic acid (EDTA)buffer, and the concentration of each sample was estimatedby agarose (0.8 %) electrophoretically, with λ DNA(Bangalore Genei, India) as the reference standard. Theextracted DNA was stored in triplicate vials at −20 °C untiluse to prevent any damage from frequent freezing andthawing.

AFLP analysis

The AFLP was carried out using the protocol described byVos et al. (1995) with minor modification as described below.

174 Tree Genetics & Genomes (2014) 10:173–188

Table 1 The candidate plus trees (CPT) of Pongamia pinnata selected from different agro-ecological zones of the Southern Peninsular India forAmplified fragment length polymorphism analysis

Agro-ecological zones SLno.

Provenancea Code Latitude Longitude Altitude Pod characteristics Oil content(%)

Texture Tip Size Shape

Central dry zone ofKarnataka (Zone 1)

1 Chitradurga CPT146

N 14 03 49.8 E 76 10 41.3 680 Smooth Beaked Small Flat 38.33

2 Davanagere CPT161

N 14 30 28.8 E 76 17 16.8 684 Smooth Mucronate Medium Flat 37.90

3 Chitradurga CPT131

N 14 19 00.0 E 76 4355.04

600 Smooth Mucronate Small Bulged 35.05

4 Davanagere CPT181

N 14 22 39.0 E 76 06 51.6 690 Smooth Mucronate Small Bulged 34.78

5 Davanagere CPT166

N 14 2936.48

E 75 50 48.6 572 Smooth Mucronate Small Flat 33.73

6 Chitradurga(B)

CPT139

N 14 08 41.4 E 76 18 41.7 745 Smooth Mucronate Small Bulged 32.81

7 Chitradurga(B)

CPT135

N 14 08 55.3 E 76 39 35.3 610 Smooth Mucronate Small Flat 29.93

Eastern dry zone ofKarnataka (Zone 2)

8 Bangalorerural

CPT109

N 12 40 21.7 E 77 30 06.0 746 Rough Beaked Large Bulged 36.48

9 Kolar CPT76

N 13 45 58.3 E 77 45 02.0 740 Smooth Mucronate Mediun Bulged 35.47

10 Tumkur CPT98

N 13 41 51.8 E 76 54 32.4 700 Rough Beaked Medium Bulged 36.45

11 Tumkur (RR) CPT103

N 13 43 32.5 E 76 53 09.9 740 Smooth Beaked Small Bulged 35.27

12 Kolar CPT69

N 13 45 41.8 E 77 51 08.3 769 Smooth Mucronate Medium Bulged 34.96

13 Tumkur CPT85

N 13 18 04.5 E 77 08 29.8 860 Rough Beaked Small Bulged 34.41

14 Bangalorerural

CPT110

N 12 41 23.4 E 77 31 36.9 780 Rough Beaked Medium Bulged 33.24

15 Kolar (B) CPT78

N 13 42 14.6 E 77 37 52.9 770 Smooth Beaked Small Flat 33.29

16 Tumkur CPT100

N 13 48 10.6 E 76 51 28.9 670 Rough Mucronate Small Bulged 33.20

Southern dry andtransition zoneof Karnataka(Zone 3)

17 Chamarajnagar CPT188


18 Hassan CPT36

N 13 12 53.9 E 75 58 43.1 865 Smooth Mucronate Medium Flat 34.66

19 Hassan CPT22

N 13 18 21.6 E 76 17 36.5 887 Smooth Beaked Medium Flat 33.37

20 Mandya (B) CPT155


21 Hassan CPT11

N 12 46 46.0 E 76 03 09.4 940 Smooth Mucronate Large Bulged 32.36

22 Mandya (B) CPT151

N 12 33 38.4 E 77 01 50.7 676 Rough Mucronate Medium Bulged 29.28

23 Chamarajnagar CPT189

N 11 59 20 E 76 57 12.7 706 Smooth Mucronate Small Flat 28.75

Transition zone betweenKarnataka andAndhra Pradesh(Zone 4)

24 Chittor CPT231

N 13 32 40.1 E 78 25 55.0 757 Rough Beaked Small Flat 36.13

25 Anantapur CPT223

N 14 11 52.4 E 77 48 25.8 452 Smooth Mucronate Medium Bulged 34.95

26 Chittor CPT232

N 13 38 43.5 E 78 43 41.3 540 Rough Mucronate Small Flat 33.71

27 Anantapur (B) CPT229

N 13 49 13.2 E 77 33 00.3 625 Smooth Beaked Medium Flat 32.89

28 Chittor CPT230

N 13 32 33.9 E 78 24 05.2 907 Rough Beaked Medium Flat 30.79

Transition zone betweenKarnataka and TamilNadu (Zone 5)

29 Salem CPT214

N 11 26 38.2 E 77 49 40.5 229 Rough Beaked Small Bulged 35.53

30 Krishnagiri N 12 36 3.36 525 Rough Mucronate Small Bulged 34.19

Tree Genetics & Genomes (2014) 10:173–188 175

The template DNA (0.5 μg) was double digested with amixture of restriction endonucleases EcoRI/Mse I and PstI/Mse I (New England Biolabs, Labmate, Bangalore) for 3 h at37 °C and ligated with EcoRI (5 pmol)/Mse I (50 pmol)adaptors and Pst I (5 pmol)/Mse I (50 pmol) adaptors,respectively, to the restricted fragments at 37 °C, overnight.The restricted ligated DNA samples were analyzed by agarosegel (0.8 %) electrophoresis.

The digested-ligated DNA mixture was used for pre-selective amplification. The pre-selective amplification primers

with one selective nucleotide extension, complementary toEcoRI, Mse I, and Pst I adaptors (Table 2) were used foramplification in the thermocycler (Primus 96, Peqlab,Germany). The amplification cycles were same as that of Voset al. (1995). The pre-selective amplified samples wereanalyzed with gel electrophoresis (0.8 %). The dilutedamplified samples were used for the selective amplificationprocess. The selective amplification cycles were carried outusing twelve primer combinations (Table 2) for EcoRI/MseIand three primer combinations for PstI/MseI in a total reaction

Table 1 (continued)

Agro-ecological zones SLno.

Provenancea Code Latitude Longitude Altitude Pod characteristics Oil content(%)

Texture Tip Size Shape

CPT198

E 78 0748.72

31 Krishnagiri CPT201

N 12 2823.04

E 78 11 40.5 476 Smooth Mucronate Medium Bulged 33.45

32 Dharmapuri(B)

CPT203

N 12 05 49.8 E 78 08 10.2 473 Rough Mucronate Small Bulged 32.41

33 Hosur CPT196

N 12 3930.06

E 78 0020.16

667 Rough Mucronate Medium Bulged 30.58

B black soil, RR red rockyaMajority of provenances had red soil except those indicated in parenthesis

Fig. 1 Location mapping ofPongamia pinnata CPT markedin different agro-ecological zonesof the study region for AFLPanalysis


mixture of 25 μl. The amplification cycle consisted of twostages. The first stage comprised of 11 cycles with initialdenaturation temperature of 94 °C for 2 min and each cycle at94 °C for 30 s of DNA denaturation, 65 °C for 30 s of primerannealing in the first cycle with subsequent reduction intemperature by 0.7 °C for the next 10 cycles, followed by72 °C for 1 min of extension. The second stage comprised of24 cycles with 94 °C for 30 s, 56 °C for 30 s and 72 °C for 60 sand final extension of 72 °C for 2 min.

The PCR products were taken in 20 μl of loading bufferand the mixture was denatured at 95 °C for 15 min and cooledimmediately on ice. The sample (4 μl) was loaded onto 4.5 %

polyacrylamide gel electrophoresis (PAGE) apparatus(Bangalore Genei); 1X TBE was used as running buffer. Thesamples were resolved electrophoretically at constant voltage(1,500 V) for 2 h.

The gel plates were fixed in 10% acetic acid for 30min andwashed with milli-Q water for 10 min (2 washes). The gelplates were kept in silver stain (1.029 g AgNO3, 1.5 ml of40 % H2CO) for 30 min. The gel was washed in milli-Q waterfor 10 s and later placed in developing solution (3 % NaCO3,1.5 ml of 40 % H2CO, 150 μl of NaSO3) until the bandsdeveloped. The gel was later fixed in 10 % acetic acid for5 min and washed in milli-Q water to remove acetic acid and

Table 2 The oligonucleotideadaptors, primers and theirsequence used in AFLP analysis

Name Code Sequence

EcoRI adaptor EcoRI AD I,EcoRI AD II

5′-CTCGTAGACTGCGTACC-3′,3′-CATCTGACGCATGGTTAA-5′

MseI adaptor MseI AD I,MseI AD II

5′-GACGATGAGTCCTGAG-3′,3′-TACTCAGGACTCAT-5′

PstI adaptor PstI AD I,PstI AD II

5′-TGTACGCAGTCTAC-3′,3′-ACGTACATGCGTCAGATGCTC-5′

EcoRI + 1(A) E-A 5′-GACTGCGTACCAATTCA-3′

EcoRI + 1(C) E-C 5′-GACTGCGTACCAATTCC-3′

EcoRI + 1(T) E-T 5′-GACTGCGTACCAATTCT-3′

MseI + 1(C) M-C 5′-GATGAGTCCTGAGTAAC-3′

MseI + 1(G) M-G 5′-GATGAGTCCTGAGTAAG-3′

PstI + 1(G) P-G 5′-GACTGCGTACATGCAG/G-3′

PstI + 1(C) P-C 5′-GACTGCGTACATGCAG/C-3′

PstI + 2(GC) P-GC 5′-GACTGCGTACATGCAG/GC-3′

PstI + 2(CG) P-CG 5′-GACTGCGTACATGCAG/CG-3′

PstI + 2(CA) P-CA 5′-GACTGCGTACATGCAG/CA-3′

EcoRI + 2(CA) E-CA 5′-GACTGCGTACCAATTC/CA-3′

MseI + 2(TA) M-TA 5′-GATGAGTCCTGAGTAA/TA-3′

EcoRI + 3(AGG) E-AGG 5′-GACTGCGTACCAATTC/AGG-3′

EcoRI + 3(ACC) E-ACC 5′-GACTGCGTACCAATTC/ACC-3′

EcoRI + 3(AAC) E-AAC 5′-GACTGCGTACCAATTC/AAC-3′

EcoRI + 3(ACG) E-ACG 5′-GACTGCGTACCAATTC/ACG-3′

EcoRI + 3(CAC) E-CAC 5′-GACTGCGTACCAATTC/CAC-3′

EcoRI + 3(CAA) E-CAA 5′-GACTGCGTACCAATTC/CAA-3′

EcoRI + 3(CAG) E-CAG 5′-GACTGCGTACCAATTC/CAG-3′

EcoRI + 3(TAC) E-TAC 5′-GACTGCGTACCAATTC/TAC-3′

MseI + 3(CGA) M-CGA 5′-GATGAGTCCTGAGTAA/CGA-3′

MseI + 3(CTC) M-CTC 5′-GATGAGTCCTGAGTAA/CTC-3′

MseI + 3(CTT) M-CTT 5′-GATGAGTCCTGAGTAA/CTT-3′

MseI + 3(CAG) M-CAG 5′-GATGAGTCCTGAGTAA/CAG-3′

MseI + 3(GCC) M-GCC 5′-GATGAGTCCTGAGTAA/GCC-3′

MseI + 3(CTA) M-CTA 5′-GATGAGTCCTGAGTAA/CTA-3′

MseI + 3(CTG) M-CTG 5′-GATGAGTCCTGAGTAA/CTG-3′

MseI + 3(GTG) M-GTG 5′-GATGAGTCCTGAGTAA/GTG-3′


also to prevent gel cracking. The gels were documented usinggel doc instrument (Alpha Innotech, Germany) and analyzedfor the intensity of bands.

Data analysis

AFLP gel plates were scored manually for the presence orabsence of an allele at a particular locus as 1 and 0,respectively, across all 33 genotypes for each primercombination. Only those reproducible and distinct at aparticular locus were considered for data analysis andfragment polymorphism (%) was determined. The totalnumber of polymorphic bands, number of monomorphicbands, number of unique bands and agro-ecological zoneand provenance-specific bands were determined for eachprimer combination across the accessions.

The informativeness of AFLP primer combinations wereassessed by following parameters. The polymorphicinformation content (PIC) for each fragment was calculatedaccording to Roldan-Ruiz et al. (2000). PICwas also averagedover the fragments for each primer combination. The MI foreach primer combination was determined as proposed byVarshney et al. (2007). The RP of each primer combinationwas measured according to Prevost and Wilkinson (1999)

The POPGENE version 1.31 (Yeh et al. 1999) was used tocalculate the genetic diversity parameters among theaccessions from different agro-ecological zones for all theAFLP fragments. The Nei's gene diversity index (h ; Nei1973), the Shannon index (I ) and the level of gene flow(Nm; McDermott and McDonald 1993; Slatkin and Barton1989), the total gene diversity (HT), the variability withinpopulations (HS), and the inter-population differentiation(GST) were determined.

The partitioning of molecular variance among agro-ecological zones, among provenance within zones and withinthe provenance was calculated for all the AFLP fragments byanalysis of molecular variance (AMOVA) technique(Excoffier et al. 1992) using ARLEQUIN software version3.1 (Schneider et al. 2001). All significance tests werecalculated by performing 1,023 permutations. Based on theAFLP marker scores, Euclidean distances were generated anddendrogram was constructed using un-weighted pair groupmethod using arithmetic averages (UPGMA; Sneath andSokal 1973) using the software STATISTICA (version 6)and biplot was computed based on the principal componentanalysis (PCA) of molecular data using software PC-ORD(version 5; McCune and Mefford 1999).

Results and discussion

In recent years, molecular markers particularly AFLPprofiling have been extensively used in genetic diversity

analysis (Elias et al. 2000), inheritance studies (Lerceteauand Szmidt 1999), and gene tagging studies (Negi et al.2000). In the present study, 33 CPT representing differentagro-ecological zones of Karnataka, Andhra Pradesh, andTamil Nadu were screened for molecular diversity usingAFLP markers. The genotype fingerprints were generatedusing different primer combinations.

Polymorphism as revealed by AFLP markers

A total of 15 primer combinations were used to generateAFLP fingerprints of 33 pongamia accessions (Table 3).AFLP marker is the most suited DNA marker system forassessing the genetic diversity in pongamia populations sincevery little sequence information on pongamia genome isavailable. Only prominent and intense bands were consideredfor analyzing the genetic diversity. The fragments generatedfrom all primer combinations ranged in size from 100 to 1,000 bp and very few high base pair bands were reproducible.Eleven primer combinations from 8 EcoRI and 8 Mse Iprimers produced reliable, reproducible and good qualityAFLP profiles. All the 11 primer combinations produced532 bands with an average of 48 bands per primercombination. There were 396 polymorphic bands with anaverage of 36 polymorphic bands per primer combination;136 bands weremonomorphic. The percentage polymorphismvaried from 47.06 % to 98.80 % with an average of 70.26 %per primer combination. The most polymorphic primercombinations out of 11 primer combinations were E-ACG/M-GCC (98.80 %; Fig. 2) followed by E-CAG/M-GTG(93.65 %). The frequency distribution of polymorphicfragments across the AFLP primer combinations showedhigher number of polymorphic fragments in the frequencyclass of 0.0–0.10 (125) and 0.90–1.0 (60; Fig. 3).

The level of fragment polymorphism (%) as revealed byindividual AFLP primer combinations in each of the agro-ecological zone was tabulated (Table 4). E-AAC/M-CAGshowed high level of polymorphism of 47.17 % for centraldry zone (Zone 1), while E-ACG/M-GCC showed high percent polymorphism of 53.01 % for eastern dry zone ofKarnataka (Zone 2). On the other hand, E-ACG/M-GCCshowed high level of polymorphism of 78.31 % for southerndry and transition zone of Karnataka (Zone 3), while E-AAC/M-CAG showed maximum polymorphism of 62.26 % fortransition zone between Andhra Pradesh and Karnataka (Zone4), and 44% polymorphismwas observed for accessions fromtransition zone between Karnataka and Tamil Nadu due to E-CAC/M-CTA.

High polymorphism level indicated the efficiency of theAFLP markers in detecting the genetic diversity in a set ofaccessions selected from different agro-ecological zones. Outof 15 AFLP primer combinations, 11 produced efficient andreproducible results, with negative results for P/M


combination. The gel profiles from primer combination PstI/EcoRI could have resulted in a very few restricted fragmentswhich were not well resolved on denaturing polyacrylamidegels. Since most of the eukaryotic DNAs are rich in AT bases,Mse I (TTAA recognition site) combined with EcoRI enzymerestriction (Vos et al. 1995) could be used to predict thefrequency of nucleotide sequences of pongamia genome. Thiskind of AFLP profiles could be further used to study thenucleotide diversity, as opined by Innan et al. (1999) in P.pinnata for survey of population on a large scale.

Of the 15 primer combinations, only E-ACG/M-GCC, E-AAC/M-CAG, E-CAG/M-GTG was considered to be moreeffective for the pongamia accessions studied. Similar resultson the effectiveness of E-AAC/M-CAG were reported in themarker based characterization of pongamia from northGuwahati (Kesari et al. 2010). Thudi et al. (2010) reportedthe effectiveness of five primer combinations for diversitystudies on pongamia with respect to marker attributes.However, the primer combinations examined in this studywere not effective. Thus, the efficiency of primercombinations might depend on the source of plant materialused for the analysis. Such kind of collective data on primerinformation are helpful in genomic studies. Thepolymorphism level of 98.8 % detected by AFLP primers inthe present study is in accordance with the studies conductedon pongamia from other parts of India (Thudi et al. 2010). E-AAC/M-CAG and E-ACG/M-GCC showed the highestpolymorphism for all the zones studied except Zone 5(Transition zone between Tamil Nadu and Karnataka). Theabove two primer combinations could be further used for

analyzing the genetic diversity within the identified agro-ecological zones in the present study with a large set of CPT.

Unique bands and agro-ecological zone/provenance-specificbands

The unique bands produced by different primer combinationswere specific to single accession. A total of 66 unique bands(16.6 % of polymorphic bands) with an average of six uniquebands per primer combination (Table 3) were observed. Themaximum number of unique bands (15) was detected by theprimer combinations E-ACG/M-GCC and E-CAA/M-CTGfollowed by 11 unique bands by E-CAG/M-GTG. Theminimum number of unique bands (2) was detected by theprimer combinations E-AAC/M-CAG and E-TAC/M-GCC.The present study indicated that many unique bands wererecorded in accessions from the southern dry and transitionzone of Karnataka (Zone 3) followed by eastern dry zone ofKarnataka (Zone 2). Very few unique bands were detected inaccessions from transition zone between Andhra Pradesh andtransition zone between Tamil Nadu and Karnataka. The CPT19 from Hassan provenance produced unique bands for sevenprimer combinations, when compared to other CPT.

The agro-ecological zone-specific and provenance-specificbands produced by different primer combinations wereidentified (Table 3). E-AGG/M-CGA produced specific bandsbetween 800 and 900 bp for provenances from outsideKarnataka (transition zone between Karnataka and AndhraPradesh, Tamil Nadu). Tumkur, Kolar, and Bangalore ruralprovenances showed specific bands between 100 and 200 bp.

Table 3 Polymorphism and primer informativeness of 11 AFLP primer combinations

Primercombination

Totalbands

Monomorphicbands

Polymorphicbands

Uniquebands

Zone/provenance-specific bands

Fragmentpolymorphism (%)

PIC MI RP

E-AGG M-CGA 68 36 32 5 + 47.06 0.112 3.58 23.51

E-AGG M-CTC 47 15 32 4 + 68.09 0.194 6.23 9.57

E-ACC M-CTT 32 15 17 4 - 53.12 0.121 2.05 16.60

E-AAC M-CAG 53 16 37 2 + 69.81 0.241 8.91 30.72

E-ACG M-GCC 83 1 82 15 + 98.80 0.209 17.13 50.84

E-CAC M-CTA 25 7 18 - - 72.00 0.264 4.75 15.87

E-CAA M-CTG 64 10 54 15 - 84.38 0.160 8.64 46.12

E-CAG M-GTG 63 4 59 11 - 93.65 0.193 11.38 48.30

E-TAC M-CAG 19 9 10 3 - 52.63 0.045 0.45 6.54

E-TAC M-GTG 53 12 41 5 + 77.36 0.165 6.76 26.78

E-TAC M-GCC 25 11 14 2 - 56.00 0.157 2.19 8.72

Total 532 136 396 66 - - 1.861 72.07 283.57

Minimum 19 1 10 2 - 47.06 0.045 0.45 6.54

Maximum 83 36 82 15 - 98.80 0.264 17.13 50.84

Average 48.36 12.36 36 6.6 - 72.26 0.169 6.55 25.77

PIC polymorphism information content, MI marker index, RP resolving power


The zone-specific markers were exhibited by accessions ofcentral dry zone between 100 and 400 bp for the primercombination E-AGG/M-CTC. E-TAC/M-GTG primercombination produced zone-specific markers between 100and 200 bp. The accessions from transition zone betweenAndhra Pradesh and Karnataka showed zone-specific bandsat 100 bp and between 300 and 200 bp for primercombinations of E-AAC/M-CAG and E-TAC/M-GTG,respectively. The E-ACG/M-GCC primer combinationproduced provenance-specific markers for Chitradurga andDavanagere provenances between 900 and 100 bp.

The 66 unique bands identified in this study provided abaseline data for identifying pongamia accessions for treeimprovement program. The primer combinations -E-ACG/M-GCC, E-CAA/M-CTG and E-CAG/M-GTG were foundto be most reliable and efficient in detecting unique bandsacross the 33 accessions studied. The presence of accession-specific and agro-ecological zone-specific bands from AFLPanalysis in a single agro-ecological zone could have resultedfrom several independent evolutionary events. Markers withaccession and zone specificity have also been reported inneem (Singh et al. 1999). Similar kind of unique bands havealso been reported in jatropha (Tatikonda et al. 2009) andpongamia (Thudi et al. 2010) accessions using AFLPmarkers.Such of the unique bands could be further used for thedevelopment of SCAR markers tagged to specific agronomictraits (Negi et al. 2000) and useful to select the diverseaccessions for tree improvement. A high number of uniquebands has been identified in accessions from Zones 2 and 3.These bands could be used to understand the relation andsimilarity between the accessions of same zone anddissimilarity between the accessions of different zones.Specific bands also could be used to analyze the accessionsof pongamia with an unknown geographical origin ormolecular profiling of accessions from a different location.

AFLP marker attributes

The discriminatory power or informativeness of all AFLPprimer combinations were assessed by recording differentmarker attributes like PIC and MI. The informativeness ofthe primer combinations as revealed by PIC, MI has beenwidely used in many genetic diversity studies (Roldan-Ruizet al. 2000; Shen et al. 2010; Varshney et al. 2007). Prevostand Wilkinson (1999) used a new concept of RP to assess thediscriminatory power of primers. In the present study, the PICvalue for 396 polymorphic fragments ranged from 0.058 to0.499 with an average of 0.278 per fragment (Fig. 4). Themaximum number of polymorphic fragments (118) showedthe PIC value between 0.05 and 0.10, and 54 fragmentsshowed PIC value between 0.45 and 0.50. The remaining224 fragments showed PIC value between 0.10 and 0.45. Inorder to evaluate different primer combinations, the PIC

Fig. 3 Frequency distribution of polymorphic fragments generated byAFLP primer combinations

Fig. 2 AFLP profile of Pongamia pinnata accessions using primercombination E-ACG/M-GCC (Lane M =100 bp ladder used as marker,Lanes 1–33=list of accessions mentioned in Table 1)


values for all the polymorphic fragments from an individualprimer combination were averaged (Table 3). The highest PICvalue (0.264) was recorded for the primer combination of E-CAC/M-CTA and the lowest PIC value (0.045) for E-TAC/M-CAG. The average PIC value per primer combination was0.169. Based on the overall PIC value for AFLP primercombinations, E-CAC/M-CTA primer combination could berecommended over others for genetic diversity studies inpongamia from different agro-ecological zones of India. Theresults obtained are at par with the PIC value of AFLP primersused in the diversity analysis of pongamia genotypes by Thudiet al. (2010).

The marker index was used to study the overall utility of aprimer combination. The marker index was calculated for allthe 11 primer combinations (Table 3) which ranged from 0.45to 17.13. An average marker index of 6.55 per primer showedmoderate utility of all the primer combinations under study.

The maximumMI value of 17.13 was recorded by E-ACG/M-GCC, and the minimum MI value of 0.45 was recorded by E-TAC/M-CAG. E-ACG/M-GCC with the highest MI valuecould be recommended for diversity studies in pongamia.The marker index showed a positive significant correlation(r =0.570, P <0.005) with PIC. The findings corroboratedwith those of Tatikonda et al. (2009) in jatropha (r =0.660).The PIC and MI range for all the primer combinations studiedis in accordance with those recorded in sesame (Laurentin andKarlovsky 2007) and ryegrass (Roldan-Ruiz et al. 2000).

The discriminatory feature of AFLP primer combinationswere indicated by studying the RP of the primers (Table 3).The RP ranged from 6.54 to 50.84 with an average of 25.78per primer combination, a maximum RP of 50.84 for primercombination E-ACG/M-GCC, and a minimum RP of 6.54 forprimer combination E-TAC/M-CAG. The RP had positivesignificant correlation (r =0.871, P <0.001) with markerindex. No specific correlation was observed between RP andPIC. The RP has been well established in all kinds ofdominant and co-dominant markers like RFLP, AFLP, RAPD,and SSR (Powell et al. 1996). Prevost and Wilkinson (1999)reported a strong relationship between the efficiency of aprimer combination to discriminate genotypes and RP.Therefore, the primer combination E-ACG/M-GCC with thehighest RP value (50.84) should be most informative fordiscriminating the genotypes. It is also comparatively higherthan that of the earlier report on pongamia genetic diversity(Thudi et al. 2010). A positive and significant correlationobserved between RP and MI indicated that the use of PIC,MI, and RP in combination could be effectively used todiscriminate the accessions from different agro-ecologicalzones. Correlation between PIC, MI, and RP has also beenreported in Jatropha curcas (Tatikonda et al. 2009). Kayiset al. (2010) reported that the RP of AFLPwas higher than thatof RAPD and ISSR markers. Therefore, AFLP markers could

Table 4 Fragment polymorphism (%) as revealed by 11 primer combinations for different agro-ecological zones of the Southern Peninsular India

Primercombination

Central dry zoneof Karnataka (Zone 1)

Eastern dry zone ofKarnataka (Zone 2)

Southern dry andtransition zone ofKarnataka (Zone 3)

Transition zone betweenKarnataka and AndhraPradesh (Zone 4)

Transition zonebetween Karnatakaand Tamil Nadu (Zone 5)

E-AGG M-CGA 16.18 35.29 20.59 11.76 8.82

E-AGG M-CTC 17.02 27.66 44.68 36.17 19.15

E-ACC M-CTT 21.88 25.00 34.38 25.00 25.00

E-AAC M-CAG 47.17 50.94 32.08 62.26 39.62

E-ACG M-GCC 30.12 53.01 78.31 28.92 27.71

E-CAC M-CTA 16.00 40.00 52.00 56.00 44.00

E-CAA M-CTG 21.88 28.12 73.44 18.75 26.56

E-CAG M-GTG 36.00 46.03 73.02 31.75 20.63

E-TAC M-CAG 15.79 10.53 36.84 5.26 0

E-TAC M-GTG 32.08 39.62 54.72 28.30 11.32

E-TAC M-GCC 24.00 36.00 48.00 28.00 32.00

Fig. 4 Frequency distribution of PIC values for polymorphic fragmentsgenerated by AFLP primer combinations


be used for further studies on genetic diversity in pongamiagrown in other agro-ecological regions of India and othertropical countries.

Population analysis

The total gene diversity across different agro-ecological zoneswas calculated based on 532 amplified fragments (Table 5).The central dry zone of Karnataka (Zone 1) showed low Nei'sindices of gene diversity (h ) of 0.094 and Shannon's indices(I ) of 0.140. The southern dry and transition zone ofKarnataka (Zone 3) showed high Nei's gene diversity of0.159 and high Shannon's indices of 0.247, whereas moderatelevels of Nei's gene diversity and Shannon's indices wereexhibited by the other agro-ecological zones. The geneticpopulation structure and estimate of gene flow for five zonalpopulations have been tabulated (Table 6). The total genediversity (H t) was 0.169 and variability within the agro-ecological zones among provenances (HS) was 0.115. Theinter-population differentiation between zones (GST) was0.296 and the level of gene flow (Nm) estimated was 1.34.This revealed a moderate level of migration between thepopulations studied. The total variability showed a positivecorrelation (r =0.949, P <0.001) with variability within theagro-ecological zones among provenances. The inter-population differentiation between zones showed significantnegative correlation (r =−0.948, P <0.001) with the estimateof gene flow.

Based on the Nei's gene diversity and Shannon's index,high gene diversity existed within accessions from Zone 3.This might have resulted since three provenances fromsouthern dry and transition zone were involved in the studyas compared to the less number of provenances from centraldry zone. Similar trends have also been reported with respectto Nei's gene diversity and Shannon's indices in differentpopulations of almond (Sorkheh et al. 2007). The populationgenetic structure is a key analysis in evolutionary biology.Each plant species is characterized by a set of geographicalfeatures, life cycle, nuclear and maternal markers todistinguish the type of gene flow (Duminil et al. 2007).Similarly, genetic structure of pongamia population could beassociated with various geographical and life cycleparameters. The negative correlation recorded between GST

and Nm indicates that high Nm values are related to lowergenetic differentiation among populations. These results are inconfirmation with those of Loveless and Hamrick (1984) whoattributed the higher genetic differentiation to restricted geneflow.

The AMOVAwas computed to access the overall variationamong the agro-ecological zones, among the provenanceswithin the zones and within each provenance (Table 7). TheAMOVA revealed that 22.22 % variance occurred among theagro-ecological zones, 33 % variance occurred among

provenances within zones and 44.78 % variance occurredwithin each provenance. The molecular variance among theagro-ecological zones (among groups) and provenanceswithin the zones (among population within groups) wassignificant. Most of the variation was observed within eachprovenance between CPT. These results indicate thatirrespective of cross-pollination in pongamia (Solomon Rajuand Purnachandra Rao 2006), high genetic variation wasevident within the provenances, since the selected CPT were100 m apart and high level of cross-pollination could beexpected. With respect to the provenance variation, thevariation level was moderate, since the provenances selectedfor the study were isolated apart. The agro-ecological zonevariation was low since these zones are geographicallyisolated from one another. Reports are available on lowvariations among agro-ecological zones in tropical legumetrees like Pterocarpus officinalis (Rivera-Ocasio et al. 2002)and Caesalpinia echinata (Cardoso et al. 1998).

The distribution of pongamia varied depending upon thetopography and climatic conditions like temperature,humidity, light, and genetic distance. This could have resultedin the occurrence of different populations of pongamia that arebeing exposed to varying selection pressures, capability ofadaptation to local environment and eventually leading togenetic differentiation among populations. Therefore, thestatus of natural distribution area formed important criteriain the selection of pongamia CPT. In addition, when the gene

Table 5 Gene diversity data for Pongamia pinnata CPT collected fromdifferent agro-ecological zones of the Southern Peninsular India

Agro-ecological zones h I

Central dry zone of Karnataka(Zone 1)

0.094±0.164 0.140±0.240

Eastern dry zone of Karnataka(Zone 2)

0.120±0.169 0.182±0.249

Southern dry and transition zoneof Karnataka (Zone 3)

0.159±0.165 0.247±0.245

Transition zone between Karnatakaand Andhra Pradesh (Zone 4)

0.116±0.168 0.172±0.246

Transition zone between Karnatakaand Tamil Nadu (Zone 5)

0.101±0.172 0.149±0.250

h Nei's gene diversity, I Shannon's index

Table 6 The genetic population structure and estimate of gene flowwithin five populations of Pongamia pinnata CPT collected fromdifferent agro-ecological zones of the Southern Peninsular India

Markers HT HS GST Nm

532 0.169±0.027 0.115±0.013 0.296 1.340

HT Total variability, HS Variability within population, GST inter-population differentiation, Nm Estimate of gene flow


flow is unlimited within provenances between the agro-ecological zones, the population would converge into a singlegenetic unit but when the gene flow is reduced to moderatelevel, inter-population differentiation would increase due togenetic drift (Hutchison and Templeton 1999).

Dendrogram analysis

The cluster analysis was performed for all the 532 AFLPbands using the UPGMA method (Fig. 5) and Euclideandistance was computed (Table 8). The dendrogram obtainedusing AFLP data grouped the CPT from different agro-ecological zones into three major clusters (I, II, and III). TheCPT 161 and CPT 146 showed minimum Euclidean distanceof 5.2, while CPT 155 and CPT 22 showedmaximum value of14.2 across the different accessions grouped. Cluster I with 18accessions was further subdivided into groups A and B. Group

A consisted of seven accessions (CPT 146, CPT 139, CPT131, CPT 135, CPT 181, CPT 161, and CPT 166) from centraldry zone of Chitradurga and Davanagere provenances. GroupB comprised of 11 accessions, of which 9 CPT (CPT 109,CPT 103, CPT 98, CPT 76, CPT 69, CPT 85, CPT 110, CPT100, and CPT 78) were from eastern dry zone of Karnataka(Bangalore rural, Kolar and Tumkur provenance) and 2 CPT(CPT 188 and CPT 189) from southern dry and transition zoneof Karnataka (Chamarajanagar provenance). On the otherhand, Cluster II consisted of two sub-groups C and D. GroupC in turn consisted of 5 CPT (CPT 155, CPT 151, CPT 11,CPT 36, and CPT 22) mainly from southern dry and transitionzone of Karnataka, and Group D mainly consisted of 9 CPT(CPT 198, CPT 201, CPT 203, CPT 196, CPT 231, CPT 223,CPT 232, CPT 229 and CPT 230) collected from differentprovenances outside Karnataka. Cluster III consisted of asingle CPT 214 from Salem provenance. The principalcomponent analysis of the data obtained by AFLP profilingwas used to group the accessions by two dimensional graph(Fig. 6).

The UPGMA method and PCA analysis of CPT clusteringbased on 532 AFLP markers showed a similar kind ofgrouping with minor variations. The UPGMA methodclustered accessions into three main clusters, while PCAsegregated the accessions into four groups. The major sub-groups A, B, C and D formed by UPGMA method were alsoobserved in the PCA clustering. A majority of accessionsclustered into respective groups were based on theirgeographical locations. An attempt was made to correlatethe oil content and morphological characteristics of pod tothe clustering pattern based on the AFLP markers. However, aspecific trend was not observed between these characters andclustering pattern. Group A mainly consisted of CPT fromZone 1with further clustering of CPT 146, CPT 139, CPT 135and CPT 131 from Chitradurga provenance and accessionsCPT 161, CPT 181 and CPT 166 from Davanagereprovenance separately. Group B was sub-divided into two

Table 7 Analysis of molecular variance (AMOVA) for Pongamiapinnata CPT collected from different agro-ecological zones of theSouthern Peninsular India based on AFLP markers

Source of variance Sum ofsquares

Variancecomponents

Variation(%)

Significanced

Among agro-ecological zonesa

135,078.5 11.37 22.22 P<0.000001

Among provenancewithin zonesb

95,202.9 16.89 33.00 P<0.000001

Within provenancec 214,809.4 22.93 44.78 P<0.000001

a Agro-ecological zones consisted of central dry zone of Karnataka,eastern dry zone of Karnataka, southern dry and transition zone ofKaranataka, transition zone between Karnataka and Andhra Pradesh,transition zone between Karnataka and Andhra Pradesh, and transitionzone between Karnataka and Tamil Nadub Provenances consisted of districts as listed in Table 1c Consisted of CPT collected from different provenancesd Significance test for 1,023 permutations

Fig. 5 Dendrogram constructedbased on UPGMA clusteranalysis and Euclidean distanceof AFLP markers of Pongamiapinnata CPT collected fromdifferent agro-ecological zones ofSouthern Peninsular India (I, IIand III are the three major clustersand A, B, C and D represents thesub-clusters)


Table 8 Distance matrix measure between the Pongamia pinnata CPT collected from different agro-ecological zones of the Southern Peninsular India

CPT146

CPT161

CPT131

CPT181

CPT166

CPT139

CPT135

CPT109

CPT76

CPT98

CPT103

CPT69

CPT85

CPT110

CPT78

CPT100

CPT188

CPT36

CPT146

0

CPT161

5.2 0

CPT131

7.1 6.7 0

CPT181

7.8 6.6 7.1 0

CPT166

7.6 7.5 7.7 7.5 0

CPT139

8.8 8.4 8.5 8.1 7.1 0

CPT135

8.8 8.7 9.1 9.1 8.3 7.7 0

CPT109

9.6 9.1 9.4 8.8 9.1 8.8 8.3 0

CPT76

9.3 9.2 9 9.4 8.9 8.8 8.9 7.4 0

CPT98

9.5 9.2 9.3 9.7 9.1 9.8 9.1 7.7 6.7 0

CPT103

10.3 9.6 9.5 9.7 9.6 9.7 10.1 8.5 7.4 7.5 0

CPT69

9.9 9.4 9.5 9.2 9.4 9.4 9.6 8.6 7.9 8.5 7.3 0

CPT85

10.1 9.9 9.9 9.9 9.6 9.9 9.4 8.9 8.6 8.3 8.5 8.8 0

CPT110

10.4 10.2 10.1 10.4 9.4 9.9 9.7 9.4 8.8 9.1 9 8.8 8.9 0

CPT78

9.8 9.6 9.3 9.7 9.7 9.8 9.9 9 8.2 9 8.3 8.9 7.7 8.6 0

CPT100

10 9.8 9.8 9.4 9.5 9 9.7 8.2 9.2 9.6 9.2 8.6 8.2 9 7.5 0

CPT188

10.4 10 10.1 10.4 9.9 9.9 9.9 9.9 9.2 9.3 8.8 8.6 8.8 9.2 8.4 7.9 0

CPT36

9.9 10.2 9.7 10 9.4 9.4 9.7 9.6 9.2 9.2 9.6 8.8 8.9 8.7 8.5 8.4 8.2 0

CPT22

13.2 13.2 13.4 13.6 13.5 13.9 13.7 13.5 13.2 13.6 13.8 13.4 13.8 13.8 13.5 13.6 13.6 13.5

CPT155

10.7 10.6 10.2 10.5 10.6 10.3 10.6 10.1 10.6 10.4 10.4 10.4 9.8 10 9.8 9.2 9.8 8.8

CPT11

11 10.9 10.2 10.6 10.5 9.9 10.5 9.9 10.1 9.4 9.5 9.7 9.2 9 9.6 9.1 9.1 8.5

CPT151

10.3 10.4 10 10.4 10.2 10 10.5 9.8 10 9.8 9.7 9.8 9.5 9.5 9.2 8.8 9.2 8.4

CPT189

10.3 10.3 10.1 10.4 10.6 10.5 10.8 10.2 10.3 10.2 10.1 10 9.8 10.3 9.5 9.1 9.2 9.3

CPT231

10.7 10.4 10.3 10.1 9.9 9.4 10.4 10 10.1 10.5 9.9 9.8 9.6 9.5 9.8 8.8 9.5 9.4

CPT223

10.7 10.6 9.7 11 10.1 10.6 11 10.4 9.8 10 9.6 10.3 10 10.3 9.9 10.6 10.7 10.7

CPT232

11 11 10 10.8 10.3 10 11 10.3 9.6 10.1 9.6 9.9 10 10.4 9.3 10 10.3 10.1

CPT229

10.9 10.6 10.1 10.6 10.1 9.9 10.6 9.9 9.2 9.7 9.2 9.7 9.3 9.6 9.1 9.4 9.4 9.9

CPT230

10.4 10.5 9.7 10.7 10 10.4 10.9 10.3 9.5 9.6 9.2 9.7 9.6 9.9 9.3 9.9 9.9 9.8

CPT214

10.1 10.1 9.9 10.6 10.1 10 10.9 10.3 9.6 9.9 9.4 9.6 9.8 10.2 9.7 10.1 9.8 9.5

CPT198

9.8 9.6 9.6 9.6 9.8 9.5 10.2 9.5 9.1 9.4 9.5 8.9 9.4 10.2 9.4 9.3 9.8 9.1

10.5 10.1 9.5 10 9.9 9.9 10.4 9.8 8.8 8.9 9.1 9.3 9.2 9.5 9 9.3 9.4 8.7


sub-groups. In the first sub-group accessions CPT 98 and CPT103 from Tumkur provenance and accessions CPT 76 andCPT 69 from Kolar provenance were grouped. AccessionsCPT 109 and CPT 110 from Bangalore rural moved intodifferent sub-groups with a distance of 9.4 in the dendrogram.The CPT 103 and CPT 98 (Tumkur) have clustered with CPT109 (Bangalore rural). This type of clustering could beattributed to the presence of a common seed stock of theCPT. Similarly, CPT 110 (Bangalore rural) was grouped withCPT 100 (Tumkur). This kind of heterogeneous clusteringwithin the dendrogram indicated that the genotypes couldmigrate from one location to another or there is an exchangeof germplasm at the provenance level. The CPT 188 and CPT189 (Chamrajnagar provenance) belonging to Zone 3 weregrouped together within the Group B mainly consisting ofaccessions from Zone 2.

Group C consisted of accessions from Zone 3. The CPT155 and CPT 151from Mandya provenance could be groupedtogether for the reason that these accessions grow in irrigatedland. Accessions from Hassan originating from a singledendrogram tree link indicated that these accessions couldhave originated from the same seed source. Group D hasaccessions CPT 231, CPT 232, and CPT 230 from Transitionzones between Andhra Pradesh, Tamil Nadu and Karnataka.Accessions fromChittor provenance have been grouped into asingle sub-group. Group D was further sub-grouped into two.No specific trend has been observed in accessions from Zone5. Cluster III had only one accession CPT 214 belonging toZone 5 of Salem provenance. CPT 214 showed highEuclidean distance (>10) with all accessions belonging todifferent zones and corresponding provenances but lowEuclidean distance measured with CPT from Zone 5. In

Table 8 (continued)

CPT146

CPT161

CPT131

CPT181

CPT166

CPT139

CPT135

CPT109

CPT76

CPT98

CPT103

CPT69

CPT85

CPT110

CPT78

CPT100

CPT188

CPT36

CPT201

CPT203

10.2 9.8 9.3 10.1 10 10.1 10.7 9.8 9.4 9.5 9.5 9.5 9.6 9.7 9.4 9.4 9.7 9.2

CPT196

9.7 9.6 8.8 9.5 9.6 9.4 9.9 9.5 9.1 9.3 9.4 9.6 9.4 9.8 9.1 9.2 9.9 9

CPT22

CPT155

CPT11

CPT151

CPT189

CPT231

CPT223

CPT232

CPT229

CPT230

CPT214

CPT198

CPT201

CPT203

CPT196

CPT22

0

CPT155

14.2 0

CPT11

13.9 8 0

CPT151

14 5.2 7.8 0

CPT189

13.6 9.1 8.3 8.7 0

CPT231

14.1 9.7 9.2 9.4 8.8 0

CPT223

14.1 10.6 10.4 10.2 9.6 9.3 0

CPT232

14.2 9.9 9.7 9.8 9.8 9.7 8.5 0

CPT229

14.1 9.5 8.9 9.6 9.5 9.5 8.5 7.1 0

CPT230

14 10.1 9.6 9.8 9.8 9.8 9.2 8.1 7 0

CPT214

14.1 10.2 9.5 9.9 9.5 9.5 8.7 8.9 7.7 7.4 0

CPT198

13.3 10.1 9.8 9.7 9.3 8.7 9.1 9.3 8.5 8.8 7.9 0

CPT201

13.9 9.6 8.8 9.2 9 9.2 9 8.5 8.5 8.7 8.2 7.9 0

CPT203

13.7 9.6 8.9 9.4 8.9 9.2 8.9 9.2 8.7 8.7 8.3 7.9 5.4 0

CPT196

13.6 9.6 8.7 9.1 9.3 9.8 9.9 9.6 9.1 9.3 9.3 8.8 6.5 6.4 0


contrast, our previous work showed random clustering basedon morphological markers with no correlation betweengeographic locations (Pavithra et al., unpublished). The highgenetic diversity across accessions from different zones asrevealed by the dendrogram clustering provides a prerequisitestudy for the selection of genotypes for tree improvement,germplasm maintenance and conservation. Singh et al. (1999)grouped Thailand exotics and Indian lines of neem accessionsinto separate clusters. In the present study, grouping of CPTfrom central dry zone suggested the influence of geographicisolation on the level of polymorphism. Ntundu et al. (2004)also reported the effect of geographic isolation on clustering ofthe legume species-groundnut. The results of the present studyon AFLP profiling are also on par with the clustering, basedon geographical origin as reported in walnut genotypes(Kafkas et al. 2005).

Conclusion

The present study provided AFLP fingerprinting of pongamiaCPT from the Southern Peninsular India. The primercombinations with high PIC, MI, and RP could be used foranalysis of large populations of pongamia genotypes. Theeastern dry zone of Karnataka and southern dry and transitionzone of Karnataka contained relatively higher levels of genediversity and high number of unique bands, indicating thepotential for germplasm collections from these zones andprovenances. Furthermore, attempts in pongamiaimprovement program for high oil content, increased seedbiomass, and stress tolerance should take into consideration

the findings of the present study as the DNA profiles of theselected zones and provenances of representative genotypeshave been well characterized. The study further indicated thatpopulation of pongamia with maximum genetic diversityneeds to be considered for conservation strategies in Indiaand other tropical countries.

Acknowledgments The authors acknowledge the financial support andfacilities received from the Department of Agriculture, the KarnatakaState Biofuel Development Board, the Government of Karnataka, andthe University of Agricultural Sciences, GKVK, Bengaluru, and theDepartment of Applied Botany, School of Biosciences, KuvempuUniversity, Shimoga.

Data archiving statement We followed the standard Tree Genetics andGenomes policy. The marker information has been listed in Treegenesdatabase and the accession number is TDGR009.

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Genetic analysis of Pongamia pinnata (L.) Pierre populations using AFLP markers