A consensus list of microsatellite markers for olivegenotyping

Luciana Baldoni Æ Nicolo G. Cultrera Æ Roberto Mariotti Æ Claudia Ricciolini ÆSergio Arcioni Æ Giovanni G. Vendramin Æ Anna Buonamici Æ Andrea Porceddu ÆVania Sarri Æ Maria A. Ojeda Æ Isabel Trujillo Æ Luis Rallo Æ Angjelina Belaj ÆEnzo Perri Æ Amelia Salimonti Æ Innocenzo Muzzalupo Æ Alberto Casagrande ÆOrietta Lain Æ Rachele Messina Æ Raffaele Testolin

Received: 4 January 2009 / Accepted: 9 April 2009 / Published online: 13 May 2009

� Springer Science+Business Media B.V. 2009

Abstract Cultivar identification is a primary con-

cern for olive growers, breeders, and scientists. This

study was aimed at examining the SSR markers

retrieved from the literature and currently used in

olive study, in order to select those most effective

in characterizing the olive accessions and to make

possible the comparison of data obtained by

different laboratories. Olive microsatellite profiles

were assessed by four independent laboratories,

which analyzed 37 pre-selected SSR loci on a set

of 21 cultivars. These SSR markers were initially

tested for their reproducibility, power of discrimi-

nation and number of amplified loci/alleles. Inde-

pendent segregation was tested for each pair of

SSRs in a controlled cross and the allelic error rate

was quantified. Some of them were finally selected

as the most informative and reliable. Most of the

alleles were sequenced and their sizes were deter-

mined. Profiles of the reference cultivars and a list

of alleles with their sizes obtained by sequencing

are reported. Several genetic parameters have been

analysed on a larger set of cultivars allowing for a

deeper characterization of the selected loci. Results

Electronic supplementary material The online version ofthis article (doi:10.1007/s11032-009-9285-8) containssupplementary material, which is available to authorized users.

L. Baldoni (&) � N. G. Cultrera � R. Mariotti �C. Ricciolini � S. Arcioni

CNR-Istituto di Genetica Vegetale, 06128 Perugia, Italy

e-mail: luciana.baldoni@igv.cnr.it

G. G. Vendramin � A. Buonamici

CNR-Istituto di Genetica Vegetale, 50019 Sesto

Fiorentino (Firenze), Italy

A. Porceddu

DSGVA, University of Sassari, 07100 Sassari, Italy

V. Sarri

Dipartimento di Biologia Cellulare e Ambientale, Sez.

Biologia Cellulare e Molecolare, University of Perugia,

06100 Perugia, Italy

M. A. Ojeda � I. Trujillo � L. Rallo

Departamento de Agronomıa, Campus Universitario de

Rabanales, University of Cordoba, 14080 Cordoba, Spain

A. Belaj

Centro ‘‘Alameda del Obispo’’-IFAPA, 14083 Cordoba,

Spain

E. Perri � A. Salimonti � I. Muzzalupo

CRA-Centro di Ricerca per l’Olivicoltura e l’Industria

Olearia, 87036 Rende (CS), Italy

I. Muzzalupo

CRA-Centro di Ricerca per le Produzioni Foraggere e

Lattiero-Casearie, 26900 Lodi, Italy

A. Casagrande � R. Testolin

Istituto di Genomica Applicata, Parco Scientifico e

Tecnologico, 33100 Udine, Italy

O. Lain � R. Messina � R. Testolin

Dipartimento di Scienze Agrarie e Ambientali, University

of Udine, 33100 Udine, Italy

123

Mol Breeding (2009) 24:213–231

DOI 10.1007/s11032-009-9285-8

of this study provide a list of recommended

markers and protocols for olive genotyping as well

as the allelic profile of a set of reference cultivars

that would be useful for the establishment of a

universal database of olive accessions.

Keywords SSR � Fingerprinting �Cultivar characterization � Olea europaea �Repeat motif � Allelic ladder

Introduction

Fingerprinting based on SSR markers has become a

popular tool for characterizing cultivars, for similar-

ity evaluation and for paternity and kinship analyses

in crop plant species (Lacape et al. 2007).

In olive, similar to other plant species, all SSRs so

far identified carry di-nucleotide repeats, due to their

high frequency in genomes (Carriero et al. 2002;

Cipriani et al. 2002; De La Rosa et al. 2002; Dıaz

et al. 2006; Rallo et al. 2000; Sabino Gil et al. 2006;

Sefc et al. 2000). They are mainly AG/CT repeats, the

most common motif in plants (Morgante and Olivieri

1993). Genotyping with di-nucleotide microsatellites

is difficult because separation of neighbouring alleles

requires very precise and reliable protocols for allele

separation and identification to avoid allele misi-

dentification (Amos et al. 2007). Weeks et al. (2002)

reported that 83% of discrepancies among laborato-

ries in scoring di-nucleotide microsatellites were due

to erroneous length attribution during the binning

process.

The problems related to this kind of markers and

the lack of agreement on a common set of SSR loci

makes comparison of fingerprinting data among

laboratories very problematic. In several recently

published papers dealing with olive cultivar finger-

printing, different sets of SSR markers have been

chosen and significant discrepancies in allele size

assignment have also been observed when the same

loci were compared (Bandelj et al. 2002; La Mantia

et al. 2005; Montemurro et al. 2005; Muzzalupo et al.

2008; Rekik et al. 2008; Sarri et al. 2006; Stambuk

et al. 2007). These discrepancies are likely due to the

different methodologies applied for allele sizing, but

they could be the consequence of genotyping errors,

even if the occurrence of intra-cultivar variation

could not be excluded, as for example observed by

Banilas et al. (2003) analysing accessions of ‘Lado-

lia’, an ancient olive variety of Cyprus. Despite

fingerprinting protocols have become more accurate

in the long run, genotyping errors still occur, leading

to discordant microsatellite profiles for the same

genotype.

A previous work has demonstrated that the

discrimination power of a few SSR markers was

sufficiently high to distinguish more than 100 olive

genotypes (Muzzalupo et al. 2008; Sarri et al. 2006),

suggesting that the proper selection of a commonly-

accepted set of microsatellites would represent an

extremely important step in strengthening the olive

cultivars discrimination. On the contrary, the numer-

ous analyses performed by means of SSRs on

important and diffused cultivars (i.e. Leccino and

Frantoio) have not yet definitively characterized their

multi-locus genotype, because they have been

screened with different loci (Bandelj et al. 2002;

Belaj et al. 2004a) or, when common loci were used,

the alleles length have been differently sized (Bandelj

et al. 2002; Muzzalupo et al. 2008; Sarri et al. 2006).

Up to now, the high genetic diversity of the

cultivated olive, the confusion about the names given

to each genotype, the local distribution of most of

varieties and the large number of collections estab-

lished in each country represent important factors

which did not allow the development of a reliable

cultivar database of SSR profiles.

Furthermore, the olive cultivars are propagated

mostly asexually, by cuttings or grafting, thus each

olive cultivar represents several ‘‘ramets’’ belonging

to one genetically defined ‘‘genet’’. Mutations leading

to clonal variants may occur in natural and agricul-

tural conditions and the presence of clonal variation

within olive cultivars is still object of dispute and not

clarified by the use of other molecular markers (Belaj

et al. 2004b; Gemas et al. 2004). SSR markers have

demonstrated the ability to detect clonal variants in

tree species like aspen (Suvanto and Latva-Kar-

janmaa 2005) and grape (This et al. 2006), where

many microsatellite mutations have been shown to

exist in a chimeric state.

In this study, a large set of previously published

olive SSRs has been evaluated and a ranked list of

best 11-based on reproducibility, quality of scoring,

information content, independent segregation and dis-

crimination capacity has been selected. It represents

214 Mol Breeding (2009) 24:213–231

123

the first attempt to establish a consensus list of

markers to make data comparison among laboratories

feasible and to establish a common olive database.

These results should allow the alignment of the olive

genotyping protocols to those of other fruit crops,

such as peach (Aranzana et al. 2003), grape (This et al.

2004; Cipriani et al. 2008), cacao (Saunders et al.

2004), apple (Liebhard et al. 2002) and strawberry

(Govan et al. 2008), where a list of microsatellites

recommended for fingerprinting already exists,

together with a list of most common alleles and/or

reference genotype profiles.

Another goal of this work consists of the release of

information on the ability of SSR markers to

correctly assign olive cultivars to their areas of

cultivation/origin. In fact, olive oil being one of the

most adulterated food products in Europe, numerous

efforts are made in order to develop molecular tools

able to establish the oil cultivar composition (Testolin

and Lain 2005). To achieve this objective, a robust

method to track the origin of olive cultivars needs to

be developed.

Materials and methods

Plant material and DNA extraction

To get a first evaluation of the usefulness of SSRs for

cultivar fingerprinting and identification, a set of

analyses was performed considering 21 olive culti-

vars (Table 1). In order to avoid confusion about

sample identity, most of the samples are represented

by a single individual and only four were collected

from two different collections. For each accession,

fresh young leaves were collected from plants of

either the World Olive Germplasm Bank of Cordoba-

Spain (WOGB) or the olive germplasm collection of

Cosenza-Italy (Centro di Ricerca per l’Olivicoltura e

l’Industria Olearia, CRA-OLI). Independent DNA

extraction was performed by Lab 2 (CNR-Istituto di

Genetica Vegetale, Perugia, Italy) and by Lab 4

(Departamento de Agronomıa, Univ. Cordoba,

Spain), allowing evaluation of the influence of

DNA quality on the SSR profiles.

DNA was extracted in Lab 2 using the Qiagen

DNeasy Plant Mini Kit, following manufacturer’s

instructions, and in Lab 4 using the method described

by De La Rosa et al. (2002). Lab 2 has provided DNA

samples to Lab 1 (Dipartimento di Scienze Agrarie e

Ambientali, University of Udine, Italy) and Lab 3

(CRA-OLI).

Creation of a SSR ranking list

Seventy-seven SSRs were retrieved from the litera-

ture (Carriero et al. 2002; Cipriani et al. 2002; De La

Rosa et al. 2002; Sefc et al. 2000) and tested to select

a sub-set of SSR markers useful for the characteriza-

tion of olive cultivars. The most recently published

SSRs (Dıaz et al. 2006; Sabino Gil et al. 2006) were

unavailable at the time this work began and were not

included in the evaluation.

Among the available SSRs, forty were immedi-

ately discarded because in previous papers (Belaj

et al. 2003; Carriero et al. 2002; Doveri et al. 2008) or

in preliminary tests of PCR amplification and elec-

trophoretic separation, they showed poor amplifica-

tion or very low polymorphism. The remaining 37

SSRs, representing the most extensively used micro-

satellites for olive cultivar evaluation (Bandelj et al.

2002; Belaj et al. 2004a; Lopes et al. 2004; La Mantia

et al. 2005; Montemurro et al. 2005; Muzzalupo et al.

2006; Muzzalupo et al. 2008; Omrani-Sabbaghi et al.

2007; Poljuha et al. 2008; Rallo et al. 2003; Rekik

et al. 2008; Sarri et al. 2006), were processed on the

panel of 21 cultivars.

In a first step, these loci were analyzed to assess

the following parameters: (1) quality of the peak

signal, ranked as strong, medium and weak; (2)

stuttering level, scaled as low (no or low stuttering),

medium and high; (3) number of amplified loci (if

more than one); and (4) estimated frequency of null

alleles (Brookfield 1996), calculated by using Identity

1.0 software (Wagner and Sefc 1999).

To guarantee that the information carried by the

SSR markers was nearly independent, the patterns of

co-segregation of the 37 SSRs were tested on a

mapping population derived by crossing two highly

heterozygous cultivars (Baldoni, unpublished data).

This analysis was carried out by Lab 2 and indepen-

dent segregation was evaluated for each pair of SSRs.

To evaluate the concordance of the results

obtained by different labs and estimate the potential

sources of errors, which may lead to incorrect

genotyping and affect the conclusions of most

analyses (Hoffman and Amos 2005), the mean allelic

error rate (ER), defined as the ratio between the

Mol Breeding (2009) 24:213–231 215

123

Table 1 List of the 77 olive cultivars used for the evaluation

of the SSR markers

Cultivar Number

of clones

Country

of main

diffusion

Repository of

samples/register

number

Istarska Belica Croatia WOGBa

Oblica Croatia WOGB

Toffahi Egypt WOGB

Bouteillan France WOGB

Lucques France WOGB

Oliviere France WOGB

Picholine France WOGB

Verdale France WOGB

Amigdalolia Greece WOGB

Kalamon Greece WOGB/105

Kerkiras Greece WOGB

Konservolia Greece WOGB/219

Koroneiki Greece WOGB/218

Mastoidis Greece WOGB/345

Maureya Greece WOGB

Mirtolia Greece WOGB

Merhavia Israel WOGB

Ascolana Tenera Italy WOGB

Biancolilla Italy CRA-OLIb/347

Borgiona Italy CRA-OLI

Bosana Italy CIFCOc

Caiazzana Italy CRA-OLI

Canino 3 Italy CRA-OLI/32

Capolga Italy CRA-OLI

Carolea Italy WOGB/736–CRA-OLI/19

Cassanese Italy CRA-OLI

Cellina di Nardo’ Italy CRA-OLI

Dolce Agogia 3 Italy IGVd

Frantoio 4 Italy WOGB/80–CRA-OLI/92

Gargna’ Italy IGV

Gentile di Chieti Italy IGV

Leccino 3 Italy WOGB/82–CRA-OLI/93

Mignola di

Cartoceto

Italy CRA-OLI

Moraiolo 10 Italy WOGB/78–CRA-OLI/94

Nostrale di Rigali 1 Italy CRA-OLI

Nostrana di

Brisighella

Italy IGV

Ogliarola Salentina Italy CRA-OLI/64

Table 1 continued

Cultivar Number

of clones

Country

of main

diffusion

Repository of

samples/register

number

Olialonga Italy CIFCO

Orbetana Italy CRA-OLI

Ottobratica Italy CRA-OLI

Passalunara Italy IGV

Piantone di

Mogliano

Italy CRA-OLI

Raia 4 Italy CRA-OLI

Raio 3 Italy CRA-OLI

Semidana Italy CIFCO

Zaituna Italy IGV

PicholineMarocaine

Morocco WOGB/101

Arbequina Spain WOGB/231

Blanqueta Spain WOGB

Changlot Real Spain WOGB

Cornezuelo de Jaen Spain WOGB

Cornicabra Spain WOGB/10

Empeltre Spain WOGB

Farga Spain WOGB

Gordal Sevillana Spain WOGB

Hojiblanca Spain WOGB/2

Lechin de Granada Spain WOGB

Lechin de Sevilla Spain WOGB/5

ManzanillaCacerena

Spain WOGB/430

Manzanilla de Jaen Spain WOGB

Manzanilla de

Sevilla

Spain WOGB

Picual Spain WOGB/9

Picudo Spain WOGB

Royal de Cazorla Spain WOGB

Sevillenca Spain WOGB

Verdial de Huevar Spain WOGB

Villalonga Spain WOGB

Kaissy Syria WOGB

Zaity Syria WOGB

Chemlali Tunisia WOGB/744

Ouslati Tunisia WOGB

Zalmati Tunisia WOGB

Ayvalik Turkey WOGB/97

Izmir Sofralik Turkey WOGB

Memecik Turkey WOGB/93

Uslu Turkey WOGB

216 Mol Breeding (2009) 24:213–231

123

number of allelic mismatches and 2nt, the number of

replicated alleles, in our case being nt = 4 (Pomp-

anon et al. 2005), has been quantified on the panel of

the selected SSRs.

PCR amplification and visualization

Each lab carried out independent SSR analyses on the

common set of 21 DNA samples, using different Taq

enzymes, cycling conditions, amplicon separation

and visualization methods.

In Lab 1 DNA amplification was carried out using

the following standard conditions: PCR reactions

were performed using 200 lM each dNTP, 0.3 lM

each primer (forward primer was labeled with either

FAM or HEX fluorescent dyes), 20 ng genomic DNA

and 0.3 U of HotMaster Taq polymerase (Eppen-

dorf). The thermal profile was as follows: 95�C for

2 min for 1 cycle; 94�C for 20 s, annealing temper-

atures as indicated in the original published papers

for 20 s, and 65�C for 40 s for 25 cycles; then 65�C

for 7 min. Amplified products were run on a

MegaBACE 500 capillary sequencer (GE Healthcare,

USA) using ET-ROX dye (Et400-R size standard, GE

Healthcare, USA) as internal standard. Dye-labeled

amplicons were sized using Genetic Profiler v2.0

software (GE Healthcare, USA) and then checked

manually with different controls, as reported in the

results section.

Lab 2 used the QIAGEN Multiplex PCR Kit

(QIAGEN), multiplexing three SSR loci, each carry-

ing a HEX, FAM or TAMRA label at the 50-end of

one of the primers and following the manufacturer’s

instructions. The following PCR cycle was used on a

GeneAmp PCR System 9600 Thermal Cycler

(Applied Biosystems, USA): activation at 95�C

15 min; amplification (35 cycles) at 94�C for 30 s,

57�C for 90 s, and 72�C for 60 s; final extension at

72�C for 10 min. Amplicons were visualized by a

MegaBACE 1000, 96 capillary sequencer (GE

Healthcare, USA), using ET-ROX dye (Et400-R size

standard, GE Healthcare, USA) as internal standard

and Genetic Profiler v2.0 software (GE Healthcare,

USA).

Lab 3 followed the PCR conditions reported in the

original published papers and alleles were separated

by a 2100 Bioanalyzer (Agilent Technologies, Wald-

bronn, Germany) using a DNA 500 LabChip Kit,

according to Muzzalupo et al. (2007).

Lab 4 followed the PCR conditions reported in

Belaj et al. (2004a). Allele analysis was carried out

on an automatic capillary sequencer ABI 3130

Genetic Analyzer (Applied Biosystems/HITACHI)

using the internal standard GeneScan 400 HD-Rox.

After the automatic binning and calling—indepen-

dently performed by each lab—allele lengths were

compared in order to define the allelic profiles of each

sample.

Allele sequencing

To assign the correct size to alleles and to verify the

presence of sequence polymorphisms within the

repeats or along the flanking regions, most alleles

(53 out of 85 obtained on 21 cultivars) of the selected

loci were sequenced.

PCR products were run on 3% (w/v) agarose gels

(FMC BioProducts), containing ethidium bromide

(1 lg ml-1) in TBE 19 buffer, at 100 V for 7 h.

Bands were excised from the gel and purified using the

QIAquick Gel Extraction kit (Qiagen). The amount of

recovered DNA was quantified using a 2100 Bioan-

alyzer (Agilent Technologies) on a DNA 500 Chip.

When alleles of a cultivar differed by few repeats

(thus not distinguishable on the agarose gel), ampli-

fication products were run on PAGE to get better

separation of bands. DNA recovered from band

excision was directly sequenced using the BigDye

Terminator technique (PE Applied Biosystems). PCR

reactions were run on an ABI Prism 310 Automatic

Sequencer (PE Applied Biosystems, Foster City, CA,

USA). Each allele was sequenced from different

Table 1 continued

Cultivar Number

of clones

Country

of main

diffusion

Repository of

samples/register

number

Yun Celebi Turkey WOGB

The 21 cultivars used for the SSR selection are reported in bold

Samples of four cultivars were obtained from two different

collections: no discrepancies were observed among thema WOGB, World Olive Germplasm Bank, Cordoba, Spainb CRA-OLI, CRA-Centro di Ricerca per l’Olivicoltura e

l’Industria Olearia, Rende (CS), Italyc CIFCO, Consorzio Interprovinciale Frutticoltura, Cagliari e

Oristano, Italyd CNR-IGV, CNR-Istituto di Genetica Vegetale, Perugia, Italy

Mol Breeding (2009) 24:213–231 217

123

cultivars (unless when present in a single cultivar)

from both ends at least twice and up to 9 times (3.1

on average at each locus) in the case of homoplasy or

when the allele was present in many genotypes. For

each SSR locus, allele sequence data were aligned

using BIOEDIT software.

Allelic ladder construction

Allelic ladders were constructed from a pool of

cultivars according to the following criteria: (a)

inclusion of most if not all allelic variants of the

cultivar set, and (b) each allele represented once, in

order to obtain homogeneous signals in the electro-

phoretic runs.

The software LadderFinder (http://www.applied

genomics.org/tools.php) allows to select allelic lad-

ders according to the above criteria by using the

Edmonds’s maximal matching algorithm (Edmonds

1965) on a non-bipartite graph which models the

problem.

PCRs were carried out individually for each locus

on each individual cultivar of the pool identified by

LadderFinder and the amplified products were mixed

in a variable volume ratio to uniform peak heights of

the samples when run on the sequencer.

Validation of the selected olive SSRs on a larger

set of olive cultivars

In order to evaluate the power of the selected SSR

markers to distinguish the olive cultivars, to estimate

population differentiation and to capture intra-cultivar

clonal variation, a larger set of genotypes, including 77

cultivars and 31 clonal variants belonging to eight

varieties, were then analyzed by Lab. 2 (Table 1). To

detect the highest possible polymorphism, the clonal

variants have been chosen among ancient olives (100

or more years old), recognized as belonging to known

varieties (G. Pannelli, pers. comm.).

The 77 cultivars were derived from the most

important regions of the Mediterranean basin and

originated from different gene pools (Breton et al.

2008). Most of them have been previously analyzed

with different sets of SSR markers (Belaj et al. 2004a;

Muzzalupo et al. 2008; Sarri et al. 2006).

The analysis of SSR data was carried out by Lab. 2

using the Match option implemented in GenAlEx

version 6 software (Peakall and Smouse 2005),

applying the matching at all loci and that at all but

one locus for two or more sample options. Individuals

identical at all loci or with a single mismatch were

dropped from the data set.

The power of cultivar discrimination at each locus

and for increasing loci combinations was estimated as

the Information Index implemented in GenAlEx and

calculated on a single-locus basis. To estimate overall

genetic diversity, the following measures were also

calculated for the 77 cultivars using GenAlEx:

Number of alleles per locus (Na), Effective number

of alleles (Ne); Observed heterozygosity (Ho),

Expected heterozygosity (He), Inbreeding coefficient

(Fis). AMOVA was used to estimate the degree of

genetic differentiation among populations using the

Fst estimator. The significance of the estimates per

locus was tested by permutations (9,999 replicates).

Estimated frequency of null alleles (F(null)) and

departure from Hardy–Weinberg equilibrium

(HWE) were tested with CERVUS 3.0 (Kalinowski

et al. 2007). Significance of departure from HWE was

tested applying sequential Bonferroni correction.

The spatial genetic structuring analysis was per-

formed through STRUCTURE (Pritchard et al. 2000).

The individual genome’s quote derived from different

gene pools was estimated by means of 200,000 burn-

in steps followed by 1,000,000 MCMC, according to

previous testing of the power of STRUCTURE to

identify stocked individuals (Larsen et al. 2005) and

assuming an admixture model and correlated allele

frequencies. No prior information was used to define

the clusters. The choice of the most likely number of

clusters (K) was carried out by calculating an ad hoc

statistic DK, based on the rate of change in the log

probability of data between successive K values, as

described by Evanno et al. (2005).

In order to evaluate the ability of the SSR marker

set to estimate population differentiation, the 77

cultivars were attributed to three different geograph-

ical areas of origin (West, Central and East Mediter-

ranean), according to their main cultivation regions,

taking into account previous observations on the

allocation of variability among cultivars (Belaj et al.

2003; Sarri et al. 2006) and considering the results

derived from the STRUCTURE analysis. The Wes-

tern group contains Moroccan, Portuguese and

Spanish cultivars from the Andalusia region, the

Central group comprises the Spanish varieties from

Catalonia and those from France and Central-North

218 Mol Breeding (2009) 24:213–231

123

Italy; South-Italian, Greek, Turkish and North-East-

ern African varieties were included in the Eastern

group.

AMOVA and assignment test were performed

using GenAlEx. The population assignment test was

carried out applying the ‘leave one out’ option.

Results

Microsatellite reproducibility

The main characteristics of the 37 microsatellite loci

are reported in Table 2. Four loci showed a medium

to weak peak signal (EMO-02, EMO-03, EMO-13,

GAPU12 and GAPU71A), 12 exhibited high to

medium peak stuttering, additional peaks and other

problems that prevented identification of the true

alleles (DCA11, EMO-02 and EMO-03). Six cases of

amplification of two or more loci, possibly overlap-

ping, were ascertained (DCA4, EMO-02, EMO-13,

GAPU47, GAPU89 and UDO-009). The estimated

frequency of null alleles ranged from 0 to 32%

(UDO-008, GAPU47 and DCA10) and 14 loci

displayed values higher than 5%. Twenty-six of the

37 SSRs, revealing one or more drawbacks, were

discarded from the panel test. It is noteworthy that the

11 SSRs included in the final list still show some

minor shortcomings, but the low number of micro-

satellites retrieved from the literature did not allow

for the application of more stringent selection criteria.

Pherogram examples of these markers are reported in

Table ESM1.

Some discrepancies in allele size estimates were

observed among labs. This variation, up to 5 bp in

length, was generally consistent among labs and

within loci. The main sources of discrepancies were

associated to the use of different sequencers and the

type of internal ladders used. These problems were

mitigated within each lab by multiple processing runs

of the same locus and by excluding aberrant runs

from the pool.

Allelic ladders

The allelic ladders may represent a helpful tool to

identify corresponding alleles between labs. The list of

cultivars which should be considered for the construc-

tion of the allelic ladders at each locus is reported in

Table 3 and examples of allelic ladders are provided in

Fig. 1. The allelic ladder of DCA18, in spite of its

compound repeat sequences, showed a regular peri-

odicity of 2 bp (Fig. 1a). The pherogram for DCA7

(Fig. 1b), a locus whose alleles show 2-bp incremental

steps, confirmed the regularity of intervals, even if the

different mobility between alleles and the internal

ladder lead to imprecise rounding to an integer.

Table 4 reports the consensus allelic profiles of the

21 olive cultivars analyzed at the 11 SSRs recom-

mended for fingerprinting.

Assigning the true size of alleles, map location of

SSRs and error rate estimation

Allele sequencing has allowed to characterize the

repeat motifs, to define the real size of alleles and to

detect the presence of odd sequences within the

repeat motifs or along the flanking regions. The

repeat motifs at each locus are reported in Table 5.

The sequence of two representative alleles per locus

has been submitted to GenBank and the accession

numbers are given in Table 5.

Some microsatellites showed compound repeat

sequences, carrying different di-nucleotide repeats

(DCA18 and DCA16), mono- or/and tri-nucleotides

(GAPU71B and DCA14) and short indels in the

flanking region (GAPU103A). The simultaneous

variation of both repeats clearly originated irregular

incremental steps. The marker GAPU103A (Fig. 2a)

had two odd alleles (157 and 159 bp) in an even

series. These alleles carried a 21-bp indel at position

30, which was not present in the even alleles. On the

other hand, also loci carrying perfect di-nucleotide

repeats may show discrepancies. Similarly, locus

GAPU71B had one di-nucleotide repeat (AG) and

one tri-nucleotide repeat (AAG) (Fig. 2b). The allele

257 bp long of the marker DCA15 (Fig. 2c),

sequenced in three different genotypes, displayed an

odd value in an even series, characterized by the

presence of a short 3-nucleotide repeat (GAA), which

was absent in other alleles. Cases of size homoplasy

were also observed at some loci. As an example, the

case of locus EMO-L, whose alleles carried single

base mutations (allele 192A and 192B, position 1)

and two base pair indels (positions 2 and 3), is

reported in Fig. 3.

The differences between sequence length and the

fragment running size obtained by different lab

Mol Breeding (2009) 24:213–231 219

123

ranged from -2.0 to ?2.9 bp. Based on these data,

fragment sizes were revised accordingly.

Taking into consideration the mapping data, only

loci located on different linkage groups have been

selected (Table 5), with the exception of those

showing a very weak linkage, as UDO-043 and

GAPU101, located in the same linkage group but at a

high map distance (19.33 cM). When the distance

between the marker pairs was very low (data not

shown), only one locus was retained and the other/s

Table 2 Main

characteristics of the 37

SSRs tested in the four

different labs on 21 olive

genotypes

Legends: Peak stuttering,

evaluated as low (no or low

stuttering), medium or high;

Peak intensity, assessed as

strong, medium or weak;

Number of loci, estimated

by the number of band per

genotype; Main drawbacks

for SSRs exclusion, (1)

More than 1 locus, (2) Null

alleles [ 0.05, (3) Medium

or high peak stuttering, (4)

Medium or low peak

intensity (5) Linkage among

ranked SSR markers, (6)

Allele homoplasy

SSR Peak

stuttering

Peak

signal

No. loci Frequency of

null alleles

Main

drawbacks

Selected

UDO–043 Low Strong 1 0 –

DCA9 Low Strong 1 0 –

GAPU103A Low Strong 1 0 –

DCA18 Low Strong 1 0 –

DCA16 Low Strong 1 0 –

GAPU101 Low Strong 1 0 –

DCA3 Low Strong 1 0 –

GAPU71B Low Strong 1 0 –

DCA5 Low Strong 1 0 –

DCA14 Low Strong 1 0.04 –

EMO-90 Low Strong 1 0 –

Rejected

DCA7 Medium Strong 1 0.08 2–3

DCA17 Medium Strong 1 0 3

DCA13 High Strong 1 0.01 3

DCA15 Low Strong 1 0.09 2

GAPU45 Low Strong 1 0.05 6

DCA4 Low Strong 2 0.14 1–2

EMO-L Low Strong 1 0.06 2–6

EMO-30 Low Strong 1 0.03 5

UDO-039 Low Strong 1 0.25 2

DCA8 Low Strong 1 0 5

UDO-008 Medium Strong 1 0.32 2–3

DCA11 Medium Strong 1 0.14 3–5

GAPU47 Low Strong 2 0.32 1–2

GAPU89 Low Strong 2 0 1

DCA10 Medium Strong 1 0.32 2–3

GAPU59 Low Strong 1 0 5

EMO-88 Low Strong 1 0.27 2

GAPU11 Medium Strong 1 0.21 2–3

EMO-03 High Medium 1 0 3–4

UDO-024 Low Strong 1 0.06 2–5

UDO-009 Low Strong 2 0 1–3

UDO-012 Medium Strong 1 0.09 2–3–5

GAPU12 Low Medium 1 0.18 2–4–5

EMO-13 Medium Medium 2 0.12 1–2–3–4

GAPU71A Low Medium 1 0.01 4

EMO-02 High Weak 2 – 1–3–4

220 Mol Breeding (2009) 24:213–231

123

Table 3 Alleles at the 11 SSR loci and list of cultivars used to construct the allelic ladder at each locus

Locus Alleles Genotype (or alternative genotypes) Ladder

alleles

Missing

alleles

UDO-043 172, 174, 176, 178, 186, 204,

208, 210, 212, 214, 216

Canino (or Mastoidis) 176–186 204

Koroneiki 172–214

Biancolilla 210–216

Lechin de Sevilla 174–178

Kalamon (or Picual) 208–212

DCA9 162, 166, 172, 182, 184, 192,

194, 198, 206, 210

Manzanilla Cacerena 162–184 166, 192

Frantoio (or Koroneiki, Mastoidis) 182–206

Biancolilla 194–210

Konservolia 172–198

GAPU103A 136, 150, 157, 159, 162, 174,

184, 186, 190

Hojiblanca 150–190 184

Canino 159–174

Chemlali (or Lechin de Sevilla) 136–157

Kalamon 162–186

DCA18 163, 167, 169, 171, 173, 175,

177, 179, 181, 185, 187

Canino 167–179 175

Lechin de Sevilla 169–177

Mastoidis 163–171

Hojiblanca 173–181

Kalamon 185–187

DCA16 124, 126, 146, 150, 154, 156, 174 Lechin de Sevilla 126–146 124

Canino 150–150

Picholine Marocaine 154–174

GAPU101 182, 190, 192, 198, 200, 206, 218 Arbequina 182–206 190

Carolea (or Lechin de Sevilla, Picual) 192–218

Leccino 198–200

DCA3 232, 237, 239, 243, 245, 249,

253, 255

Carolea (or Kalamon, Konservolia) 232–253

Frantoio 237–243

Lechin de Sevilla (or Memecik) 245–249

Ogliarola Salentina 239–255

GAPU71B 121, 124, 127, 130, 144 Arbequina (or Cornicabra, Frantoio,

Hojiblanca, Leccino, Lechin de Sevilla)

124–144 121

Ayvalik 127–130

DCA5 194, 198, 202, 204, 206, 208 Frantoio (or Leccino, Ogliarola Salentina) 198–206 202, 204

Ayvalik 194–208

DCA14 173, 179, 181, 183, 189, 191 Chemlali (or Lechin de Sevilla, Moraiolo,

Ogliarola Salentina)

173–191 189

Mastoidis 179–183

Leccino 181–181

EMO-90 186, 188, 190, 194, 198 Canino 190–190 198

Cornicabra (or Lechin de Sevilla,

Ogliarola Salentina, Picual)

188–188

Ayvalik 186–194

Alleles not included in the ladders are also indicated. Sequenced alleles are reported in bold

Mol Breeding (2009) 24:213–231 221

123

was/were discarded. Similar assumptions have been

applied for the identification of human marker sets,

which include cases of markers located on the same

chromosome (Butler 2006, http://www.cstl.nist.gov/

biotech/strbase). DCA9, DCA3, DCA5 and EMO-90

were all located in distinct linkage groups, while

DCA18, GAPU103A, DCA16 and GAPU71B resul-

ted unlinked.

Values of allelic error rate were quite high and

ranged from 0.007 (DCA9 and GAPU101) to 0.062

(DCA16) (Table 5). The main causes of discrepan-

cies between labs were allele drop out (DCA15) and

binning errors, mainly for those loci where allele size

increments were of 2 bp (DCA17).

Discrimination power of the selected

microsatellites to estimate intra- and inter-cultivar

variability and population differentiation

The analysis of a larger set of genotypes has allowed

to identify new alleles at each locus, except for DCA3,

showing eight alleles in both set of samples. The allele

frequency at all loci is reported in Table ESM2. The

parameters estimated for the selected 11 loci on the set

of 77 cultivars are reported in Table 6. The Informa-

tion Index (I) showed values ranging from 2.425 for

the most discriminant UDO-043 to 1.404 for the least

discriminant EMO-90 and the best 11 SSRs were

ordered in a list ranked according to their I values.

Probability of identity for locus UDO-043 was 0.024;

when calculating PI over multiple best loci, as the

product of the individual PI’s, the following scores

were obtained: UDO-043 ? DCA9: 0.0006; UDO-

043 ? DCA9 ? GAPU103A: 2.52E-05.

All clones resulted identical to the reference

genotypes with, at the most, a single case of allele

drop out at one locus. The Match option of GenAlEx

clearly recognized all clonal variants, when one locus

mismatching was allowed. These differences may be

attributed to the mean error rate per allele (in this

case considered as the ratio between the number of

allelic mismatches and the number of replicated

alleles in all clones of each genotype) and not to real

genetic divergences. Loci showing mismatches were

DCA14 and DCA16.

A total of 135 alleles at 11 microsatellite loci were

detected across the 77 accessions analyzed. The

number of alleles per locus ranged from 7 at locus

EMO-90 to 21 at UDO-043, with a mean value of

12.2. The number of effective alleles per locus (Ne)

ranged from 3.16 for DCA14 and EMO-90 to 8.63 for

UDO-043.

A

Allele lengths correspond to sequenced alleles 163, 167, 169, 171, 173, 175, 177, 179, 181, 185, 187.

B

Allele lengths correspond to sequenced alleles 131, 133, 143, 145, 147, 149, 151, 165, 167.

SSR DCA7 Allelic ladder

0

500

1000

130.9

132.8 142.6

144.5

146.4

148.4

150.3

163.9

165.8

SSR DCA18 Allelic ladder

0

500

1000

162.1

166.2

168.3

170.4

172.3

174.2

176.4

178.5

180.7

184.8

186.5

Fig. 1 Allelic ladders produced for the microsatellites DCA18

(A) and DCA7 (B). Peaks representing true alleles are marked

with the symbol (•). Values in boxes are the sizes estimated by

the software. The correct size of alleles deduced from

sequences is reported below each graph. The inconsistency

between estimated and true allele sizes is evident in both SSRs

222 Mol Breeding (2009) 24:213–231

123

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Mol Breeding (2009) 24:213–231 223

123

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224 Mol Breeding (2009) 24:213–231

123

10 20 30 40 50 60 GAPU103A allele 136 TGAATTTAAC TTTAAACCCA CACATTGTG- ---------- ---------- TCTCTCTCTCGAPU103A allele 150 TGAATTTAAC TTTAAACCCA CACATTGTG- ---------- ---------- TCTCTCTCTCGAPU103A allele 157 TGAATTTAAC TTTAAACCCA CACATTGTGT CTTTCATGGT GGATCAGACG TCTCTCTCTCGAPU103A allele 159 TGAATTTAAC TTTAAACCCA CACATTGTGT CTTTCATGGT GGATCAGACG TCTCTCTCTCGAPU103A allele 184 TGAATTTAAC TTTAAACCCA CACACAGTG- ---------- ---------- TCTCTCTCTC

70 80 90 100 110 120 GAPU103A allele 136 TCTCTC---- ---------- ---------- ---------- ---------- ----CAAACCGAPU103A allele 150 TCTCTCTCTC TCTCTCTCTC ---------- ---------- ---------- ----CAAACCGAPU103A allele 157 TCTCTC---- ---------- ---------- ---------- ---------- ----CAAACCGAPU103A allele 159 TCTCTCTC-- ---------- ---------- ---------- ---------- ----CAAACCGAPU103A allele 184 TCTCTCTCTC TCTCTCTCTC TCTCTCTCTC TCTCTCTCTC TCTCTCTCTC TCTCCAAACC

130 140 150 160 170 180 GAPU103A allele 136 AAGAATGCTA TTCATCAGTA TTCATTATGA ACAATAATGT GTGACATCAT TGCCACATTG GAPU103A allele 150 AAGAATGCTA TTCATCAGTA TTCATTATGA ACAATAATGT GTGACATCAT TGCCACATTG GAPU103A allele 157 AAGAATGCTA TTCATCAGTA TTCATTATGA ACAATAATGT GTGACATCAT TGCCACATTG GAPU103A allele 159 AAGAATGCTA TTCATCAGTA TTCATTATGA ACAATAATGT GTGACATCAT TGCCACATTG GAPU103A allele 184 AAGAATGCTA TTCATCAGTA TTCATTATGA ACAATAATGT GTGACATCAT TGCCACATTG

190 200GAPU103A allele 136 TCTAATGGAT AAAATCGAGC GATGCGAPU103A allele 150 TCTAATGGAT AAAATCGAGC GATGCGAPU103A allele 157 TCTAATGGAT AAAATCGAGC GATGCGAPU103A allele 159 TCTAATGGAT AAAATCGAGC GATGCGAPU103A allele 184 TCTAATGGAT AAAATCGAGC GATGC

10 20 30 40 50 60 GAPU71B allele 121 GATCAAAGGA AGAAGGGGAT AAAGTGGGAA GAGAGAGAGA GAG------- ----------GAPU71B allele 124 GATCAAAGGA AGAAGGGGAT AAAGTGGGAA GAGAGAGAGA GAG------- ----------GAPU71B allele 127 GATCAAAGGA AGAAGGGGAT AAACTGGGAA GAGAGAGAGA GAG------- ----------GAPU71B allele 130 GATCAAAGGA AGAAGGGGAT AAAGTGGGAA GAGAGAGAGA GAG------- -------AAGGAPU71B allele 144 GATCAAAGGA AGAAGGGGAT AAACTGGGAA GAGAGAGAGA GAGAGAAGAA GAAGAAGAAG

70 80 90 100 110 120 GAPU71B allele 121 ------AAGA AGAAGAAGAA GAAAAATGAG AATGAGAAGA GAGGGAGTGT TCGTCAAAATGAPU71B allele 124 ---AAGAAGA AGAAGAAGAA GAAAAATGAG AATGAGAAGA GAGGGAGTGT TCGTCAAAATGAPU71B allele 127 AAGAAGAAGA AGAAGAAGAA GAAAAATGAG AATGAGAAGA GAGGGAGTGT TCGTCAAAATGAPU71B allele 130 AAGAAGAAGA AGAAGAAGAA GAAAAATGAG AATGAGAAGA GAGGGAGTGT TCGTCAAAATGAPU71B allele 144 AAGAAGAAGA AGAAGAAGAA GAAAAATGAG AATGAGAAGA GAGGGAGTGT TCGTCAAAAT

130 140GAPU71B allele 121 CCCGCAAGCG TACGGATTTG TTGTGAPU71B allele 124 CCCGCAAGCG TACGGATTTG TTGTGAPU71B allele 127 CCCGCAAGCG TACGGATTTG TTGTGAPU71B allele 130 CCCGCAAGCG TACGGATTTG TTGTGAPU71B allele 144 CCCGCAAGCG TACGGATTTG TTGT

10 20 30 40 50 60 DCA15 allele 246 GATCTTGTCT GTATATCCAC ACACACACAC ---------- ---------- AAAAAGAATADCA15 allele 257 GATCTTGTCT GTATATCCAC ACACACACAC ACA------- -AAGAAGAAG AAAAAGAATADCA15 allele 266 GATCTTGTCT GTATATCCAC ACACACACAC ACACACACAC ACACACACAC ACAAAGAATA

70 80 90 100 110 120 DCA15 allele 246 GAAAAACAGA CAGAAAAAGA ATCAGAGACA ACACTATACT GAGAAATTAT ACGTCTGCTA DCA15 allele 257 GAAAAACAGA CAGAAAAAGA ATCAGAGACA ACACTATACT GAGAAATTAT ACGTCTGCTA DCA15 allele 266 GAAAAACAGA CAGAAAAAGA ATCAGAGACA ACACTATACT GAGAAATTAT ACGTCTGCTA

130 140 150 160 170 180 DCA15 allele 246 ACTACACAGG TGGGAAAAAA TTACCAGTTC TGGGGTCAAC TTCTCAGCTG ACTTTGCAGC DCA15 allele 257 ACTACACAGG TGGGAAAAAA TTACCAGTTC TGGGGTCAAC TTCTCAGCTG ACTTTGCAGC DCA15 allele 266 ACTACACAGG TGGGAAAAAA TTACCAGTTC TGGGGTCAAC TTCTCAGCTG ACTTTGCAGC

190 200 210 220 230 240 DCA15 allele 246 TGTCAAAACA TAATGCATAT TAAATTAAAA AACTAAAAGA GAAAGGCTAA TTAGAGCGAT DCA15 allele 257 TGTCAAAACA TAATGCATAT TAAATTAAAA A-CTAAAAGA GAAGGGCTAA TTAGAGCGAT DCA15 allele 266 TGTCAAAACA TAATGCATAT TAAATTAAAA AACTAAAAGA GAAAGGCTAA TTAGAGCGAT

250 260DCA15 allele 246 AAATGCGTCA AGATGGAAAA GGTATADCA15 allele 257 AAATGCGTCA AGATGGAAAA GGTATADCA15 allele 266 AAATGCGTCA AGATGGAAAA GGTATA

B

A

C

Fig. 2 Sequence alignment of alleles detected at the microsatellites GAPU103A (A), GAPU71B (B) and DCA15 (C)

Mol Breeding (2009) 24:213–231 225

123

The expected heterozygosity (He) per locus ranged

from 0.726 at locus DCA5 to 0.884 at locus UDO-

043, with an average value of 0.808. There is a

tendency of higher observed (Ho) than expected (He)

heterozygosity in most of loci, which is reflected in

the negative Fis values.

Null alleles were detected at a negligible fre-

quency only at the DCA14 locus. Fst values ranged

from 0.027 of GAPU71B to 0.053 for DCA5 and

EMO-90. The departure from Hardy–Weinberg equi-

librium resulted significant (P \ 0.01) only for

GAPU103A locus.

The STRUCTURE analysis revealed a low level of

population structure and, at the most probable K = 3,

most of the cultivars were assigned to different gene

pools (data not shown).

AMOVA revealed that a small but significant

amount (4%, P \ 0.001) of the total variance was due

to differences among the three geographic areas. Loci

displaying the highest differentiation among the three

groups were DCA5 and GAPU103A.

The assignment test showed that all cultivars of the

Western area were correctly assigned, while high

percentages of incorrect assignment were observed

Fig. 3 Example of size homoplasy at microsatellite EMO-L.

Alleles 192A and 192B have the same length but different base

composition, as shown by the nucleotides included in boxes.

Box 1 shows a SNP C/T; Box 2 represents an AA insertion at

100 bp present in the allele 192B and absent in 192A (and in

all other alleles). The difference in the repeat motif, (CT)9 in

192A and (CT)8 in 192B, as indicated in Box 3, compensates

the previous indel, thus originating length homoplasy

Table 6 Description of some genetic parameters estimated for the selected SSR markers using the profile of 77 cultivars

Rank SSR I Na Ne Ho He Fis Fst F(null) HWE

1 UDO-043 2.425 21 8.63 0.855 0.884 0.033 0.049 ?0.0149 NS

2 DCA9 2.306 18 7.95 0.870 0.874 0.005 0.030 ?0.0037 NS

4 GAPU103A 2.137 16 6.56 0.844 0.848 0.004 0.049 ?0.0005 **

3 DCA18 2.092 13 6.89 0.896 0.855 -0.048 0.048 -0.0267 NS

6 DCA16 1.983 13 6.42 0.857 0.844 -0.015 0.046 -0.0074 NS

5 GAPU101 1.929 8 6.46 0.974 0.845 -0.152 0.043 -0.0714 NS

7 DCA3 1.881 8 6.09 0.922 0.836 -0.103 0.043 -0.0493 NS

8 GAPU71B 1.725 9 5.13 0.909 0.805 -0.129 0.027 -0.0615 NS

9 DCA5 1.697 11 3.65 0.870 0.726 -0.198 0.053 -0.1204 NS

10 DCA14 1.517 11 3.16 0.635 0.683 0.071 0.037 ?0.0218 NS

11 EMO-90 1.404 7 3.16 0.714 0.684 -0.045 0.053 -0.0420 NS

Legends: I, information index; Na, number of alleles; Ne, effective number of alleles; Ho, observed heterozygosity; He: expected

heterozygosity; Fis: inbreeding coefficient; Fst, degree of gene differentiation among populations; F(null), estimated frequency of null

alleles; HWE, Hardy–Weinberg equilibrium test.(NS: Not significant, ** P \ 0.01 after Bonferroni correction)

226 Mol Breeding (2009) 24:213–231

123

for the Central and Eastern Mediterranean areas (17.8

and 42.4%, respectively). When analysing the locus

by locus ability to correctly assign cultivars, the best

performance has been obtained for GAPU101,

DCA5, DCA16, DCA18 and GAPU71B (data not

shown).

It is interesting to note that there are some

cultivars that never, or rarely, are assigned to their

area of cultivation. In particular, Arbequina, Farga,

Oliviere, Gentile di Chieti and Raio, a priori included

in the Central Mediterranean area, were attributed by

most of ten best-assigning loci to the Eastern area,

while Ogliarola Salentina, Olialonga, Ottobratica,

Zaituna, Ayvalik, Izmir Sofralik, Kerkiras and Tof-

fahi, were always assigned to the Central area instead

of the Eastern one.

Discussion

Organization and distribution of SSR loci in olive

genome

The microsatellite markers analyzed in this study have

all been selected by screening genomic libraries

enriched in di-nucleotide (GA and CA) repeats

(Carriero et al. 2002; Cipriani et al. 2002; De La Rosa

et al. 2002; Sefc et al. 2000). Allele sequencing has

provided valuable information about the mutational

mechanisms of olive microsatellites. Sequences show

more compound motifs than expected, generating the

irregular allelic series observed at several loci.

The mechanisms producing SSR variation are

complex and may depend on several factors, includ-

ing the type and number of the repeats or the

sequence of the flanking regions (Almeida and

Penha-Goncalves 2004). Loci carrying complex

motifs are generally more prone to produce allelic

series with irregular incremental steps. This was the

case, for instance, of DCA18, GAPU103A, DCA16

and DCA14 loci, where the simultaneous variation of

both/all repeats created irregular incremental steps. In

any case, the structure of the repeated motif did not

correlate with the effective number of alleles and loci

carrying perfect repeats, like UDO-043 and DCA9,

showed a higher allele number than complex micro-

satellites, like GAPU71B and DCA14.

In olive, allele size differences not in accordance

with the expected repeat variation were mainly due to

the presence of compound di- and tri-nucleotide

repeats, and, in one case, GAPU103A, to an indel in

the flanking region. Allelic homoplasy has also been

observed, as in the case of EMO-L locus, confirming

what already documented in olive by Muzzalupo

et al. (2006).

Discrepancies between expected and obtained

allele patterns made difficult the binning of alleles,

as already reported for other species. In maize, for

example, it has been shown that most of microsat-

ellites have allele size distributions that do not fit the

stepwise model, due to the presence of indels in the

flanking regions and/or interruptions of the repeat

stretches, and this largely contribute to the observed

length polymorphism (Fernandez et al. 2008; Lia

et al. 2007; Matsuoka et al. 2002). Similar patterns

have also been found in woody species like grape

(This et al. 2004). Even in apparently regular series

rounded to an integer, allele lengths caused frequent

artificial odd/even transition of integers and vice

versa. This is due to the fact that the interval between

neighboring alleles does not exactly correspond to the

repeat length. In most cases the discrepancies were

indeed due to the estimated length of the core unit

that was slightly different from the expected 2 bp of

the di-nucleotide repeats.

About 30% of 37 microsatellites displayed non

random segregation and about 22% of them were

associated in a single linkage group. The spotted

apportioning of these repetitive sequences, whose

structural origin needs further evaluation, suggests to

pay particular attention for their application, not only

for fingerprinting, but also for evolutionary and

population genetic analyses, because co-segregating

loci can drive to erroneous estimations of population

differentiation and phylogenetic inferences. For these

reasons only independent (or nearly independent)

SSRs were accepted in the final list.

SSR data comparison among labs and selection

of a common set of markers

Comparison of data obtained by different labs on a

common set of cultivars revealed cases of allele drop

out and, more frequently, discrepancies in allelic

length, mainly due to the run conditions and the use

of different internal ladders, which affected the

relative mobility estimates. In some cases, for

identical alleles, discrepancies up to five nucleotides

Mol Breeding (2009) 24:213–231 227

123

were recorded. It is noticeable that differences among

allele lengths remain constant within each locus, thus,

in case of doubts, instead of allele lengths we

recommend to give more credit to differences in bp

between adjacent alleles.

Misidentification of true peaks and allele drop out

are quite common in high throughput genotyping

assays, reaching values higher than 21% when

standard automated approaches are applied (Amos

et al. 2007). The application of new algorithms able

to ensure a most accurate allele binning and of

software that can help automating several steps of the

genotyping process (Jayashree et al. 2006) can

significantly reduce the problem of allele calling

errors.

Our data fit with those published by Doveri et al.

(2008) when considering the six genotypes and five

microsatellites in common, but the allele sizes differ

by 1 bp (DCA9) and 3 bp (DCA3 and DCA14),

confirming that length differences may occur among

labs and stressing the need to use reference profiles.

In order to make data comparison among labora-

tories feasible, we proposed a consensus list of the

most outstanding SSR markers based on their repro-

ducibility, quality of scoring, information content,

independent segregation and discrimination capacity.

Moreover to reduce the probability of mistyping

alleles we recommend the use of allelic ladders

constructed with a set of genotypes carrying true-

sized alleles confirmed by sequencing. The genera-

tion of allelic ladders using known-profiled cultivars

will allow univocal allele binning and assigning the

correct size to new alleles.

These recommendations are likely sufficient to

guarantee reliable fingerprinting in olive and to

make comparable the data produced by different

laboratories.

Discrimination capacity

The discrimination power of the selected loci is in

accordance with the results reported by published

papers. For example, UDO-043 showed the highest

PD value among a set of nine UDO loci when used to

identify 35 Spanish and Italian cultivars (Belaj et al.

2004a). Sarri et al. (2006) which included in their

work some of the 11 selected markers, but not

the most informative UDO-043, GAPU103A and

GAPU101, obtained the best discrimination scores

with DCA9, DCA16 and DCA17.

The selected SSR markers were unable to detect

polymorphisms at intra-cultivar level. If mutations

occur in very long living trees, which are asexually

propagated, it seems unlikely that such mutations

may be detected by scanning a very low portion of

the genome, as it is done with the few microsatellite

markers we propose. The SSR analysis of numerous

clones of Picholine Marocaine cultivar (Khadari et al.

2008), as well as that of natural populations of Olea

europaea subsp. laperrinei (Baali-Cherif and Besnard

2005), has led to similar results.

A low genetic differentiation among the areas of

cultivation was estimated, particularly for the Eastern

Mediterranean varieties. Several studies have argued

that phylogeographic patterns in olive are elusive

(Besnard et al. 2002; Lumaret and Ouazzani 2001;

Lumaret et al. 2004). The vegetative propagation

through cutting and grafting has favoured an intense

exchange of plant material and therefore the mixing

of genotypes of different origin and their multiple and

multidirectional diffusions. Different patterns of olive

cultivars spread have been found (Besnard et al.

2001; Breton et al. 2008) and others are possibly still

unidentified. For instance, the results obtained for the

East Mediterranean may be justified by the significant

structure within that area, particularly considering

Greek and Turkish cultivars, as previously observed

by Owen et al. (2005), other than by the relatively

low number of cultivars included in the study. These

reasons may explain the difficulty of the selected loci

to correctly assign cultivars to their area of cultiva-

tion. Thus, even using highly discriminating markers,

the problem of assigning cultivars to their area of

cultivation will be possible only for a portion of

autochthonous cultivars and in those areas where the

introduction of genetic pools from abroad has been

limited, like it seems for the extreme West Mediter-

ranean (Besnard et al. 2001).

It is interesting to note a tendency of negative Fis

values at 7/11 loci, indicating an excess of hereto-

zygosity relative to random mating. In fact, under

clonal reproduction as in olive, Fis values are

expected to be negative because clonal reproduction

enables the two alleles at each locus to independently

accumulate mutations and therefore diverge within

individuals (Halkett et al. 2005).

228 Mol Breeding (2009) 24:213–231

123

Application of a common protocol for SSR

fingerprinting in olive

Lack of agreement on the molecular markers to be

used for olive fingerprinting has made impossible, up

to now, to figure out the level and distribution of

variability. Besides the important results obtained by

several groups working independently at the charac-

terization and identification of olive cultivars, the

comparison of data among laboratories still repre-

sents a very difficult task.

The use of a common set of microsatellite markers

and the application of stringent protocols can repre-

sent a better world-wide applicable tool for olive

DNA typing, either for population genetic studies

(Breton et al. 2008) or for cultivars discrimination

(Baldoni et al. 2006; Belaj et al. 2004b).

The use of the selected SSRs, characterised by a

very high discrimination power, and the application

of a common strategy for data comparison will finally

allow data convergence, a preliminary condition for

genotype identification and certification, as well as

for data exchange and managing, useful for the

creation of an universal molecular database of olive

genetic resources.

Acknowledgments The authors are very grateful to Dr. Juan

Caballero, Dr. Carmen del Rıo and Prof. Diego Barranco,

responsible of the evaluation, maintenance and identification of

the World Olive Germplasm Bank, Cordoba, Spain, for

providing the DNA samples. A special thanks is also due to

the other sample providers. The work has been supported by the

Project RIOM-MIPAAF ‘‘Research and Innovation for Olive

growing in Italian Southern regions’’, the Project OLIBIO–

MIPAAF ‘‘Development of competitive, sustainable, traceable

and safe systems to produce high quality olive oils by organic

agriculture’’ and the Project FISR ‘‘Improving flavor and

nutritional properties of plant food after first and second

transformation’’, Activity ‘Molecular characterization of main

olive varieties’.

References

Almeida P, Penha-Goncalves C (2004) Long perfect dinucle-

otide repeats are typical of vertebrates, show motif pref-

erences and size convergence. Mol Biol Evol 21(7):1226–

1233. doi:10.1093/molbev/msh108

Amos W, Hoffman JI, Frodsham A, Zhang L, Best S, Hill VS

(2007) Automated binning of microsatellite alleles:

problems and solutions. Mol Ecol Notes 7:10–14. doi:

10.1111/j.1471-8286.2006.01560.x

Aranzana MJ, Pineda A, Cosson P, Dirlewanger E, Ascasibar J,

Cipriani G, Ryder D, Testolin R, Abbott A, King GJ,

Iezzoni AF, Arus P (2003) A set of simple-sequence

repeat (SSR) markers covering the Prunus genome. Theor

Appl Genet 106:819–825

Baali-Cherif D, Besnard G (2005) High genetic diversity and

clonal growth in relict populations of Olea europaeasubsp. laperrinei (Oleaceae) from Hoggar, Algeria. Ann

Bot (Lond) 96(5):823–830. doi:10.1093/aob/mci232

Baldoni L, Tosti N, Ricciolini C, Belaj A, Arcioni S, Pannelli

G, Germana MA, Mulas M, Porceddu A (2006) Genetic

structure of wild and cultivated olives in the central

Mediterranean basin. Ann Bot (Lond) 98:935–942. doi:

10.1093/aob/mcl178

Bandelj D, Jakse J, Javornik B (2002) DNA fingerprinting of

olive varieties by microsatellite markers. Food Technol

Biotechnol 40(3):185–190

Banilas G, Minas J, Gregoriou C, Demoliou C, Kourti A,

Hatzopoulos P (2003) Genetic diversity among accessions

of an ancient olive variety of Cyprus. Genome 46(3):370–

376. doi:10.1139/g03-011

Belaj A, Satovic Z, Cipriani G, Baldoni L, Testolin R, Rallo L,

Trujillo I (2003) Comparative study of the discriminating

capacity of RAPD, AFLP and SSR markers and of their

effectiveness in establishing genetic relationships in olive.

Theor Appl Genet 107(4):736–744. doi:10.1007/s00122-

003-1301-5

Belaj A, Cipriani G, Testolin R, Rallo L, Trujillo I (2004a)

Characterization and identification of the main Spanish

and Italian olive cultivars by simple-sequence-repeat

markers. HortScience 39(7):1557–1561

Belaj A, Rallo L, Trujillo I, Baldoni L (2004b) Using RAPD

and AFLP markers to distinguish individuals obtained by

clonal selection of ‘Arbequina’ and ‘Manzanilla de Se-

villa’ olive. HortScience 39(7):1566–1570

Besnard G, Baradat P, Berville A (2001) Genetic relationships

in the olive (Olea europaea L.) reflect multilocal selection

of cultivars. Theor Appl Genet 102(2–3):251–258. doi:

10.1007/s001220051642

Besnard G, Khadari B, Baradat P, Berville A (2002) Oleaeuropaea (Oleaceae) phylogeography based on chloro-

plast DNA polymorphism. Theor Appl Genet 104:1353–

1361. doi:10.1007/s00122-001-0832-x

Breton C, Pinatel C, Medail F, Bonhommea F, Berville A

(2008) Comparison between classical and Bayesian

methods to investigate the history of olive cultivars using

SSR-polymorphisms. Plant Sci 175:524–532. doi:

10.1016/j.plantsci.2008.05.025

Brookfield JFY (1996) A simple new method for estimating null

allele frequency from heterozygote deficiency. Mol Ecol

5:453–455. doi:10.1111/j.1365-294X.1996.tb00336.x

Butler JM (2006) Genetics and genomics of core short tandem

repeats loci used in human identity testing. J Forensic Sci

51(2):253–265. doi:10.1111/j.1556-4029.2006.00046.x

Carriero F, Fontanazza G, Cellini F, Giorio G (2002) Identi-

fication of simple sequence repeats (SSRs) in olive (Oleaeuropaea L.). Theor Appl Genet 104:301–307. doi:

10.1007/s001220100691

Cipriani G, Marrazzo MT, Marconi R, Cimato A, Testolin R

(2002) Microsatellite markers isolated in olive (Olea eu-ropaea L.) are suitable for individual fingerprinting and

Mol Breeding (2009) 24:213–231 229

123

reveal polymorphism within ancient cultivars. Theor Appl

Genet 104:223–228. doi:10.1007/s001220100685

Cipriani G, Marrazzo MT, Di Gaspero G, Pfeiffer A, Morgante

M, Testolin R (2008) A set of microsatellite markers with

long core repeat optimized for grape (Vitis spp.) geno-

typing. BMC Plant Biol 8:127. doi:10.1186/1471-2229-

8-127

De La Rosa R, James CM, Tobutt KR (2002) Isolation and

characterization of polymorphic microsatellites in olive

(Olea europaea L.) and their transferability to other

genera in the Oleaceae. Mol Ecol Notes 2:265–267. doi:

10.1046/j.1471-8286.2002.00217.x

Dıaz A, De la Rosa R, Martın A, Rallo P (2006) Development,

characterization and inheritance of new microsatellites in

olive (Olea europaea L.) and evaluation of their useful-

ness in cultivar identification and genetic relationship

studies. Tree Genet Genomes 2:165–175. doi:10.1007/

s11295-006-0041-5

Doveri S, Sabino Gil F, Dıaz A, Reale S, Busconi M, da

Camara Machado A, Martın A, Fogher C, Donini P, Lee D

(2008) Standardization of a set of microsatellite markers

for use in cultivar identification studies in olive (Oleaeuropaea L.). Sci Hortic (Amsterdam) 116:367–373. doi:

10.1016/j.scienta.2008.02.005

Edmonds J (1965) Maximum matching and polyhedron with 0,

1-vertices. J Res Natl Inst Stand B 69:125–130

Evanno G, Regnaut S, Goudet J (2005) Detecting the number

of clusters of individuals using the software STRUC-

TURE: a simulation study. Mol Ecol 14:2611–2620. doi:

10.1111/j.1365-294X.2005.02553.x

Fernandez MP, Nunez Y, Ponz F, Hernaiz S, Gallego FJ, I-

banez J (2008) Characterization of sequence polymor-

phisms from microsatellite flanking regions in Vitis spp.

Mol Breed 22:455–465. doi:10.1007/s11032-008-9189-z

Gemas VJV, Almadanim MC, Tenreiro R, Martins A, Fe-

vereiro P (2004) Genetic diversity in the Olive tree (Oleaeuropaea L. subsp. europaea) cultivated in Portugal

revealed by RAPD and ISSR markers. Genet Resour

Crop Evol 51:501–511. doi:10.1023/B:GRES.0000024152.

16021.40

Govan CL, Simpson DW, Johnson AW, Tobutt KR, Sargent DJ

(2008) A reliable multiplexed microsatellite set for

genotyping Fragaria and its use in a survey of 60 F. X

ananassa cultivars. Mol Breed 22:649–661. doi:10.1007/

s11032-008-9206-2

Halkett F, Simon JC, Balloux F (2005) Tackling the population

genetics of clonal and partially clonal organisms. Trends

Ecol Evol 20(4):194–201. doi:10.1016/j.tree.2005.01.001

Hoffman JI, Amos W (2005) Microsatellite genotyping errors:

detection approaches, common sources and consequences

for paternal exclusion. Mol Ecol 14:599–612. doi:

10.1111/j.1365-294X.2004.02419.x

Jayashree B, Reddy PT, Leeladevi Y, Crouch JH, Mahalakshmi

V, Buhariwalla HK et al (2006) Laboratory information

management software for genotyping workflows: appli-

cations in high throughput crop genotyping. BMC Bio-

informatics 7:383. doi:10.1186/1471-2105-7-383

Kalinowski ST, Taper ML, Marshall TC (2007) Revising how

the computer program CERVUS accommodates geno-

typing error increases success in paternity assignment.

Mol Ecol 16:1099–1106. doi:10.1111/j.1365-294X.2007.

03089.x

Khadari B, Charafi J, Moukhli A, Ater M (2008) Substantial

genetic diversity in cultivated Moroccan olive despite a

single major cultivar: a paradoxical situation evidenced by

the use of SSR loci. Tree Genet Genomes 4:213–221. doi:

10.1007/s11295-007-0102-4

La Mantia M, Lain O, Caruso T, Testolin R (2005) SSR-based

fingerprints reveal the great genetic diversity of Sicilian

olive (Olea europaea L.) germplasm. J Hortic Sci Bio-

technol 80(5):628–632

Lacape JM, Dessauw D, Rajab M, Noyer JL, Hau B (2007)

Microsatellite diversity in tetraploid Gossypium germ-

plasm: assembling a highly informative genotyping set of

cotton SSRs. Mol Breed 19:45–58. doi:10.1007/s11032-

006-9042-1

Larsen PF, Hansen MM, Nielsen EE, Jensen LF, Loeschcke L

(2005) Stocking impact and temporal stability of genetic

composition in a brackish northern pike population (Esoxlucius L.), assessed using microsatellite DNA analysis of

historical and contemporary samples. Heredity 95:136–

143. doi:10.1038/sj.hdy.6800671

Lia VV, Bracco M, Gottlieb AM, Poggio L, Confalonieri VA

(2007) Complex mutational patterns and size homoplasy

at maize microsatellite loci. Theor Appl Genet 115:981–

991. doi:10.1007/s00122-007-0625-y

Liebhard R, Gianfranceschi L, Koller B, Ryder CD, Tarchini

R, Van De Weg E, Gessler C (2002) Development and

characterisation of 140 new microsatellites in apple

(Malus 9 domestica Borkh.). Mol Breed 10:217–241.

doi:10.1023/A:1020525906332

Lopes MS, Mendonca D, Sefc KM, Gil FS, Machado AD

(2004) Genetic evidence of intra-cultivar variability

within Iberian olive cultivars. HortScience 39(7):1562–

1565

Lumaret R, Ouazzani N (2001) Ancient wild olives in Medi-

terranean forests. Nature 413:700. doi:10.1038/35099680

Lumaret R, Ouazzani N, Michaud H, Vivier G, Deguilloux M-

F, Di Giusto F (2004) Allozyme variation of oleaster

populations (wild olive tree) (Olea europaea L.) in the

Mediterranean Basin. Heredity 92:343–352. doi:10.1038/

sj.hdy.6800430

Matsuoka Y, Mitchell SE, Kresovich S, Goodman M, Doebley

J (2002) Microsatellite in Zea-variability, patterns of

mutations, and use for evolutionary studies. Theor Appl

Genet 104:436–450. doi:10.1007/s001220100694

Montemurro C, Simeone R, Pasqualone A, Ferrara E, Blanco A

(2005) Genetic relationships and cultivar identification

among 112 olive accessions using AFLP and SSR mark-

ers. J Hortic Sci Biotechnol 80(1):105–110

Morgante M, Olivieri AM (1993) PCR-amplified microsatel-

lites as markers in plant genetics. Plant J 3(1):175–182.

doi:10.1111/j.1365-313X.1993.tb00020.x

Muzzalupo I, Lombardo N, Musacchio A, Noce ME, Pelleg-

rino G, Perri E, Sajjad A (2006) DNA sequence analysis

of microsatellite markers enhances their efficiency for

germplasm management in an Italian olive collection. J

Am Soc Hortic Sci 131(3):352–359

Muzzalupo I, Pellegrino M, Perri E (2007) Detection of DNA

in virgin olive oils extracted from destoned fruits. Eur

230 Mol Breeding (2009) 24:213–231

123

Food Res Technol 224(4):469–475. doi:10.1007/s00217-

006-0340-y

Muzzalupo I, Lombardo N, Salimonti A, Perri E (2008)

Molecular characterization of Italian olive cultivars by

microsatellite markers. Adv Hortic Sci 22(2):142–148

Omrani-Sabbaghi A, Shahriari M, Falahati-Anbaran M, Mo-

hammadi SA, Nankali A, Mardi M, Ghareyazie B (2007)

Microsatellite markers based assessment of genetic

diversity in Iranian olive (Olea europaea L.) collections.

Sci Hortic (Amsterdam) 112(4):439–447. doi:10.1016/j.

scienta.2006.12.051

Owen CA, Bita EC, Banilas G, Hajjar SE, Sellianakis V, Ak-

soy U et al (2005) AFLP reveals structural details of

genetic diversity. Theor Appl Genet 110:1169–1176. doi:

10.1007/s00122-004-1861-z

Peakall R, Smouse PE (2005) GenAlEx 6: genetic analysis in

Excel. population genetic software for teaching and

research. Mol Ecol Notes 6(1):288–295. doi:10.1111/j.

1471-8286.2005.01155.x

Poljuha D, Sladonja B, Setic E, Milotic A, Bandelj D, Jakse J,

Javornik B (2008) DNA fingerprinting of olive varieties in

Istria (Croatia) by microsatellite markers. Sci Hortic

(Amsterdam) 115:223–230. doi:10.1016/j.scienta.2007.

08.018

Pompanon F, Bonin A, Bellemain E, Taberlet P (2005)

Genotyping errors: causes, consequences and solutions.

Nat Rev Genet 6:847–859. doi:10.1038/nrg1707

Pritchard JK, Stephens M, Donnelly P (2000) Inference of

population structure using multilocus genotype data.

Genetics 155:945–959

Rallo P, Dorado G, Martın A (2000) Development of sim-

ple sequence repeats (SSRs) in olive tree (Olea europaeaL.). Theor Appl Genet 101:984–989. doi:10.1007/

s001220051571

Rallo P, Tenzer I, Gessler C, Baldoni L, Dorado G, Martın A

(2003) Transferability of olive microsatellite loci across

the genus Olea. Theor Appl Genet 107:940–946. doi:

10.1007/s00122-003-1332-y

Rekik I, Salimonti A, Grati Kamoun N, Muzzalupo I, Perri E,

Rebai A (2008) Characterisation and identification of

Tunisian olive tree varieties by microsatellite markers.

HortScience 43(5):1371–1376

Sabino Gil F, Busconi M, Da Camara Machado A, Fogher C

(2006) Development and characterization of microsatellite

loci from Olea europaea. Mol Ecol Notes 6:1275–1277.

doi:10.1111/j.1471-8286.2006.01513.x

Sarri V, Baldoni L, Porceddu A, Cultrera NGM, Contento A,

Frediani M, Belaj A, Trujillo I, Cionini PG (2006)

Microsatellite markers are powerful tools for discrimi-

nating among olive cultivars and assigning them to geo-

graphically defined populations. Genome 49(12):1606–

1615. doi:10.1139/G06-126

Saunders JA, Mischke S, Leamy EA, Hemeida AA (2004)

Selection of international molecular standards for DNA

fingerprinting of Theobroma cacao. Theor Appl Genet

110:41–47. doi:10.1007/s00122-004-1762-1

Sefc KM, Lopes MS, Mendonca D, Rodrigues dos Santos M,

Laimer da Camara Machado M, Da Camara Machado A

(2000) Identification of microsatellite loci in olive (Oleaeuropaea) and their characterization in Italian and Iberian

olive trees. Mol Ecol 9:1171–1173. doi:10.1046/j.1365-

294x.2000.00954.x

Stambuk S, Sutlovic D, Bakaric P, Petricevic S, Andelinovic S

(2007) Forensic botany: potential usefulness of micro-

satellite-based genotyping of Croatian olive (Olea euro-paea L.) in forensic casework. Croat Med J 48:1–7

Suvanto LI, Latva-Karjanmaa TB (2005) Clone identification

and clonal structure of the European aspen (Populustremula). Mol Ecol 14(9):2851–2860. doi:10.1111/j.1365-

294X.2005.02634.x

Testolin R, Lain O (2005) Genetic traceability in olive oil:

DNA extraction and PCR amplification of microsatellite

markers. J Food Sci 70(1):108–112

This P, Jung A, Boccacci P, Borrego J, Botta R, Costantini L

et al (2004) Development of a standard set of microsat-

ellite reference alleles for identification of grape cultivars.

Theor Appl Genet 109:1448–1458. doi:10.1007/s00122-

004-1760-3

This P, Lacombe T, Thomas MR (2006) Historical origins and

genetic diversity of wine grapes. Trends Genet 22(9):511–

519. doi:10.1016/j.tig.2006.07.008

Wagner HW, Sefc KM (1999) Identity 1.0. Centre for Applied

Genetics, University of Agricultural Sciences, Vienna, 5

pp

Weeks DE, Conley YP, Ferrell RE, Mah TS, Gorin MB (2002)

A tale of two genotypes: consistency between two high-

throughput genotyping centres. Genome Res 12:430–435.

doi:10.1101/gr.211502

Mol Breeding (2009) 24:213–231 231

123