Genetic Resources and Crop Evolution

Analysis of genetic diversity in Tunisian durum wheat cultivars and relatedwild species by SSR and AFLP markers

Maher Medini1, Sonia Hamza1,*, Ahmed Rebai2 and Michael Baum3

1Laboratory of Genetics and Plant Breeding, Institut National Agronomique de Tunisie, Avenue Charles

Nicolle, Tunis 1082, Tunisia; 2Laboratory of Plant Protection and Transformation, Centre de

Biotechnologie de Sfax, BP ‘K’, Sfax 3038, Tunisia; 3ICARDA, Germplasm Program, P.O. Box 5466,

Aleppo, Syria; *Author for correspondence (e-mail: hamza.sonia@inat.agrinet.tn)

Received 29 October 2002; accepted in revised form 30 May 2003

Key words: AFLP, Durum wheat, Genetic diversity, Microsatellite, SSR, Triticum durum

Abstract

Thirty-four durum wheat cultivars representing the Tunisian durum (Triticum durum Desf.) wheat collection

and seven wild species of wheat relatives (Triticum turgidum L., T. dicoccon Schrank., T. dicoccoides (Korn)

Schweinf., T. araraticum Jakubz., T. monococcum L., Aegilops geniculata Roth, and Aegilops ventricosa

Tausch) were analysed with amplified fragment length polymorphism (AFLP) and microsatellite (SSR)

markers. Both marker systems used were able to differentiate durum wheat cultivars from the wild relatives

and to specifically fingerprint each of the genotypes studied. However, the two marker systems differed in the

amount of detected polymorphisms. The 15 SSR markers were highly polymorphic across all the genotypes.The total number of amplified fragments was 156 and the number of alleles per locus ranged from 3 to 24

with an average of 10.4. Two SSR markers alone, Xwms47 and Xwms268, were sufficient to distinguish all 34

durum wheat genotypes. The five AFLP primer pair combinations analysed yielded a total of 293 bands, of

which 31% were polymorphic. The highest polymorphic information content (PIC) value was observed for

SSRs (0.68) while the highest marker index (MI) value was for AFLPs (7.16) reflecting the hypervariability

of the first and the distinctive nature of the second system. For durum wheat cultivars, the genetic similarity

values varied between 31.3 and 81% for AFLPs (with an average of 54.2%), and between 3.6 and 72.7% for

SSRs (with an average of 19.9%). The rank correlation between the two marker systems was moderate, withr ¼ 0.57, but highly significant. Based on SSR markers, highest genetic similarity (GS) values were observed

within the modern cultivars (37.3%), while the old cultivars showed a low level of GS (19.9%). Moreover, the

modern cultivars showed low PIC and MI values. UPGMA Cluster analysis based on the combined AFLP

and SSR data separated the wild wheat species from the durum wheat cultivars. The modern cultivars were

separated from the old cultivars and form a distinct group.

Introduction

Wheat is the most important cereal crop in Tunisia

where it covers 60% of the cereal area. Prior to1969, wheat breeding was based on the exploitation

of the variability in the local durum wheat popula-

tions and the evaluation of foreign germplasm; many

cultivars were developed by mass and pedigree

selection from segregant populations (Degha€��s et al.

1998). Population increase and food consumption

led to changes in the breeding strategy in order to

develop varieties more productive and suitableto intensive agriculture. Breeding programs using

exotic germplasm resulted in the development of a

number of modern cultivars, which replaced the

former heterogenous populations. By shifting to

# Springer 200552: 21–31, 2005.

more productive cultivars the national yield

increased from 500 kg/ha (1930–1970) to 1280 kg/

ha (1980–1990).

At present, Tunisian durum wheat germplasm

does not exceed 45 varieties and it consists of: (i)old varieties selected from local populations (1890–

1930); (ii) intermediate cultivars issued from crosses

between old varieties (1930–1970); and (iii) modern

cultivars bred at the international breeding centers

and further selected in Tunisia (1970–today).

Tunisian durum wheat germplasm has been well

characterized for morphological, physiological and

agronomic traits and it has been shown that localgermplasm is diverse and it is a source of genes for

resistance/tolerance to biotic and abiotic stresses as

well as technological qualities (Bœuf 1932; Rezgui

et al. 1998; Karmous et al. 2002). However, to date,

no information was available on genetic variation

of Tunisian durum wheat germplasm at the mole-

cular level. Genetic diversity estimation based on

DNA markers is more informative when the mar-kers used are distributed on the whole genome.

PCR based markers such as RAPD, AFLP

and SSR, have been shown to be powerful tools for

studying genetic diversity and discriminating

durum wheat cultivars (Dograr et al. 2000; Eujayl

et al. 2002; Soleimani et al. 2002; Pujar et al. 1999;

Incirli and Akkaya 2001). The objectives of the

present study were to evaluate the genetic diversityof durum wheat in a representative set of the

Tunisian national collection and to differentiate

durum wheat collections from wild relatives by

utilising AFLP and SSR markers.

Materials and methods

Plant material and DNA isolation

Forty tetraploid and one diploid wheat genotypes

were used in this study (Table 1). They consisted of

34 durum wheat cultivars of the Tunisian national

collection, comprising 25 old varieties selected

from local and foreign populations, two intermedi-

ate varieties from a cross between old cultivars, and

the whole collection of seven modern cultivars

released between 1980 and 1996. Seven accessionsof wild related species that are commonly used for

hybridisation and development of intermediate and

new varieties were provided by the International

Center for Agricultural Research in the Dry Areas

(ICARDA). Additionally, two Aegilops species

found in Tunisia (Ae. geniculata and Ae. ventricosa

accessions collected from the North (Ariana) and

the Center (Kef) of Tunisia, respectively) wereincluded in the analysis, as they are the most wide-

spread wild wheat relatives in Tunisia, possibly

harbouring useful disease resistance genes for

wheat improvement.

Total genomic DNA was isolated from fresh leaf

material (ten plants per genotype) at the adult stage

by a modification of the method described by Saghai-

Maroof et al. (1984). DNA concentration was esti-mated using a spectrophotometer as well as by visual

comparison with known concentrations of phage

lambda DNA.

Molecular analysis

AFLP assays

AFLP analysis was performed as described byBarrett and Kidwell (1998). One microgram of

genomic DNA was digested with restriction

enzymes PstI and MseI and double stranded

adaptators ligated to the fragment ends. This was

followed by a pre-amplification step using non selec-

tive primers. Selective amplification was performed

on the pre-amplified fragment mixture using a total

of five PstI:MseI primer combinations (Table 2).

SSR assays

For SSR analyses, DNA concentration was

adjusted to 10 ng/�L. Fifteen primer pairs described

by Roder et al. (1998) were used in the present study

(Table 3). PCR amplifications were carried out in a

Perkin Elmer thermocycler model (PE9700) in a

25 �L reaction mixture. Each reaction contained

1� Taq polymerase buffer, 200 �M each dNTP,0.5 �M of each of the two primers, 1 U Taq poly-

merase (Promega), and 80 ng of genomic DNA as

template. Amplifications were performed as follows:

94 �C for 2 min, 40 cycles of (94 �C 30 s, 55 or 60 �C30 s, 72�C 45 s), 72 �C for 10 min.

For both assays (AFLP and SSR), PCR

products were mixed with loading buffer, 5–10 �L

of mixture were denatured and loaded into wells in0.4 mm thick 6% denaturing polyacrylamide

gels (35� 50 cm) resolved at constant power

(75 W) in 1�TBE running buffer for 2.15 h.

22

Table 1. Genotypes used in AFLP and SSR analyses, their genome, their pedigree (when available), their breeding institute or region of

origin and the year of introduction or release (in case of inbred cultivars).

Species Genome Varieties Pedigree x Origin ¥ Year

Durum wheat AABB

(Triticum durum) Modern cultivars

Karim* JO‘S’/AA‘S’//FG‘S’ CIMMYT 1980

Khiar* CHEN‘S’//RUS‘S’/FG‘S’// CIMMYT 1992

Mexi75/3/SHWA

Razzak* DMx69-331/Karim INRAT 1987

Ben Bechir* Galo469/3/JO‘S’//61-130/LDS CIMMYT 1978

Magherbi* GLL‘S’/3/BR180/LK//GZ/61.130 CIMMYT 1972

Om Rabia* JO/Haurani ICARDA 1996

Amel 72* ¼ BR‘S’ ¼ BEL116E/2*TC60//ZB/WLS

/3/TME/2*TC60//ZB/WLS

CIMMYT 1972

Intermediate cultivars

INRAT69 ¼ D58-25 ¼ Kyperounda�Mahmoudi 981 INRAT 1969

Badri D56-3A ¼ Zenati Bouteille//Mahmoudi 152/Mrari INRAT 1969

Old cultivars

Agili Morocco 1913

Derbessi Indigenous 1907

Jenah Khottifa Indigenous 1907

Hamira Indigenous

Bidi Algeria 1907

Biada RP1 Unknown

Adjini Morocco 1893

Biskri Algeria 1907

Azizi Indigenous 1893

Aouedj Unknown 1893

Sbei Indigenous 1907

Souri Foreign 1907

Realforte Unknown

Mahmoudi Morocco 1893

Taganrog Unknown

Medea Algeria 1893

Chili Chili 1932

MG18159 Unknown

Richi Unknown

Swabaa Elgia Indigenous

Mekki Morocco 1893

Kyperounda Cyprus 1954

Roussia Indigenous

Hedba Unknown

Zenati Bouteille Algeria

Wild wheat species Accession no. Turkey

Triticum turgidum AABB 82702 Jordan

Triticum dicoccoides AABB 45742 Turkey

Triticum dicoccon AABB 45085 Azerbaijan

Triticum araraticum AAGG 45211 Unknown

Triticum monococcum AA 45197

Aegilops species

Aegilops geniculata UUMM Tunisia

Aegilops ventricosa DDNN Tunisia

*Inbred lines; x Jori ¼ JO‘S’; ZB ¼ Zenati Bouteille; ¥ CIMMYT: The International Maize and Wheat Improvement Center; ICARDA:

International Centre for Agricultural Research in the Dry Areas; INRAT: Institut National de Recherche Agronomique de Tunisie.

23

Bands were visualised by the silver-staining method

as described by Pillen et al. (2000). Dried gels were

visually scored and scanned for records.

Data analysis

Marker polymorphism

To measure the informativeness of the markers, the

average polymorphic information content (PIC)

was calculated for each marker system according

to the formula given by Powell et al. (1996):

PIC ¼ 1 �Xn

i¼1

f 2i

where fi is the frequency of the ith allele in the set

of 41 genotypes. This value provides an estimate

of the discrimination power of a molecular locus

by taking into account not only the number of

alleles per locus but also their relative frequencies

in the studied population (L€uubberstedt et al.

2000).The marker index (MI) is determined in order to

balance the level of polymorphism detected (PIC)

and the extent to which an assay can identify multi-

ple polymorphisms. It was calculated according to

the formula:

MI ¼ PIC � � � �

where � is the proportion of polymorphic bands

and � is the number of loci per assay unit (Powell

et al. 1996).

Estimates of genetic similarity

Data obtained with the two assays were scored in abinary form as presence (1) or absence (0) of bands

for each individual, and entered into a data matrix.

Genetic similarity (GS) between two genotypes i

and j was calculated for each marker system and

Table 2. Oligonucleotide sequences used in the AFLP analysis.

Oligonucleotide Sequence

Adapters

PstI Forward adapter

50 CTC GTA GAC TGC GTA CAT GCA 30

Reverse adapter

50 TGT ACG CAG TCT AC 30

MseI Forward adapter

50 GAC GAT GAG TCC TGA G 30

Reverse adapter

50 TAC TCA GGA CTC AT 30

Non-selective primers

PstI-A 50 GAC TGC GTA CAT GCA GA 30

MseI-C 50 GAT GAG TCC TGA GTA A C 30

Selective primers

PstI-ACG 50 GAC TGC GTA CAT GCA G ACG 30

PstI-ACA 50 GAC TGC GTA CAT GCA G ACA 30

PstI-AGG 50 GAC TGC GTA CAT GCA G AGG 30

PstI-AAG 50 GAC TGC GTA CAT GCA G AAG 30

MseI-CGC 50 GAT GAG TCC TGA GTA A CGC 30

MseI-CAA 50 GAT GAG TCC TGA GTA A CAA 30

MseI-CAC 50 GAT GAG TCC TGA GTA A CAC 30

Table 3. List of SSR markers used, their map position, primer sequence according to Roder et al. (1998), and their number of alleles,

PIC and MI values generated in the present germplasm.

SSR locus Chromosome(s) Motif Number of alleles PIC MI

Xwms497 1A, 2A (GT)29imp 3 0.14 0.14

Xwms268 1B (GA)17TA(GA)27 24 0.89 0.89

Xwms445 2A (CT)19 4 0.62 0.62

Xwms47 2A, 2B (CT)7TT(CT)16 16 0.85 0.85

Xwms429 2B (CT)25 11 0.88 0.88

Xwms674 3A (CT)16CCC(GT)4 7 0.57 0.57

Xwms299 3B (GA)31TAG)4 10 0.85 0.85

Xwms637 4A (CA)18 6 0.78 0.78

Xwms251 4B (CA)28 7 0.68 0.68

Xwms304 5A (CT)22 9 0.76 0.76

Xwms499 5B (GA)32 14 0.89 0.89

Xwms570 6A (CT)14(GT)18 6 0.41 0.41

Xwms132 6B (GA)24(GAA)6imp 13 0.75 0.75

Xwms60 7A (CA)30 12 0.80 0.80

Xwms46 7B (GA)2GC(GA)33 14 0.86 0.86

Average 10.4 0.72 0.72

24

across marker systems according to the formula

given by Nei and Li (1979):

GSij ¼ 2Nijl ðNi þ NjÞwhere Nij is the number of bands in common

between cultivars i and j, and Ni and Nj are

the total number of bands in genotype i and j,

respectively, with regard to all assay units. Thus

GSij reflects the proportion of bands in common

between two genotypes and may range from 0(no common bands) to 1 (identical profiles of two

lines).

Coefficient of co-ancestry

Coefficients of co-ancestry ( f ) were calculated for

pair of the seven modern cultivars from the

pedigree records described in Table 1. f were com-

puted for all pairs of lines as fellows (Kempthorne

1969)

fA;B ¼ fA;c�D ¼ 12ðfA;C þ fA;DÞ

where f between the A and B lines is equal to the

average of fs between A and the parental lines of B

(C and D).

The calculations followed Cox et al. (1985)

assumptions: (1) ancestors are unrelated; (2) allcultivars, ancestors and parental lines are homozy-

gous; and (3) a cultivar derived from a cross

obtains one-half of its genes from each parent.

Cluster analysis

The similarity matrices obtained with the two sets

of data – SSR and AFLP – were converted to

distance measures (d ) using a formula d ¼ 1�GS

and used to generate UPGMA (unweighted pair

group method with arithmetic averages, Sneath

and Sokal 1973) dendrograms, visualised withTreecon (Van de Peer and De Wachter 1994).

Estimation of correlation coefficient

Correlation between similarity matrices derived

from SSR and AFLP was tested by Mantel’s test

(Mantel 1967) with the NTSYS-PC (Version 2.1)

program (Rohlf 2000). We also computed linear

(denoted r) and Spearman rank (denoted rs) corre-lation coefficients between GS values of AFLP

and SSR. Significance P-values for these correla-

tions were calculated based on their finite sample

distribution (Kendall and Stuart 1979).

Correspondence between the two similarity

matrices GS was tested with the Mantel Z statistic

(Mantel 1967). Significance of Z was determined

by comparing the observed Z values with a criticalZ value obtained by calculating Z for one matrix

with 1000 permuted variants of the second matrix.

All computations were performed with appropriate

procedures of the NTSYS-PC version 2.1 software

(Rohlf 2000).

Results

AFLP patterns

Five AFLP primer combinations yielded a total of

293 amplification products, 91 of them were poly-

morphic across all the 41 genotypes with an aver-

age of 18.2 polymorphic bands (Table 4). Eachprimer combination was able to distinguish the 41

genotypes. When considering either of the durum

wheat collections or only the old cultivars (Table 1),

the average number of polymorphic bands was 17.2

and 17, respectively. However, this value decreased

dramatically within the modern cultivars (12.9).

SSR patterns

A total of 156 fragments were obtained from the 15

SSR primers and all the bands were polymorphic

across all the genotypes screened. The number of

alleles per locus ranged from 3 to 24, with an

average of 10.4 alleles per locus (Table 3). When

considering durum wheat cultivars, 14 of the 15

used SSR primers yielded 112 polymorphic bands

with an average of eight alleles per SSR locus. Thisaverage was similar within old cultivars (7.43), but

it fell to 3.17 when considering just modern culti-

vars (Table 4).

The maximum number of alleles was detected

at Xwms268 (Table 3). This SSR marker, which is

mapped to a single locus on chromosome 1B

(Roder et al. 1998), showed two PCR products on

17 genotypes. However, the Xwms497, which ismapped to two loci (1A and 2A), showed a single

monomorphic band for all durum wheat geno-

types. Two microsatellite primer pairs, WMS268

25

and WMS47, were sufficient to differentiate all the

durum wheat genotypes.

SSR markers that mapped either to chromo-

QJ;somes of the homologous groups A or B werealso amplified in wild species carrying a different

genome. Among eight markers mapped on the

B genome, six amplified in species carrying a different

genome (T. monococum (AA), T. araraticum

(AAGG), Ae. ventricosa (DDNN) and Ae. geniculata

(MMUU)). B-genome SSR markers amplified

four and three bands and A-genome SSR markers

amplified two bands in Ae. ventricosa andAe. geniculata, respectively. However, other A-genome

SSR markers did not amplify any PCR product in

species carrying the A genome. Among the eight

markers mapped in this genome, only three

(Xwms60, Xwms445, and Xwms674) were amplified

in diploid species carrying the A genome (T. mono-

coccum) and six (Xwms60, Xwms304, Xwms445,

Xwms570, Xwms637, and Xwms674) were amplifiedin the tetraploid species carrying A+G genomes

(T. araraticum).

Diversity

Across all the analysed genotypes, the average

polymorphic information content values were dif-

ferent between the two marker systems (Table 4).

The SSRs have shown higher PIC values than theAFLPs (0.72 and 0.39, respectively). However, the

MI was definitely higher for AFLPs than for SSRs

(7.16 and 0.81, respectively), which reflects the

ability of the AFLP technique to discriminate by

few primer combinations all the genotypes tested.

For the SSR assays, the PIC values were different

when considering separately old and moderncultivars (respectively, 0.68 and 0.44), whereas the

AFLP assays generated close PIC values (0.35,

and 0.38, respectively) in the same materials.

Nevertheless, differences were detected between

the MI values (5.9 and 4.9, respectively) for AFLP.

Genetic similarity

Similarity matrices constructed on shared allele

bases revealed that the average genetic similarity

between genotypes was lower using SSRs (15% for

all genotypes and 19.9% for durum wheat cultivars;

Table 5) than AFLPs (39.4% for all genotypes

and 54.2% for durum wheat cultivars). With AFLP

markers, GS average values were quite similar in

old and modern cultivars (50.5 and 57.7%, respec-tively), whereas with SSR markers, modern culti-

vars GS was higher than that for old cultivars (37.3

and 19.9%, respectively).

Coefficients of co-ancestry (f) were calculated for

21 pairs of modern cultivars (data not shown). fs

values ranged from 0.000 to 0.500 with a mean of

0.053.

Cluster analysis

In order to illustrate the relatedness between the 41

wheat genotypes, a combined UPGMA cluster

Table 4. Analyses of banding patterns generated by SSR and AFLP assays.

Marker Genotypes

Number of

genotypes

Number of

assay units

Total no.

of bands

Number of

bands per

assay unit

Proportion of

polymorphic

bands

Number of

polymorphic

bands per

assay unit

Number of

loci per

assay unit PIC MI

SSR All genotypes 41 15 (primer

pairs)

156 10.4 1 10.4 1.13 0.72 0.81

Durum wheat

cultivars

34 14 112 8.0 1 8.0 1.07 0.68 0.72

Old cultivars 25 14 104 7.43 1 7.43 1.07 0.68 0.72

Modern cultivars 7 12 38 3.17 1 3.17 1.08 0.44 0.48

AFLP All genotypes 41 5 (primer

combination)

293 58.6 0.31 18.2 58.6 0.39 7.16

Durum wheat

cultivars

34 5 293 58.6 0.29 17.2 58.6 0.38 6.5

Old cultivars 25 5 293 58.6 0.29 17.0 58.6 0.35 5.9

Modern cultivars 7 5 293 58.6 0.22 12.9 58.6 0.38 4.9

26

based on the GS matrix calculated by the combina-

tion of 247 polymorhic bands obtained with SSR

and AFLP data was developed (Figure 1). Thecluster analysis clearly separated the wild wheat

species from the durum wheat cultivars. The

modern cultivars were separated from the old culti-

vars and form a distinct group. Indeed, the modern

cultivars are more productive, insensitive to day-length, earlier than old cultivars and with exception

of ‘Amel’, they are related by pedigree (Table 1).

Table 5. Maximum, minimum and mean genetic similarity estimates (expressed in percentage) calculated from AFLPs and SSRs for all

the genotypes (41), durum wheat cultivars (34), old cultivars (25) and modern cultivars (7). Coefficient of Co-ancestry was calculated

only for modern cultivars.

Number of genotypes

AFLPs SSRs Parentage

Max Min Mean Max Min Mean Max Min Mean

All genotypes 41 81 10.8 39.4 72.7 0.0 15

Durum wheat 34 81 31.3 54.2 72.7 3.6 19.9

Old cultivars 27 75 31.3 50.5 61.9 3.6 19.9

Modern cultivars 7 81 50 57.7 72.7 17.9 37.3 0.500 0.000 0.053

Figure 1. Dendrogram of 34 durum wheat cultivars and seven related species revealed by UPGMA cluster analysis based on genetic

similarity (GS) estimates calculated from the combined AFLP and SSR data.

27

The old cultivars were clustered into two majors

groups. However ‘Zenati Bouteille’ and ‘Derbessi’

were not included. Despite the fact that the old

cultivar ‘Zenati Bouteille’ has been used to breed

the cultivars ‘Amel’ and ‘Badri’ (see pedigree), thiscultivar is well separated from its progeny.

Comparison between SSR and AFLP

To compare the results obtained with the two tech-

niques, the correlation was tested using linear (r)

correlation and Spearman rank (rs) correlation of

the pair wise GS values. Correlations between bothGS matrices were highly significant (r ¼ 0.57, P <0.0001; rs ¼ 0.63, P < 0.0001). The normalised

Mantel statistic Z test was also significant between

GS matrices (Z ¼ 0.678, P < 0.002).

Discussion

The two DNA marker techniques used in the pre-

sent study were able to clearly discriminate betweenthe 41 cultivated and wild wheat genotypes studied.

Using AFLP, only five primer pair combinations

were sufficient to generate 91 polymorphic mar-

kers, revealing differences between related modern

cultivars. A total of 156 fragments were obtained

from the 15 SSR primers amplified and all bands

were polymorphic across all the genotypes used.

The two microsatellite primer pairs, WMS268 andWMS47 alone were sufficient to differentiate all the

durum wheat genotypes.

The present study shows that the polymorphic

information content (PIC) for SSRs is greater than

for AFLP. This is related to the variation of the

average number of polymorphic bands per assay

unit. This average ranged from a maximum of

100% for SSR to only 31% for AFLP. The highamount of variability has also contributed to the

lower estimates of GS based on SSR markers

(mean 19.9%) as compared to AFLP markers

(mean 54.2%). Our results are in agreement with

other studies comparing the level of polymorphism

detected with AFLP and SSR markers in wheat

(Bohn et al. 1999; Donini et al. 2000), barley

(Russell et al. 1997), maize (L€uubberstedt et al.2000), hops (Jak�sse et al. 2001), and in soybean

(Powell et al. 1996). However, the calculation of

the marker index (introduced by Powell et al. 1996)

highlights the distinctive nature of the AFLP assay:

the high marker index or diversity index is a reflec-

tion of the efficiency of AFLPs to simultaneously

analyse a large number of bands rather than the

level of polymorphism detected.Relative to other diversity studies on wheat,

more alleles (an average of 15 alleles) were detected

in our study at loci containing (GA) repeats. Roder

et al. (1995) also reported (GT) repeats to be more

polymorphic than other repeats in wheat, whereas,

Akagi et al. (1997) found that the highly poly-

morphic microsatellites of rice consist of (AT)

repeats. The average number of eight alleles perlocus for durum wheat cultivars recorded in the

present study was relatively higher than those ear-

lier reported on durum wheat cultivars (average of

6.5 in Dograr et al. 2000 and 4.5 in Eujayl et al.

2002). This difference might relate to the utilisation

of old cultivars with a mean value of 7.43 alleles per

locus versus 3.17 among modern cultivars as well as

the selection of the specific primer.Specifically, we have compared the genetic varia-

bility between old durum wheat cultivars and mod-

ern cultivars. Genetic similarity estimates based on

SSR allele polymorphism differed between the two

sets of cultivars in contrast to GS estimates based

on AFLP data. Based on SSR data, modern culti-

vars were genetically more similar to one another

than old ones. This may suggest that a high pro-portion of SSR markers used in our study might be

associated with chromosomal regions selected dur-

ing breeding programs that have more fixed allelic

state. This could be chromosomal regions harbor-

ing a number of agronomic important loci (such as

those determining vernalisation requirement, semi-

dwarf habit or yield) that are more uniform in the

modern varieties. Microsatellite frequency wasshown to be higher in transcribed regions, espe-

cially in the untranslated portions, than in other

genomic DNA fractions (Morgante et al. 2002) and

have a significant association with the low-copy

fraction of plant genomes. Therefore, SSR markers

were more under the influence of selection, adapta-

tion and use than neutral AFLP markers not

affected by selection (Stachel et al. 2000; Saghai-Maroof et al. 1994). The more widespread applica-

tion of SSR markers, especially EST-derived SSR

markers to the evaluation of germplasm collection

will provide an opportunity to examine functional

diversity in phenotypically well characterized

28

accessions (Eujayl et al. 2002). Old varieties will be

the appropriate material for genetic diversity stu-

dies in relation to the environment adaptation since

their adaptation is secular (Bœuf 1932).

The SSR markers used in our study were notspecific to a single genome. Several markers ampli-

fying loci of the A or B genome also amplified loci

of Ae. ventricosa and Ae. geniculata, carrying the

UM and DN genomes respectively. Microsatellites

of bread wheat were transferable to homoeologous

species (Guadagnuolo et al. 2001), however,

this transferability was far weaker than that

observed from wheat to the corresponding ances-tral diploid species genome (Sourdille et al. 2001).

The recent findings of Morgante et al. (2002) show-

ing a significant association of SSR with the low-

copy fraction of plant genomes may also suggest a

considerable amount of conservation of micro-

satellite and microsatellite flanking sequences

between species which might be exploited for com-

parative genomic analysis.Genetic relatedness between genotypes was

tested using UPGMA cluster analysis. A dendro-

gram based on the combination of AFLP and SSR

data with a total of 247 polymorphic markers was

developed in order to describe the relation-

ship between the varieties. Interestingly, we have

noticed that all the modern cultivars were sepa-

rated from old cultivars and were clustered in onegroup. Despite the fact that most of the modern

cultivars are related by pedigree, they share several

agro-morphological parameters that are different

from those observed in old cultivars (plant height,

yield, earliness and insensitivity to day-length). The

insensitivity to the day-length is a consequence of

sowing twice a year in the south and north of

Mexico by the CIMMYT wheat program. Thisphenomenon would largely contribute to the dis-

tinction of the modern from the old varieties

(Gharbi pers. comm). Some groups obtained in

this study were consistent with the classification

based on morphological spike characteristics

(Bœuf 1932). Indeed, the subgroup composed of

the varieties ‘Bidi, ‘Roussia’, ‘Hamira’, Jenah

Khottifa’, and ‘Souri’ share the similar colourtrait of the glumes and awn (russet), and barbs

(russet or black). The relatedness between ‘Agili’,

‘Sbei’, ‘Biadha RP1’, ‘Adjini’, ‘Mekki’ and ‘Biskri’

is supported by similar spike characteristics (white

glumes and black or white barbs).

In our study associations were found between

GS-AFLP and GS-SSR with a pool of related and

unrelated varieties. According to Bohn et al. (1999)

correlation between different marker estimates is

observed only for related cultivars. The combi-nation of unrelated parent correlations between

GS estimates determined with different marker sys-

tems can be expected only if linkage disequilibrium

exists between the different marker loci in the gen-

otypes examined (Bohn et al. 1999). In our study,

the chance for close linkage between AFLP and

SSR marker loci was low since only 15 SSR and

92 polymorphic AFLP markers were used with lowprobability to be tightly linked. Therefore, one

reason that may account for the association

observed between GS measures is that linkage dis-

equilibrium exists among the cultivars forming

three different groups (wild, old and modern

wheat) as it was demonstrated by the dendrogram

(Bohn et al. 1999).

The correlation between parentage coefficient ( f )and GS estimates was not significant despite the

fact that modern cultivars were related by pedigree

(data not shown). In our case, this lack of correla-

tion can be attributed to the low number of culti-

vars with known pedigree (7 cultivars). However,

low correlations between f and GS based on mole-

cular markers were also reported in other studies

(Plaschke et al. 1995; Autrique et al. 1996; Bohnet al. 1999). This discrepancy between pedigree and

molecular markers based on GS estimates may be

the result of assumptions that were made in the

calculation of genetic relatedness based on pedi-

gree. These estimates may be biased due to selec-

tion pressure, unequal parental contribution, and

also the relatedness of ancestors without a known

pedigree. Bohn et al. (1999) emphasised that bias isgenerated by the breeding method employed on

wheat and barley where segregants are selected

for phenotypic similarity to the superior parent.

However, DNA markers have the advantage of

directly detecting sequence variation among culti-

vars and therefore, are not influenced by these

assumptions that are inherent to pedigree analysis

(Manifesto et al. 2001; Soleimani et al. 2002).The present study is an investigation of DNA

markers for the characterisation of Tunisian

wheat germplasm. Cultivation of modern cultivars

with limited genetic diversity, over large areas of

Tunisia could result in a loss of the local genetic

29

diversity as well as increasing uniformity in the elite

germplasm. On the other hand, old durum wheat

cultivars known to possess characters for adapta-

tion to North African continental dryland dis-

played a high genetic diversity and were shown tobe genetically distant from modern cultivars.

Breeding programs should use the old cultivars

for hybridisation in order to obtain heterotic

hybrids as well for the conservation of adaptive

and resistant traits within Tunisian germplasm.

Moreover, molecular characterisation of old durum

wheat cultivars should be followed by an agro-

nomic and morphologic evaluation in order todevelop DNA markers linked to adaptation traits

to biotic and abiotic conditions.

Acknowledgements

We are thankful to Dr M.S. Gharbi and M. Kasri,

Tunisian wheat breeders for providing durum

wheat cultivars and the two Aegilops species. This

work is part of the collaborative project‘Development of Biotechnological Research in the

Arab States’ with ICARDA. Financial support was

provided by the Arab Fund for Economic and

Social Development.

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