Euphytica 133: 359–366, 2003.© 2003 Kluwer Academic Publishers. Printed in the Netherlands.

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Assessment of EST- and genomic microsatellite markers for varietydiscrimination and genetic diversity studies in wheat

Fiona Leigh1, Vince Lea1, John Law1, Petra Wolters2, Wayne Powell2,3 & Paolo Donini1,∗1NIAB, Huntingdon Road, Cambridge CB3 0LE, U.K.; 2DuPont Agricultural Biotechnology, Delaware TechnologyPark, Suite 200, 1 Innovation Way, Newark, DE 19714, U.S.A.; 3Present address: Scottish Crops Research Institute,Invergowrie, Dundee DD2 5DA, U.K.; (∗author for correspondence: e-mail: paolo.donini@niab.com)

Received 23 July 2002; accepted 11 June 2003

Key words: EST-SSR, genic microsatellites, genomic microsatellites, molecular markers, variety identification,wheat

Summary

It is likely that in the near future sequence information from sequencing programmes and EST libraries will gener-ate an abundance of genic microsatellite markers. This study is focused on the assessment of their likely impact andperformance vis-à-vis their genomic counterparts. Microsatellites from two sources were used to assess the geneticdiversity in 56 old and new varieties of bread wheat on the UK Recommended List. A set of 12 microsatellitemarkers generated from genomic libraries and 20 expressed sequence tag (EST)-derived microsatellites were usedin the study, and the performance of both marker sets assessed. The EST-derived or genic microsatellites deliveredfingerprints of superior quality, amplifying clear products with few stutter bands. Diversity levels as revealed bygenic microsatellites are similar to the few published results. The PIC values for the genic markers were generallylower than those calculated for the genomic microsatellites, though advantages of both marker classes for varietyidentification applications are discussed.

Introduction

The degree of genetic variation in wheat (Triticum aes-tivum L. emend. Fiori et Paol.) has been assessed witha number of DNA molecular markers (Koebner et al.,2001). Considering that most cereals, including wheat,have genomes that consist largely (>75%) of repetit-ive and non coding DNA (Flavell et al., 1974), it maybe inferred that the great majority of wheat diversitystudies have concentrated on the silent portion of itsgenome(s). In other words, the majority of anonym-ous DNA markers are likely to have no genic functionnor close linkage to transcribed sequences and maynot be subject to selection pressure. The few stud-ies conducted with RFLP probes derived from cDNAclones revealed low levels of polymorphism (Gale etal., 1990). However, a recent evaluation of the distri-bution of different classes of SSRs in the genomes ofwheat, Arabidopsis, maize and rice by Morgante et al.(2002) revealed that ‘the frequency of microsatellites

was significantly higher in ESTs than in genomic DNAacross all species’.

To date, no published results are available for stud-ies on hexaploid bread wheat using microsatellitesspecifically developed from genic or EST regions. Aset of EST-derived simple sequence repeats (SSR) de-veloped by DuPont (Newark, DE, USA) were used byEujayl et al. (2001, 2002) to assess the genetic vari-ation among tetraploid durum wheat (Triticum durum)cultivars. The genetic variation detected with thesemarkers was compared to that revealed by genomicSSRs on the same wheat cultivars. The EST-SSRswere shown to be less polymorphic than the genomicSSRs, but were still informative tools for assessinggenetic relationships. Comparisons between SSRs de-rived from EST sequence data and those isolated fromgenomic libraries have also been made in rice (Oryzasativa L.) (Cho et al., 2000) and grape (Vitis spp)(Scott et al., 2000). In grape, the EST SSRs werepolymorphic and highly transferable across cultivars,

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species and genera (Scott et al., 2000). In both cases,the EST derived SSRs were less polymorphic than thegenomic SSRs screened.

Before they can be marketed, newly bred vari-eties of plants must undergo statutory testing. Partof this registration process is known as distinctness(D), uniformity (U), and stability (S) testing which iscurrently based largely on field trials where species-specific defined sets of morphological characters areused to assess D between listed and candidate varieties(Donini et al., 2000a). The present study was under-taken to test the performance of both types of SSRmarkers to assess their potential for applications inDUS testing and genetic diversity studies. With regardto potential DUS application of DNA markers, one ofthe criticisms which has emerged is that the use of an-onymous DNA sequences (from non coding regions)may undermine the solidity of the variety protectionsystem. Using anonymous sequences for D purposeswould also in principle challenge the current philo-sophy of the testing, which relies on morphology, andthus on the expression of functional genes.

The EST-derived SSRs used in this study are un-likely to be associated with any of the genes that aredirectly responsible for the morphological charactersscored under the current field-based DUS testing, norwould they necessarily be the object of active selectionin the breeding process. However, the genes are likelyto have a biological function and could map in regionswhere functional genes are subject to selection. Theuse of such genic markers in diversity studies thus hasthe potential for providing an insight into the selec-tion and recombination events that occur during thebreeding process. In order to test the performance ofthe two sets of SSR markers on selected germplasmaccessions, the data generated with the EST-SSRs wascompared with that obtained from a set of 12 genomicor ‘anonymous’ SSRs.

Molecular markers such as SSRs form useful toolsin assessment of genetic diversity and the use ofmarkers from genic regions may be functionally moreinformative than the ‘anonymous’ SSRs that are morewidely used. Our experimental materials include 56‘United Kingdom’ wheat varieties from the NIAB Re-commended Lists of 1934 to 1994 to represent themajority of the UK acreage grown in each decade.Varieties entering evaluation trials from the 1970’s on-wards were subject to the DUS part of the statutoryNational Listing procedures. Thus, in practice, the ‘ge-netic distance’ between varieties was measured, usingthe current morphology standards. In addition, 10 non-

UK varieties with no a priori shared breeding historiesfrom Japan, India, China, Greece and New Zealandwere used. These varieties form the ‘world’ (W) setof our study and provide examples of the genetic di-versity that may exist within a wider sample of thespecies. The two sets of material may be representativeof the levels of diversity encountered in wheat, andthese have been previously assessed with other markertypes (Donini et al., 1998, 2000b; Law et al., 1998).

Materials and methods

SSR amplification and detection

Bulked DNA from 66 varieties of wheat (Donini etal., 1998; 2000b) (see Table 1) was amplified with 20EST-derived (‘genic’) wheat microsatellites (providedby W. Powell, DuPont, Newark DE, USA) and 12 ‘ge-nomic’ microsatellites (Bryan et al., 1997; Donini etal., 1998) previously found to map to 14 loci (one ofthe SSR markers maps to 3 loci). For the EST-SSRs,each 12.5 µl reaction consisted of 75 mM Tris-HCl(pH 8.8 at 25 ◦C), 20 mM (NH4)2SO4, 0.1% Tween-20, 2.5 mM MgCl2, 0.25 mM dNTPs, 0.2 µM eachprimer pair, 1 unit Taq and 12.5 ng DNA. The forwardprimer was used in a 1:9 unlabelled:IRD labelled ratioto overcome the inhibitory effect that the IRD label hason the kinetics of the PCR. The reaction was amplifiedin a Perkin Elmer 9700 thermocycler under touchdownconditions (94 ◦C for 3 minutes; 23 cycles of 94 ◦Cfor 30 seconds, 62 ◦C for 30 seconds (decreasing by0.5 ◦C each cycle), 72 ◦C for 1 minute; 23 cycles of94 ◦C for 30 seconds, 50 ◦C for 30 seconds, 72 ◦Cfor 1 minute). Each reaction was overlaid with 10µl of Chillout WaxTM (MJ Research, UK). FollowingPCR cycling, amplicons of different size range fromtwo or more SSR markers were combined to max-imise fingerprinting throughput, and heat-denatured.The PCR products were multi-loaded on denaturingsequencing gels (Sequagel-XR) and electrophoresedon a LI-CORTM IR2, dual-laser, DNA automatedsequencing system.

Data analysis

The profiles produced were scored manually; each al-lele was scored as present (1) or absent (0) for eachof the 14 genomic SSR loci and 20 EST-SSR mark-ers. The only subsequent selection consisted in theremoval of the 3 monomorphic markers from the cal-culations, following an initial screening. Each SSR

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Table 1. Wheat varieties used for SSR analysis

‘UK’ wheat varieties ‘World’ wheat varieties

Admiral Hustler Elite Lepeuple Pilot Xios

Flame Longbow Flamingo Pilot S Myokonos

Genesis Mission Hybrid 46 Redman S YU-18

Hereward Norman Hybrid 46/131 Redman Xian-8

Hunter Rapier Professeur Marchal Staring HD2189

Mercia Stetson Rothwell Perdix Victor C591

Riband Virtue Thor Yeoman Norin 6

Spark Bouquet Bersee Yeoman II Takahe

Torfrida Cappelle Desprez Holdfast Squarehead II Kopara

Armada Champlein King II Steadfast Chinese Spring

Avalon Maris Nimrod Juliana Warden

Brigand Maris Ranger Little Joss IronIII

Fenman Maris Widgeon Masterpiece Tjalve

Galahad West Desprez Minister Weibull 17338

profile was given a ‘quality’ score in the range 1 to5 (Table 2), where 1 denotes a PCR product of expec-ted size, with a clear signal and no stuttering, while5 defines a defective amplification. The PIC (poly-morphic information content) for each primer pair wascalculated as described in Anderson et al. (1993). Mir-roring the distinctness test in DUS testing followed theapproach of Law et al. (1998); the criteria to estab-lish distinctness were based on the number of patterndifferences (allelic state) exceeding the threshold foreach variety compared to each other variety. For ex-ample, with 6 varieties A, B, C, D, E and F, varietyA is considered distinct (D) with a threshold criteriaof 1 (D-1) if the number of pattern differences acrossthe set of markers exceeds 1 when A is compared toB, C, D, E and F. That is, there is at least 1 patterndifference between A and B, one difference betweenA and C, one difference between A and D, etc. Thestringency increases as the threshold requirement forD increases from 1 to 5 pattern differences. Dendro-grams were computed using the TREECON softwarepackage (Van der Peer & De Wachter, 1994).

Results

The 20 ‘genic’ microsatellites all produced profilessuitable for scoring when used to amplify the set ofwheat varieties. The ‘quality’ scores assigned to eachof the EST-SSRs and genomic-SSR markers were allin the range of 1 to 2, denoting bands with a strong,clear signal, with little or no stuttering (Table 2).

However, it was generally found that the EST-SSRsgenerated banding patterns of superior quality andwith fewer stutter bands, when compared to thoseobtained from the genomic SSR markers.

The EST-SSRs detected 72 alleles in the UK vari-ety set and 84 alleles in the UK + world set, comparedto 58 and 78, respectively, for the genomic SSRs(Table 2). The number of alleles amplified by eachEST-SSR microsatellite primer pair ranged from 1 to10 in the UK wheat variety set and from 1 to 14 inthe UK + world variety set (Table 2). The number ofalleles amplified by each genomic-SSR microsatelliteprimer pair was comparable or slightly inferior, andranged from 1 to 8 in the UK variety set and from3 to 12 in the UK + world set (Table 2). Eleven ofthe EST-SSRs amplified a low number of alleles (1–3) compared with two of the genomic SSRs. Sevenand two of the EST-SSRs amplified medium (4–9) andhigh (10+) numbers of alleles, respectively, comparedto eleven and one for the set of genomic SSRs.

A wide range of polymorphic information con-tent (PIC) values was observed in the set ofgenic microsatellites (Table 2). Three microsatellites(DuPW123, DuPW168 and DuPW210) were mono-morphic on the entire set of wheat varieties. Theremaining 17 SSRs were polymorphic with an averagePIC of 0.421 when all 66 varieties were assessed, and0.387 when only the 56 UK varieties were considered(Table 3). DuPW167 and DuPW398 had PIC valuesof 0.768 (0.770 UK set only) and 0.747 (0.714 UKset only), respectively, indicating a high efficiency in

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Table 2. Comparison of EST-SSRs and genomic SSRs. UK = UK variety set (56 varieties); UK+W = UK and ‘World’varieties (66 varieties)

SSR No. of alleles Rare allelesa Null allelesb Multiple allelesb Qc PIC

UK UK+W UK UK+W UK UK+W UK UK+W UK UK+W

EST-SSRsDuPW004 4 4 0 0 Y Y N N 1 0.688 0.701

DuPW023 2 2 1 1 Y Y N N 1 0.035 0.086

DuPW043 6 6 3 2 N N Y Y 2 0.454 0.521

DuPW108 3 4 2 1 N N N N 2 0.327 0.421

DuPW115 4 4 1 1 N N Y Y 1 0.524 0.582

DuPW123 1 1 0 0 N N N N 1 0 0

DuPW124 2 4 0 1 N N N Y 1 0.436 0.535

DuPW135 2 3 1 1 N N Y Y 1 0.035 0.059

DuPW165 2 2 1 1 Y Y N N 1 0.035 0.030

DuPW167 9 10 6 5 N N Y Y 2 0.770 0.768

DuPW168 1 1 0 0 N N N N 1 0 0

DuPW173 5 5 2 2 N N Y Y 2 0.637 0.653

DuPW205 2 2 0 0 N N Y Y 2 0.459 0.454

DuPW210 1 1 0 0 N N N N 2 0 0

DuPW216 3 3 1 1 N N Y Y 1 0.331 0.349

DuPW217 8 9 6 7 N N Y Y 2 0.654 0.662

DuPW227 2 3 1 2 N N Y Y 1 0.035 0.059

DuPW238 3 3 1 1 N N Y Y 1 0.418 0.446

DuPW254 2 3 1 2 N N N N 1 0.035 0.088

DuPW398 10 14 6 9 N N Y Y 2 0.714 0.747

Total 72 84 33 37

Genomic SSRsPSP3033 5 6 2 4 N Y N N 2 0.556 0.556

PSP3081 6 6 2 2 N N Y Y 2 0.77 0.785

PSP3047 2 4 0 1 N N N N 1 0.484 0.549

PSP3034 8 12 2 4 Y Y Y Y 2 0.831 0.857

PSP3071 4 5 0 1 N N N N 2 0.739 0.749

PSP3050 4 7 2 4 N Y N N 2 0.504 0.57

PSP3030-4B 5 5 1 1 N N N N 1 0.465 0.537

PSP3030-2B 3 3 2 2 N N N N 1 0.103 0.088

PSP3030-3B 1 3 0 2 N N N N 1 0 0.030

PSP3088 5 5 2 2 N N N N 1 0.605 0.610

PSP3137 4 5 2 2 N Y N Y 1 0.535 0.558

PSP3103 5 9 2 4 N N N Y 2 0.631 0.714

PSP3080 4 4 2 1 Y Y N N 2 0.534 0.539

PSP3009 2 4 0 2 N N N N 2 0.448 0.464

Total 58 78 19 32

a Rare alleles defined as frequency ≤0.05.b Detection of null alleles/multiple alleles: Yes/No.c Q = quality criteria for microsatellite pattern:1 = clear product of expected size / no stutter bands2 = product clearly scorable / faint stutter bands3 = ladder of stutter bands that can not be scored4 = products of non-expected size/ high background signal5 = fails to amplify.

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Table 3. PIC values for EST-SSRs and genomic SSRs.UK = UK variety set; UK+W = UK and ‘World’varieties

EST-SSRs Genomic SSRs

UK UK+W UK UK+W

Maximum 0.770 0.768 0.868 0.878

Minimum 0.035 0.030 0.103 0.030

Mean 0.387 0.421 0.554 0.543

distinguishing between the varieties under study. Theaverage PIC of the 14 genomic microsatellite mark-ers (0.554 for all varieties and 0.543 for UK only)was somewhat higher than that found for the genicmicrosatellite set.

Null alleles were observed in three of the 17 poly-morphic genic SSRs and five of the 14 genomic SSRloci (Table 2), while multiple alleles were observedwith a higher frequency at the genic SSR loci whencompared to the genomic SSR loci (12/17 comparedwith 4/14, Table 2). The frequency at which the differ-ent alleles occurred was highly variable. Nine of theseventeen polymorphic EST-SSRs amplified ‘uniquealleles’ that appeared only once in the complete set of(66) UK + world varieties, while eleven of the seven-teen polymorphic EST-SSRs amplified alleles whichappeared only once in the set of 56 UK varieties (datanot shown). Rare alleles, occurring at a locus withfrequency ≤0.05 were ca. 45% of the total numberof alleles amplified by the EST-SSRs in both the UK(33/72) and UK + world (37/84) variety sets (Table 2).The genomic SSRs generated rare alleles at a lowerfrequency in both the UK (33%, 19/58) and in theUK+ world (41%, 32/78) variety sets (Table 2).

The ability of each set of SSR markers to dis-criminate between varieties was very similar (Table 4)and fell as the stringency criteria consisting of the‘number of allelic differences’ between varieties in-creased. At first glance the percentage discriminationrates (Table 4) appear to be very similar for bothsets of germplasm (UK varieties only and the UK +world varieties). For the UK material (56 varieties)both sets of SSR markers were able to distinguishbetween 96% of the varieties when taking into ac-count single allele differences between varieties (D-1,Table 4). This discrimination level was reached us-ing 17 EST-SSR markers, whereas only 14 genomicSSR markers were required to give a similar levelof resolution. If the distinctness criterion was made

Table 4. Percentage Discrimination Rate in wheat. The ability todiscriminate between wheat varieties using different stringenciesfor the distinctness criterion (D-1, etc.); the D-numbers indicatethe number of allelic differences between varieties which definedifferent distinctness thresholds

UK varieties only (56)

Percentage Discrimination Rate

All SSRs Genomic SSRs EST-SSRs

Number of SSRs 31 14 17

Distinctness criteria

D-1 100.0 96.4 96.3

D-2 100.0 89.3 74.1

D-3 96.3 58.9 51.9

D-4 85.2 28.6 20.4

D-5 75.9 14.3 3.7

UK varieties (56) + World varieties (10)

Percentage Discrimination Rate

All SSRs Genomic SSRs EST-SSRs

Number of SSRs 31 14 17

Distinctness criteria

D-1 100.0 97.0 96.9

D-2 100.0 90.9 78.1

D-3 96.9 65.2 57.8

D-4 87.5 39.4 29.7

D-5 79.7 24.2 15.6

more stringent, that is, if the number of differencesin the allelic status at more than one locus is in-creased when defining varieties to be distinct from oneanother, the EST-SSRs appeared to be less discrimin-ating than the genomic SSRs. At the two-marker alleledifference threshold (D-2), the discrimination rates are89.3% for the genomic and 74.1% for the genic sets,but the discrimination power falls drastically at theD-5 distinctness criteria threshold, with the genomicmarkers scoring considerably higher (14.3%) than theEST-SSRs (3.7%). A similar pattern of results can beobserved for the combined UK and world variety sets(Table 4). For both groupings of varieties, the differ-ential in discrimination power between the two classesof markers is maintained and widens as the stringencycriterion for variety distinctness increases from D-1 toD-5. Dendrograms (data not shown) indicated that asingle pair of varieties were not resolved with bothprimer sets, although the pair of varieties was differ-ent with the sets. Nonetheless, relationships seen with

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the two data sets were broadly similar, with ‘world’varieties as a separate cluster from ‘UK’ varieties.

Discussion

The above results indicate that the EST-derived SSRmarker set used in this study is slightly less efficientat discriminating between hexaploid bread wheat vari-eties compared to a second panel of genomic SSRmarkers. Although the SSRs are in untranslated re-gions of genes and there are no functional constraintsto their mutation, nonetheless there is likely to beselection for particular allelic combinations at genicloci thus reducing the number of alleles in the breed-ing pool and hence lowering the observed levels ofpolymorphism in associated SSRs.

The EST-SSR (‘genic’ microsatellites) marker setwas able to distinguish between 96.9% of the 66varieties, compared to 97.0% for the genomic SSRs(Table 4). This equivalent rate of discrimination re-quired 17 genic SSRs as opposed to 12 genomic SSRmarkers mapping at 14 loci, and reflects the generallylower PIC values of the EST-SSRs (Table 3).

The percentage discrimination rate for varying dis-tinctness thresholds, and for both marker and varietysets was severely affected by the increase in the num-ber of allelic differences between any two varieties inorder for them to be deemed as distinct (Table 4). Bothmarker sets suffered a severe loss of discriminationpower at the D-3 distinctness threshold, although ge-nomic SSRs markers showed a higher efficiency. TheEST-SSRs showed a significant drop in discriminationpower at the D-5 threshold (3.7% and 15.6% on theUK and UK + world variety sets, respectively). How-ever, the variety discrimination power for the higheststringency conditions (D-4, D-5) was maintained atacceptable levels when all SSR markers were used inconjunction (75.9% and 79.7% on the UK and UK +world variety sets, respectively).

In general, the level of genetic variation detec-ted in bread wheat was similar to the only previousstudy conducted in durum wheat, where genomic mi-crosatellites were compared to the same test-set ofgenic microsatellites (Eujayl et al., 2001, 2002). Theauthors reported that the same set of EST-SSRs wasable to distinguish between 64 durum breeding lines,and a comparable number of varieties was used in ourstudy.

The EST-SSRs thus retain the ability to detectpolymorphism in both hexaploid bread wheat (gen-

omes A, B and D) and tetraploid durum (genomesA and B) which share both the A and B genomes.This marker transferability across species suggeststhat the EST-microsatellite loci examined, althoughpolymorphic in their basic repeat number, have notaccumulated mutations in their flanking regions to theextent of impairing the use of the same primer sets indifferent species. The markers thus allow discrimina-tion between varieties of each species, while retainingfunctional priming sites which have not been signific-antly affected by mutations since the time of speciesdivergence, a few thousands years ago. This is suppor-ted by recent findings by Morgante et al. (2002) whohypothesised that ‘most microsatellites reside in re-gions pre-dating the recent genome expansion in manyplants’. Three of the 20 EST-SSR markers detectednull alleles in some of the 66 varieties, against five ofthe 14 genomic SSR markers (Table 2). Null alleles arepossibly the result of point mutations such as singlenucleotide polymorphisms or other mutation eventsoccurring at the priming sites residing in the SSRflanking regions, or could originate from large-scalegenomic rearrangements, including insertion-deletionevents within the region of the amplicon. Because noselection was made during the development of eithermarker set against their ability to detect null alleles,it is reasonable to infer that such mutations are lesstolerated in the transcribed portion of the genome, andthis affects the rate at which microsatellite flanking re-gions are free to evolve. The SSRs are not part of thetranslated region of the gene, so point mutations couldbe tolerated, but major insertions or deletions wouldbe likely to be selected against. This may explain thehigher proportion of null alleles detected by the gen-omic SSR marker set, although a larger data set wouldbe needed to test this possibility.

Using DNA from bulks of 30 grains for each vari-ety minimises the detection of ‘off-type’ alleles withina variety. Thus, the alleles present at a frequency be-low the assay sensitivity, which is affected by thedetection limit of signal strength and PCR competitioneffects, cannot be visualised. Multiple alleles were ob-served for some SSRs that were reported to map toa single locus and in which only one allele was nor-mally observed. As the DNA samples used for thisstudy were extracted from the flour of 30 wheat grains,this apparent heterogeneity may be used as a crudeindicator for the uniformity at the SSR loci. Whenmultiple alleles were observed in the bulks (Table 2),it may be inferred that either the seed lots were a)heterogeneous mixtures of homozygous individuals at

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one locus, or b) composed of individuals which wereheterozygous at one locus. Past studies have indicatedthat in order for one allele to be detected unequi-vocally using PCR, it must be present in more than10% of the sample sub-components, otherwise thereis a high chance that it will fall below the sensitiv-ity threshold of the microsatellite assay (unpublishedresults). Unexpected multiple bands indicate that a sig-nificant proportion of the individuals within a varietywere heterogeneous or heterozygous. To resolve thecause of the presence of >1 alleles at a locus, whenonly one would be expected due to the autogamousnature of wheat, a uniformity assessment of the vari-ety should ideally be performed by genotyping severalindividuals. This would reveal whether the detectionof ‘multiple alleles’ is to be attributed to heterozygos-ity in individuals, or to the presence of individualsin the bulk that are homozygous for different alleles.Multiple alleles may also be the result of multilocusmarkers. Rare alleles (frequency ≤0.05) are usefulif they appear in a variety that will be studied indepth, for example, when examining essential deriv-ation (EDV) issues. In such cases, the presence of anallele that is peculiar to a variety allows easy trackingof closely linked chromosome segments in the pro-geny. However, if a microsatellite marker detects rarealleles in one or a few varieties of a set, while all othervarieties share one other allele, then the microsatelliteis of limited use for general discrimination, and thiswill also be reflected by a low PIC value. Also, whilerare alleles may be useful in terms of identificationpower for specific varieties, they have also shown tobe the potential source of uniformity problems withina variety (unpublished results). Rare alleles were moreabundant in the set of EST-SSRs (44% and 46% in theUK and UK + world sets, respectively) when com-pared to those generated by the genomic SSRs (33%and 41% in the UK and UK + world sets, respect-ively) (Table 2). The higher proportion of rare allelesin the EST-SSRs is coupled, and possibly correlatedto their lower discriminatory power and lower PICvalues. Considering that the EST-SSRs generally amp-lified higher numbers of alleles on both variety sets(72 and 84 alleles for the UK and the UK + worldsets, respectively) when compared to genomic SSRs(58 and 78 alleles for the UK and the UK + worldsets, respectively), then the lower PIC values and thehigher proportion of rare alleles can be explained by amore uneven frequency distribution of alleles betweenvarieties.

There are several factors indicating that EST-SSRsmay in the near future complement and outnumberthe genomic SSR markers developed in the years fol-lowing the birth of microsatellite technology. SSRsidentified from EST sequence databases have the ad-vantage of being inexpensive and rapidly identified(almost a by-product of sequencing projects), and,as shown in this study, they also produce high qual-ity profiles. They are also more representative of allthe repeat motifs, when compared to SSRs isolatedfrom enrichment procedures which are, by the natureof their isolation, biased towards particular motifs(Karagyosov et al., 1993). EST-SSR’s also reveal vari-ation in transcribed genes, thus allowing functionaldiversity to be examined in relation to adaptive vari-ation (Eujayl et al., 2001). Genic markers thus have thepotential to define (and track) the specific varietal ori-gin of genes and could be used to identify non-randomassociation of alleles. Such markers may therefore beuseful to study the degree of selection pressure on thechromosome segments where they map, and to definethe degree of linkage disequilibrium for functionalgenes of known or putative function. Sets of ‘genic’microsatellites derived from extensive sequencing ofEST libraries will in the future complement the sets ofmicrosatellites that were abundantly generated in thefirst years of this marker technology.

The dendrograms generated using the two setsof markers (data not shown) are generally present-ing similar outcomes, with clustering reflecting someagreement at both higher and lower grouping orders.The ‘world set’ of varieties is thus largely distinct fromthe UK germplasm. The differences between dendro-grams are explained by the relatively small number ofmarkers in each SSR set, and by the fact that genicand genomic microsatellites sample different regionsof the genome, thus giving a different perspective ondiversity. As such, the two sets of markers are com-plementary and more likely to reflect a real picture ofgenetic distances when used in conjunction.

On the basis of our results, EST-SSRs and gen-omic SSRs appear to be roughly equivalent in theirpotential applications, each set possessing its ownadvantages and disadvantages. A suitable combina-tion of EST-SSRs and genomic-SSR markers couldbe designed that offers a compromise between desir-able inter-varietal distinctness power and undesirableintra-variety non-uniformity levels, and may thus findoptimal use in Distinctness, Uniformity and Stabil-ity testing applications. An appropriate assortment ofSSR marker types and an informed choice for spe-

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cific end-uses may also provide invaluable tools forboth variety identification and discrimination. Thesemarker sets would be potentially of use throughout theagricultural production chain from breeder, throughseed production and certification, farm-scale cultiv-ation, harvest and storage, and on to food and feedprocessing and consumption.

Acknowledgements

We thank the Department for Environment, Food &Rural Affairs (DEFRA), UK, for financial support ofthis research under project SDP00/03, and the two an-onymous referees and the Communicating Editor fortheir helpful comments on the manuscript.

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