Marker assisted selection in crop plants

E. Francia, G. Tacconi, C. Crosatti, D. Barabaschi, D. Bulgarelli, E. Dall’Aglio &G. Vale*C.R.A – Istituto sperimentale per la Cerealicoltura, Sezione di Fiorenzuola d’Arda, Via S. Protaso 302, 29017Fiorenzuola d’Arda (PC), Italy (*requests for offprints; Fax: +39-0523-983750; E-mail: gp.vale@iol.it)

Received 27 May 2004; accepted in revised form 15 February 2005

Key words: crop improvement, genetic mapping, genome analysis, marker assisted selection, PCR-basedmarkers, QTLs, synteny

Abstract

Genetic mapping of major genes and quantitative traits loci (QTLs) for many important agricultural traitsis increasing the integration of biotechnology with the conventional breeding process. Exploitation of theinformation derived from the map position of traits with agronomical importance and of the linkedmolecular markers, can be achieved through marker assisted selection (MAS) of the traits during thebreeding process. However, empirical applications of this procedure have shown that the success of MASdepends upon several factors, including the genetic base of the trait, the degree of the association betweenthe molecular marker and the target gene, the number of individuals that can be analyzed and the geneticbackground in which the target gene has to be transferred. MAS for simply inherited traits is gainingincreasing importance in breeding programs, allowing an acceleration of the breeding process. Traitsrelated to disease resistance to pathogens and to the quality of some crop products are offering someimportant examples of a possible routinary application of MAS. For more complex traits, like yield andabiotic stress tolerance, a number of constraints have determined severe limitations on an efficient utili-zation of MAS in plant breeding, even if there are a few successful applications in improving quantitativetraits. Recent advances in genotyping technologies together with comparative and functional genomicapproaches are providing useful tools for the selection of genotypes with superior agronomical perfor-mancies.

Abbreviations: G · E – genotype · environment; LD – linkage disequilibrium; MAS – marker assistedselection; NILs – near isogenic lines; Q · E – QTL · environment; RILs – recombinant inbred lines

Introduction

Plant breeding, in its conventional form, is based onphenotypic selection of superior genotypes withinsegregating progenies obtained from crosses.Application of this methodology often encountereddifficulties related principally to genotype · env-ironment (G · E) interactions. In addition, severalphenotyping procedures are often expensive, timeconsuming or sometimes unreliable for particulartraits (i.e. for some traits related to abiotic stresstolerance).

Molecular marker-assisted selection (MAS) isan approach that has been developed to avoidthe problems connected with conventional plantbreeding changing the selection criteria fromselection of phenotypes towards selection of genes,either directly or indirectly. Molecular markersare clearly not environmentally regulated and areunaffected by the conditions in which the plantsare grown and are detectable in all stages ofplant growth. With the availability of an arrayof molecular markers and genetic maps, MAShas become possible both for traits governed by

Plant Cell, Tissue and Organ Culture (2005) 82: 317–342 � Springer 2005DOI 10.1007/s11240-005-2387-z

major genes as well as for quantitative trait loci(QTLs). Usefulness of a given molecular marker isdependent from its capability in revealingpolymorphisms in the nucleotide sequence allow-ing discrimination between different molecularmarker alleles. These polymorphisms are revealedby molecular techniques such as restriction frag-ment length polymorphisms (RFLP), amplifiedfragment length polymorphisms (AFLP), micro-satellite or simple sequence length polymor-phisms (SSR), random amplified polymorphicsequences (RAPD), cleavable amplified poly-morphic sequences (CAPS), single strand con-formation polymorphisms (SSCP), singlenucleotide polymorphisms (SNPs) and other(Mohan et al., 1997; Rafalski, 2002).

A successful application of molecular markersto assist breeding procedures rely on several factors:– a genetic map with molecular markers linked tothe major gene(s) or QTLs of agronomic interest;

– a tight association between the markers and themajor gene(s) or the QTLs;

– adequate recombinations between the markersassociated to the trait(s) of interest and the restof the genome and

– the possibility of analyzing a large number ofindividuals in a time and cost effective manner.

The success of MAS also depends on the locali-zation of the marker with respect to the targetgene. In a first case, the molecular marker can belocated directly within the gene of interest. Thiskind of relationship is clearly the most favorableand in most cases requires the availability of thetarget gene cloned. This situation is now possiblefor several disease resistance genes and for genesrelated to quality traits in tomato (discussed be-low). In a second case, the marker is geneticallyassociated to the trait of interest. In this case loweris the genetic distance between the marker and thegene and more reliable is the application of themarker in MAS because only in few cases the se-lected marker allele will be separated from thedesired trait by a recombination event. Most of thesuccessful applications of MAS discussed belowrely on this class of molecular markers. In a thirdcase, the target gene(s) can be represented by oneor more QTLs. In this case genomic regions to beselected are often chromosome segments; it istherefore preferable either to have two polymor-phic markers flanking the target QTL, and/or oneor more markers within the QTL genomic region.

Some examples related to both approaches for theintrogression of QTLs into different geneticbackgrounds are discussed below.

To date, MAS has shown to be effective forrelatively simple traits that are controlled by asmall number of genes and several examples areprovided in this review on the advantages and onthe successful application of MAS for this class oftraits. For more complex traits, MAS has provento be less effective; several reasons at the basis ofthese difficulties, some examples of successfulapplication of MAS for quantitative traits andperspectives for increasing efficiency of MAS forQTLs are discussed.

Marker assisted selection aims

For plant breeders, the most useful applicationof MAS is to use DNA-based markers for basi-cally three purposes:– tracing favorable allele(s) (dominant or reces-

sive) across generations; in order to accumulatefavorable alleles,

– identifying the most suitable individuals amongsegregating progenies, based on the allelic com-position of a part or of the entire genome and

– breaking the possible linkage of favorable alleleswith undesirable loci.When the expression of a target trait is regu-

lated by a single gene, or by a gene responsible fora high percentage of the phenotypic variance of thetrait, the transfer of a single genomic region from adonor to a recipient line can produce significanttrait improvement. MAS is now increasinglyemployed for accelerating the recovery of therecurrent parent in backcross (BC) programmes.Compared with conventional backcrossing, the useof molecular markers can improve the efficiency ofBC breeding at least in three ways:– for traits that are difficult to phenotype, selectionfor a marker allele from the donor parent at alocus near the target gene can increase the effi-ciency and accuracy of selection;

– markers can be used to select BC progeny withless amount of donor parent germplasm in thegenome outside the target region and to selectrare progenies resulting from recombinationnear the target gene, thus minimizing the effectsof linkage drag and

– in the transfer of recessive genes through con-ventional breeding, additional selfing genera-

318

tions after every backcross are required, leadingto a procedure that is prohibitively low formost breeding purposes.The probability of selecting superior genotypes

is low for low to moderate heritability. In classicalbreeding, plant breeders cope with this problem byproducing and testing progeny from numerouscrosses, using low selection pressure, using repli-cate testing and testing advanced generations.Breeders selecting for low to moderate heritabilitytraits have the following dilemmas:– when the heritabilities of the traits to be selectedare low or moderate and small samples ofprogeny are tested, the probability of selectingan outstanding genotype is very low;

– large numbers of progeny must be selected (lowselection intensities must be used) to ensure thepresence of one or more superior genotypes inthe selected sample and

– even when low selection intensities are used, themost outstanding genotypes produced by a crossmight not be present in the selected sample whenheritability is low and samples are small.MAS has therefore emerged as a strategy for

increasing selection gains with respect to pheno-typic selection alone and quantitative genetic the-ory suggests that the effectiveness of MAS isinversely proportional to the heritability of a giventrait (Lande and Thompson, 1990; Knapp, 1998).Knapp (1998) developed a theory to estimate theprobability of selecting one or more superiorgenotypes by MAS and defined a parameter toestimate the cost efficiency of MAS relative tophenotypic selection. Depending on the selectionpressure, the genotypic superiority target and thetrait heritability, it is estimated that a breederusing phenotypic selection must test 1.0 to 16.7times more progeny than a breeder using MAS tobe assured of selecting one or more superiorgenotypes. Thus, MAS can substantially decreasethe resources needed to accomplish a selection goalfor a low to moderate heritability trait when theselection goal and the selection intensity are high.The parameter defined by Knapp (1998) predictthat MAS is most efficient than phenotypic selec-tion when breeders use high selection intensitiesand set high selection goals. Selection intensity canbe increased to exclude inferior genotypes whenthe heritability is increased by using MAS.

An empirical comparison on the efficiency ofMAS and phenotypic selection for the simulta-

neous improvement of quantitative traits in-volved in seedling emergence and eating-qualitycharacteristics has been carried out in three dif-ferent sweet corn populations (Yousef and Juvik,2001). In this study, selection of QTLs affectingseedling emergence, kernels sucrose concentra-tion and kernels tenderness was assessed byusing five molecular markers per QTL. MASand phenotypic selection have then been com-pared for the selection gain in each of the threesingle traits and for the selection gain in themultiple traits (i.e. emergence plus kernel ten-derness): in both cases, MAS was superior tophenotypic selection. Selection efficiencies weretherefore evaluated on the basis of gains overone cycle of selection and estimated evaluationcosts. A total of 52 paired comparison weremade between MAS and phenotypic selection; in38% of the paired combinations MAS resultedin significantly higher gains than phenotypicselection across the three populations, whilephenotypic selection was significantly grater thanMAS in only 4%. Even if the estimates of theMAS and phenotypic selection costs provided inthis study may not be applicable to everybreeding scheme, it appears that MAS can eco-nomically compete with phenotypic selection,particularly with the advances in DNA technol-ogy and the gains resulting from reduced sizeand duration of breeding programs.

Theoretical and analytical investigations(Lande and Thompson, 1990; Wittaker et al.,1995) have shown that the maximum selectionefficiency for quantitative traits may be obtainedby using a combination of molecular and pheno-typic information. For maize grain yield and per-cent stalk lodging, an index including bothphenotypic and marker information predictedperformancies of testcrosses better than pheno-typic or marker information alone. For grainyield, combining marker information with phe-notypic information allowed a reduction of 40%in the number of lines to be tested (Eathingtonet al., 1997). Thus, DNA markers could allow theidentification of high performing genotypes inearly generations and therefore have a strong im-pact on breeding programs by reducing the num-ber of lines that have to be tested and thereforeaccelerating the breeding process (Eathingtonet al., 1997; Schneider et al., 1997; Frisch et al.,1999).

319

MAS for improvement of quantitative traits

Most of the traits of agronomic importance, suchas yield, some classes of disease resistance genes,several abiotic stress tolerance genes and qualitytraits, are complex and regulated by several genes.Difficulties in manipulating these traits are deri-ved from their genetic complexity, principally thenumber of genes involved, the interactions betweengenes (epistasis) and environment-dependentexpression of genes. Quantitative traits often havea low heritability, with many QTLs segregating forthe trait, each with small effect individually. Theresult is that effects of individual regions are noteasily identified, and multiple genomic regionsmust be manipulated at the same time in order tohave a significant impact. For this reason, repli-cates of field tests are required to characterizeaccurately the effects of QTLs and to evaluate theirstability across environments. Although significantQTL effects should be detected across severalenvironments, variation in expression due to QTLby environmental interactions (Q · E) remains amajor constraint to the discovery of QTL that willconfer a consistent advantage across a wide rangeof environments; on the other side the identifica-tion of G · E as well as Q · E effects, it may per-mit identification of genotypes adapted to specificenvironments (Fox et al., 1997).

Given this complexity, integrated approachesare therefore required to increase the probabilityof an useful application of MAS for QTLs(Figure 1). In fact, despite the proliferation ofQTL mapping works in recent years, a number ofconstraints have determined severe limitations onan efficient utilization of QTL mapping informa-tion in plant breeding through MAS. These con-straints include– the identification of major QTLs controlling the

trait of interest;– uncertainty of the QTL position, notably for

those with a small effect (the confidence intervalfor QTLs location determined with current QTLanalysis techniques sometimes is up to 30 cMfor small populations);

– deficiencies in QTL analysis leading either to anoverestimation or underestimation of the num-ber and effects of QTLs;

– problems connected with the identification ofQTL-marker associations applicable over dif-ferent sets of breeding materials;

– possibility of loosing the target QTL duringMAS through double-cross-overs betweenmarkers (this possibility is increasing with in-creased length of the marker interval analyzed);

– difficulty in precisely evaluating epistatic effectsand

– difficulty in evaluating Q · E interactions.Improved field designs (Gleeson, 1997) and sta-tistical approaches for QTL can led to a bettercharacterization of the target genes map position.In recently devised mathematical methods, such ascomposite interval mapping (CIM), field datafrom different environments can be integrated intoa joint analysis to evaluate Q · E and thus identifyQTLs that are stable across environments (Jangand Zeng, 1995). Besides, with a detailed linkagemap, CIM allow a better identification of linkedQTLs (in coupling phase) from the same parentalline. In addition, analysis methods have beenproposed to accommodate QTL mapping data for

Molecular marker-based genetic maps

Phenotypic evaluation of agronomic traits in multi-environments

QTL analysis of input traits

Identification of markers linked to

QTLsepistasis G x E effects

Additive effects

Estimation of the QTL allele

effects

Definition of a target combination of markers alleles

association mapping

(haplotype analysis)

MAS application in breeding programs

or Accomodation of the effects of

epistasis and GxE

Figure 1. An integrated approach for a possible exploitation ofQTL data in MAS.

320

the effects of G · E interactions (Crossa et al.,1999), of epistasis (Boer et al., 2002) and of G · Einteractions and epistasis at the same time (Podlichet al., 2004). It is possible that such integratedapproaches would better allow estimation of QTLeffect for MAS application in breeding programs.

An emerging approach for identifying genesunderlying phenotypic variation in complex traitsis represented by linkage disequilibrium (LD – i.e.nonrandom association between alleles at linkedloci) mapping (association mapping). Linkagemapping carried out on a bi-parental cross, sam-ples only a small fraction of the possible alleles inthe population from which the parents originated.LD, on the contrary, infers associations betweengenotypes (haplotypes) and phenotypic variationby examining genetic polymorphisms that havebeen generated into different genetic backgroundsthrough many past generations of recombination(Nordborg and Tavare, 2002). Because no map-ping populations need to be created, the keyadvantages is that association tests can be per-formed relatively quickly and inexpensively. In LDmapping, a whole genome may be scanned toidentify regions that are associated with a partic-ular phenotype, or alleles at a few selected candi-date genes may be tested for association with aphenotype (Rafalski, 2002). The primary obstacleto association studies is represented by the pres-ence, within the population, of subgroups with anunequal distribution of alleles. In such popula-tions, false-positive associations can be detectedbetween a marker and a phenotype, even if themarker is not physically linked to the locusresponsible for the phenotypic variation (Bucklerand Thornsberry, 2002). Thus, a crucial first stepin LD mapping is to define a set of subpopula-tions. An effective method controlling for popu-lation structure in association tests, uses a geneticsimilarity matrix estimated from molecular markerdata. This method was successfully implementedon a maize population consisting of three subpop-ulations, reducing the number of false positive(Thornsberry et al., 2001). The extent of LD isaffected by many factors, including populationhistory and the frequency of recombination in theexamined genome segment (Rafalski, 2002). Inplant species where population bottlenecks haveoccurred, such as sugarcane and sugar beet, LDextending for several cM was found in RFLP andAFLP marker analysis (Jannoo et al., 1999; Kraft

et al., 2000). Similarly, data available for soybean,which also has a very narrow genetic base, showthat LD decays at distances of 2.0–2.5 cM (Zhuet al., 2003). Maize studies have shown that LDdecays over 1500 bp distances, but other studieshave also shown that LD values may vary consid-erably (Morgante and Salamini, 2003). LD map-ping has successfully be applied in the model plantArabidopsis (Olsen et al., 2004) and maize(Thornsberry et al., 2001) to identify QTLs associ-ated with variation in flowering time. Associationmethods that incorporate estimates of populationstructure is therefore providing a powerful ap-proach to identifying alleles responsible for varia-tion in a variety of quantitative traits.

MAS for yield

Although some investigations provided exampleson the pratical application of MAS to increaseyield, it is becoming clear that integratedapproaches involving traditional methods of agri-cultural improvement (Lande and Thompson,1990) and combination of crop modeling and QTLmapping (Figure 1; Yin et al., 2003) are requiredto select a crop ideotype for a given environment.In the following sections some selected examples ofsuccessful applications of MAS to increase yield inimportant crop plants like maize, rice, barley andsoybean are provided.

Maize

Marker-mediated backcrossing is a selectionscheme used in maize to monitor the transfer offavorable alleles at QTLs (foreground selection)and to hasten the return to the recipient genotypein the remainder of the genome (backgroundselection) (Bouchez et al., 2002; Figure 2). A sim-ilar approach of marker-mediated backcrossinghas been used to generate series of maize NILsderived from an elite recipient line (the recurrentline) and a exotic donor line (Stuber et al., 1999).Marker facilitated backcrossing and marker-facilitated selfing were used for foreground andbackground selection. As few as two BCs and oneselfing (to fix the introgressed segment) genera-tions were sufficient to generate different NILs,each with different introgressed genomic regions.When crossed to a tester line and evaluated in

321

replicated field trials, different NILs revealed tohave received donor segments increasing ordecreasing their yield performancies. This breedingscheme not only creates enhanced elite lines, butalso provide materials for the identification andmapping of yield QTLs. A possible disadvantageof this approach is that favorable epistatic effectsbetween QTLs may not be identified.

Development of a reliable method for pre-dicting hybrid performance in maize, withoutgenerating and testing hundreds or thousands ofsingle-cross combinations, has been the goal ofnumerous studies, using both marker data andcombinations of marker and phenotypic data. Inorder to explore heterosis (hybrid vigor) andG · E interaction, Stuber et al. (1992) used across between two widely used elite maize in-breds, B73 and Mo17. They identified and mappedQTL alleles that were predicted to increase hybridyield. Markers were used to introgress the QTLsinto the inbred lines, and the hybrids from the en-hanced inbred lines yielded better than hybridsfrom inbred lines that lacked the marker-intro-gressed QTLs (Stuber, 1994). Whenever a QTL forgrain yield was detected, the heterozygote had ahigher phenotype than the respective homozygote(with only one exception) suggesting not onlyoverdominance (or pseudo-overdominance) butalso that these detected QTLs play a significant rolein heterosis. This conclusion was reinforced by ahigh correlation between grain yield and proportionof heterozygous markers. However, for traits gov-erned largely by additive gene action (this type of

gene action might prevail for some loci affectinggrain yield) the heterozygous QTL genotype wouldnot be the most favorable. For this reason, aneffective prediction of hybrid performance based onmarkers solely would requires knowledge of QTLslinked to the markers.

Rice

QTL alleles for yield components traits derived fromthe wild rice relative Oryza rufipogon have recentlybeen extensively studied by using advanced BCpopulations (AB-QTL; Tanksley andNelson, 1996).In these studies, despite its inferior performance,53% (Thomson et al., 2003) and 33% (Septiningsihet al., 2003) of the QTLs alleles originating fromO. rufipogon had a beneficial effect for yield and yieldcomponents in the recipient rice elite cultivars. Thelower percentage reported in the second study maybe explained by an higher genetic similarity betweenthe elite line andO. rufipogon at the yield QTL allelesor by the fact that in this cross the elite cultivar mayhave more favorable alleles at most of the identifiedloci. Some of the O. rufipogon yield QTLs identifiedwere not linked to any deleterious negative QTLsand would directly be useful to develop breedingmaterials. In several different instances, theO. rufipogon alleles showed the same effect in dif-ferent genetic backgrounds and environments, sup-porting the stability of these yield QTLs.

A thousand grain weight (TGW) QTL hasrecently been identified on chromosome 6 by usingBC inbred lines derived from a cross between thehigh-yield rice japonica cv. Nipponbare and thelow-yield indica cv. Kasalath (Ishimaru, 2003).The QTL allele increasing TGW is derived fromthe low yield cv., and when introgressed by MASinto a Nipponbare NIL, this QTL increase TGWand yield per plant by 10 and 15% respectivelywithout any effect on plant type. The genomicregion in which this yield QTL is located is taggedby several molecular markers that can be be usedto introgress this QTL to increase yield in highyielding rice cultivars.

Barley

The transfer of high grain yield QTL alleles onchromosomes 2 and 3 from the widely adapted cv.Baronesse to the high malting quality cv.Harrington has involved a marker-assisted selec-

Figure 2. Marker-assisted introgression scheme of favorablealleles at QTL applied in maize by Bouchez et al. (2002).

322

tion scheme (Schmierer et al., 2004). Several BC3Harrington isolines have displayed yields equal toBaronesse across test sites and years, and in mostcases the malting quality of Harrington has beenmaintained or improved. In this work the chro-mosome 3 QTL seems to be more effective on yieldthan the chromosome 2 QTL.

Soybean

The limited diversity in elite soybean germplasmhas prompted an interest in evaluating G. soja, thewild ancestor of soybean, as a new source ofgenetic diversity for improving the crop. Concibi-do et al. (2003) mapped a QTL allele from G. sojaPI 407305 that was associated with 12% yieldincrease across testing environments. The QTLallele from G. soja was then introgressed into sixgenetic backgrounds by marker-assisted BC toassess the adaptability of the G. soja yield QTLacross genetic backgrounds. The efficacy of theyield QTL was limited to two out of the sixbackgrounds, demonstrating the potential of usingexotic germplasm to improve soybean yield, atleast in some genetic backgrounds. Also Wanget al. (2004) used G. soja as a potential source ofuseful genetic variation for soybean improve-ment. They mapped four QTL for yield acrossenvironment in a series of BC populationsdeveloped using a G. soja line as a donor parentand a soybean cultivar as a recurrent parent. Forthese yield QTLs, the marker allele of G. max cul-tivar was responsible for the greater yield than themarker allele from G. soja.

MAS in breeding for disease and pest resistance

Most of the successful applications of MAS toplant breeding have been those for major diseaseresistance (R) genes. The introgression of an Rgene into an elite breeding line by traditionalbreeding can take up to 10–15 years. It is alsocomplicated by the need of performing time-consuming and labor-intensive artificial inocula-tion tests to assess the resistant phenotype, whichrequires maintenance of the pathogens or the pestson the host (or alternate hosts) if they are obligateparasites. Molecular markers tightly linked to Rgenes can obviate the need for resistance testing toidentify resistant individuals from early genera-

tions of resistance-segregating breeding popula-tions. Phenotypic assay to assess resistance tosoybean cyst nematode (SCN) in soybean or toleaf stripe in barley takes more than 5 weeks andextensive greenhouse space. Identification of reli-able PCR-based markers for MAS of the resistantphenotype has therefore led to an effectiveimprovement of the resistance breeding procedureagainst these soybean (Cregan et al., 2000) andbarley (Arru et al., 2003) diseases.

In addition, MAS can provide specific advanta-ges in resistance breeding, allowing faster responseto a breakdown in resistance, rapid introgression ofmultiple genes from diverse germplasm, pyramiding,and selection of rare recombinants between tightlylinked resistance genes (Michelmore, 2003).

Developing molecular markers

Markers linked to a given R gene have beenobtained by Bulk Segregant Analysis (BSA – Mi-chelmore et al., 1991), or by using fully mappedpopulations. Although the BSA approach tofinding a linked marker in barley has been esti-mated to be approximately one-third the cost ofusing fully mapped populations, BSA is limited totraits controlled by one or two major genes (Barret al., 2000).

New tools are providing assistance in deter-mining the map position of R genes; arrays ofcontiguous genomic Bacterial Artificial Chromo-somes (BAC) clones are becoming available forrice, corn and soybean. Hybridization to suchcontigs can provide a rapid and accurate methodfor mapping cloned sequences and can replacesegregation analysis; such a procedure has theadditional advantage that it does not requirepolymorphism between the parents of a mappingpopulation (Michelmore, 2000). This approachhas recently been used to map the gall midge Rgene Gm7 in rice: an AFLP fragment associated toGm7 has been used as a probe to screen rice BACand YAC genomic libraries and the identificationof a hybridizing YAC clone has allowed geneticand physical mapping of the R gene (Sardesaiet al., 2002).

R genes against a variety of different pathogenshave now been cloned from a variety of cropplants (Baker et al., 1997; Hammond-Kosack andParker, 2003). Most of these genes have been iso-lated through a marker-facilitated approach called

323

as map-based or positional cloning. The methodrely on– the development of large populations segregatingfor the trait of interest;

– utilization of markers flanking the target gene toscreen these populations in order to identify rareindividuals derived from one or more gametescontaining a crossover in the given interval;

– analysis of these individuals in relation to all themarkers within the region of interest and iden-tification of molecular markers tightly associatedor co-segregating with the target gene.These markers are suitable for the screening of

a large insert genomic library (BAC, PAC orYAC). In theory, the ideal marker would be theone lying at a physical distance from the targetgene that is less than the average insert size of thegenomic library from which one expects to isolatethe gene. The identification of such markers wouldavoid the chromosome walking steps via overlap-ping clones, a long and problematic process, byallowing a direct landing on the large insertgenomic clone containing the target gene (Tanks-ley et al., 1995). Development and mapping of newmolecular markers derived from sequencing of theends of the BAC or YAC clones would ensure ifthe clones identified contain the target gene.Modifications and optimizations of the methodhave allowed the isolation of several R genes fromdifferent plant species (e.g. Buschges et al., 1997;Zhou et al., 2001; Feuillet et al., 2003; Song et al.,2003).

The vast majority of these clonedR genes encodeproteins belonging to the nucleotide-binding/leucine-rich repeat (NB-LRR), extracellular LRR(eLRR) or LRR-Kinase superfamilies (Hammond-Kosack and Parker, 2003). Because sequences of thecloned R genes and of the immediately surroundinggenomic regions (frequently including ESTs) areavailable, precise (R-gene derived) or physicallyclose molecular markers for these R genes can beeasily generated.

In addition to known resistance genes, plantgenomes contain hundreds of other NBS-LRRencoding genes, called Resistance Gene Analogs(RGAs). Analysis of the Arabidopsis and ricegenomes has revealed a content of about 150 and600 predicted NBS-LRR encoding genes, respec-tively (The Arabidopsis Genome Initiative, 2000;Goff et al., 2002). RGAs may represent undiscov-ered resistance genes of complete or partial effect.

Alternatively, they may perform some functionunrelated to resistance (Madsen et al., 2003). RGAsequences identified by a PCR-based approachoften map to regions containing known diseaseresistance genes, as revealed for several crop plantsincluding soybean, potato, maize, barley and to-mato (for references see Pan et al., 2000; Madsenet al., 2003). Since RGAs that map close geneti-cally often reflect physical proximity (Leister et al.,1999; Wei et al., 2002) markers derived from RGAsequences could also be useful in MAS of R genesduring breeding. For instance, an RGA polymor-phic marker was recently used to develop a SCARmarker co-segregating with the BYDV resistancegene Bdv2 in wheat and this marker has success-fully been used to assess BYDV resistance in awheat breeding program (Zhang et al., 2004).

Assisted breeding for resistance

Several plant disease resistance genes have nowbeen tagged by molecular markers (selectedexamples are reported in Table 1) and examples ofpractical application of MAS are now available.

Genes conferring resistance to the two mostdestructive rice diseases, bacterial blight caused byXanthomonas oryzae and blast caused by the fungusMagnaporthe grisea, have been mapped (Mohanet al., 1997). A MAS assisted BC program has re-cently been used to pyramid into the high yieldingrice cultivar PR106, three bacterial blight resistancegenes (Xa21, xa13 and xa5) conferring resistance toall the bacterial races present in the specific culti-vation area (Singh et al., 2001). The broad-spec-trum blast resistance gene Pi5(t) in rice, derivedfrom the African cultivar Moroberekan, has re-cently been genetically and physically mapped(Jeon et al., 2003). Analysis of BAC end sequencestotalling around 70 kb has revealed a cluster ofNBS-LRR sequences at the Pi5(t) locus that mayconstitute a ‘‘natural pyramid’’ of resistance genesresponsible for the broad-spectrum resistance.A marker-assisted approach based on RFLP andPCR markers has successfully been used to pyra-mid three major genes for blast resistance (Pi1, Piz-5 and Pita); when tested with isolates virulenttowards a single R gene, the pyramided linesshowed enhanced resistance, demonstrating acomplementary effect of the three R genes whenpresent together (Hittalmani et al., 2000).

324

Table 1. Selected examples of gene-marker association for disease resistance in different crops

Disease/pest R gene Molecular markers Note References

Wheat

Leaf rust (Puccinia

recondita f.sp. tritici)

Lr34 from

T. aestivum

SSR Lr34 expresses resistance

in a quantitative way

Suenaga et al. (2003)

Lr 35 from

T. speltoides

STS and CAPS Adult plant leaf rust

R gene

Seyfarth et al. (1999)

Lr47 from

T. speltoides

CAPS R to a wide spectrum

of leaf rust strains

Helguera et al. (2000)

Stem rust (Puccinia

graminis f.sp. tritici)

Sr31 STS markers Broad spectrum R gene Mago et al. (2002)

Powdery mildew

(Blumeria graminis

f.sp. tritici)

Qpm.vt-1A,

Qpm.vt-2A,

Qpm.vt-2B

SSRs Quantitative resistance

from T. aestivum cv.

Massey, effective since

1981

Liu et al. (2001)

Pm4a CAPS Ma et al. (2004)

Pm5e SSR Huang et al. (2003)

Fusarium head blight

(Fusarium graminearum)

Qfhs.ndsu-3BS

from T. aestivum

cv Sumai3

RFLPs Sumai is the principal head

blight resistance source

in wheat

Anderson et al. (2001)

Yellow rust (Puccinia

striiformis f.sp. tritici)

Yr15 RAPD and SSR R to a wide spectrum of

yellow rust strains

Chague et al. (1999)

YrMoro STS Smith et al. (2002)

Rice

Bacterial blight

(Xanthomonas oryzae

pv. oryzae)

xa5 STS Three bacterial blight R

genes were pyramided in

the high yielding

susceptible

cultivar PR106

Singh et al. (2001)

xa13 STS

Xa21 STS

Rice blast (Pyricularia

oryzae)

Pi5(t) CAPS Broad-spectrum

resistance

Jeon et al. (2003)

Pi1, Piz5, Pita RFLP and SAP Pyramiding of three

blast R genes

Hittalmani et al.

(2000)

Piz, Pizt PCR-based SNP Hayashi et al. (2004)

Pi-b, Pi-k, Pita2 SSRs Fjellstrom et al.

(2004)

Gall midge (Orseolia

oryzae)

Gm7 SA598 SCAR Sardesai et al. (2002)

Brown planthopper bph2 STS Murai et al. (2001)

Barley

Barley yellow mosaic

virus

rym4/rym5 SSR rym4 and rym5 on

chromosome 3H, are

allelic with respect to

BaMMV

Graner et al. (1999)

Williams (2003)

rym4, rym9, rym11 SSRs Werner et al. (2003)

Leaf rust (Puccinia

hordei)

Rph7 CAPS Effective in Europe Graner et al. (2000)

Rph15 CAPS Broadly effective Weerasena et al.

(2004)

325

For pathogens that can easily generate newvirulent pathotypes, such as rusts and mildews,high and durable levels of resistance should beachievable by MAS. Of almost 50 known Lrresistance genes against the damaging leaf rustdisease of wheat, the slow rusting genes Lr34 andLr46 have been effective over a long period oftime, in different environments and against differ-ent pathotypes of the fungus (Kolmer, 1996; Singhet al., 1998). Several studies have described

enhanced resistance effects derived from combi-nations of Lr34 with other Lr genes, such as Lr2,Lr12, Lr13, Lr16, which give hypersensitive resis-tance responses (Kolmer, 1996; Kloppers andPretorius, 1997). Microsatellite markers linked toLr34 have been identified (Suenaga et al., 2003).Molecular markers have also been developed forother leaf rust genes including Lr1, Lr9, Lr19,Lr24, Lr25, Lr28, Lr29, Lr32 and the highlyeffective Lr35 mediating a resistant hypersensitive

Table 1. (Continued)

Disease/pest R gene Molecular markers Note References

Maize

Sugarcane mosaic virus

(SCMV)

Scm1 and Scm2 SCAR and CAPS Finely mapped to maize

chromosomes 6 (Scm1)

and 3 (Scm2)

Dussle et al. (2002)

Sugar beet

Rhizomania (BYNVV ) Rr1 introgressed

from Beta maritima

SCAR F6

SCAR N9

F6 is linked in coupling

with the Rr1 allele;

dominant allele of N9

is linked to the Rr1

allele in repulsion

Barzen et al. (1997)

Tomato

Black mold (Alternaria

alternata)

QTLs introgressed

from L. cheesmanii

CAPS and RFLP QTL on chromosome

2 is the most effective

Robert et al. (2001)

Corky root rot (Pyreno-

chaeta lycopersici)

py-1 introgressed

from L. peruvianum

CAPS A recessive gene

conferring R to a

soil-borne fungus

Doganlar et al. (1998)

Powdery mildew

(Oidium lycopersicum)

Ol-1 introgressed

from L. hirsutum

SCAR Incompletely

dominant gene

Huang et al. (2000)

Root knot nematodes

(Meloydogyne spp.)

Mi introgressed from

L. peruvianum

RAPD Williamson et al.

(1994)

Mi3 introgressed

from L. peruvianum

RAPD and RFLP Yaghoobi et al. (1995)

Apple

Scab (Venturia

inaequalis)

Vf introgressed

from Malus

Floribunda 821

ALO7-SCAR

cosegregating with

M19-CAPS and

M18-CAPS

Effective against most

of the pathogen races

Tartarini et al. (1999)

King et al. (1999)

Vm introgressed

from M. micromalus

STS Cheng et al. (1998)

Mildew (Podosphaera

leucotricha)

Pl1 SCAR Kellerhals et al. (2000)

Grapevine

Powdery mildew

(Uncinula necator)

Run1 introgressed

from Muscardinia

rotundifolia

AFLP, CAPS,

SCAR

Effective in the field

against the most

frequent genotypes of

the fungus in Bordeaux

and Montpellier

Pauquet et al. (2001)

Donald et al. (2002)

326

response (for references see Seyfarth et al., 1999;Huang and Gill, 2001), potentially allowing com-binations of slow-rusting genes with others con-ferring an hypersensitive response. Resistance tothe causative agent of the wheat powdery mildewdisease (Blumeria graminis f. sp. tritici) is governedby both race-specific resistance genes and adultplant resistance (APR) genes. APR retards infec-tion, growth and reproduction of the pathogen inadult plants but not in seedlings. Three adult plantpowdery mildew resistance QTLs, which have beeneffective for more than 20 years, have been map-ped in the wheat cultivar Massey (Liu et al., 2001).Recombinant inbred lines containing Massey-derived alleles at all three loci had a mean diseaseseverity of 3.4%, whereas the RI lines with allelesof the susceptible parent at all three loci had amean disease severity of 22.3%. Microsatellitemarkers identified as associated with the threeQTLs have therefore potential for use in MAS forAPR to powdery mildew, with or without con-current selection for race-specific powdery mildewresistance genes. Molecular markers linked tomajor wheat powdery mildew R genes and suitablefor MAS have been identified for Pm4a (Ma et al.,2004), Pm5e (Huang et al., 2003), as well as forother Pm genes (for references see Ma et al., 2004;Huang et al., 2003).

Sources of resistance to some pathogens areoften not present in the cultivated species but inrelated wild germplasm. Suppression of recombi-nation with the introgressed segments can lead to‘linkage drag’ of genes with unwanted effects onthe phenotype, persisting through many BCs.Molecular markers can be used to select for anintrogression segment of minimum size in order toreduce or eliminate deleterious effects resultingfrom linkage drag. For example, accessions ofMalus with a minimum chromosome segmentfrom M. floribunda containing the scab resistancegene Vf have been selected (King et al., 1999).Similarly, MAS allowed selection of wheat-ryerecombinant lines containing the highly effectiverust (Lr26, Sr31/SrR, Yr9) and powdery mildew(Pm8) resistance genes from rye without the dele-terious grain quality characteristics (sticky doughand a reduction in dough strength) of the closelylinked rye Sec-1 locus (Mago et al., 2002). Ingrapevine (Vitis vinifera) the monogenic dominantpowdery midew resistance gene Run1 has beenintroduced from the wild muscadine grape Mus-

cadina rotundifolia. A map of the Run1 region wasmade using AFLP markers (Pauquet et al., 2001)and the markers identified are suitable for selectinggood quality genotypes with the smallest M.rotundifolia genomic fragment containing Run1.Three AFLP markers were identified as beingappropriate for subsequent MAS of Run1 ingrapevine cultivars. In potato, introduction ofresistance traits also relies mostly on wild germ-plasm, and is therefore frequently associated withlinkage drag. During introgression of resistance tolate blight (Phytophthora infestans) from Solanumbulbocastaneum, several resistant individualsshowing the recurrent parent alleles at molecularmarkers on 6 or more chromosomes were identi-fied already from the BC2 populations. These lineswere selected as superior parental materials forsubsequent BC generations (Naess et al., 2000).Genes conferring resistance to the most importantpotato diseases have been mapped and molecularmarkers to assist the selection of the resistantphenotype have been identified (reviewed in Geb-hardt and Valkonen, 2001).

Linkage drag problems are potentially greaterwhen the resistant phenotype is governed byQTLs. No genetic resistance to blackmold hasbeen reported in cultivated tomato (L. esculetum).QTLs for blackmold resistance have been intro-gressed into cultivated tomato from the wild spe-cies Lycopersicon cheesmanii using MAS mediatedby RFLP and PCR-based markers flanking andwithin the chromosomal regions containing theQTLs (Robert et al., 2001). A QTL on chromo-some 2 provided a positive effect on blackmoldresistance; other two QTLs were associate withincreased level of resistance but associated to del-eterious agronomical traits.

Utilization of molecular markers is a valuablealternative means of diagnosis for assessing diseaseresistance genes when the phenotypic trait is gov-erned by recessive or incompletely dominantgenes. Tomato resistance to corky root rot and topowdery mildew is governed by the recessive py-1(Doganlar et al., 1998) and the incompletelydominant Ol-1 (Huang et al., 2000) resistancegenes respectively. Co-dominant CAPS markersassociated to py-1 and dominant SCAR associatedin coupling and repulsion phase to Ol-1 areallowing these genes to be incorporated intomodern cultivars and speeding up breeding pro-grammes for resistance to these two diseases.

327

The availability of molecular markers to selectfor resistance in long-generation cultivated tree orwoody species can be a great advantage, especiallyfor those species which are self-incompatible orhave a long juvenile period. The fungus Venturiainaequalis is the causative agent of apple scab,the most important apple disease. Identifiedcodominant PCR-based markers closely flankingthe Vf resistance gene allow the identification ofseedling homozygous for the resistance gene andthereby make traditional test-cross experimentsunnecessary (King et al., 1999; Tartarini et al.,1999). Similarly, a codominant STS marker forthe peach Mij gene, which makes rootstocks resis-tant to the root-knot nematodes M. incognita andM. javanica, can allow the selection of homozy-gous resistant individuals (Lu et al., 1999).

Molecular markers can also provide informa-tion on the virulence functions present in thepathogen population of a given cultivation area,and thereby help to assess the likelihood of thepathogen in overcoming the deployed R gene. Forexample, molecular markers diagnostic for theability of root-knot nematodes of the genusMeloidogyne (M. incognita, M. javanica, M. are-naria) to overcome the tomato resistance gene Mihave been obtained (Xu et al., 2001). Similarly, atleast three mutations associated with high levels ofresistance to Bt toxins have been identified in in-sect pests (reviewed in Tabashnik, 2001). Typicalbioassays are not effective in detecting the presenceof Bt toxin resistance alleles when these arerecessive or rare (as is most often the case). Dis-covery of genes underlying Bt resistance in keyinsect pests therefore enables a DNA-based mon-itoring strategy for these rare mutational eventswhich will play an important role in managinginsect resistance to Bt crops.

MAS for low-temperature stress tolerance

Besides a requirement for vernalization, overwin-tering crops also require frost and cold tolerance.Cold tolerance is recognized as having a complexquantitative inheritance, making therefore prob-lematic MAS approaches to increase tolerancephenotypic values. Nevertheless, some few exam-ples of successful utilization of MAS for improvingcold tolerance in crop plants are available.

In barley, two tightly linked QTLs for low-temperature tolerance were identified on chromo-some 5H (Francia et al., 2004); these QTLs werecoincident with QTLs regulating mRNA levels aswell as protein accumulation of two well charac-terized cold-regulated (COR) genes (Vagujfalviet al., 2003; Francia et al., 2004 respectively).Several genes with the CBF transcription factorsignature mapped in a cluster in this region. Sincea CRT/DRE recognition site, a potential site forinteraction with a CBF transcription factor, wasfound in the genomic regulatory sequence of oneof the two COR genes, the identified CBF genesrepresent candidates for the gene underlying theQTL (Francia et al., 2004). Because it has beendemonstrated that CBF1 overexpression inducesCOR genes and enhances freezing tolerance inArabidopsis (Jaglo-Ottosen et al., 1998), theseresults support the hypothesis that members of theCBF gene family may regulate the stress responsesof a wide range of plant species. PCR-basedmarkers (a RAPD marker and an STS derivedfrom the sequence of a wheat RFLP mapped in thefrost tolerance QTLs region on chromosome 5H)have recently been validated for their ability inassessing frost tolerance level in two sets of winterand spring barley genotypes and in doubled-hap-loid lines derived from a cross between a highlytolerant and a susceptible genotype (Toth et al.,2004). These two markers were shown to dis-criminate efficiently between frost-tolerant andfrost-susceptible genotypes. Their use in differentbreeding materials will clarify how much would bethe gain in frost tolerance obtained by the onlyMAS respect to phenotypic selection in stressingenvironments.

Some stress-implicated genes have been shownto co-segregate with stress tolerance QTLs incrops. Two Dehydrin loci (Dhn1/Dhn2 and Dhn9)are located in the region of Triticeae chromosomegroup 5 known to contain QTLs for cold and salttolerance and ABA accumulation (reviewed inCattivelli et al., 2002). A potential application ofthese findings has derived from a study on cowpea;in this plant the accumulation of the 35 kDadehydrin was correlated with chilling toleranceduring seedling emergence. Allelic variations in thecoding region of the dehydrin structural gene mapto the same position as the dehydrin proteinpresence/absence trait, which in turn is associated

328

with chilling tolerance/susceptibility during seed-ling emergence (Ismail et al., 1999).

Rice has evolved in tropical and subtropicalareas, and hence its cultivation is vulnerable tolow-temperature stress in temperate-growingregions and in high-elevation environments. An-thers at booting stage are known to be susceptibleto low temperature, so cold stress results in de-layed heading or maturation and yield reductiondue to spikelet sterility. Abe et al. (2002) reportedthe tight association of a SNP in a rice alternativeoxidase gene (OsAOX1a) with two closely linkedQTLs (Ctb1 and Ctb2) for low temperature toler-ance of anthers at the booting stage mapped tochromosome 4. They found that the allelic varia-tion in molecular mass of AOX isoforms amongvarieties differing in low temperature toleranceco-segregate to the presence of the QTL. Theseresults suggest that exploitation of this SNP rep-resent a good tool for MAS of the cold toleranceQTL. Ctb1 locus has recently been physicallymapped and seven candidate genes for this QTLhave been identified (Saito et al., 2004).

MAS for drought stress tolerance

Drought is by far the most significant environ-mental stress in worldwide agriculture. Althoughplants are exposed to many types of environmentalstresses, osmotic stress, whether by drought,salinity or low temperature, constitutes the mostserious limitation to plant growth, productivityand distribution.

Many studies on drought resistance havemonitored the physiological and biochemical sta-tus of stressed plants compared with unstressedplants. Important mechanisms of drought resis-tance deduced from these studies mainly includethe following:– drought escape via a short life cycle, photope-

riod sensitivity and developmental plasticity;– drought avoidance via enhanced water uptake

and reduced water loss;– drought tolerance via osmotic adjustment (OA)

and antioxidant capacity; and– drought recovery via desiccation tolerance

(Zhang et al., 1999).Direct selection for grain yield under water-stres-sed conditions is difficult due to low heritabilityand significant G · E interactions (Ceccarelli

et al., 1991). An alternative strategy is representedby the selection of a range of morpho-physiologi-cal characters suggested to be indicators of in-creased grain yield under drought conditions.Even if it is difficult to identify traits that provide aconsistent advantage on yield across variable wa-ter-limited environments, a catalogue of such traitshas been proposed (Turner, 1997). In this sense,OA is believed to be important for allowing plantsto maintain turgor and avoid meristem damagewhen faced with extreme desiccation; in addition,other mechanisms are probably also important fordrought tolerance, including:– the ability of the roots to exploit deep soil

moisture to meet evapotranspirational demand,– moderatation of water-use by reduction of leafarea and shortening of growth period, and

– the limitation of nonstomatal water loss fromleaves (i.e. through the cuticle).A plethora of traits are therefore involved in

increasing drought stress tolerance, making amolecular breeding approach for drought toler-ance a complex task. In this section some selectedexamples are reported on the mapping and on thepossible exploitation of traits involved in droughtstress tolerance.

Osmotic adjustment

When cells are subjected to slow osmotic stress,compatible solutes are accumulated resulting inthe maintenance of a higher turgor potential at agiven leaf water potential. Genetic variation in OAhas been reported in a number of plant species andcultivars and differs with respect to the types ofsolutes accumulated (i.e. amino acids, sugars,polyols, quaternary amines, ions, and organicacid) (Bohnert and Jensen, 1996).

OA of wheat was found to be influenced byalternate alleles at a single locus on chromosome7A, with high response being recessive (Morganand Tan, 1996). Control of OA by the 7A locuswas based primarily on potassium accumulation,and secondarily on amino acid accumulation. Inrice, a QTL for OA under drought stress wasidentified on chromosome 8 across rice popula-tions (Lilley et al., 1996; Robin et al., 2003).Comparative mapping indicates that the region ofrice chromosome 8 containing the OA QTL ishomeologous with a segment of wheat chromo-some 7S which contains the OA locus identified by

329

Morgan and Tan (1996) and with a barley chro-mosome 1 region where a QTL for relative watercontent in stressed conditions was identified(Teulat et al., 2003). Similarly, a rice QTL for OAlocated on chromosome 3 reside in a genomicregion syntenic with the homeologous region ofmaize chromosome 1; in maize this region isassociated with various physiological and agro-nomic traits affecting drought tolerance (Zhanget al., 2001). These results suggest that duringcereal evolution, genes in these genomic regions inrice, wheat, barley and maize have been conservedto respond to drought conditions and mighttherefore contains useful genes for the improve-ment of drought resistance in cereal crops. The riceQTL region controlling OA on chromosome 3 hasrecently been subjected to saturation mapping andnew markers as well as stress-related EST havebeen added to this chromosomal region and, fol-lowing the authors suggestions (Nguyen et al.,2004), these markers can now be used for MAS ofthe favorable QTL allele.

Root penetration and morphology

Constitutive and adaptative root growth have beenimplicated in the improved performance of riceunder rainfed lowland conditions. A causal rela-tionship between root traits and yield performan-cies under drought stress was found in the study ofBabu et al. (2003). In this work, QTLs controllingproduction traits under irrigated and droughtstress conditions were mapped using a rice DHpopulation. On chromosome 4, a major regioncontrolling grain yield components under droughtstress was identified. In this study, root traits (likeroot penetration index, root thickness at stem base,root pulling force and root morphology) identifiedin the same mapping population in other studies(references in Babu et al., 2003) showed positivecorrelations with yield and yield components underdrought stress on chromosome 4 region.

A marker-assisted BC program was recentlyused to improve root traits affecting drought stresstolerance in the elite rice cultivar IR64, (Shenet al., 2001). Foreground selection of the Azucena(an upland tropical japonica variety) allele at fourQTLs for deeper roots was performed strictly onthe basis of the genotypes at the marker loci up tothe BC3F2. Selected NILs for the four target QTLsshowed, depending of the target QTLs, improved

root length (by 12 to 27% with respect to IR64) orimproved deep root weight (two NILs had thehighest phenotypic gain, outperforming IR64 byup to 75%).

Efforts for the identifications of gene functionsresponsible for the QTLs affecting root traits haverecently involved the mapping of candidate genesfor root traits under drought stress (Zheng et al.,2003; Nguyen et al., 2004). Genes, identified asdifferentially expressed under drought conditionsand/or putatively involved in root growth/modifi-cation, were found to map to chromosomalregions that affects root traits. The tight geneticlinkage between these candidate genes and theQTLs for root traits does not definitely demon-strate a causal relationship; associations of thesedata with fine mapping experiments and with thedevelopment of NILs for a given QTLs wouldreinforce a possible causal relationship. Notwith-standing, the availability of additional sequencestightly linked to QTLs conferring drought resis-tance provide an additional set of markers usefulto select the favorable QTL alleles.

Carbon isotope discrimination

Carbon isotope discrimination (CID) provides anintegrated measurement of TE (TranspirationEfficiency, the ratio of dry matter produced towater transpired) of C3 crop species (Ehdaie et al.,1991). During photosynthesis, plants favor incor-poration of the light carbon isotope (12C) over theheavy isotope (13C), with the result that CID ispositively correlated with the ratio of internal leafCO2 concentration to ambient CO2 concentration(Ci/Ca) and negatively associated with TE (Ehdaieet al., 1991). Thus, a high Ci/Ca leads to a higherCID and a lower TE. The major advantage ofusing CID in selection is its high heritability, whichis primarily due to small G · E interactions indryland areas (Merah et al., 2001).

Teulat et al. (2002) have reported the first QTLstudy for CID measured in mature grains fromplants grown in Mediterranean field conditions,identifying ten QTLs involved in CID variation.Eight QTLs for CID overlap with QTLs previouslyidentified in the same population and affectingplant water status, OA and/or yield components.These regions are of interest in terms of plantbreeding as they control both important drought-adaptative traits for cereals and yield components.

330

Phenological traits

When drought stress occurs just before or duringthe flowering period, a delay in silking is observed,resulting in an increase in the length of theanthesis-silking interval (ASI) in maize. Thisasynchrony between male and female floweringhas been associated with a grain-yield decreaseunder drought (Ribaut et al., 1997). Under con-ditions of low yield, selection for secondary traitssuch as ASI, which are highly correlated with grainyield and have relatively high heritability, mayincrease selection efficiency (Bolanos andEdmeades, 1996). Selection for reduced ASI intropical open-pollinated varieties has been shownto correlate with improved yields under droughtstress. Molecular markers allowed the identifica-tion of four genomic regions for the expression ofboth yield and ASI in maize (Ribaut et al., 1997).In three of these regions, the allelic contributionsfor short ASI corresponded to grain yield increase,while for one genomic region the allelic contribu-tion for short ASI corresponded to a yield reduc-tion. In the design of a breeding strategy, selectionfor QTLs involved in the expression of ASI shouldtherefore be combined with selection for grainyield.

Stay-green is an important form of droughtresistance mechanism in sorghum, conferringresistance to premature senescence under soilmoisture stress during the post-flowering period.QTL studies with RILs and NILs have identifiedseveral genomic regions associated with resistanceto pre-flowering and post-flowering drought stress(Haussman et al., 2002; Sanchez et al., 2002). Inthe work of Sanchez et al. (2002), three stay greenQTLs, Stg1, Stg2 and Stg3, accounting for 20, 30and 16% of phenotypic variance respectively aredescribed. At least Stg2, considered as the mostimportant QTL, was found to be consistent acrossseveral different mapping populations and envi-ronments; for this QTLs, NILs have been devel-oped by marker-assisted BC breeding and physicalcontig of sequences have been obtained with sor-ghum BAC clones. Because the average markerinterval for the genomic regions of the QTLs Stg1and Stg2 is 1.3 and 1.7 cM respectively, severalinformation are therefore available to allow theexploitation of this favorable trait. In addition,sorghum could serve as a bridge species for com-parative mapping analysis between the grass rela-

tives (Paterson et al., 2000); breeding for droughtresistance is also an important objective for othercrops like maize or wheat and the stay-greenphenomenon has also been reported in these crops.After fine mapping in the region of the sorghumQTLs, DNA markers could be used to pinpointthe corresponding orthologous regions in maize orwheat.

Seed germination

An additional approach to minimizing agriculturallosses incurred by drought stress is to develop, viagenetic means, plant cultivars that can escape orwithstand periods of drought. Selection andbreeding for drought tolerance is also difficultbecause tolerance is a developmentally regulated,stage-specific phenomenon (Richards, 1996). Mostcommercial cultivars of tomato are sensitive todrought stress at all stage of plant development,with seed germination and early seedling growthbeing the most sensitive. A tomato populationsegregating for seed germination rate underdrought stress has been obtained from a crossbetween a Lycopersicon esculentum breeding lineand a germination stress-tolerant Lycopersiconpimpinellifolium (Foolad et al., 2002). A total of119 RFLP markers were used in a distributionalextreme marker analysis to measure statisticaldifferences in marker allele frequencies betweenrapidly germinating and slow germinating lines.This resulted in identification of four QTLs forrate of germination under drought stress. At theQTLs located on chromosomes 1 and 9 thefavorable alleles were contributed by theL. pimpinellifolium parent and had large effects,while at the QTLs on chromosomes 8 and 12 thefavorable alleles were contributed by theL. esculentum recurrent parent. The overall resultsindicate that drought tolerance during seed ger-mination in tomato is genetically controlled andcould potentially be improved by directional phe-notypic selection or MAS.

MAS for salinity and aluminum tolerance

Irrigation is a common cause of agricultural landdegradation, because salt dissolved in the irriga-tion water is left in the soil following evapotra-spiration. In regard to salinity, rice has been

331

particularly studied, due to its preference for irri-gation, its sensitivity to salinity and its relativelysmall genome. Lee et al. (2003) found that reduc-tion in several growth parameters were signifi-cantly lower in indica (tolerant) varieties than injaponica varieties. Tolerant indica varieties weregood Na+ excluders, absorbed high amounts ofK+, and maintained a low Na+/K+ ratio in theshoot. Tolerant japonica varieties absorbed lessNa+ but were not as good excluders as indicavarieties. Taken as a whole, these results indicatethat, for all parameters measured, the tolerancelevel of indica was higher than that of japonica.These results have recently been confirmed in astudy where QTL mapping for physiological traitsrelated to rice salt-tolerance has been performed(Lin et al., 2004). In this study QTL analysis hasbeen carried out on a segregating populationderived from a cross between a high salt-tolerantindica variety and a susceptible elite japonicavariety. Two major QTLs with very large effects,one on chromosome 7 for shoot Na+ concentra-tion (called as qSNC-7) and one on chromosome 1for shoot K+ concentration (called as qSKC-1),explained 48.5 and 40.1% of the total phenotypicvariance respectively; the additive affect of thealleles of the salt-tolerant variety at the qSNC-7and qSKC-1 QTLs led respectively to a reductionof shoot Na+ concentration and to an increase ofshoot K+ concentration. In three F3 linesbelonging to the segregating population, the allelesof the salt-tolerant variety at a discrete number ofQTLs (three to four, including qSNC-7 andqSKC-1) for physiological traits related to salt-tolerance were pyramided: these lines showed alevel of seedling survival under salt stress equal orsuperior to the one observed for the resistantparent. These results would indicate that a breed-ing method of QTLs pyramiding using MAS couldbe applied for the development of varieties withhigh level of salt tolerance.

Aluminum toxicity is a major limiting factorfor agriculture in tropical and acidic soils. Usingbread wheat (T. aestivum) recombinant inbredlines, a single locus for Al tolerance (referred to asAltBH) was found on the long arm of chromosome4D (Riede and Anderson, 1996). A single genecontrolling aluminum tolerance was also found inbarley on chromosome 4H (Alp; Tang et al., 2000)and microsatellite markers associated to this locushave been identified (Raman et al., 2003); the

microsatellite marker Bmag353 has been validatedin a F3 population segregating for Al toleranceand the marker was found to predict the Al tol-erance phenotype with over 95% accuracy. Previ-ous reports showed that there is a conservedgenomic region on the long arm of homoeologouschromosome 4 for Al tolerance among wheat(AltBH), rye (Alt3) and barley (Alp) (Miftahudinet al., 2002). On the basis of common markers itwas suggested that the AltBH, Alt3 and Alp genesare orthologous loci because of the high level ofsynteny among chromosomes 4DL, 4RL and 4HLand they may share common function. One of themechanisms for Al tolerance in the Triticeae is Alexclusion; this mechanism is mediated byAl-activated release of organic acids (malic acid),which chelate Al3+ in the rhizosphere and preventits entry into the root apex. A rice major QTL forAl tolerance, contributed by O. rufipogon, hasrecently been mapped on chromosome 3 and, onthe basis of comparative map analysis, this QTLappear to be orthologous to the genomic regioncarrying the major Al tolerance gene on group 4 ofthe Triticeae (Nguyen et al., 2003). Even if physi-ological evidences of Al tolerance in rice are stilllacking, the fact that collinear genomes pinpointssimilar traits to the same chromosomal regionraise the suspect that these loci are encoded bydifferent alleles of a single gene. If this hypothesiswill turn to be true, the Al tolerance locus on thelong arm of homoeologous chromosome 4 of theTriticeae will provide a useful source in breedingfor Al tolerance across several crop plant species.

MAS for quality traits

Most quality traits show continuous variation andare influenced by environmental conditions. Not-withstanding, some example of quality traits, forwhich MAS is a reliable approach for selection,are now available for several important cropsincluding tomato, barley, wheat, cotton, and rice(Table 2).

Tomato

Soluble-solids content is of paramount importancefor processing tomatoes, because lines with highersugar content require less energy input (i.e. lesscooking) during the concentration process. To

332

uncover the molecular basis of sugar content var-iation, a QTL for total soluble solids (sugar andacids), named Brix9-2-5, derived from the green-fruited tomato species Lycopersicon pennellii, wascharacterized (Fridman et al., 2000). The geneticbasis of the QTL was dissected by positionalcloning and was shown to be the Lin5 gene, codingfor a fruit-specific apoplastic invertase hypothes-ised to modulate fruit sink strength. TheL. pennellii Brix allele increased glucose (+28%)and fructose (+18%) contents in various geneticbackgrounds of cultivated tomato and across dif-ferent environments, and confers around 3-foldincrease in soluble solid content (up to 15% of thefruit’s fresh weight). Brix9-2-5 was shown to bepartially dominant and independent of fruit weightand yield. Another QTL increasing soluble solidscontent has recently been introgressed in cultivatedtomato from L. chmielewskii chromosome 1, gen-erating a NIL with a 56-cM introgression (Fraryet al., 2003). Cross of this NIL with a cultivatedtomato line, generate an F2 segregating popula-tion. From this population subNILs with areduced size of the L. chmielewskii introgressedfragment were generated through the identificationof recombinant plants identified by RFLP analysisof markers flanking the introgression. Additionalmarker analysis to determine the exact recombi-nation break points allowed the identification of a

subNIL with only 19-cM introgression containingthe soluble solids QTL.

Another important quality trait in tomato isthe fruit shape. The recessive pear-shaped tomatofruit trait ovate identified by early 20th-centurygeneticists has been mapped to a locus on chro-mosome 2. Recent genetic analyses have identifiedovate as a major quantitative trait controlling fruitelongation and neck constriction in both tomatoand eggplant. The major QTL ovate has recentlybeen cloned from tomato and encodes for a pro-tein with regulatory functions (Liu et al., 2002).Sequencing of the OVATE gene both in the wildtype (round-fruited) and in an ovate genotype hasled to the identification of an early stop codon inthe ovate genotype causing the transition fromround to pear shaped.

Fruit size is another important quantitativetrait. In the last 20 years a great number of QTLsfor fruit size have been detected in severalLycopersicon species, 28 of which have been con-firmed in independent studies (Grandillo et al.,1999). In one tomato population, the QTL fw2.2was found to be responsible for 30% of the vari-ation in fruit size. The gene underlying the majorQTL fw2.2 has been cloned (Frary et al., 2000).The gene product has sequence similarity to thehuman oncogene c-H-rasp21m, and is early ex-pressed during floral development.

Table 2. Selected examples of important quality traits in plants

Species Trait Locus/gene Molecular markers References

Tomato Total soluble solids

(sugar and acids)

Brix9-2-5 RFLP, SCAR, CAPS Fridman et al. (2000)

Tomato Total soluble solids

(sugar and acids)

Brix TA1150 QTL RFLP and SCAR Frary et al. (2003)

Tomato Fruit elongation and

neck constriction

ovate RFLP, SCAR, CAPS Liu et al. (2002)

Tomato Fruit size fw2.2 RFLP and CAPS Frary et al. (2000)

Barley Malting quality QTL1 (chr. 1H)

and QTL2 (chr. 4H)

RFLP Han et al. (1997)

Barley Malting quality QTLs on chr. 3H,

6H and 7H

RFLP Igartua et al. (2000)

Wheat (Triticum

aestivum)

Dough strength

(HMW glutenin)

Glu-1 alleles SCAR Radovanovic and

Cloutier (2003),

Ma et al. (2003)

Cotton Fiber strength QTLFS1 SSR and RAPD Zhang et al. (2003)

Rice Eating and cooking

quality

Waxy SSR Zhou et al. (2003)

333

A MAS BC scheme (Bouchez et al., 2002;Figure 2) has recently been applied for the intro-gression of five QTLs controlling fruit qualitytraits derived from L. esculentum var. cerasiformeinto three different genetic backgrounds of culti-vated tomato (Lecomte et al., 2004). In this studyit was observed that as few as three BCs weresufficient to recover most of the recipient genomefor chromosomes in which no QTLs effect hadbeen detected. Because quality traits QTLs werenot precisely mapped, to reduce the risk of loosingthe QTLs, almost no selection for genetic back-ground was performed on chromosomes carryingthe quality traits QTLs; large regions of the donorchromosomes were therefore transferred leading tounfavorable linkage drag. It is therefore advisablethat QTLs map position should be preciselydefined in order to allow a selection pressure alsoin chromosome regions where target QTLs arelocated. Nevertheless, in this study, marker-assistedBC was efficient in improving quality traits in thegenetic backgrounds used.

Malting barley

Methods for assessing the suitability of barley formalting involve analyses of grain characters,micromalting and laboratory analyses of maltquality traits. Barley grain and malt quality char-acters generally exhibit quantitative variation andare influenced by genetic and environmental fac-tors and by G · E interactions (extensively re-viewed in Fox et al., 2003). MAS would thereforebe desirable in breeding programs to help over-come environmental effects and to minimize costlyand time-consuming laboratory analysis.

In a six-row Steptoe · Morex barley popula-tion, major QTLs controlling malt extract per-centage, alpha-amylase activity, diastatic power,and malt beta-glucan content, have been identifiedon chromosomes 1 (QTL1) and 4 (QTL2). The largeand consistent effects of these two QTL regionsacross different environments make them goodcandidates for MAS. RFLP markers Brz andAmy2, WG622 and BCD402B, respectively flank-ing QTL1 and QTL2, were used in a study com-paring different selection strategies (Han et al.,1997). Genotypic selection (G) alone was used, inaddition to phenotypic selection (P), tandemgenotypic and phenotypic selection (first G andthen P, GfiP), and combined phenotypic and

genotypic selection (G and P together, G+P).MASforQTL1 (GfiP andG+P)wasmore effective thanphenotypic selection, whileMAS for QTL2 was notas effective as phenotypic selection due to a lack ofQTL2 effects in the background used.

In the two-row Harrington (North Americamalting barley industry standard culti-var) · TR306 barley population, QTLs for grainand malt quality traits were identified on chro-mosomes 3 (3H), 6 (6H) and 7 (5H). Barley lines,derived from same cross where the QTLs had beenmapped, were selected on the basis of their mar-ker-genotype at two identified QTLs on chromo-some 7 (5H) (Igartua et al., 2000). Selected lineswere phenotypically superior and the magnitude ofthe effects for these regions were closed to theestimates calculated in the mapping population.MAS was therefore effective within this popula-tion, but there are not indications of whether theseQTLs would be useful beyond this particularcross.

Wheat

The most important quality parameters in wheatrelate to physical (rheological) properties of thedough during bread making, such as extensibilityand resistance to extension. These propertiesdepend on the endosperm gluten proteins, whichcomprise two major fractions: gliadins and glute-nins (Ma et al., 2003). Generally, high molecularweight (HMW) glutenins have been found to bemore important than gliadins and low molecularweight (LMW) glutenins for dough rheologicalproperties. Breadmaking qualities, especiallydough strength, are dependent on the compositionof HMW glutenin subunits, particularly the allelesGlu-A1b and Glu-D1d.

SDS-PAGE of seed proteins is used forscreening wheat lines for glutenin polypeptideprofiles. This method is relatively efficient becauseallelic variation at multiple loci can be assessed ina single gel lane. PCR-based molecular markersbased on sequence variations of the coding andpromoter regions of the wheat HMW gluteningenes at the Glu-1 locus have been developed(Radovanovic and Cloutier, 2003). When tested ina DH population segregating for bread makingquality, DNA and SDS-PAGE protein markersshowed discrepancies of only 2 to 8.5% dependingon the marker assayed.

334

PCR-based molecular markers have also beendeveloped for the Glu-A1 locus in Australiancommercial wheat varieties (Ma et al., 2003). Thesecultivars show only one or two predominant allelesat each HMW glutenin (Glu-1) homoeologouslocus. Products of a single multiplexed PCR reac-tion permitted the discrimination of the majorHMW glutenins in one simple assay. These mark-ers are currently used in MAS for HMW gluteninsin DH-based wheat breeding programs.

Cotton

Cotton is a high-value per acre crop that providesa raw material for the textile industry. Heritabilityof cotton yield components and fiber propertiesis moderate to high (around 40–80%), based oncurrent estimates. However, genetic control offiber quality is affected by G · E interactions,specifically by differences in water managementregimes (Paterson et al., 2003).

Molecular markers linked to fiber-strengthQTLs have been recently identified using a segre-gating population derived from a cross between aGossypium anomalum introgression line, with goodfiber quality properties, and a standard cottonvariety (Zhang et al., 2003). Three SSRs and sixRAPDs markers were identified to be linked at twoQTLs for fiber strength. A major QTL (QTLFS1)associated to eight markers explained more than30% of the phenotypic variation. MAS using twoRAPD markers and a SSR marker was used toassist the transfer of QTLFS1 in four different ge-netic backgrounds. The major QTL was geneticallystable in the different backgrounds and lead to asubstantial increase in fiber strength of the im-proved lines. It was concluded that MAS is efficientto increase fiber strength, especially using the SSRmarker associated to the major QTL, since thismarker could identify the homozygous genotype.

Rice

In China there is a strong emphasis on improvingthe quality of indica hybrid rice varieties.‘‘Zhenshan 97’’, the female parent of a number ofwidely cultivated hybrids, is of poor quality be-cause of its high amylose content (AC), hard gelconsistency (GC), low gelatinization temperature(GT) and chalky endosperm. These three traits forcooking and eating quality are controlled by the

genomic region containing the Waxy locus. Theeating and cooking quality of Zhenshan 97A(male-sterile) has been improved by introgressionof the Waxy gene region from Minghui 63 (therestorer line) (Zhou et al., 2003). MAS was usedduring three generations of backcrossing. An SSRmarker waxy, representing the Waxy gene, wasused to select for the presence of the MinghuiWaxy region; two RFLP markers defining a 6.1cM interval and flanking the Waxy locus wereused to select recombinants between the flankingmarkers and the Waxy gene (to ensure that theintrogressed region was shorter than the intervaldefined by the two RFLP markers). A total of 118AFLP fragments were used in background selec-tion to recover the genetic background of Zhen-shan at unlinked loci. The obtained selected linesand their hybrids with Minghui 63, or Shanyou 63,showed a reduced AC and an increased GC andGT, coupled with reduced grain opacity. Resultsfrom this study also confirmed that the Waxy re-gion has major effects on the three traits forcooking and eating quality.

Conclusions and perspectives

The recent progress in the area of plant molecularbiology and plant genomics have the potential toinitiate a new Green Revolution. However, thesediscoveries need to be implemented in new culti-vars to realize that potential. Some potential lim-itations of MAS strategies have been highlightedfor both simply and complex inherited traits.A cautious optimism has been expressed aboutMAS of complex traits. Although molecularmarkers have been successfully associated withQTLs, these associations have actually demon-strated limited usefulness in plant breeding pro-grams. Complex traits are the most difficult tohandle during a breeding program, but areresponsible for most breeding progress in criticaltraits such as yield, yield stability and adaptation.Before selection of complex traits can be fullyrealized, efforts are required to obtain more reli-able and comprehensive data about quantitativetrait by using correctly sized mapping populations(i.e. small mapping populations are not adequatefor QTL mapping) (Young, 1999). Economics isalso a key determinant for the applicationof molecular genetics in genetic improvement

335

programs. The use of MAS can be justified when itreplaces more expensive or tedious assays, or re-sults in increased precision in the identification ofthe desired genotypes. What becomes criticaltherefore is the balance between added value andadditional cost. Dekkers and Hospital (2002) haverecently reviewed some of the potential limitationsof MAS strategies, and concluded that the use ofMAS will be determined by the economic benefitrelative to conventional selection.

In the immediate future, the key for the effi-ciency of MAS in large breeding population willdepends from the implementation and integrationof different points.

The first crucial point is therefore the avail-ability of cost-efficient and high-throughputgenotyping methods. A variety of high-throughputgenotyping technologies (Rafalski, 2002) are justbecoming sufficiently inexpensive to allow their usein plant breeding. A new generation of molecularmarkers based on the detection of SNPs promiseshigh-throughput assays at relatively low costs,along with the potential for high levels of multi-plexing. Implementation of this multiplexingtechnology in plant improvement strategies canprovide cost-effective tools for selection of multi-ple traits in breeding populations.

Second, exploiting information derived fromcomparative genetic maps, genomic regions con-ferring positive traits across syntenic species mightbe directly applied across species for theimprovement of the trait. Comparative geneticmaps show that chromosomal segment structure(orthology or conserved synteny) and markerorder (colinearity) are conserved across plantspecies over substantial evolutionary distances(Paterson et al., 2000). Most of the economicallyimportant species of the grass have detailed com-parative maps such that both gene content andgene order often can be predicted across species(Gale and Devos, 1998); similarly, genetic andgenomic information can be shared among manyleguminous species (e.g. soybean and mung bean)(Boutin et al., 1995) or among solanaceous species(e.g. tomato, pepper, potato) (Livingstone et al.,1999). When genetic mapping in collinear genomespinpoints similar traits to the same chromosomalregions, there is a good reason to suspect that theseloci are encoded by ortholog genes. Even in theabsence of an orthologous candidate gene, theinformation from the model plant of the family

(e.g. rice for the grass family and tomato for thenightshade family) can be applied to increase themarker saturation of a defined chromosome re-gion, as it may be required for the identification ofmarkers suitable for MAS or for the fine mappingof a gene for positional cloning.

Third, for improving polygenic traits in a quicktime-frame and in a cost effective manner, recentadvances in MAS strategies have suggestedimproved selection schemes in which MAS isapplied only once during the breeding process to arange of agronomically important traits (Singlelarge-scale-MAS – Ribaut and Betran, 1999) orinvolve the mapping of loci for all agronomicimportant traits (Breeding by design – Pelemanand van der Voort, 2003), and selection schemes inwhich MAS operates by cyclically re-estimatingthe value of the QTL alleles each time a new set ofgermplasm is generated during the breeding pro-cess (Mapping as you go – Podlich et al., 2004).Empirical applications of these improved selectionschemes will provide indications on their efficiencyin improving MAS for complex traits.

A fourth important point is represented by theincreasing amount of information on the topic thatare publicly available. Recent large-scalesequencing projects have produced a large amountof single-pass sequences of cDNAs from differentplant species. The number of ESTs deposited ingene bank for wheat, maize, barley and soybeanhas mounted to 562,000, 416,000, 380,000 and342,000 sequences respectively (http://www.ncbi.nlm.nih.gov/dbEST/). Because SSRs and SNPsbased markers can be obtained quite easily fromESTs (Morgante et al., 2002; Rafalski, 2002), thedevelopment of molecular markers has recentlyshifted from anonymous DNA fragments to genes.Transcription-based genetic maps have thus beenobtained from different crop species includingwheat (Gao et al., 2004), barley (Graner et al.,2004) and rice (Wu et al., 2002). The availability ofmarker-dense transcriptional maps has twoimportant implications for the improvement ofcomplex traits in plant breeding. First, it cancontribute candidate genes during the mapping ofQTLs. Second, when relatively stringent sequencesimilarity threshold are used, the EST loci formconnecting points between related genomes (e.g.wheat, barley and rice) allowing the exploitation ofthe information derived from syntenic genomicregions across species, i.e. in the case of the grass

336

family, provides the opportunity for a transfer ofgenetic information from rice to barley and otherTriticeae. Also for the tools directly useful forMAS, publicly available information are increas-ing. As an example, in the MAS wheat project,funded by the USDA, all the information andprotocols used in the MAS-wheat project in wheatMAS for traits related to pathogens resistance andquality are publicly available through the projectWEB site (http://maswheat.ucdavis.edu).

As a fifth point it has to be mentioned that theavailability of comprehensive cDNA and oligon-cleotide arrays is now providing an option for thedevelopment of functional genomics-based strate-gies for the investigation of quantitatively inher-ited traits, using at least two strategies. The first isa functional association strategy: cDNA array canreveal that gene expression within a given tissuevaries between genotypes differing for a given trait.Genetic mapping of identified candidate genes canthen reveal congruency between the map positionof the candidate gene and the presence of a QTL(Graner et al., 2004). The second strategy allowthe identification of QTLs by a methodologycalled as eXtreme array mapping (XAM). Thismethod can estimate the differences in allele fre-quency between pools of lines, selected for extremephenotypes, by hybridization of total genomicDNA to a GeneChips (Wolyn et al., 2004).

Thus, because different approaches areimproving the strategies on which MAS rely on, anincreased complementarity between moleculartechnologies and conventional breeding isexpected in the near future for a more efficientimprovement of the crop plants.

Acknowledgements

This work was supported by the Italian MiPAF,Project ‘‘Protezione delle piante mediante l’uso dimarcatori molecolari (PROMAR)’’, the CEREA-LAB project and the E.C. COST Action 860SUSVAR. The authors thanks Dr Nicholas Collinsfor critically reading and improving the review.

References

Abe F, Saito K, Miura K & Toriyama K (2002) A single

nucleotide polymorphism in the alternative oxidase gene

among rice varieties differing in low temperature tolerance.

FEBS Lett. 527(1–3): 181–185

Anderson JA, Stack RW, Liu S, Waldron BL, Fjeld AD, Coyne

C, Moreno-Sevilla B, Mitchell Fetch J, Song QJ, Cregan PB

& Frohberg RC (2001) DNA markers for Fusarium head

blight resistance QTLs in two wheat populations. Theor.

Appl. Genet. 102: 1164–1168

Arru L, Faccini N, Govoni C, Cattivelli L, Pecchioni N, Delogu

G, Stanca AM, & Vale G (2003) The PCR-based marker

MWG2018 linked to the Rdg2a leaf stripe resistance gene is a

useful tool for assesing barley resistance in breeding pro-

grams. Crop Sci. 43: 1036–1042

Babu RC, Nguyen BD, Chamarerk V, Shanmugasundaram P,

Chezhian P, Jeyaprakash P, Ganesh SK, Palchamy A,

Sadasivam S, Sarkarung S, Wade LJ & Nguyen HT (2003)

Genetic analysis of drought resistance in rice by molecular

markers. Crop Sci. 43: 1457–1469

Baker B, Zambryski P, Staskawicz B & Dinesh-Kumar SP

(1997) Signaling in plant-microbe interactions. Science 276:

726–733

Barr AR, Jefferies SP, Warner P, Moody DB, Chalmers KJ &

Langridge P (2000) Marker assisted selection in theory and

pratice. Proceedings VIII Barley Genetic Symposium, Vol. I

(pp. 167–178).

Barzen E, Stahl R, Fuchs E, Borchardt DC & Salamini F (1997)

Development of coupling-repulsion-phase SCAR markers

diagnostic for the sugar beet Rr1 allele conferring resistance

to rhizomania Mol. Breed. 3: 231–238

Boer MP, Ter Braak CJF & Jansen (2002) A penalized

likelihood method for mapping epistatic quantitative trait

loci with one-dimensional genome searches. Genetics 162:

951–960

Bohnert HJ & Jensen RG (1996) Strategies for engineering

water-stress tolerance in plants. Trends Biotechnol. 14: 89–97

Bolanos J & Edmeades GO (1996) The importance of the

anthesis-silking interval in breeding for drought tolerance in

tropical maize. Field Crop Res. 48: 65–80

Bouchez A, Hospital F, Causse M, Gallais A & Charcosset A

(2002) Marker-assisted introgression of favorable alleles at

Quantitative Trait Loci between maize elite lines. Genetics

162:1945–1959

Boutin SR, Young ND, Olson T, Yu Z-H, Shoemaker RC &

Vallejos C (1995) Genome conservation among three legume

genera detected with DNA markers. Genome 38: 928–937

Buckler IV ES & Thornsberry JM (2002) Plant molecular

diversity and application to genomics. Curr. Opin. Plant Biol.

5: 107–111

Buschges R, Hollricher K, Panstruga R, Simons G, Wolter M,

Frijters A, van Daelen R, van der Lee T, Diergaarde P,

Groenendijk J, Topsch S, Vos P, Salamini F & Schulze-Lefert

P (1997) The barley Mlo gene: a novel control element of

plant pathogen resistance. Cell 88: 695–705

Cattivelli L, Baldi P, Crosatti C, Di Fonzo N, Faccioli P, Grossi

M, Mastrangelo AM, Pecchioni N & Stanca AM (2002)

Chromosome regions and stress-related sequences involved in

resistance to abiotic stress in Triticeae. Plant. Mol. Biol.

48(5–6): 649–665

Ceccarelli S, Acevedo E & Grando S (1991) Breeding for yield

stability in unpredictable environments: single traits, inter-

action between traits, and architecture of genotypes. Euphy-

tica 56: 169–185

337

Chague V, Fahima T, Dahan A, Sun GL, Korol AB, Ronin YI,

Grama A, Roder ME & Nevo E (1999) Isolation of

microsatellite and RAPD markers flanking the Yr15 gene

of wheat using NILs and bulked segregant analysis. Genome

42: 1050–1056

ChengFS,WeedenNF,BrownSK,AldwinckleHS,Gardiner SE

& Bus VG (1998) Development of a DNA marker for Vm, a

gene conferring resistance to apple scab. Genome 41: 208–214

Concibido VC, Vallee BL, Mclaird P, Pineda N, Meyer J,

Hummel L, Yang J, Wu K & Delannay X (2003) Introgres-

sion of a quantitative trait locus for yield from Glycine soja

into commercial soybean cultivars. Theor. Appl. Genet. 106:

575–582

Cregan PB, Mudge J, Fickus EW, Danesh D, Denny R &

Young ND (2000) Two simple sequence repeat markers to

select for soybean cyst nematode resistance conditioned by

the rhg1 locus. Theor. Appl. Genet. 99: 811–818

Crossa J, Vargas M, van Eeuwijk FA, Jang C, Edmeades GO &

Hoisington D (1999) Interpreting genotype · environment

interaction in tropical maize using linked molecular markers

and environmental covariables. Theor. Appl. Genet. 99:

611–625

Dekkers JCM & Hospital F (2002) The use of molecular

genetics in the improvement of agricultural populations. Nat.

Rev. Genet. 3: 22–32

Doganlar S, Dodson J, Gabor B, Beck-Bunn T, Crossman C &

Tanksley SD (1998) Molecular mapping of the py-1 gene for

resistance to corky root rot (Pyrenochaeta lycopersici) in

tomato. Theor. Appl. Genet. 97: 784–788

Donald TM, Pellerone F, Adam-Blondon A-F, Bouquet A,

Thomas MR & Dry IB (2002) Identification of resistance

gene analogs linked to a powdery mildew resistance locus in

grapevine. Theor. Appl. Genet. 104: 610–618

Dussle CM, Quint M, XuML, Melchinger AE & Lubberstedt T

(2002) Conversion of AFLP fragments tightly linked to

SCMV resistance genes Scmv1 and Scmv2 into simple PCR-

based markers. Theor. Appl. Genet. 105: 1190–1195

Eathington SR, Dudley JW & Rufener GK (1997) Usefulness of

marker-QTL associations in early generation selection. Crop

Sci. 37: 1686–1693

Ehdaie B, Hall AE, Farquhar GD, Nguyen HT & Waines JG

(1991) Water-use efficiency and carbon isotope discrimina-

tion in wheat. Crop Sci. 31: 1282–1288

Feuillet C, Travella S, Stein N, Albar L, Nublat A & Keller B

(2003) Map-based isolation of the leaf rust disease resistance

gene Lr10 from the hexaploid wheat (Triticum aestivum L.)

genome. Proc. Natl. Acad. Sci. USA 100: 15253–15258

Fjellstrom R, Conaway-Bormans C, McClung AM, Marchetti

MA, Shank AR & Park WD (2004) Development of DNA

markers suitable for marker assisted selection of three Pi

genes conferring resistance to multiple Pyricularia grisea

pathotypes. Crop Sci. 44: 1790–1798

Foolad MR, Zhang LP & Subbiah P (2002) Genetics of drought

tolerance during seed germination in tomato: inheritance and

QTL mapping. Genome 46: 536–545

Fox GP, Panozzo JF, Li CD, Lance RCM Inkerman PA &

Henry RJ (2003) Molecular basis of barley quality. Aust. J.

Agricult. Res. 54: 1081–1101

Fox PN, Crossa J & Romagosa I (1997) Genotype · environ-

ment and adaptation. In: Kempton R & Fox P (eds)

Statistical Methods for Plant Variety Evaluation (pp. 117–

138) Chapman & Hall, London

Francia E, Rizza F, Cattivelli L, Stanca AM, Galiba G, Toth B,

Hayes PM, Skinner JS & Pecchioni N (2004) Two loci on

chromosome 5H determine low-temperature tolerance in a

‘Nure’ (winter) · ‘Tremois’ (spring) barley map. Theor.

Appl. Genet. 108: 670–680

Frary A, Nesbitt TC, Frary A, Grandillo S, van der Knaap E,

Cong B, Liu JP, Meller J, Elber R, Alpert KB & Tanksley SD

(2000) fw2.2: A quantitative trait locus key to the evolution

of tomato fruit size. Science 289: 85–88

Frary A, Doganlar S, Frampton A, Fulton T, Uhlig J, Yates H

& Tanksley S (2003) Fine mapping of quantitative trait loci

for improved fruit characteristics from Lycopersicon chmie-

lewskii chromosome 1. Genome 46: 235–243

Fridman E, Pleban T & Zamir D (2000) A recombination

hotspot delimits a wild-species quantitative trait locus for

tomato sugar content to 484 bp within an invertase gene.

Proc. Natl. Acad. Sci. USA 97: 4718–4723

Frisch M, Bohn M & Melchinger AE (1999) Comparison of

selection strategies for marker-assisted selection backcrossing

of a gene. Crop Sci. 39: 1285–1301

Gale MD & Devos KM (1998) Comparative genetics in the

grasses. Proc. Natl. Acad. Sci. USA 95: 1971–1794

Gao LF, Jing RL, Huo NX, Li Y, Li XP, Zhou RH, Chang XP,

Tang JF, Ma ZY & Jia JZ (2004) One hundred and one new

microsatellite loci derived from ESTs (EST-SSRs) in bred

wheat. Theor. Appl. Genet. 108: 1392–1400

Gebhardt C & Valkonen JPT (2001) Organization of genes

controlling disease resistance in the potato genome. Annu.

Rev. Phytopathol. 39: 79–102

Gleeson AC (1997) Spatial analysis. In: Kempton RA & Fox

PN (eds) Statistical Mathods for Plant Variety Evaluation

(pp. 68–85) Chapman & Hall, London

Goff SA, Ricke D, Lan T-H, Presting G, Wang R, Dunn M,

Glazebrook J, Sessions A, Oeller P, Varma H, Hadley D,

Hutchison D, Martin C, Katagiri F, Lange BM, Moughamer

T, Xia Y, Budworth P, Zhong J, Miguel T, Paszkowski U,

Zhang S, Colbert M, Sun W-L, Chen L, Cooper B, Park S,

Wood TC, Mao L, Quail P, Wing R, Dean R, Yu Y,

Zharkikh A, Shen R, Sahasrabudhe S, Thomas A, Cannings

R, Gutin A, Pruss D, Reid J, Tavtigian S, Mitchell J,

Eldredge G, Scholl T, Miller RM, Bhatnagar S, Adey N,

Rubano T, Tusneem N, Robinson R, Feldhaus J, Macalma

T, Oliphant A & Briggs S (2002) A draft sequence of the rice

genome (Oryza sativa L. ssp. japonica). Science 296: 92–100

Grandillo S, Ku HM & Tanksley SD (1999) Identifying the loci

responsible for natural variation in fruit size and shape in

tomato. Theor. Appl. Genet. 99: 978–987

Graner A, Streng S, Kellermann A, Schiemann A, Baner E,

Waugh R, Pellio B & Ordon F (1999) Molecular mapping of

the rym5 locus encoding resistance to different strains of the

barley yellow mosaic virus complex. Theor. Appl. Genet. 98:

285–290

Graner A, Streng S, Drescher A, Jin Y, Borovkova I &

Steffenson B (2000) Molecular mapping of the leaf rust

resistance gene Rph7 in barley. Plant Breed. 119: 389–392

Graner A, Kota R, Perovic D, Potokina E, Prasad M, Scholz

U, Stein N, Thiel T, Varshney RK & Zhang H (2004)

Molecular mapping: shifting from the structural to the

338

functional level. Proc. 9th International Barley Genetic

Symposium (pp. 49–57).

Hammond-Kosack KE & Parker JE (2003) Deciphering plant–

pathogen communication: fresh perspectives for molecular

resistance breeding. Curr. Opin. Biotec. 14: 177–193

Han F, Romagosa I, Ullrich SE, Jones BL, Hayes PM,

Wesenberg DM (1997) Molecular marker-assisted selection

for malting quality traits in barley. Mol. Breed. 3: 427–437

Haussmann BIG, Mahalakshmi V, Reddy BVS, Seetharama N,

Hash CT & Geiger HH (2002) QTL mapping of stay-green in

two sorghum recombinant inbred populations. Theor. Appl.

Genet. 106: 133–142

Hayashi K, Hashimoto N, Daigen M & Ashikawa I (2004)

Development of PCR-based SNP markers for rice blast

resistance genes at the Piz locus. Theor. Appl. Genet. 108:

1212–1220

Helguera M, Khan IA & Dubcavsky (2000) Development of

PCR markers for wheat leaf rust resistance gene Lr47. Theor.

Appl. Genet. 101: 625–631

Hittalmani S, Parco A, Mew TV, Zeigler RS & Huang N (2000)

Fine mapping and DNA marker-assisted pyramiding of the

three major genes for blast resistance in rice. Theor. Appl.

Genet 100: 1121–1128

Huang CC, Cui YY, Weng CR, Zabel P & Lindhout P

(2000) Development of diagnostic PCR markers closely

linked to the tomato powdery mildew resistance gene Ol-1

on chromosome 6 of tomato. Theor. Appl. Genet 101: 918–

924

Huang L & Gill BS (2001) An RGA-like marker detects all

known Lr21 leaf rust resistance gene family members in

Aegilops tauschii and wheat. Theor. Appl. Genet. 103: 1007–

1013

Huang XQ, Wang LX, Xu MX & Roder MS (2003) Microsat-

ellite mapping of the powdery mildew resistance gene Pm5e

in common wheat (Triticum aestivum L.). Theor. Appl.

Genet. 106: 858–865

Igartua E, Edney M, Rossnagel BG, Spaner D, Legge WG,

Scoles GJ, Eckstein PE, Penner GA, Tinker NA, Briggs KG,

Falk DE & Mather DE (2000) Marker-based selection of

QTL affecting grain and malt quality in two-row barley.

Crop Sci. 40: 1426–1433

Ishimaru K (2003) Identification of a locus increasing rice yield

and physiological analysis of its function. Plant Physiol. 133:

1083–1090

Ismail AM, Hall AE & Close TJ (1999) Allelic variation of a

dehydrin gene co-segregates with chilling tolerance during

seedling emergence. Proc. Natl. Acad. Sci. USA 96: 13566–

13570

Jaglo-Ottosen KR, Gilmour SJ, Zarka DG, Schabenberger O &

Thomashow MF (1998) Arabidopsis CBF1 overexpression

induces COR genes and enhances freezing tolerance. Science

280: 104–106

Jannoo N, Grivet L, Dookum A, D’Hont A & Glaszmann JC

(1999) Linkage disequilibrium among modern sugarcane

cultivars. Theor. Appl. Genet. 99: 1053–1060

Jeon J-S, Chen D, Yi G-H, Wang GL & Ronald PC (2003)

Genetic and physical mapping of Pi5(t), a locus associated

with broad-spectrum resistance to rice blast. Mol. Gen.

Genom. 269: 280–289

Jiang C & Zeng Z-B (1995) Multiple trait analysis of genetic

mapping for quantitative trait loci. Genetics 140: 1111–

1127

King GJ, Tartarini S, Brown L, Gennari F & Sansavini S (1999)

Introgression of the Vf source of scab resistance and

distribution of linked marker alleles within the Malus gene

pool. Theor. Appl. Genet. 99: 1039–1046

Kloppers FJ & Pretorius ZA (1997) Effects of combinations

amongst genes Lr13, Lr34 and Lr37 on components of

resistance in wheat to leaf rust. Plant Pathol. 46: 737–750

Knapp SJ (1998) Marker-assisted selection as a strategy for

increasing the probability of selecting superior genotypes.

Crop Sci 38: 1164–1174

Kolmer JA (1996) Genetics of resistance to wheat leaf rust.

Annu. Rev. Phytopathol. 34: 435–455

Kraft T, Hansen M & Nilsson NO (2000) Linkage disequilib-

rium and fingerprinting in sugar beet. Theor. Appl. Genet.

101: 323–326

Lande R & Thompson R (1990) Efficiency of marker-assisted

selection in the improvement of quantitative traits. Genetics

124:743–756

Lecomte L, Duffe P, Buret M, Servin B, Hospital F & Causse

M (2004) Marker-assisted introgression of five QTLs con-

trolling fruit quality traits into three tomato lines revealed

interactions between QTLs and genetic backgrounds. Theor.

Appl. Genet. 109: 658–668

Lee KS, Choi WY, Ko JC, Kim TS & Gregorio GB (2003)

Salinity tolerance of japonica and indica rice (Oryza sativa L.)

at the seedling stage. Planta 216: 1043–1046

Leister D, Kurth J, Laurie DA, Yano M, Sasaki T, Graner A &

Schulze-Lefert P (1999) RFLP- and physical mapping of

resistance gene homologues in rice (O. sativa) and barley (H.

vulgare). Theor. Appl. Genet. 98: 509–520

Lilley JM, Ludlow MM, McCouch SR & O’Toole JC (1996)

Locating QTL for osmotic adjustment and deydration

tolerance in rice. J. Exp. Bot. 47: 1427–1436

Lin HX, Zhu MZ, Yano M, Gao JP, Liang ZW, Su WA, Hu

XH, Ren ZH & Chao DY (2004) QTLs for Na+ and K+

uptake of the shoots and roots controlling rice salt tolerance.

Theor. Appl. Genet. 108: 253–260

Liu JP, Van Eck J, Cong B & Tanksley SD (2002) A new class

of regulatory genes underlying the cause of pear-shaped

tomato fruit. Proc. Natl. Acad. Sci. USA 99: 13302–13306

Liu S, Griffey CA & Shagai Maroof MA (2001) Identification

of molecular markers associated with adult plant resistance

to powdey mildew in common wheat cultivar Massey. Crop

Sci. 41: 1268–1275

Livingstone KD, Lackney VK, Blauth JR, van Wijk R & Jahn

MK (1999) Genome mapping in Capsicum and the evolution

of genome structure in the Solanaceae. Genetics 152: 1183–

1202

Lu Z-X, Sossey-Alaoui K, Reighard GL, Baird WMV & Abbot

AG (1999) Development and characterization of a codominat

marker linked to root-knot nematode resistance, and its

application to peach rootstock breeding. Theor. Appl. Genet.

99: 115–122

Ma W, Zhang W & Gale KR (2003) Multiplex-PCR typing of

high molecular weight glutenin alleles in wheat. Euphytica

134: 51–60

339

Ma ZQ, Wei JB & Cheng SH (2004) PCR-based markers for the

powdery mildew resistance gene Pm4a in wheat. Theor. Appl.

Genet. 109: 140–145

Madsen LH, Collins NC, Rakwalska M, Backes G, Sandal N,

Krusell L, Jensen J, Waterman EH, Jahoor A, Ayliffe M,

Pryor AJ, Langridge P, Schulze-Lefert P & Stougaard J

(2003) Barley disease resistance gene analogs of the NBS-

LRR class: identification and mapping. Mol. Gen. Genomics

269: 150–161

Mago R, Spielmeyer W, Lawrence GJ, Lagudah ES, Ellis JG

&ÆPryor A (2002) Identification and mapping of molecular

markers linked to rust resistance genes located on chromo-

some 1RS of rye using wheat-rye translocation lines. Theor.

Appl. Genet. 104: 1317–1324

Merah O, Deleens E, Teulat B & Monneveux P (2001)

Productivity and carbon isotope discrimination of different

durum wheat organs under Mediterranean conditions. CR

Acad. Sci. Paris, Science de la vie, Serie III 324: 51–57

Michelmore R (2000) Genomic approaches to plant disease

resistance. Curr. Opin. Plant Biol. 3: 125–131

Michelmore RW (2003) The impact zone: genomics and

breeding for durable disease resistance. Curr. Opin. Plant

Biol. 6: 397–404

Michelmore RW, Paran I & Kesseli RV (1991) Identification of

markers linked to disease-resistance gene by bulked segregant

analysis: a rapid method to detect markers in specific

genomic regions by using segregating populations. Proc.

Natl. Acad. Sci. USA 88: 9828–9832

Miftahudin G, Scoles J & Gustafson JP (2002) AFLP markers

tightly linked to the aluminium tolerance gene Alt3 in rye

(Secale cereale L.). Theor. Appl. Genet. 104: 626–631

Mohan M, Nair S, Bhagwat A, Krishna TG, Yano M, Bhatia

CR & Sasaki T (1997) Genome mapping, molecular markers

and marker-assisted selection in crop plants. Mol. Breed. 3:

87–103

Morgan JM & Tan MK (1996) Chromosomal location of a

wheat osmoregulation gene using RFLP analysis. Aust. J.

Plant Physiol. 23: 803–806

Morgante M & Salamini F (2003) From plant genomics to

breeding practice. Curr. Opin. Biotechnol. 14: 214–219

Morgante M, Hanafey M & Powell W (2002) Microsatellites

are preferentially associated with nonrepetitive DNA in plant

genomes. Nat. Genet. 30: 194–200

Murai H, Hashimoto Z, Sharma PN, Shimizu T, Murata K,

Takumi S, Mori N, Kawasaki S & Nakamura C (2001)

Construction of a high-resolution linkage map of a rice

brown planthopper (Nilaparvata lugens Stal) resistance gene

bph2. Theor. Appl. Genet. 103: 526–532

Naess SK, Bradeen JM, Wielgus SM, Haberlach GT, McGrath

JM & Helgeson JP (2000) Resistance to late blight in

Solanum bulbocastaneum is mapped to chromosome 8. Theor.

Appl. Genet. 101: 697–704

Nguyen BD, Brar DS, Bui BC, Nguyen TV, Pham LN &

Nguyen HT (2003) Identification and mapping of the QTL

for aluminium tolerance introgressed from the new source,

Oryza rufipogon Griff., into indica rice (Oryza sativa L.).

Theor. Appl. Genet. 106: 583–593

Nguyen TTT, Klueva N, Chamareck V, Aarti A, Magpantay

G, Millena ACM, Pathan MS & Nguyen HT (2004)

Saturation mapping of QTLs regions and identification of

putative candidate genes for drought resistance in rice. Mol.

Gen. Genomics 272: 35–46

Nordborg M & Tavare S (2002) Linkage disequilibrium: what

history has to tell us. Trends Genet. 18: 83–90

Olsen KM, Halldorsdottir SS, Stinchombe JR, Weinig C,

Schmitt J & Purugganan MD (2004) Linkage disequilibrium

mapping of Arabidopsis CRY2 flowering time alleles.

Genetics 167: 1361–1369

Pan Q, Wendel J & Fluhr R (2000) Divergent evolution of plant

NBS-LRR resistance gene homologues in dicot and cereal

genomes. J. Mol. Evol. 50: 203–213

Paterson AH, Bowers JE, BurowMD, Draye X, Elsik CG, Jang

C-X, Katsar CS, Lan T-H, Lin Y-R, Ming R & Wright RJ

(2000) Comparative genomics of plant chromosomes. Plant

Cell 12: 1523–1539

Paterson AH, Saranga Y, Menz M, Jiang CX & Wright RJ

(2003) QTL analysis of genotype · environment interactions

affecting cotton fiber quality. Theor. Appl. Genet. 106: 384–

396

Pauquet J, Bouquet A, This P & Adam-Blondon A-F (2001)

Establishment of a local map of AFLP markers around the

powdery mildew resistance gene Run1 in grapevine and

assessment of their usefulness for marker assisted selection.

Theor. Appl. Genet. 103: 1201–1210

Peleman JD & van der Voort JR (2003) Breeding by design.

Trends Plant Sci. 8:330–334

Podlich DW, Winkler CR & Cooper M (2004) Mapping as you

go: an effective approach for marker-assisted selection of

complex traits. Crop Sci. 44: 1560–1571

Radovanovic N & Cloutier S (2003) Gene-assisted selection for

high molecular weight glutenin subunits in wheat doubled

haploid breeding programs. Mol. Breed. 12: 51–59

Rafalski JA (2002) Applications of single nucleotide poly-

morphisms in crop genetics. Curr. Opin. Plant Biol. 5: 94–

100

Raman H, Karakousis A, Moroni JS, Raman R, Read BJ,

Garvin DF, Kochian LV & Sorrels ME (2003) Development

and allele diversity of microsatellite markers linked to the

aluminium toloerance gene Alp in barley. Aust. J. Agric. Res.

54: 1315–1321

Ribaut JM & Betran J (1999) Single large-scale marker-assisted

selection (SLS-MAS). Mol. Breed. 5: 531–541

Ribaut JM, Jiang C, Gonzales-de Leon D, Edmeades GO &

Hoisington DA (1997) Identification of quantitative trait loci

under drought conditions in tropical maize 2-yield compo-

nents and marker-assisted selection strategies. Theor. Appl.

Genet. 94: 887–896

Richards RA (1996) Defining selection criteria to improve yield

under drought. Plant Growth Regul. 20: 157–166

Riede CR & Anderson JA (1996) Linkage of RFLP markers to

an aluminum tolerance gene in wheat. Crop Sci. 36: 905–909

Robert M, West MAL, Inai S, Caines A, Arntzen L, Smith JK

& St. Clair DA (2001) Marker-assisted introgression of

blackmold resistance QTL alleles from wild Lycopersicon

cheesmanii to cultivated tomato (L. esculentum) and evalu-

ation of QTL phenotypic effects. Mol. Breed. 8: 217–233

Robin S, Pathan MS, Courtois B, Lafitte R, Carandang S,

Lanceras S, Amante M, Nguyen HT & Li Z (2003) Mapping

osmotic adjustment in an advanced back-cross inbred pop-

ulation of rice. Theor. Appl. Genet. 107: 1288–1296

340

Saito K, Hayano-Saito Y, Maruyama-Funatsuki W, Sato Y &

Kato A (2004) Physical mapping and putative candidate gene

identification of a quantitative trait locus Ctb1 for cold

tolerance at the booting stage of rice. Theor. Appl. Genet.

109: 515–522

Sanchez AC, Subudhi PK, Rosenow DT & Nguyen HT (2002)

Mapping QTLs associated with drought resistance in sor-

ghum (Sorghum bicolor L. Moench). Plant Mol. Biol. 48:

713–726

Sardesai N, Kumar A, Rajyashri KR, Nair S & Mohan M

(2002) Identification of an AFLP marker linked to Gm7, a

gall midge resistance gene and its conversion to a SCAR

marker for its utility in marker aided selection in rice. Theor.

Appl. Genet. 105: 691–698

Schmierer DA, Kandemir N, Kudrna DA, Jones BL, Ullrich

SE &Kleinhofs A (2004) Molecular marker-assisted selection

for enhanced yield in malting barley. Mol. Breed. Published

on line

Schneider KA, Brothers ME & Kelly JD (1997) Marker-

assisted selection to improve drought resistance in common

bean. Crop Sci. 37: 51–60

Septiningsih Septiningsih EM, Trijatmiko KR, Moeljopawiro S

& McCouch SR (2003) Identification of quantitative trait loci

for grain quality in an advanced backcross population

derived from the Oryza sativa variety IR64 and the wild

relative O. rufipogon. Theor. Appl. Genet. 107: 1419–1432

Seyfarth R, Feuillet C, Schachermayr G, Winzeler M & Keller

B (1999) Development of molecular marker for the adult

plant leaf rust resistance gene Lr35 in wheat. Theor. Appl.

Genet. 99: 554–560

Shen L, Courtois B, McNally KL, Robin S & Li Z (2001)

Evaluation of near-isogenic lines of rice introgressed with

QTLs for root depth through marker-aided selection. Theor.

Appl. Genet. 103: 75–83

Singh RP, Mujeeb-Kazi A & Huerta-Espino J (1998) Lr46: a

gene conferring slow-rusting resistance to leaf rust in wheat.

Phytopathology 88: 890–894

Singh S, Sidhu JS, Huang N, Vikal Y, Li Z, Brar DS, Dhaliwal

HS & Khush GS (2001) Pyramiding three bacterial blight

resistance genes (xa5, xa13 and Xa21) using marker-assisted

selection into indica rice cultivar PR106. Theor. Appl. Genet.

102: 1011–1015

SmithPH,KoebnerRMD&BoydLA(2002)Thedevelopmentof

aSTSmarker linked toayellowrust resistancederived fromthe

wheat cultivar Moro. Theor. Appl. Genet. 104: 1278–1282

Song J, Bradeen JM, Naess SK, Raasch JA, Wielgus SM,

Halberlach GT, Liu J, Kuang H, Austin-Phillips S, Buell CR,

Hengelson JP & Jang J (2003) Gene RB cloned from Solanum

bulbocastaneum confers broad spectrum resistance to potato

late blight. Proc. Natl. Acad. Sci USA 100: 9128–9133

Stuber CW (1994) Success in the use of molecular markers for

yield enhancement in corn. Proc. 49th Annual Corn and

Sorghum Industry Res. Conf. American Seed Trade Assoc.

49: 232–238

Stuber CW, Lincoln SE, Wolff DW, Helentjaris T & Lander ES

(1992) Identification of genetic factors contributing to

heterosis in a hybrid from two elite maize inbred lines using

molecular markers. Genetics 132: 823–839

Stuber CW, Polacco M & Lynn M Senior (1999) Synergy of

empirical breeding, marker-assisted selection, and genomics

to increase crop yield potential. Crop Sci. 39: 1571–1583

Suenaga K, Singh RP, Huerta-Espino J & William HM (2003)

Microsatellite markers for genes Lr34/Yr18 and other quan-

titative loci for leaf rust and stripe rust resistance in bread

wheat. Phytopathology 93: 881–890

Tabashnik BE (2001) Breaking the code of resistance. Two

reports shed light on the molecular basis of resistance to Bt

toxins. Nat. Biotechnol. 19: 922–924.

Tang Y, Sorrels ME, Kochian LV & Garvin DF (2000)

Identification of RFLP markers linked to barley aluminum

tolerance gene Alp. Crop Sci. 40: 778–782

Tanksley SD & Nelson JC (1996) Advanced backcross

QTL analysis: a method for the simultaneous discovery

and transfer of valuable QTLs from unadapted germ-

plasm into elite breeding lines. Theor. Appl. Genet. 92:

191–203

Tanksley SD, Ganal MW & Martin GB (1995) Chromosome

landing: a paradigm for map-based gene cloning in plants

with large genomes. Trends Genet. 11: 63–68

Tartarini S, Gianfranceschi L, Sansavini S & Gessler C (1999)

Development of reliable PCR markers for the selection of the

Vf gene conferring scab resistance in apple. Plant Breed. 118:

183–186

Teulat B, Merah O, Sirault X, Borries C, Waugh R & This D

(2002) QTLs for grain carbon isotope discrimination in field-

grown barley. Theor. Appl. Genet. 106: 118–126

Teulat B, Zoumarou-Wallis N, Rotter B, Ben Salem M, Bahri

H & This D (2003) QTL for relative water content in field-

grown barleys and their stability across Mediterranean

environments. Thoer. Appl. Genet. 108:181–188

The Arabidopsis Genome Initiative (2000) Analysis of the

genome of the flowering plant Arabidopsis thaliana. Nature

408: 796–815

Thomson MJ, Tai TH, McClung AM, Hinga ME, Lobos KB,

Xu Y, Martinez C & McCouch SR (2003) Mapping

quantitative trait loci for yield, yield components, and

morphological traits in an advanced backcross population

between Oryza rufipogon and the Oryza sativa cultivar

Jefferson. Theor. Appl. Genet. 107:479–493

Thornsberry JM, Goodman MM, Doebley J, Kresovich S,

Nielsen D & Buckler IV ES (2001) Dwarf8 polymorphisms

associate with variation in flowering time. Nat. Genet. 28:

286–289

Toth B, Francia E, Rizza F, Stanca AM, Galiba G & Pecchioni

N (2004) Development of PCR-based markers on chromo-

some 5H for assisted selection of frost-tolerant genotypes in

barley. Mol. Breed. Published on line

Turner NC (1997) Further progress in crop water relations.

Adv. Agron. 58: 293–338

Vagujfalvi A, Galiba G, Cattivelli L & Dubcovsky J (2003) The

cold-regulated transcriptional activator Cbf3 is linked to the

frost-tolerance locus Fr-A2 on wheat chromosome 5A. Mol.

Genet. Genom. 269: 60–67

Wang D, Graef GL, Procopiuk AM & Diers BW (2004)

Identification of putative QTL that underlie yield in inter-

specific soybean backcross populations. Theor. Appl. Genet.

108: 458–467

Weerasena JS, Steffenson BJ & Falk AB (2004) Conversion of

an amplified fragment lenght polymorphism marker into a

co-dominant marker in the mapping of the Rph15 gene

conferring resistance to barley leaf rust, Puccinia hordei Otth.

Theor. Appl. Genet. 108: 712–719

341

Wei F, Wing RA & Wise RP (2002) Genome dynamics and

evolution of the Mla (powdery mildew) resistance locus in

barley. Plant Cell 14: 1903–1917.

Werner K, Friedt W, Laubach E, Waugh R & Ordon F

(2003) Dissection of resistance to soil-borne yellow-mosaic-

inducing viruses of barley (BaMMV, BaYMV, BAYMV-2)

in a complex breeders’ cross by means of SSR and

simultaneous mapping of BaYMV/BaYMV-2 resistance of

var. ‘‘Chikurin Ibaraki 1’’. Theor. Appl. Genet. 106: 1425–

1432

Williams KJ (2003) The molecular genetics of disease resistance

in barley. Aust. J. Agric. Res. 54: 1065–1079

Williamson VM, Ho J-Y, Wu FF, Miller N & Kaloshian I

(1994) A PCR-based marker tightly linked to the nematode

resistance gene,Mi, in tomato. Theor.Appl. Genet. 87: 757–763

Wittaker JC, Curnow RN, Haley CS & Thompson R (1995)

Using marker-maps in marker-assisted selection. Genet. Res.

66: 255–265

Wolyn DJ, Borevitz JO, Loudet O, Scwartz C, Maloof J, Ecker

JR, Berry CC & Chory J (2004) Light-response quantitative

trait loci identified by composite interval mapping and

eXtreme array mapping in Arabidopsis thaliana. Genetics

167: 907–917

Wu J, Maehara T, Shimokawa T, Yamamoto S, Harada C,

Takazaki Y, Ono N, Mukai Y, Koike K, Yazaki J, Fujii F,

Shomura A, Ando T, Kono I, Waki K, Yamamoto K, Yano

M, Matsumoto T & Sasaki T (2002) A comprehensive rice

transcript map containing 6591 expressed sequence tag sites.

Plant Cell 14: 525–535

Xu J, Narabu T, Mizukubo T & Hibi T (2001) A molecular

marker correlated with selected virulence against the tomato

resistance gene Mi in Meloidogyne incognita, M. javanica and

M. arenaria. Phytopathology 91: 377–382

Yaghoobi J, Kaloshian I, Wen Y & Williamson VM (1995)

Mapping a new nematode resistance locus in Lycopersicon

peruvianum. Theor. Appl. Genet. 91: 457–464

Yin X, Stam P, Kropff MJ & Schapendonk HCM (2003) Crop

modeling, QTL mapping, and their complementary role in

plant breeding. Agron. J 95: 90–98

Young ND (1999) A cautiously optimistic vision for marker-

assisted breeding. Mol. Breed. 5: 505–510

Yousef GG & Juvik JA (2001) Comparison of phenotypic and

marker-assisted selection for quantitative traits in sweet corn.

Crop Sci. 41: 645–655

Zhang Z, Xu J, Xu Q, Larkin P & Xin Z (2004) Devlopment of

novel PCR markers linked to the BYDV resistance gene Bdv2

useful in wheat for marker-assisted selection. Theor. Appl.

Genet. 109: 433–439

Zhang J, Zheng HG, Aarti A, Pantuwan G, Nguyen TT,

Tripathy JN, Sarial AK, Robin S, Babu RC, Nguyen BD,

Sarkarung S, Blum A & Nguyen HT (2001) Locating

genomic regions associated with components of drought

resistance in rice: Comparative mapping within and across

species. Theor. Appl. Genet. 103: 19–29

Zhang JX, Nguyen HT & Blum A (1999) Genetic analysis of

osmotic adjustment in crop plants. J. Exp. Bot. 50: 291–302

Zhang TZ, Yuan YL, Yu J, Guo WZ & Kohel RJ (2003)

Molecular tagging of a major QTL for fiber strength in

Upland cotton and its marker-assisted selection. Theor.

Appl. Genet. 106: 262–268

Zheng BS, Yang L, Zhang WP, Mao CZ, Wu YR, Yi KK, Liu

FY & Wu P (2003) Mapping QTLs and candidate genes for

rice root traits under different water-supply conditions and

comparative analysis across three populations. Theor. Appl.

Genet. 107: 1505–1515

Zhou F, Kurth J, Wei F, Elliot C, Vale G, Yahiaoui N, Keller

B, Somerville S, Wise R & Schulze-Lefert P (2001) Cell-

autonomous expression of barley Mla1 confers race-specific

resistance to the powdery mildew fungus via a Rar1-

independent signaling pathway. Plant Cell 13: 337–350

Zhou PH, Tan YF, He YQ, Xu CG & Zhang Q (2003)

Simultaneous improvement for four quality traits of

Zhenshan 97, an elite parent of hybrid rice, by molecu-

larmarker-assistedselection.Theor.Appl.Genet.106:326–331

Zhu YL, Song QJ, Hyten DL, Van Tassel CP, Matukumalli

LK, Grimm DR, Hyatt SM, Fickus EW, Young ND &

Cregan PB (2003) Single nucleotide polymorphisms (SNPs)

in soybean. Genetics 163: 1123–1134

342