Euphytica (2005) 143: 135–144DOI: 10.1007/s10681-005-3058-1 C© Springer 2005

Identification of molecular markers linked with crown rust (Pucciniacoronata f. sp. lolii) resistance in perennial ryegrass (Lolium perenne)using AFLP markers and a bulked segregant approach

H. Muylle1,∗, J. Baert1, E. Van Bockstaele1,2, B. Moerkerke3, E. Goetghebeur3 & I. Roldan-Ruiz1

1Department of Plant Genetics and Breeding, Agricultural Research Centre, Caritasstraat 21, 9090-Melle Belgium;2Department of Plant Production, Ghent University, Coupure Links 653, 9000-Gent Belgium; 3Departmentof Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281-S9, 9000-Gent Belgium;(∗author for correspondence: e-mail: h.muylle@clo.fgov.be)

Received 8 July 2004; accepted 2 March 2005

Key words: AFLP, BSA, crown rust resistance, MAS, perennial ryegrass, permutation testing

Summary

Crown rust resistance is an important selection criterion in ryegrass breeding. The fungal disease caused by P.coronata causes yield loss and a reduced quality of the fodder crop. Molecular markers were used to unravel thegenomic organization of crown rust resistance in a segregating L. perenne population. Two genomic regions involvedin crown rust resistance were identified that together explained 35% of the phenotypic variance present. Bulkedsegregant analysis in combination with AFLP markers was a suitable method to identify DNA markers associatedwith genomic regions of major effect. One cluster of AFLP markers explained 6.1% of the variance and mapped tolinkage group 2, a genomic region known to contain crown rust resistance genes. A second cluster of AFLP markersdetected a novel genomic region of major effect that explained 27.7% of the phenotypic variance in crown rustresistance. This cluster was unlinked to the cluster on linkage group 2. Divergent selections performed within thesegregating F1 population on the basis of genotype and phenotype revealed that the markers associated with crownrust resistance identified in this study have potential for marker assisted selection. Selection of plants on the basisof markers was more straightforward than the selection on the basis of phenotype.

Introduction

Perennial ryegrass or L. perenne is important in forage/livestock systems. It is used as a forage species, mainlyas pasture, hay crop or ground cover. It has a high yieldand fast establishment potential and relatively good re-sistance to drought stress, is winter hard and persis-tent. As a result, it is often the preferred forage grassin temperate regions of the world.

Crown rust (caused by the biotrophic fungusPuccinia coronata f. sp. lolii) is widely distributedin Lolium growing areas throughout the world.Consequences of crown rust infection can be found atshort- and long-term levels. Short-term losses are dueto reduced yield, palatability and nutritional quality.

Among the most important long-term losses are re-duced productivity and reduced persistence (Plummeret al., 1990; Smith et al., 1998).

The severity of crown rust infections can be re-duced by good management practices, such as the useof resistant cultivars (Wilkins & Humphreys, 2003).Resistant cultivars are needed, especially with the cur-rent tendency to minimize nitrogen input, because theless nitrogen applied, the more crown rust invades rye-grass stands. Given the economic impact of crownrust infections, the improvement of the genetic resis-tance is one of the major goals in ryegrass breedingprograms.

Several models have been proposed regardingthe genetic control of resistance to P. coronata and

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were reviewed by Kimbeng (1999). Evidence forboth qualitative (McVeigh, 1975; Lellbach, 1999;Dumsday et al., 2003) and quantitative (McVeigh,1975; Hayward, 1977; Reheul & Ghesquiere, 1996)inheritance has been revealed. Different perennialryegrass genotypes seem to carry major genes orquantitative trait loci (QTL) for crown rust resistance.Furthermore, a negative correlation seems to existbetween resistance and yield performance (Hides &Wilkins, 1978; Schmidt, 1980; Reheul & Ghesquiere,1996). However, it is unknown whether this negativecorrelation is due to linkage or pleiotrophism orwhether it is just an effect of the relaxation of theselection pressure for agronomic characteristics duringselection of crown rust resistance (Kimbeng, 1999).Thus, a better understanding of the genetic control ofcrown rust resistance in different germplasm sourcesand of the relationship between resistance and agro-nomic performance is required for the development ofefficient selection methods in ryegrass breeding.

The advent of molecular marker technologies hasopened the door for the dissection and genetic charac-terization of complex traits and for the identificationof beneficial allelic variants. Once markers associatedwith the trait of interest have been identified, marker as-sisted selection (MAS) can be used to more efficientlyand effectively manipulate resistance genes in breed-ing programs. Bulked segregant analysis (BSA), de-veloped by Michelmore et al. (1991), in combinationwith the AFLP technique has proven to be a powerfulmethod for identifying DNA-markers that are tightlylinked with genes underlying monogenic traits or ma-jor genes underlying quantitative traits in various plantspecies (Uzun et al., 2003; Dussle et al., 2003; Asnaghiet al., 2004; Touzet et al., 2004).

Recently, BSA has been applied to identifygenomic regions conferring crown rust resistancein perennial ryegrass (Dumsday et al., 2003). SSRmarkers were used to determine the location of amajor locus or loci for resistance to crown rust inryegrass on linkage group 2 (LG 2). In their study,Dumsday et al. (2003) used germplasm from Australiaand New Zealand and urediniospores collected inone Australian location. In this study, we analyzethe mode of inheritance of crown rust resistance ina segregating population derived from a pair crossbetween perennial ryegrass genotypes of Europeanorigin, using urediniospores collected in one Belgianlocation. Additionally, BSA in combination with theAFLP markers is used to identify molecular markersassociated with the crown rust resistance genes.

Finally, the usefulness of these molecular markers formarker assisted selection (MAS) is evaluated.

Materials and methods

Segregating population

A pair cross was made between a diploid, crown rust re-sistant and a diploid, crown rust susceptible L. perenneplant. The degree of crown rust resistance of the par-ent plants was previously determined in the fieldsof the breeding company Advanta, at Rilland (TheNetherlands). The cross was carried out in an isolationcell and F1 seed from each crossing partner was har-vested separately.

Evaluation of crown rust resistance

F1 seeds were sown in pots (4 × 4 × 7 cm) filledwith common soil. Trays of 96 pots were kept in thegreenhouse at 20–25 ◦C during the day and at 15 ◦Cduring the night. When necessary, illumination wassupplemented for a maximum of 14 h. A total of 265F1 plants were artificially infected with crown rustthree times. Of these, 166 F1 plants were derivedfrom seed harvested from the resistant parent and99 F1 plants were derived from seed harvested fromthe susceptible parent. The inoculation was carriedout as described in Adams et al. (2000) when theplants were six weeks old. The same plants werere-inoculated twice more at intervals of one month.Urediniospores for the artificial infection tests wereharvested in the fields of DvP-CLO (Melle, Belgium)in 1997 from plants of different perennial ryegrassvarieties.

The plants were scored for resistance reaction twoweeks after each inoculation using a scale derivedfrom Birckenstaedt et al. (1994). The rating valuesof Birckenstaedt et al. (1994) represent a relative es-timate of the leaf area occupied by crown rust pustulesand range from 1 (resistant) to 9 (susceptible). Thisscale was modified slightly and scores between 1 (re-sistant) and 6 (susceptible) were used in the presentstudy. Scores 1–5 were identical to the ratings of theBirckenstaedt scale, while score 6 comprised scores6–9 of the Birckenstaedt scale. This was done as thedifferentiation of scores 6–9 was not clear in this study.After each resistance evaluation the plants were cut andallowed to regrow for at least 2 weeks before a newround of inoculation.

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Bulked segregant analysis

Before phenotypic evaluation for crown rust resis-tance, young leaf material was harvested, frozen inliquid nitrogen, lyophilised and stored under vacuumconditions. About 100 mg of lyophilised leaf materialwas ground using a mill (Retsch MM200). GenomicDNA was prepared using a modified CTAB protocol(Weising et al., 1991). AFLP analysis was performedaccording to Roldan-Ruiz et al. (2000) with availablekits. Two different enzyme combinations were tested:EcoRI/MseI and HindIII/MseI.

Resistant and susceptible bulked samples were con-structed by pooling preamplified DNA of F1 individ-uals displaying extreme phenotypes in the phenotypictests. Three bulks of 12 individuals were made: tworesistant bulks and one susceptible bulk. One resistantbulk consisted of resistant F1 plants harvested fromthe resistant parent. The second resistant bulk con-sisted of resistant F1 plants harvested from the suscep-tible parent. The susceptible bulk consisted of the 12most susceptible F1 plants derived from both seed lots.AFLP fingerprints of the bulks and the parents werecompared. The primer combinations yielding candi-date markers from BSA were subsequently tested oneach individual within the bulks. The markers display-ing association with resistance to crown rust in the in-dividuals within the bulks were further analyzed onall the F1 individuals of the population to confirm themarker–trait association.

Statistical analysis of phenotypic and genotypic data

First of all, non-parametric correlation coefficients(Spearman’s rho) between the different rounds of in-fection were calculated and the significance of thepair-wise differences between the three inoculationrounds was determined using the Friedman test. Mann–Whitney U-tests were performed to check for the pres-ence of maternal resistance factors in the population.Calculations were performed using the statistical pack-age SPSS (Norusis, 2000).

Secondly, the AFLP-data generated for thecomplete F1 population were analyzed using non-parametric approaches. Permutation tests as describedin Churchill and Doerge (1994) were performed in S-Plus 6.0 professional release 2 (Insightful Corp.). Foreach candidate marker tested on the whole F1 popula-tion, the marker–trait association was evaluated usinga standardized regression coefficient comparing themean trait value of plants with and without the marker.

The significance of the observed test statistic (T0) wasdetermined for each marker separately by referring toits null distribution. Since the trait values were discreteand in most cases skewed, the null distribution of thet-test statistic did not follow a t-distribution. Permu-tation tests are particularly attractive in this situationsince they construct a non-parametric null distributionfor the test statistic, conditional on the observeddata.

A statistically significant association betweenmarker and trait is suggested by an unusually extremevalue of the test statistic. Randomly permuting the traitvalues among the progeny while retaining its markergenotype destroys any existing marker–trait associa-tion and hence each permutation can be seen as a ran-dom draw from the null hypothesis, as if there were noassociation. With n plants, there are n! different per-mutations possible representing an excessive compu-tational burden if exhaustive permutations would beneeded. We followed the approach of Nettleton andDoerge (2000) who constructed confidence intervals(CI) for the critical values based on a relatively smallsample of permutations.

Each marker was analyzed separately at thenominal significance level α = 0.05 (not accountingfor multiple testing). Eight hundred permutations wereused to construct 95% CI for the 2.5 and 97.5 percentilevalues (two-sided tests). A significant association withcrown rust resistance or susceptibility was determinedwhen the original test statistic of the marker was abovethe CI for the 97.5 percentile or below the CI forthe 2.5 percentile. These markers were retained forfurther analysis. If the original test statistic (T0) waswithin the CI for the critical values, results would beinconclusive and the number of permutations should beincreased.

Finally, simple linear regression of the trait onthe marker was used to calculate the coefficient ofdetermination, R2, for each DNA-marker displayingsignificant association with the crown rust resistance.This coefficient provides a measure of the proportionof the total variance explained by each selected markerand represents the magnitude of the marker-associatedphenotypic effect. All markers identified with anassociation with crown rust resistance were used inmultiple linear regression analysis to calculate partialR2 and to confirm which loci, if any, were taggingthe same genomic region. The total R2 was used tomeasure the total proportion of phenotypic variationexplained by these loci (William et al., 1997). Re-gression analyses were performed using the package

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SPSS (Norusis, 2000). Recombination frequenciesbetween candidate markers were calculated using thesoftware Joinmap version 3.0 (van Ooijen & Voorrips,2001).

Usefulness for MAS

In order to determine the usefulness of the markersfor MAS, divergent selections were carried out. Foursubpopulations of 8–11 plants each were selectedfrom the F1 population used for BSA. Selection ofthe subpopulations was based on either marker orphenotype information. Subpopulation M+ consistedof plants with the two markers associated with crownrust resistance, PC168-R1 and PC026-R3, whilesubpopulation M− consisted of plants that lackedthese markers. Subpopulation R consisted of resistantplants with a mean crown rust score of 1, accordingto the phenotypic evaluation, while subpopulation Scontained susceptible plants with a mean rust score ≥3.All four subpopulations were planted in isolation plotssurrounded by rye in the spring of 2003. The plantswere open pollinated within the subpopulations andthe progeny were evaluated for crown rust resistance asdescribed.

Results and discussion

Consistency of the rust resistance evaluations

In order to determine the degree of consistency ofthe resistance scores assigned to each F1 plant, in-dividual plants were inoculated and rated for crownrust resistance three times. The crown rust scores ob-tained during the three consecutive inoculation roundswere highly correlated. Significant correlation coeffi-cients (Spearman’s rho) were found between inocula-tion rounds 1 & 2, 2 & 3 and 1 & 3 (0.666, 0.648 and0.714, respectively).

The repeated inoculation of the same plant couldlead to (a) induction of systemic acquired resistanceresulting in increased levels of resistance over time,or to (b) weakening due to reserve depletion and de-creased levels of resistance over time. The presence ofthese effects were analyzed using Friedman tests. How-ever, no heterogeneity among rust data from the threeinoculation rounds was found, demonstrating that therepeated infection of the same plant did not result ei-ther in systemic acquired resistance nor in weakeningof the plants.

Inheritance of crown rust resistance

Lolium spp. are obligate outbreeders with a game-tophytic self-incompatibility system (Cornish et al.,1979; Van Daele et al., 2000; Thorogood et al., 2002).Therefore, we crossed two heterozygous and geneti-cally diverse genotypes contrasting in the trait of inter-est. The mode of inheritance was studied in the segre-gating F1 population.

Adams et al. (2000) reported that a maternalfactor transmitted from generation to generation ina predictable way (Baert, DvP-CLO, unpublishedresults) may play a role in crown rust resistance insome L. multiflorum plants. The presence of maternalresistance factors in the BSA population wouldconfound the interpretation of the results. Therefore,Mann–Whitney U-tests were used to determinesignificant differences between the mean crown rustscore of the two parental seed lots. No maternal effectswere identified in the population studied (Table 1).Thus, the genetic variation present in the population isencoded by the nuclear genome.

The frequency distribution of the mean crown rustscore of individual F1 plants is given in Figure 1. Based

Table 1. Mean crown rust score of the R and S F1 seed lots

Inoculation round R F1 seed lota S F1 seed lotb P-value

Round 1 1.97 1.75 0.182

Round 2 1.81 1.94 0.197

Round 3 2.07 1.75 0.087

The results of the Mann–Whitney U-tests for significant differencebetween mean crown rust scores of the seed lots are represented.aBased on 166 F1 plants harvested from the R parent.bBased on 99 F1 plants harvested from the S parent.

Figure 1. Frequency distribution of mean crown rust scores of the F1

population. Crown rust scores vary from 1, resistant to 6, susceptible.The number of plants in each category are noted on top of each bar.The population mean, the standard deviation and the total number ofplants are also given.

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on the design of the population, we can only observe thesegregation of genes that are heterozygous in the par-ent plants. In order to determine the number of geneticfactors influencing crown rust resistance, F1 individu-als with a score 1 or 2 were considered resistant andindividuals with a score of 3 to 6 as susceptible. In thispopulation, 79% of the genotypes were resistant while21% were susceptible. This discrete distribution canbe explained by the segregation of two dominant ma-jor genes for resistance that do not interact (χ2 = 2.547with a P-value of 0.110). Qualitative crown rust resis-tance has been reported previously. Similar to the situ-ation identified in this study, Lellbach (1999) reportedtwo dominant genes showing interaction with modi-fying genes in a segregating F2 population. McVeigh(1975), Wilkins (1975) and Dumsday et al. (2003) alldetected one important major gene involved in crownrust resistance.

Bulked segregant analysis

BSA using AFLP markers was used to identify mark-ers associated with genomic regions involved in crownrust resistance of ryegrass. The two parents and threebulks were screened using a total of 187 AFLP primercombinations (Table 2). Each AFLP primer combina-tion amplified more than 60 AFLP fragments. Markerspresent in the resistant parent and in the resistant bulksbut absent in the susceptible parent and the susceptiblebulk were selected for further analysis. An average of0.95 markers per primer combination displayed poly-morphism between the parents and the bulks and wereretained. In total 177 putative markers associated withcrown rust resistance were identified (Table 2). The ef-ficiency of BSA to identify putatively linked markerscan be measured by the frequency at which markerspolymorphic between the parent plants show differ-ences between bulks. We found a total of 6545 AFLPmarkers which were polymorphic between the parents.

Table 2. Number of AFLP primer combinations (PC) tested during BSA and number of markersputatively associated to crown rust resistance

Restriction enzyme No. of No. of Average no.combination Preamplification PC tested selected markers of markers/PC

EcoRI–MseI EcoRI + A-MseI + C 43 42 0.98

EcoRI + A-MseI + G 44 26 0.59

HindIII–MseI HindIII + T-MseI + C 48 74 1.54

HindIII + T-MseI + G 52 35 0.67

Total 187 177 0.95

Considering that (1) AFLP markers are distributeduniformly throughout the genome, (2) the length ofthe Lolium genome is 1190 cM (Naylor, 1960; Hay-ward et al., 1998) and (3) we are looking for a markerwithin a window of 10% recombination either side ofthe target locus, we expect 1.9% of the polymorphicloci to be linked to the target gene and fall within awindow of 20 cM around the target locus. Moveover,this figure should be halved, to 0.95%, as we onlyselected markers present in the R parent and absentin the S parent. However, the percentage observed inthis study was 2.7 and is higher than theoretically ex-pected. This could be explained by the fact that morethan one locus is segregating for resistance in the pop-ulation, as predicted by the phenotypic analysis (seeabove).

In a second round of AFLP screening, the mostpromising polymorphisms were tested in all the in-dividuals used to construct the bulks. Some of themarkers failed to show association with the resistancephenotype (data not shown). However, 18 markers pro-duced polymorphic amplification products with a highdegree of association with the resistance phenotype.Six of these selected markers were tested on all F1

individuals.

Association of markers with genetic factors involvedin crown rust resistance

As the genotypic and phenotypic data used in this studywere non-parametric (marker data were scored as 1 or0 and crown rust data were scored as categories from1 to 6), permutation tests were used to test for signif-icant association between each individual marker andcrown rust score. An example of a null distribution ob-tained after 800 permutations is given in Figure 2. Inthe example shown, the association between crown rustresistance and marker PC106-R2 was tested. The teststatistic (T0) for this marker was −8.861, which falls

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Figure 2. Statistical analysis of the association between marker PC106-R2 and mean crown rust score. The null distribution of the permutedtest statistics (800 permutations) is represented. The boundaries of the 95% CI of the 2.5 percentile and the 97.5 percentile (critical thresholdvalues) are indicated. The T0 test statistic of PC106-R2 is −8.861 and is more extreme than the CI of the 2.5 percentile of the distribution. Thismeans that there is a positive and significant (P < 0.05) association between the marker and the mean crown rust score.

outside the 95% CI of the 2.5 percentile for the permu-tation based null distribution. This indicates a signifi-cant (P < 0.05) and positive association between themarker and crown rust resistance.

All six markers tested showed a significant associ-ation with the average crown rust resistance score aswell as with each of the individual scores obtained inthe three rounds of infection (Table 3). Marker PC026-R2 did not display clear results. The test statistic forPC026-R2 in inoculation round 2 fell within the 2.5%CI, but the marker displayed significant associationwith crown rust resistance for inoculation rounds 1 and3 and for the overall average. This marker was thusretained for further analyses.

To estimate the proportion of variance explainedby each marker, a linear regression model was fittedto the data (last column of Table 3). The coefficient ofdetermination R2 was used as a measure of the mag-nitude of the marker-linked phenotypic effect. TwoR2 values of more than 20% were found (PC106-R223.5% and PC168-R1 21.7%). Dumsday et al. (2003)identified SSR markers on LG 2 that explained up to64% of phenotypic variation in crown rust resistancein a L. perenne population. It is likely that the smallpopulation size (60 F1 plants) used by Dumsday et al.(2003) resulted in discrete segregation data and anoverestimation of phenotypic effects. In comparison,we used a population size three times larger in our

study. In addition, Dumsday et al. (2003) used Rinstead of R2 to estimate the phenotypic effects of themarkers.

Linkage analysis of R associated markers

Multiple linear regression including all six markersresulted in a R2 of 35% (Table 3). This is still rather lowcompared to the study of Kover and Caicedo (2001)that summarized different resistance QTL studies andfound on average 51% of the phenotypic variance wasexplained by QTLs. Combining all markers resulted inonly a small increase in the proportion of phenotypicvariance explained or in no increase at all, suggestingthat some markers are detecting the same genetic locusfor crown rust resistance. Therefore, the recombinationfrequencies between the six selected markers werecalculated. Linkage analysis revealed two unlinkedmarker clusters associated with crown rust resistance(Figure 3). One marker cluster was formed by tightlylinked markers PC026-R3 and PC026-R4 (5 cMmap distance), and marker PC008-R1, linked 24 and19 cM from these markers, respectively. The secondmarker cluster consisted of tightly linked markersPC106-R2 and PC168-R1 (9 cM) with PC026-R2linked to marker PC106-R2 (17 cM). This explainsthe result obtained with the multiple regressionanalysis in which only a slightly higher R2 value

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Table 3. Statistical analysis of the association between selected markers and crown rust scores (three individualscores at each inoculation round and the average crown rust score)

Boundaries of 95% CI Boundaries of 95% CIof the 2.5 percentile of the 97.5 percentile

Marker Score T0 value Lower Upper Lower Upper R2 value

PC008-R1∗∗ 1 −4.072∗∗ −2.233 −1.824 1.916 2.188

2 −2.684∗∗ −2.112 −1.830 1.915 2.198

3 −3.799∗∗ −2.214 −1.778 1.765 1.983

Average −3.726∗∗ −2.220 −1.821 1.694 1.958 0.057

PC026-R2 1 −3.243∗∗ −2.541 −1.854 1.901 2.312

2 −2.078 −2.507 −1.936 1.965 2.250

3 −3.357∗∗ −2.342 −1.901 1.984 2.315

Average −3.458∗∗ −2.496 −1.959 1.968 2.370 0.049

PC026-R3∗∗ 1 −3.920∗∗ −2.215 −1.940 1.695 2.106

2 −3.211∗∗ −2.193 −1.766 1.727 2.153

3 −3.728∗∗ −2.247 −1.805 1.767 2.098

Average −3.673∗∗ −2.157 −1.889 1.788 2.055 0.055

PC026-R4∗∗ 1 −3.387∗∗ −2.126 −1.852 1.774 2.048

2 −2.079∗∗ −2.078 −1.795 1.831 2.258

3 −3.067∗∗ −2.170 −1.840 1.724 2.054

Average −2.737∗∗ −2.062 −1.929 1.608 2.007 0.032

PC106-R2∗∗ 1 −7.411∗∗ −2.288 −1.876 1.746 2.156

2 −6.288∗∗ −2.093 −1.809 1.955 2.382

3 −7.872∗∗ −2.187 −1.857 1.813 2.142

Average −8.861∗∗ −2.082 −1.816 1.850 2.251 0.235

PC168-R1∗∗ 1 −7.413∗∗ −2.452 −1.906 1.831 2.103

2 −6.609∗∗ −2.362 −1.936 1.946 2.229

3 −7.223∗∗ −2.065 −1.847 1.911 2.239

Average −7.981∗∗ −2.174 −1.909 1.868 2.400 0.217

Overall 0.350

Original T-values (T0) of all selected markers obtained in the permutation tests are given. The upper and lowerboundaries of the 2.5 and the 97.5 percentiles of the permutation distribution obtained after 800 permutationsare represented. The marker-specific and the overall R2 value is given as an estimate of phenotypic variationexplained by each marker.∗∗Value outside the 2.5 and 97.5 percentiles when testing for a two-sided association at the α = 5% significancelevel.

was seen compared to the different individual R2

values.The detection of two unlinked genomic regions in

the BSA agrees with the conclusion of the phenotypicanalysis, which suggested the involvement of two in-dependently segregating loci. Markers PC026-R3 andPC026-R4 are tightly linked with the RFLP markercdo385 in this L. perenne population (unpublished re-sults). According to Jones et al. (2002), cdo385 mapsto LG 2 in the p150/112 mapping population. It is inthis genomic region that Dumsday et al. (2003) also

located SSR markers associated with crown rust re-sistance. This means that the first cluster of markers(PC026-R3/PC026-R4/PC008-R1), could be detectingthe same genomic region as Dumsday et al. (2003).However, in our population this region only explainsa small proportion of the variation (total R2 of cluster1 = 6.1%). The second marker cluster identified inour population (PC106-R2/PC168-R1/PC026-R2), ex-plains a much higher proportion of the variation (totalR2 of cluster 2 = 27.7%) and represents a novel locusnot detected in previous studies.

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Figure 3. Linkage analysis of the selected markers based on geneticdata of the whole F1 population. Genetic distance is estimated incM.

Use of selected markers for MAS

In order to get an indication of the usefulness of themarkers identified by BSA for MAS, divergent se-lections were carried out within the F1 populationused in the bulked segregant analysis. The presenceof two AFLP markers (PC168-R1 and PC026-R3),representing the two genomic regions associated withcrown rust resistance, were used as criterion for MAS.The improvement achieved in the subpopulation se-lected on the basis of this marker information, after

Table 4. Divergent selection based on phenotypic or marker information

Constitution of the subpopulations Evaluation of the multiplied subpopulations

# F1 Mean (S.D.) crown Presence of Presence of # Evaluated Mean (S.D.) crown rustplants rust resistance score PC168-R1 (%) PC026-R3 (%) plants resistance score

Marker-based selection

M+ 10 1.23 (0.32) 100 100 155 1.93 (1.34)

M− 8 2.11 (0.69) 0 0 185 2.22 (1.55)

Phenotype-based selection

R 11 1.00 (0.00) 55 81 177 2.1 (1.57)

S 10 3.30 (1.03) 0 30 150 3.33 (1.47)

Plants selected out of the F1 population with (M+) or without (M−) the selected associated markers or with a resistant (R) orsusceptible (S) phenotype, were intercrossed. A number (‘# evaluated plants’) of plants from these subpopulations were evaluatedfor crown rust resistance.

one cycle of multiplication was compared to the im-provement achieved by selecting on the basis of thephenotype alone. An overview of the composition ofthe different subpopulations and the results obtainedin the multiplied subpopulations are given in Table4. Subpopulation M+ was composed of 10 F1 plantsdisplaying an average crown rust score of 1.23. Sub-population R was composed of 11 plants display-ing an average crown rust score of 1.00. Subpop-ulation M− was formed by 8 plants displaying anaverage score of 2.11, while subpopulation S wasformed by 10 plants displaying an average score of3.3. In general, the multiplied subpopulations dis-played higher degrees of susceptibility than the orig-inal subpopulations, but the difference was signifi-cant (P < 0.05) only in the case of subpopulationR. In fact, the multiplied subpopulation R (formedby F1 plants selected only on the basis of pheno-type) displayed a much higher degree of susceptibil-ity (average score 2.11) than the 11 original plantsused to form subpopulation R (average score 1.00).Based on the results presented in Table 4, we can con-clude that selection on the basis of DNA-marker in-formation resulted in more satisfactory results thanselection on basis of the phenotype alone. Further-more, if we take into account that the plants selectedto form the M+ subpopulation displayed a higherdegree of susceptibility than the plants selected toform the R subpopulation (1.23 for M+ and 1.00 forR), we can expect a higher efficiency by combiningboth phenotypic evaluation with DNA-marker infor-mation. The most appropriate approach in that casewould be to select those plants with the lowest diseasescores, but which also contain markers PC168-R1 andPC026-R3.

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Conclusions

Two clusters of AFLP markers associated with crownrust resistance in a mapping population of L. perennehave been identified. Together these clusters explain35% of the variance present in the population. BSA incombination with AFLP markers was proven to be agood method for identify genomic regions associatedwith the trait of interest. Tightly linked markers wereselected that will be useful for MAS.

This study confirmed the presence of a genomicregion involved in crown rust resistance on LG 2, asreported in previous studies. Furthermore, a novel ge-nomic region with major effect (27.7% of variation ex-plained) not linked to LG 2 was identified. The genomiclocation of this second region remains unknown.

Taking into account that only a small proportion ofthe phenotypic variance was explained by the AFLPmarkers identified, we can conclude that genomic re-gions other than the two identified probably play a rolein the determination of crown rust resistance in thissegregating population. A dense genetic map in com-bination with QTL analysis will help us to identify thelocation of the novel genomic region associated withcrown rust resistance identified in this study, as well asother genomic regions with minor effects.

Furthermore, our results clearly indicate that theanalysis of germplasm from different origins can leadto the identification of different genomic regions asso-ciated with resistance (for example, if we compare ourstudy to Dumsday et al., 2003). In this context, the anal-ysis of P. coronata mixtures and L. perenne germplasmshowing different infection/resistance mechanisms areof special interest in unravelling the genomic organi-sation of crown rust resistance in Lolium spp.

Even though the results of MAS are rather promis-ing, we performed the selections within the same pop-ulation used for BSA screening. Given the results ob-tained in this study and in previous studies, a betterunderstanding of the different regions controlling re-sistance in different germplasm sources is necessarybefore standard tests can be developed for MAS.

Acknowledgments

The authors thank Wendy Aartsen, Greet De Smet,Carina Pardon and Ariane Staelens for their technicalassistance. We thank dr. ir. J. Van Huylenbroeck for hisassistance in plant handling. Dr. Rindert Peerbolte andArnd Jan van Wijk (Advanta Seeds B.V. Rilland, The

Netherlands) helped with plant material and fruitfuldiscussions. This research was funded by the ‘Euro-pean GRAmineae Mapping project’ (EGRAM), Con-tract No. B104-CT97-2220. We thank them for theirsupport.

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