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Crop Science 40:1417-1425 (2000)
© 2000 Crop Science Society of America

CELL BIOLOGY & MOLECULAR GENETICS

Genetic Relationships and Fingerprinting of Rapeseed Cultivars by AFLP

Consequences for Varietal Registration

V. Lombarda, C.P. Barilb, P. Dubreuilb, F. Blouetb and D. Zhanga

a GEVES, domaine du Magneraud, BP 52, F-17700 Surgères, France
b GEVES, La Minière, F-78285 Guyancourt Cedex, France

vincent.lombard{at}geves.fr


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 Materials and methods
 Results
 Discussion
 REFERENCES
 
At present, registration and protection of rapeseed (Brassica napus L.) cultivars relies on a small number of morphological traits. As the number of cultivars increases, the ability to distinguish them on the basis of these traits becomes more difficult. New descriptors like molecular markers are required to maintain the efficiency of registration testing. The objectives of this study were to (i) evaluate the discrimination power of 17 amplified fragment length polymorphism (AFLP) primer combinations tested on a collection of 83 spring and winter rapeseed cultivars, (ii) assess the structure of the genetic diversity revealed by AFLPs, and (iii) compare three genetic distance estimators (Jaccard, Sokal, and Michener and Sokal and Michener weighted by the PIC). A total of 324 polymorphic markers were scored, with an average of 19.1 markers per primer combination. The use of only two primer combinations was sufficient to identify uniquely all the cultivars. The analysis of the genetic structure of the diversity by cluster and principal component analyses and AMOVAs (analyses of molecular variance) clearly delineated three significant factors: the cultivar type (winter or spring), the country of origin (France or Germany), and the breeding company. The three measures of genetic distance were highly correlated (P < 0.001, r = 0.96–0.98) and led to similar groupings. AFLPs were shown to be a powerful method for identifying cultivars and analyzing the genetic structure of the diversity in rapeseed.

Abbreviations: AFLP, amplified fragment length polymorphism • AMOVA, analysis of molecular variance • DUS, distinctness uniformity stability • J, Jaccard's coefficient • MSM, modified Sokal and Michener's coefficient • PIC, polymorphic information content • PCA, principal components analysis • PCR, polymerase chain reaction • RAPD, random polymorphic DNA • RFLP, restriction fragment length polymorphism • SM, Sokal and Michener's coefficient • UPGMA, unweighted pair-group arithmetic average method


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
Materials and methods
 Results
 Discussion
 REFERENCES
 
THE LEGAL RIGHT to market a newly bred cultivar in the European countries depends on the results of statutory testing. This statutory testing provides information to establish that a new cultivar is distinct (D) from all the other released cultivars, and uniform (U) and stable (S) for the traits evaluated in the tests. DUS (Distinctness Uniformity Stability) testing guarantees the quality of the new cultivar for farmers and merchants. Morever, it is also used to protect intellectual property of the plant breeder (plant breeders' rights), which encourages the continuous development of new varieties. In that context, plant breeders' rights have evolved since the introduction of the concept of essential derivation into the UPOV convention (International Union for the Protection of new Varieties of plants) of 1991. An essentially derived cultivar is a cultivar that is derived from and that conforms to the genotype of the initial cultivar and is clearly distinguishable from the initial cultivar by phenotypic characteristics, such as a single simply inherited trait. This phenomenon has increased since the development of biotechnologies which make it possible to introduce a single gene into a variety.

For most species, DUS testing only relies on the comparison of morphological traits between candidate cultivars and all the previously registered cultivars (the reference collection). This protocol has a number of limitations and is expensive and time consuming. First, for economic reasons, DUS testing requires minimal replication (two locations and two replicates per location in France), which leads to unreliable estimates of the interactions between genotypes and environment. These interactions can be important for the morphological traits used because their expression is influenced by environmental conditions. Second, candidate varieties are compared with the reference collection which increases in size year after year. To complement morphological traits, isozyme analysis has been used for cultivar identification and was included in DUS testing for some crops such as maize (Zea mays L.), wheat (Triticum aestivum L.), and barley (Hordeum vulgare L.). Isozyme analysis allows a more direct observation of the genotype of a cultivar, but the number of isozyme markers is limited.

In regard to difficulties encountered in DUS testing with morphological traits or isozyme markers, rapeseed cultivar registration and protection in France has several specific problems. First, only 13 traits are available for DUS testing. Second, the number of candidate cultivars has increased sharply in the past 5 yr with the increase of rapeseed production in Europe. Furthermore, while the inbred line was the major type of cultivar before the 1990s, the marketing of varietal associations (mixture between a sterile hybrid and pollinators) and hybrids has expanded, which requires new tests for estimating varietal purity (hybridity rate). Third, only seven isozyme systems (about 20 loci) are commonly used and the polymorphism level present in rapeseed is particularly low (Lee et al., 1996).

In this framework, DUS testing would benefit from the use of molecular markers that have been shown to be more rapid and cost-effective. In comparison with morphological traits, molecular markers have many advantages. Their expression is independent of environmental conditions, a lengthy survey of plant growth methods is not needed and the potential number of markers is nearly unlimited in relation to isozymes. Molecular markers have been successfully applied in registration activities like cultivar identification (Mailer et al., 1994), or controls of seed purity of hybrid varieties (Marshall et al., 1994). Other applications could be envisaged. First, the management of field comparison could be improved from a knowledge of the genetic structure of the reference and candidate cultivars as revealed by a set of markers well distributed over the genome. Only the most genetically similar reference varieties could be planted near candidate varieties, which would reduce the required number of reference cultivars and would improve the direct comparisons of morphological traits. Second, molecular markers could assess genetic relatedness between cultivars which is the central aspect of the essential derivation concept.

Among the available DNA molecular techniques, AFLP is a powerful technique for cultivar identification (Powell et al., 1996). It provides a large number of markers in a single analysis without requiring sequence information for their development (Vos et al., 1995). AFLPs have been successfully applied for intraspecific genetic diversity analyses in tea [Camellia sinensis (L.) O. Kuntze; Paul et al., 1997], sunflower (Helianthus annuus L.; Hongtrakul et al., 1997), and wheat (Barrett and Kidwell, 1998), and interspecific study among cassava species (Maninhot spp.; Roa et al., 1997), and for genetic linkage mapping in rice (Oryza sativa L.; Maheswaran et al., 1997). As compared with RFLP (restriction fragment length polymorphism) for which polymorphism among cultivars is low (Diers and Osborn, 1994), AFLPs have very attractive qualities for DUS testing in rapeseed. Likewise, in rapeseed it has been difficult to find stable polymorphic markers generated with RAPD (random amplified polymorphic DNA) (Mailer et al., 1994).

Because decisions about registration or/and protection of a new candidate variety have crucial economic consequences for breeders and farmers, the potential of molecular markers for DUS testing merits investigation. The aims of this study were to (i) evaluate the discrimination power of AFLP markers to identify rapeseed cultivars, (ii) analyze the structure of the genetic diversity revealed by AFLPs and (iii) compare three genetic distance measures for their ability to represent genetic relationships among cultivars.


   Materials and methods
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and methods
Results
 Discussion
 REFERENCES
 
Plant Material
Eighty-three rapeseed cultivars including both spring and winter types were studied. Most of the cultivars were chosen to represent the European genetic diversity in rapeseed. The remainder consisted of either ancestral cultivars of historical importance in rapeseed breeding, or non-European cultivars chosen to increase genetic diversity (Table 1) . GEVES supplied all seed except for cultivars Major, E, Stellar, and Yudal for which seed was supplied by Michel Renard (INRA of Rennes, France).


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  		Table 1 Description of 83 rapeseed cultivars used in AFLP analyses including cultivar type, country of origin, and breeder

 
DNA Extraction and AFLP Assays
For each cultivar, total DNA was extracted from a mixture of thirty seedlings using a CTAB procedure with minor modifications (Rogers and Bendich, 1988). AFLP analysis was performed according to the procedure described by Vos et al. (1995), using a commercially available kit (AFLP analysis system I, GIBCO BRL, Life Technologies, Inc., Rockville, MD) and following manufacturer's instructions. Seventeen primer combinations involving five EcoRI and seven MseI primers were used. EcoRI primers were radiolabeled with [ß-33P]-dATP.

After selective amplification, polymerase chain reaction (PCR) products were mixed with an equal volume of loading buffer [98% (v/v) formamide, 12.5% (w/v) saccharose, 10 mM NaOH, 0.05% (v/v) xylene cyanol, and 0.05% (v/v) bromophenol blue] and denatured at 92°C for 3 min and then heated at 70°C during the loading. Then, 4 µL were loaded on a 5% (w/v) polyacrylamide gel containing 8 M urea, which was pre-warmed for 25 min at 55 W. Gels were run with a 0.5 x TBE electrophoresis buffer [50 mM Tris, 50 mM boric acid, 1 mM EDTA, pH 8] at 55 W constant power. After electrophoresis, gels were removed by means of a 3MM Whatman filter paper, dried and exposed to X-ray film Biomax MR (Kodak) for about 5 d. The reproducibility of AFLP patterns was verified with replicates of digestion, ligation, and selective amplification from the same sample of DNA of one cultivar. No unstable bands were detected.

Statistical Analyses
AFLP Polymorphism and Discrimination Power
Only polymorphic bands (markers) with strong intensity were scored, each marker was identified by the primer combination and the band number as a suffix. Markers with a molecular weight lower than 100 bp were excluded from the data matrix. The discrimination power of each AFLP marker was evaluated by the polymorphism information content (PIC) (Anderson et al., 1993) as {insert equation or symbol}, where p is the frequency of the marker.

Levels of Genetic Diversity
Three dissimilarity coefficients were computed from the binary matrix: the Jaccard's coefficient (J) (Jaccard, 1908), the Sokal and Michener's coefficient (SM) (Sokal and Michener, 1958), and a modified Sokal and Michener's coefficient (MSM) that we defined as,




where n11 is the number of bands in common between Cultivars x and y, n10 is the number of bands present in x and absent in y, n01 is the number of bands present in y and absent in x, n00 is the number of bands both absent in x and y. For MSM, Bxj is the genotype of the Cultivar x (0 or 1) for Marker j, N is the total number of markers and PICj is the PIC for the Marker j. We defined this modified Sokal and Michener coefficient to take into account the frequency of each marker in the computation of the distance by weighting J by the inverse of the PIC. With such a weighting already proposed by Baril et al. (1997), a very rare or frequent marker has a larger contribution to the distance than a marker with an intermediate frequency. One could note that this distance does not range from 0 to 1 in comparison with the others. We could have computed Nei and Li coefficient of dissimilarity (NL) (Nei and Li, 1979) but it was closely related to J by the formula: {insert equation or symbol} (Snijders et al., 1990). This relationship between the two distances clearly implies a high correlation between them and would have led to identical rankings of genetic distances. In addition, this measure of distance is more adapted to codominant marker data transformed into a 0/1 matrix (Engqvist and Becker, 1995).

The sampling variance of each pairwise distance was empirically estimated from 1000 bootstrap samples of the data set. Then, the accuracy of the observed distance was estimated by computing the mean coefficient of variation for each distance estimator.

Description of the Genetic Diversity Structure
Dendrograms were constructed on the basis of the three distance matrices and by the Ward's method (Ward, 1963) and UPGMA (unweighted pair-group arithmetic average method) (Sneath and Sokal, 1973) to show the structure of the diversity based on the AFLP markers. A bootstrap procedure (1000 runs) was performed to provide an estimate of the robustness of the nodes. The structure of the genetic diversity was further analyzed by a principal component analysis (PCA) from the allele frequency correlation matrix.

Significance of the Genetic Structure
The structure of the genetic diversity of the collection was tested by analyses of molecular variance (AMOVA) (Excoffier et al., 1992) on the basis of available information about the origin of the cultivars. This method provides an estimate of the fraction of between-population diversity (i.e., {Phi}st), analogous to the Fst (Wright, 1951), which can be tested by a permutational procedure. In this study, AMOVA was performed to test the structure (i) among winter and spring cultivars (type of cultivars), (ii) among countries of origin for winter cultivars, and (iii) among breeding companies. For (ii) we only considered German and French cultivars because other countries were not well represented. Among French and German winter cultivars, we excluded those that were developed cooperatively by DSV (Germany) and Semences CARGILL (France). For (iii), we only used winter cultivars from DSV, DIPPE, NPZ, INRA and SERASEM, and Semences CARGILL (Table 1) because the number of cultivars from the other breeders was too small.

All statistical analyses were performed with SAS (SAS Institute Inc., 1996), except for genetic distance matrices and bootstrap procedures performed with a computer program written in FORTRAN language by P. Dubreuil, and AMOVA performed with a program written in TURBO PASCAL language by C. Dillmann (INRA of Paris, France).


   Results
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results
Discussion
 REFERENCES
 
AFLP Polymorphism and Power of Discrimination
A total of 324 polymorphic fragments were generated with the 17 primer combinations used. The number of markers per primer combination ranged from 12 to 30, with an average of 19.1. The most polymorphic primer combinations were E-AAC+M-CAA, E-AAC+M-CTT, and E-AAG+M-CTT, which produced 30 markers each (Table 2) . The fragment sizes ranged from 100 to 750 bp, with 81.8% of the markers between 100 and 400 bp. The distribution of the fragment sizes was skewed towards larger fragments. This was a mainly a result of the fact that fragments of less than 100 bp were not included in the analysis.


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  		Table 2 Level of polymorphism and power of discrimination revealed by the 17 primer combinations on 83 rapeseed cultivars

 
The frequency of polymorphic bands observed among the 83 cultivars varied from 0.06 to 0.95, with an average of 0.54 and the frequency of polymorphic fragments generated from the different EcoRI or MseI primers or primer combinations were not significantly different (ANOVA, results not shown). According to Chi-square tests, the allele frequency of 174 markers (53.7%) was significantly different between winter and spring types of cultivars at the 5% level of significance. Among these 174 markers, there was no allele that was unique to one of the two types of cultivars.

The discrimination power of each marker was estimated by the PIC (results not shown). It ranged between 0.09 and 0.50 (the expected maximum value for a biallelic locus) with an average of 0.34. A large proportion of markers have a high discrimination power. Only few markers had a very low PIC value because the bands that were absent or present in less than five cultivars were not included in the analysis.

The discrimination power of each primer combination was estimated by computing the number of groups into which the 83 cultivars could be separated (Table 2). This number ranged from 30 to 77 classes and was highly correlated with the number of markers generated by the primer combination (r = 0.83, P < 0.001). Two primer combinations were sufficient to uniquely identify all of the cultivars: E-AAC+M-CAA and E-AAC+M-CTT or E-AAC+M-CTT and E-AAG+M-CTT.

Genetic Relationships between Cultivars
The distance matrices based on the three similarity coefficients were highly correlated (r = 0.96 for J an-|-d MSM, r = 0.97 for SM and MSM, and r = 0.98 for J and SM; P < 0.001). Similarity values among cultivars ranged from 0.070 to 0.758 for J, from 0.038 to 0.599 for SM and from 0.091 to 1.963 for MSM (Table 3) . The mean coefficient of variation of the similarity values ranged from 8.2 to 9.5 % showing a good precision of the estimates (Table 3). Whatever the estimator considered, Apex and Goeland were the closest cultivars with coefficients of 0.070, 0.038, and 0.091 for J, SM, and MSM, respectively. Colvert (a winter French cultivar) and Westar (a spring Canadian cultivar) had the largest values, 0.758, 0.599, and 1.963 for J, SM, and MSM, respectively, indicating they were the most genetically dissimilar cultivars. The lowest distances were observed among closely related cultivars (Fig. 1) . Apex, Goeland, and Lady were derived from the same breeding program (full-sib lines); Darmor Nain was a selection from a BC3 population between Darmor as the recurrent parent and a dwarf line; cultivars B_ms and E_ms are the male sterile forms of Cultivars B and E.


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  		Table 3 Range and mean coefficient of variation (CV) for the three genetic distance estimators calculated from 324 AFLP markers on 83 rapeseed cultivars

 


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  		Fig. 1 Dendrogram of 83 rapeseed cultivars based on Ward cluster analysis of Modified Sokal and Michener's similarity matrix calculated from 324 AFLP markers. The values on the dendrogram gives the stability of nodes estimated with a bootstrap procedure (no number indicates support less than 10%). Cross: French winter cultivars from Semences CARGILL, pillar: French winter cultivars from INRA and SERASEM, open circle: German winter cultivars from DIPPE, diamond: German winter cultivars from DSV, star: German winter cultivars from NPZ

 
Structure of the Genetic Diversity
The dendrogram obtained with Ward cluster analysis on the modified Sokal and Michener distance matrix separated winter and spring cultivars (Fig. 1). Within the winter group, two subgroups could be easily identified. The first group only included French cultivars from two breeders (INRA and SERASEM and Semences CARGILL), in which cultivars were partitioned into two clusters. The second group included both French cultivars and the other European cultivars. Cultivars from the same breeder were often clustered. The spring group was split into two subgroups, but there was no clear structure with respect to country of origin or breeder. The bootstrap values on the dendrogram showed that the most stable nodes were the first two nodes, which grouped cultivars closely related by pedigree. Almost the same structure was observed with UPGMA cluster analysis even if the dendrogram showed a chaining effect (results not shown). Dendrograms constructed with the Ward method on the three similarity matrices were very similar even if MSM enhanced the structure revealed with the other estimators.

Associations among the cultivars revealed by PCA were represented in Fig. 2 . The first three components explained about 30% of the total variation, with 16.9, 7.9, and 4.6% for the first, the second, and the third components, respectively. Principal component analysis revealed the same global structure as the dendrogram analysis. The PCA representation showed that the spring group was more spread along the first principal component 1 (PC1), i.e., had a higher genetic diversity. It is worth noting that non-European spring cultivars were located at the periphery of the spring group showing their genetic distinctness in comparison with the European genetic pool.



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  		Fig. 2 Genetic relationships depicted among 83 rapeseed cultivars by the first three components (PC1, PC2, and PC3) derived from a principal components analysis on the AFLP data. Cross: French winter cultivars from Semences CARGILL, pillar: French winter cultivars from INRA and SERASEM, square: other French winter cultivars, balloon: German winter cultivars from DIPPE, diamond: German winter cultivars from DSV, star: German winter cultivars from NPZ, triangle: other German cultivars, vertical line: other winter cultivar, and cube: spring cultivars

 
Analyses of molecular variance tested the structure of the collection. Results showed that type of cultivar and the country of origin were significant factors (Table 4) . Thirty-three percent of the molecular variance occurred between winter and spring cultivars. Among winter cultivars, 11.4% of the variance was due to the partition between French and German cultivars. The molecular variance was also significantly structured according to breeders, except between one French (INRA and Serasem) and one German (Dippe) breeding company (Table 4).


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  		Table 4 Analysis of molecular variance (AMOVA) of three factors on molecular data matrix. {Phi}st values correspond to the fraction of the molecular diversity accounted by the factor. For the breeder factor; {Phi}st values are below the diagonal and significance level is above the diagonal

 

   Discussion
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results
 Discussion
REFERENCES
 
AFLP Polymorphism and Discrimination Power
The AFLP technique revealed a higher level of polymorphism among winter and spring rapeseed cultivars in comparison with other marker systems. With similar plant material, 43 genomic clones revealed 161 RFLP markers (3.7 markers per probe/enzyme combination) among 83 winter and spring rapeseed cultivars (Diers and Osborn, 1994). With four multicopy genomic clones, Lee et al. (1996) obtained 60 markers (15 markers per probe/enzyme combination) among 62 cultivars. Among 23 rapeseed cultivars, six primers generated 23 RAPD markers (3.8 markers per primer) (Mailer et al., 1994), whereas Charters et al. (1996) developed 44 markers with their two most polymorphic 5' anchored simple repeat primers (22 markers per primer) among 19 rapeseed cultivars. In our study, the most polymorphic primer combination revealed 30 markers and the average was 19.1 per primer combination. In terms of discrimination power, the AFLP technique was very efficient for cultivar identification. All of the 83 cultivars were distinguished with only two primer combinations even pairs of near isogenic lines such as B and B_ms as well as E and E_ms which are only different for the male sterility trait. These two primer combinations also distinguished Eurol and Idol, whereas Lee et al. (1996) reported no difference between these two cultivars determined on the basis of RFLP data.

Structure of the Genetic Diversity
The consistency of the results among PCA, clustering methods and AMOVAs showed that AFLP markers accurately revealed genetic structure among cultivars. As already reported with RFLP markers by Diers and Osborn (1994) and Lee et al. (1996), there was a clear separation between winter and spring cultivars and a clear trend toward the grouping of cultivars from the same breeder. In the present study, AFLP markers separated most of the French winter cultivars from the other European winter cultivars, showing the genetic distinctness of this group. The remaining French cultivars in the European cluster included Jet Neuf and some related French cultivars such as Darmor and Darmor Nain. Jet Neuf is of considerable historical importance in rapeseed breeding. In the late 1970s, this cultivar was widely cultivated in Europe, and was used as a parent in the development of numerous European cultivars (M. Renard, personal communication).

The robustness of the dendrograms as assessed by the bootstrap procedure further supported the ability of AFLPs to represent the genetic structure of the collection. Whereas the comparison between clustering methods (UPGMA vs. Ward), between analysis methods (cluster vs. PCA), and tests of significance of structure factors (AMOVA) revealed the stability of the structure based on the data set, the bootstrap procedure allowed one to assess the quality the data set (i.e. marker sample) (Efron, 1979; Brown, 1994). First, the low values of the mean coefficient of variation proved that the number of AFLP markers was sufficient to achieve a good level of precision of the similarity estimates between cultivars. Second, the relationship between pedigree information and structure of the dendrogram indicated that there is little evidence of selection on the AFLP markers. The bootstrap values are high for the nodes that group cultivars with the same genetic origin. Among winter cultivars, one interesting node was the one that separated the majority of French cultivars from the others. The bootstrap value was not very high (55%) although the separation corresponded to a relationship previously detected with PCA and AMOVAs. The relative instability of this node can be explained by the presence of three cultivars (Colvert, Express, and A) which had intermediate positions on the PCA between the two major winter groups. If they were removed from the analysis, the bootstrap value of this node increased from 55 to 72%. This result showed that clustering methods are not well adapted to classify individuals that have intermediate position between two groups because an individual can not belong to several classes.

Comparison between Genetic Distances
The high correlation between J and SM (r = 0.98) showed that an allelic relationship between the absence and the presence of a given band can be assumed (Peltier et al., 1995). This result has been already reported in other studies where genotypes came from the same species. Baril et al. (1997) showed that J and SM were highly correlated when computed within each of two species of Eucalyptus (r = 0.77 and r = 0.76 for each species, respectively) whereas the correlation coefficient was small for between-species distances (r = 0.36, P < 0.1%).

We also computed SM weighted by the inverse of the PIC of each marker to take into account the marker frequency in the calculation of the distance. MSM and SM were closely correlated and indicated similar relationships between cultivars. However, MSM better separated the most genetically unique cultivars (i.e., those which carry rare bands).

Choice of a Genetic Distance for Essential Derivation
In the context of essential derivation, the choice of a genetic distance is a crucial issue for estimating the level of relatedness between cultivars. The disadvantage of J is in the difficulty of finding its statistical distribution which is needed to calculate a confidence interval. This difficulty comes from the denominator, which is a random variable. In this case, the bootstrap procedure can be used to estimate empirically sampling variance, but no statistical approach is available to test pairwise distances. For analytical calculations, it is easier to work with Euclidian distances like SM or Rogers' distances (Rogers, 1972). These can be modeled as binomial variables and their statistical properties are well known (Tivang et al., 1994; Dillmann et al., 1997). However, this model requires that molecular markers are a random sample of the genome. This assumption is not fulfilled when a subset of markers is chosen to optimize genome coverage. In this case, Dillmann et al. (1997) showed that by taking into account the position of markers throughout the genome in the calculation of the Rogers' distance, the gain of precision could reach to 40% for inbred lines related by pedigree. For that purpose, a genetic map of our AFLP markers is being developed to determine the position of the markers in the rapeseed genome for the calculation of SM.

In conclusion, this study showed that AFLP markers functioned well in the assessment of genetic relatedness between rapeseed cultivars in the context of plant registration and protection. The discrimination power of AFLP markers allowed easy identification of cultivars, which would be very useful to check the conformity of seed lots of a given cultivar. AFLP markers indicated genetic relationships between cultivars that were consistent with their genetic origin. Analysis of molecular variance can be successfully used to test the significance of groupings based on information of the origin of the cultivars. This could be useful in the management of the reference collections in order to reduce the number of cultivars in direct comparison with candidate varieties in fields for DUS testing. Genetic distances between cultivars estimated with J, SM, and MSM coefficients of dissimilarity were very well correlated and led to a very similar assessment of relationships between cultivars. Associated with knowledge of the genomic distribution and frequency of the AFLPs markers, SM may be a convenient estimator of the genetic distance between cultivars.Paul Wachira Powell Waugh 1997; SAS Institute 1996


   ACKNOWLEDGMENTS
 
This study was supported by a grant from the GEVES (groupe d'étude et de contrôle des variétés et des semences) and the région Poitou-Charentes. We thank Michel Renard and his team for interesting discussions about genetic origins of the cultivars. We also thank the three anonymous reviewers for providing helpful editorial comments.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 Materials and methods
 Results
 Discussion
 REFERENCES
 
Research support provided by the GEVES (groupe d'étude et de contrôle des variétés et des semences) and the région Poitou-Charentes.

Received for publication August 18, 1999.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results
 Discussion
 REFERENCES
 




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