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BackgroundSugar beet is an obligate outcrossing species. Varieties consist of mixtures of plants from various parental combinations. As the number of informative morphological characteristics is limited, this leads to some problems in variety registration research.ResultsWe have developed 25 new microsatellite markers for sugar beet. A selection of 12 markers with high quality patterns was used to characterise 40 diploid and triploid varieties. For each variety 30 individual plants were genotyped. The markers amplified 3-21 different alleles. Varieties had up to 7 different alleles at one marker locus. All varieties could be distinguished. For the diploid varieties, the expected heterozygosity ranged from 0.458 to 0.744. The average inbreeding coefficient Fis was 0.282 ± 0.124, but it varied widely among marker loci, from Fis = +0.876 (heterozygote deficiency) to Fis = -0.350 (excess of heterozygotes). The genetic differentiation among diploid varieties was relatively constant among markers (Fst = 0.232 ± 0.027). Among triploid varieties the genetic differentiation was much lower (Fst = 0.100 ± 0.010). The overall genetic differentiation between diploid and triploid varieties was Fst = 0.133 across all loci. Part of this differentiation may coincide with the differentiation among breeders' gene pools, which was Fst = 0.063.ConclusionsBased on a combination of scores for individual plants all varieties can be distinguished using the 12 markers developed here. The markers may also be used for mapping and in molecular breeding. In addition, they may be employed in studying gene flow from crop to wild populations.

High genetic variation within sugar beet (Beta vulgaris L.) varieties hampers reliable classification procedures independent of the type of marker technique applied. Datasets on amplified fragment length polymorphisms, sequence tagged microsatellite sites, and cleaved amplified polymorphic sites markers in eight sugar beet varieties were subjected to supervised classifiers, methods in which individual assignments are made to predefined classes, and unsupervised classifiers, defined afterward on the similarity in marker composition from sampled individuals. Major issues addressed are (i) which classification method gives the most consistent results when three marker techniques are compared, and (ii) given different classification techniques available, for which marker technique is the output generated least constrained by the way data analysis is performed. Assignment tests showed a higher consistency across classifications independent from the marker technique. A good allocation to the proper variety was obtained, together with a reliable allocation pattern among the other varieties. Both aspects deal with the variation within a variety and the distance to other varieties. Assignment data were transformed into an average similarity measure, similarity by assignment (Sax,y), which is a new genetic distance measure with interesting properties.