Assignment tests for variety identification compared to genetic similarity-based methods using experimental datasets from different marker systems in sugar beet

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