Multi-trait association mapping in sugar beet (Beta vulgaris L.)

Association mapping promises to overcome the limitations of linkage mapping methods. The main objective of this study was to examine the applicability of multivariate association mapping with an empirical data set of sugar beet. A total of 111 diploid sugar beet inbreds was selected from the seed parent heterotic pool to represent a broad diversity with respect to sugar content (SC). The inbreds were genotyped with 26 simple sequence repeat markers chosen according to their map positions in proximity to previously identified quantitative trait loci for SC. For SC and beet yield (BY), the genotypic variances were highly significant (P < 0.01). Based on the global test of the bivariate mixed-model approach, four markers were significantly associated with SC, BY, or both at a false discovery rate of 0.025. All four markers were significantly (P < 0.05) associated with BY but only two with SC. The identification of markers associated with SC, BY, or both indicated that association mapping can be successfully applied in a sugar beet breeding context for detection of marker-phenotype associations. Furthermore, based on our results multivariate association mapping can be recommended as a promising tool to discriminate with a high mapping resolution between pleiotropy and linkage as reasons for co-localization of marker-phenotype associations for different traits.