Data from: Relatedness severely impacts accuracy of marker- assisted selection for disease resistance in hybrid wheat
Reif, Jochen C. et al. (2013), Data from: Relatedness severely impacts accuracy of marker- assisted selection for disease resistance in hybrid wheat, Dryad, Dataset, https://doi.org/10.5061/dryad.461nc
The accuracy of genomic selection depends on the relatedness between the members of the set in which marker effects are estimated based on evaluation data and the types for which performance is predicted. Here, we investigate the impact of relatedness on the performance of marker-assisted selection for fungal disease resistance in hybrid wheat. A large and diverse mapping population of 1,739 elite European winter wheat inbred lines and hybrids was evaluated for powdery mildew, leaf rust, and stripe rust resistance in multi-location field trials and fingerprinted with 9k and 90k SNP arrays. Comparison of the accuracies of prediction achieved with data sets from the two marker arrays revealed a crucial role for a sufficiently high marker density in genome-wide association mapping. Cross- validation studies using test sets with varying degrees of relationship to the corresponding estimation sets unraveled that close relatedness leads to a substantial increase in the proportion of total genotypic variance explained by the identified QTL and, consequently, to an overoptimistic judgment of the prospected precision of marker-assisted selection.