Multi-environment Genomic Prediction in Tetraploid Potato.

Multi-environment genomic prediction was applied to tetraploid potato using 147 potato varieties, tested for two years, in three locations representative of three distinct regions in Europe. Different prediction scenarios were investigated to help breeders predict genotypic performance in the regions from one year to the next, for genotypes that were tested this year (scenario 1), as well as new genotypes (scenario 3). In scenario 2 we predicted new genotypes for any one of the six trials, using all the information that is available. The choice of prediction model required assessment of the variance-covariance matrix in a mixed model that takes into account heterogeneity of genetic variances and correlations. This was done for each analysed trait (tuber weight, tuber length and dry matter) where examples of both limited and higher degrees of heterogeneity was observed. This explains why dry matter did not need complex multi-environment modelling to combine environments and increase prediction ability, while prediction in tuber weight, improved only when models were flexible enough to capture the heterogeneous variances and covariances between environments. We also found that the prediction abilities in a target trial condition decreased, if trials with a low genetic correlation to the target were included when training the model. Genomic prediction in tetraploid potato can work once there is clarity about the prediction scenario, a suitable training set is created, and a multi-environment prediction model is chosen based on the patterns of GxE indicated by the genetic variances and covariances.