Multienvironment genomic prediction in tetraploid potato

Stefan Wilson, Chaozhi Zheng, Chris Maliepaard, Han Mulder, Richard G.F. Visser, Fred van Eeuwijk*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Multienvironment genomic prediction was applied to tetraploid potato using 147 potato varieties, tested for 2 years, in 3 locations representative of 3 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 6 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 analyzed 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 multienvironment modeling 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 multienvironment prediction model is chosen based on the patterns of G×E indicated by the genetic variances and covariances.
Original languageEnglish
Article numberjkae011
JournalG3: Genes, Genomes, Genetics
Volume14
Issue number4
Early online date19 Feb 2024
DOIs
Publication statusPublished - 2024

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