Handling Genotype-By-Environment Interaction in Genomic Selection to Predict New Genotypes and New Environments

Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. Furthermore, genotype by environment interaction (GEI) is a widespread phenomenon in plants, and how to incorporate GEI to GS models remains a challenge. Mixed models have been used to exploit GEI by borrowing information from other environments or individuals in other contexts. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments, and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat lines for yield in 35 location-year combinations genotyped with genotyping-by sequencing (GBS). Mixed models were used to obtain either overall or by-environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years, or to predict within defined mega-environments for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context.