| Sumario: | Abstract
Background: Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant
genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic
prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this
context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-
environment information. We used 111 elite breeding lines representing the diversity of the International Rice Research Institute (IRRI)
breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15
environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict
untested environments using seven multi-environment models and three cross-validation scenarios.
Results: The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved
material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant
height (33% and 54% of pairwise correlation greater than 0.5 ) but low for grain yield (lower than 0.2 in most cases). Clustering analyses
based on environmental covariates separated Asia’s and Africa's environments into different clusters or subclusters. The predictive abilities
ranged from 0.06 to 0.79 for days to flowering, 0.25 to 0.88 for plant height, and -0.29 to 0.62 for grain yield. We found that models
integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects
(genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of
all available environments gave better results than a subset.
Conclusion: Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines
in targeted environments. The recommendation for the breeders is to use simple multi-environment models with all available information
for routine application in breeding programs.
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