Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations

• Published in: Crop science 2023-07, Vol.63 (4), p.2220-2240
• Main Authors: Rebollo, Inés, Aguilar, Ignacio, Pérez de Vida, Fernando, Molina, Federico, Gutiérrez, Lucía, Rosas, Juan Eduardo
• Format: Article
• Language: English
• ISSN: 0011-183X; 1435-0653
• DOI: 10.1002/csc2.21029

Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype‐specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega‐environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single‐step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank‐change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. Core Ideas Random regression models improved the predictive ability for grain yield in a realistic plant breeding scenario. Prediction using the complete dataset performed better than predicting within mega‐environments. Large amounts of genotype by environment interaction affect grain yield in both indica and japonica populations. Indica genotype ranking drastically changed in early sowing compared with intermediate and late sowing. Climatic variables affecting grain yield are different between our indica and japonica rice breeding populations.