Pedigree‐Based Prediction Models with Genotype × Environment Interaction in Multienvironment Trials of CIMMYT Wheat

Abstract

Genotype × environment (G × E) interaction can be studied through multienvironment trials used to select wheat (Triticum aestivum L.) lines. We used spring wheat yield data from 136 international environments to evaluate the predictive ability (PA) of different models in diverse environments by modeling G × E using the pedigree‐derived additive relationship matrix (A matrix). These analyses focused on 109 wheat lines from three Wheat Yield Collaboration Yield Trials (WYCYTs) and 168 lines from four Stress Adapted Trait Yield Nurseries (SATYNs) developed by CIMMYT for yield potential conditions and stress conditions, respectively. The main objectives of this study were to use various pedigree‐based reaction norm models to predict sites included in each of the three WYCYT nurseries and each of the four SATYN nurseries (individual population) and to predict environments (site‐year combinations) when combining the three WYCYT and four SATYN trials (combined population). Results of the PA for the individual‐ and combined‐population analyses indicated that best predictive Model 6 (E + L + A + AE + e) always included the G × E denoted as the interaction between the A matrix and environments. The most predictable sites in WYCYTs were Iran DZ (Dezful) and Pak I (Islamabad), whereas the most predictable sites in SATYNs were India I (Indore), Iran DZ, and Mex CM (Cd. Obregon). Heritability was correlated with PA for individual‐population prediction analyses, but not for combined‐population prediction analyses. Our results indicate pedigree‐based reaction norm models with G × E can be useful for predicting the performance of lines and selecting good predictable key sites (or environments) to reduce phenotyping costs.