Bayesian AMMI-Based Simulation of Genotype x Environment Interactions

Abstract

Genotype-by-environment interaction (GEI) has been studied to identify environment-stable/favorable genotypes. The GEI simulation could help refine the inference by incorporating tangible factors such as genomic and environmental information. The Bayesian additive main effect and multiplicative interaction (Bayesian AMMI) model captures the genotype-specific responses across environments, reflecting directional relationships between genotypes and environments. Thus, we propose a Bayesian AMMI-based GEI simulation framework that utilizes high-throughput environmental covariance matrices to generate GEI effects with interpretable directional structure. To demonstrate the proposed approach, two simulated phenotypes were assessed under four levels of GEI variance. In the first simulation (Sim1), GEI effects were sampled from a multivariate normal distribution defined by the GEI matrix. In the second simulation (Sim2), GEI effects were generated by extending Sim1 with the Bayesian AMMI model. In both simulations, increasing GEI variance resulted in lower correlations of phenotypes across environments and stronger genotype-specific sensitivity to environmental variation. Across five cross-validation designs, models accounting for GEI consistently outperformed one that did not, with prediction accuracy generally decreasing as GEI variance increased. Clear distinctions between the two simulated phenotypes were evident from biplot analyses: Sim2 successfully captured environmental relatedness and genotype-specific responses, whereas such structure was absent in Sim1. These results demonstrate that the proposed Bayesian AMMI-based GEI simulation framework enables interpretable visualization of GEI and supports genomic selection strategies under complex environmental conditions.