GE-BiFormer: bidirectional cross-attention integration of genomic and Enviromic data for genotype-by-environment...

Phenotypic variation is shaped by genotype, environment, and their interactions. Accurately predicting crop performance across diverse environments therefore requires models capable of capturing these complex and context-dependent relationships. Here, we developed GE-BiFormer, an explainable multimodal deep learning framework for genotype-by-environment prediction. GE-BiFormer integrates genomic and enviromic information through dual-path feature disentanglement, tokenized bidirectional cross-attention, and mixture-of-experts routing, enabling fine-grained modeling of genetic effects, environmental responses, and their interplay. We evaluated GE-BiFormer using the Genomes to Fields maize dataset. After preprocessing, the dataset contained approximately 360,000 non-missing genotype-environment-trait observations across six traits. The evaluation focused on three breeding-relevant scenarios: predicting known genotypes in unseen environments, predicting novel genotypes in known environments, and predicting novel genotypes in entirely untested environments. Across these scenarios, GE-BiFormer consistently outperformed GBLUP, classical machine learning methods, and recent deep learning approaches. External validation on an independent winter wheat dataset further demonstrated the broad applicability and cross-dataset robustness of GE-BiFormer across crop species. In addition, SHAP analysis identified biologically interpretable environmental drivers, while mixture-of-experts routing revealed trait-specific computational specialization. Together, these results demonstrate that GE-BiFormer provides a practical and interpretable framework for environment-aware genomic selection and climate-adaptive crop breeding.