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

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.