Explainable Artificial Intelligence for Genomic Prediction in Plants: A Critical Review of Methods, Biological Insights, and Practical Challenges

Abstract

Machine learning has become an important tool in plant genomic prediction for modeling complex genotype–phenotype relationships and improving breeding decisions. However, many high-performing models, particularly ensemble and deep learning approaches, remain difficult to interpret, limiting their biological applicability. This review summarizes major machine learning methods and explainable artificial intelligence (XAI) approaches used in plant genomics, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-Agnostic Explanations), attention mechanisms, permutation importance, tree-based feature importance, and gradient-based attribution methods. XAI can help identify influential SNPs, genomic regions, candidate genes, regulatory elements, and omics features associated with complex traits. For example, SHAP analysis in an almond germplasm collection identified a genomic region associated with shelling fraction, illustrating how XAI can generate testable hypotheses for further validation. The review further discusses applications in trait prediction, breeding, functional genomics, and multi-omics integration. Importantly, we emphasize major limitations, including data bias, model instability, correlated genomic markers, limited model transferability, and the common misconception that feature importance implies biological causality. We recommend integrating XAI with linkage disequilibrium pruning, stability assessment, biological annotation, and experimental validation before prioritizing candidate genes. Overall, XAI should be considered a framework for model interpretation, feature prioritization, and hypothesis generation rather than a replacement for experimental validation in plant genomics and breeding.