TOPlus: an Algorithm Integrating Prior Genetic and Environmental Information for Multi-Trait Synergic Selection in Maize Hybrid Breeding

In maize hybrid breeding, synergic multi-trait selection of elite hybrids in specific target environments remains a major challenge. Enviromic data and functional gene knowledge are rapidly increasing; however, they have not been effectively integrated into crop breeding decisions. Here, we present TOPlus, a multi-trait hybrid prioritization framework comprising predictive and selective modules. The predictive module uses environmental information to improve phenotype prediction for untested genotypes and environments, whereas the selective module incorporates functional gene priors and multiple predicted traits to prioritize hybrids for target environments. Across 17 agronomic traits, TOPlus improved average cross-environment prediction accuracy by 12% over JGRA and 3% over EADW+GW. Incorporating functional gene priors further improved hybrid prioritization: hybrids selected by TOPlus showed 5.90-19.64% higher yield than those selected by the original TOP method while maintaining comparable performance for other traits. The TOPlus algorithm internally clustered the functional genes into environmentally stable and plastic gene groups, with stable genes associated with core plant developmental processes and plastic genes enriched in stress-response and environmental-adaptation pathways. Independent validation in commercial hybrid panels demonstrated that TOPlus supports region-level suitability assessment and extrapolative deployment across diverse agroecological zones for specific candidate maize hybrid varieties. Overall, by integrating enviromic data and functional gene priors within an interpretable framework, TOPlus provides a biologically grounded and data-driven approach for cross-environment prediction and multi-trait synergic selection of hybrids for specific regions in maize breeding.