Ensemble-based genomic prediction for maize flowering time reveals novel insights into trait genetic architecture and improves prediction for breeding applications

While many genomic prediction models have been evaluated for their potential to accelerate genetic gain for multiple traits, no individual genomic prediction model has outperformed others across all applications. This problem aligns with the implications of the No Free Lunch Theorem, stating that the average performance of individual prediction models becomes equivalent across the state space of diverse prediction scenarios. Ensembles of multiple individual genomic prediction models can be a potential alternative approach. The framework of the Diversity Prediction Theorem suggests the potential for a reduction in prediction error with the inclusion of diverse prediction models in the ensemble. We investigated the performance of an ensemble approach that combines multiple genomic prediction models. We demonstrate the results using flowering time traits measured in two maize Nested Association Mapping datasets. For both datasets, the ensemble-based prediction approach achieved the highest prediction accuracy and lowest prediction error across traits. Multiple genomic regions containing key flowering time-related genes were captured by the different genomic prediction models with diverse weights, demonstrating different views of the trait genetic architecture. The combination of such diverse views contributed to the improvement of prediction performance by the ensemble-based approach over the individual prediction models. Exploiting the expectations of the Diversity Prediction Theorem, the ensemble can overcome some limitations proposed by the No Free Lunch Theorem when applying individual genomic prediction models.