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In crop breeding, genomic selection (GS) serves as a powerful tool for predicting unknown phenotypes by using genome-wide markers, aimed at enhancing genetic gain for quantitative traits. However, in practical applications of GS, predictions are not always made within populations or for individuals that are genetically similar to the training population. Therefore, exploring possibilities and effective strategies for across-population prediction becomes an attractive avenue for applying GS technology in breeding practices. In this study, we used an existing maize population of 5820 hybrids as the training population to predict another population of 523 maize hybrids using the GBLUP and BayesB models. We evaluated the impact of optimizing the training population based on the genetic relationship between the training and breeding populations on the accuracy of across-population predictions. The results showed that the prediction accuracy improved to some extent with varying training population sizes. However, the optimal size of the training population differed for various traits. Additionally, we proposed a population structure-based across-population genomic prediction (PSAPGP) strategy, which integrates population structure as a fixed effect in the GS models. Principal component analysis, clustering, and Q-matrix analysis were used to assess the population structure. Notably, when the Q-matrix was used, the across-population prediction exhibited the best performance, with improvements ranging from 8 to 11% for ear weight, ear grain weight and plant height. This is a promising strategy for reducing phenotyping costs and enhancing maize hybrid breeding efficiency.

Drought stress is seriously affecting the growth and production of crops, especially when agricultural irrigation still remains quantitatively restricted in some arid and semi-arid areas. The identification of drought-tolerant genes is important for improving the adaptability of maize under stress. Here, we found that a new member of the actin-depolymerizing factor (ADF) family; the ZmADF5 gene was tightly linked with a consensus drought-tolerant quantitative trait locus, and the significantly associated signals were detected through genome wide association analysis. ZmADF5 expression could be induced by osmotic stress and the application of exogenous abscisic acid. Its overexpression in Arabidopsis and maize helped plants to keep a higher survival rate after water-deficit stress, which reduced the stomatal aperture and the water-loss rate, as well as improved clearance of reactive oxygen species. Moreover, seventeen differentially expressed genes were identified as regulated by both drought stress and ZmADF5 , four of which were involved in the ABA-dependent drought stress response. ZmADF5 -overexpressing plants were also identified as sensitive to ABA during the seed germination and seedling stages. These results suggested that ZmADF5 played an important role in the response to drought stress.