Molecular Plant Breeding 2025, Vol.16, No.5, 278-286 http://genbreedpublisher.com/index.php/mpb 284 8.2 Integration of AI, big data, and precision breeding tools With the continuous accumulation of high-throughput omics, phenomics and environmental data, artificial intelligence (AI) and big data analysis have provided new opportunities for the genetic analysis and molecular design breeding of complex traits. AI can integrate multi-omics data for complex trait prediction and candidate gene screening, improving the efficiency of molecular marker-assisted selection and gene editing (Yu et al., 2020). Nowadays, precision breeding tools (such as CRISPR/Cas gene editing and pan-genome association analysis) have begun to be used for the improvement of aroma and flavor traits of tea plants, but their application in multi-gene complex traits still requires further optimization and verification (Zhang et al., 2021; Chen et al., 2023). 8.3 Prospects for climate-resilient, high-quality tea breeding programs Climate change will affect the quality and yield of tea trees. Future breeding needs to take into account aroma, flavor and stress resistance. Population genomics studies have found that tea plants have developed adaptive genes to environmental factors such as cold stress during domestication, suggesting that molecular breeding has the potential to achieve simultaneous improvement of high quality and climate adaptability (Zhang et al., 2021; Chen et al., 2023). Through the exploration of specific germplasm resources and functional genes, it is expected to cultivate new varieties that have both excellent flavors and can adapt to environmental changes, promoting the sustainable development of the tea industry (Yu et al., 2020; Li et al., 2022b; Zhao et al., 2022). Acknowledgments The authors appreciate the comments from two anonymous peer reviewers on the manuscript of this study. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Chen S., Wang P., Kong W., Chai K., Zhang S., Yu J., Wang Y., Jiang M., Lei W., Chen X., Wang W., Gao Y., Qu S., Wang F., Wang Y., Zhang Q., Gu M., Fang K., Ma C., Sun W., Ye N., Wu H., and Zhang X., 2023, Gene mining and genomics-assisted breeding empowered by the pangenome of tea plant Camellia sinensis, Nature Plants, 9: 1986-1999. https://doi.org/10.1038/s41477-023-01565-z Gohain B., Borchetia S., Bhorali P., Agarwal N., Bhuyan L., Rahman A., Sakata K., Mizutani M., Shimizu B., Gurusubramaniam G., Ravindranath R., Kalita M., Hazarika M., and Das S., 2012, Understanding Darjeeling tea flavour on a molecular basis, Plant Molecular Biology, 78: 577-597. https://doi.org/10.1007/s11103-012-9887-0 Gu M., Gao T., Xu M., Hong Y., Wang Y., Yu J., Zhang Y., She W., Wang P., and Ye N., 2023, Identification and analysis of alleles in the aroma biosynthesis pathways based on Camellia sinensis ‘Jinguanyin’ haplotype-resolved genomes, Trees, 37: 1627-1641. https://doi.org/10.1007/s00468-023-02447-9 Han Z., Rana M., Liu G., Gao M., Li D., Wu F., Li X., Wan X., and Wei S., 2016, Green tea flavour determinants and their changes over manufacturing processes, Food Chemistry, 212: 739-748. https://doi.org/10.1016/j.foodchem.2016.06.049 Li H., Song K., Li B., Zhang X., Wang D., Dong S., and Yang L., 2023, CRISPR/Cas9 editing sites identification and multi-elements association analysis in Camellia sinensis, International Journal of Molecular Sciences, 24(20): 15317. https://doi.org/10.3390/ijms242015317 Li J., Hao C., Jia H., Zhang J., Wu H., Ning J., Wang R., and Deng W., 2022a, Aroma characterization and their changes during the processing of black teas from the cultivar, Camellia sinensis (L.) O. Kuntze cv. Jinmudan, Journal of Food Composition and Analysis, 108: 104449. https://doi.org/10.1016/j.jfca.2022.104449 Li J., Xiao Y., Zhou X., Liao Y., Wu S., Chen J., Qian J., Yan Y., Tang J., and Zeng L., 2022b, Characterizing the cultivar-specific mechanisms underlying the accumulation of quality-related metabolites in specific Chinese tea (Camellia sinensis) germplasms to diversify tea products, Food Research International, 161: 111824. https://doi.org/10.1016/j.foodres.2022.111824 Li X., Lei W., You X., Kong X., Chen Z., Shan R., Zhang Y., Yu Y., Wang P., and Chen C., 2024, The tea cultivar ‘Chungui’ with jasmine-like aroma: from genome and epigenome to quality, International Journal of Biological Macromolecules, 281: 136352. https://doi.org/10.1016/j.ijbiomac.2024.136352 Liu C., Li J., Wang M., Jian G., Zhu C., Li H., Jia Y., Tang J., and Zeng L., 2025, (R)-linalool is a key indicator of aroma quality levels of a distinctive black tea (Camellia sinensis var. Yinghong No. 9), Industrial Crops and Products, 225: 120506. https://doi.org/10.1016/j.indcrop.2025.120506
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