International Journal of Molecular Ecology and Conservation, 2025, Vol.15, No.2, 83-90 http://ecoevopublisher.com/index.php/ijmec 88 7.2 Prospects of high-throughput omics technologies in tuber shape studies New technologies such as genomics, transcriptomics and methylomics offer opportunities for improving the study of tuber shape. Genome-wide association studies (GWAS) have identified multiple important SNP loci related to tuber shape and bud depth, which can serve as potential breeding markers. Transcriptome and methylation studies have also screened out some candidate genes, which are closely related to hormone regulation, glucose metabolism and cell wall modification (De Jesus Colwell et al., 2021). The accuracy of predicting tuber traits can be further improved if different omics data are combined with machine learning methods (such as random forest regression) (Aliche et al., 2019). 7.3 Importance of data sharing and international collaboration Data sharing and cross-border cooperation are conducive to promoting the development of potato shape research. When teams from different countries integrate their genetic resources and field records, the accuracy of genetic research (GWAS) and trait mapping (QTL) can be improved (Bist et al., 2023). Cooperation also helps to create common standards for measuring traits and supports shared databases. This makes it easier to compare and verify the results among different studies. New tools such as drone imaging also support this kind of cooperation. Drones have accelerated large-scale field trials. They also help to link genetic data with the actual growth of tubers in farmers' fields and connect genes with real-world outcomes. 7.4 Application potential of predictive models and multi-omics integration The use of predictive models containing multiple biological data offers new opportunities for tuber shape breeding. Machine learning methods (including random forest regression) can identify key genes and pathways that affect tuber traits. This provides a strong marker for selection (Acharjee et al., 2016). By integrating genomics, transcriptomics and other data types, it is possible to have a clearer understanding of how genes control the growth and morphology of tubers. It also helps to discover important candidate genes and their networks. These combined methods have accelerated the breeding speed and improved the stability of the shape of the tubers under different conditions. This eventually breeds potato varieties that are more adaptable and have higher market value. Acknowledgments We thank all the team members involved in the experiment and data analysis and the review experts who suggested revisions on 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 Acharjee A., Kloosterman B., Visser R., and Maliepaard C., 2016, Integration of multi-omics data for prediction of phenotypic traits using random forest, BMC Bioinformatics, 17: 180. https://doi.org/10.1186/s12859-016-1043-4 Ahmad D., Zhang Z., Rasheed H., Xu X., and Bao J., 2022, Recent advances in molecular improvement for potato tuber traits, International Journal of Molecular Sciences, 23(17): 9982. https://doi.org/10.3390/ijms23179982 Aliche E., Oortwijn M., Theeuwen T., Bachem C., Van Eck H., Visser R., and Van Der Linden C., 2019, Genetic mapping of tuber size distribution and marketable tuber yield under drought stress in potatoes, Euphytica, 215: 186. https://doi.org/10.1007/s10681-019-2508-0 Bist L., Sharma R., and Thakurathi B., 2023, Effect of seed tuber size on growth and yield of potato (Solanum tuberosumL.) variety Desiree in Dadeldhura, Contemporary Research: An Interdisciplinary Academic Journal, 6(2): 60251. https://doi.org/10.3126/craiaj.v6i2.60251
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