Rice Genomics and Genetics 2025, Vol.16, No.5, 245-253 http://cropscipublisher.com/index.php/rgg 251 7 Conclusions and Future Perspectives In recent years, a number of large-scale rice genome studies have been carried out one after another under the impetus of technology, uncovering an astonishing amount of variant information - often in the millions. These variations, especially their enrichment in regulatory regions and stress-resistant related genes, have attracted the attention of researchers. But strangely enough, those effects that have a significant impact on traits are often hard to detect in traditional SNP analysis. For instance, behind key agronomic traits such as grain shape, plant height and drought tolerance, there may be some structural variations (SVS) rather than single-point mutations. The causes behind these structural variations are actually not simple. Some are caused by recombination between non-allelic genes, while others result from the insertion of transposable factors. In other words, they are not merely "abnormal events" in the genome, but may also be involved in the process of species evolution and adaptation (especially changes in population structure). So, from a theoretical perspective, studying SV is not merely for breeding, but more importantly, to figure out how plants "adapt" and "shape" themselves. But from another perspective, SV is not only a scientific research material but is also increasingly becoming a "tool". They reveal some previously overlooked new alleles with breeding potential, and it is not difficult to directly apply them to molecular design breeding. Methods like GWAS and genomic prediction, when SV is taken into account, have significantly enhanced explanatory power for complex traits. This also enables some truly "useful" variations to surface, thereby serving tag selection and precise editing. Now, resources including the pan-genome and SV maps have begun to play a role in rice breeding, with the goal of achieving high yields, stable yields, stress resistance and high quality. Looking ahead, multi-omics integration is likely to be the next key direction in SV research. Not only the genome, but also the transcriptome, epigenome and even phenome must be combined to clearly understand what a certain SV is doing, how it regulates and what traits it affects. And how should these priorities be ranked? It all depends on the data. Fortunately, deep learning and bioinformatics tools are also developing rapidly nowadays. Not only has the annotation efficiency improved, but the functional prediction of rare SVS has also become reliable. When big data is combined with these tools, it is really worth looking forward to whether the SV mechanism behind complex traits can be clarified in the future. This might be a brand-new breakthrough for rice breeding. Acknowledgments We would like to express our gratitude to the reviewers for their valuable feedback, which helped improve the manuscript. 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 Y., Wang A., Barkley C., Zhang Y., Zhao X., Gao M., Edmonds M., and Chong Z., 2023, Deciphering the exact breakpoints of structural variations using long sequencing reads with DeBreak, Nature Communications, 14: 283. https://doi.org/10.1038/s41467-023-35996-1 Chiang C., Scott A., Davis J., Tsang E., Li X., Kim Y., Hadzic T., Damani F., Ganel L., Montgomery S., Battle A., Conrad D., and Hall I., 2016, The impact of structural variation on human gene expression, Nature genetics, 49: 692-699. https://doi.org/10.1038/ng.3834 Cui L., Min H., Byun M., Oh H., and Kim W., 2018, OsDIRP1, a putative RING E3 ligase, plays an opposite role in drought and cold stress responses as a negative and positive factor, respectively, in rice (Oryza sativa L.), Frontiers in Plant Science, 9: 1797. https://doi.org/10.3389/fpls.2018.01797 Ding Y., Zhu J., Zhao D., Liu Q., Yang Q., and Zhang T., 2021, Targeting cis-regulatory elements for rice grain quality improvement, Frontiers in Plant Science, 12: 705834. https://doi.org/10.3389/fpls.2021.705834 Fornasiero A., Wing R., and Ronald P., 2022, Rice domestication, Current Biology, 32: R20-R24. https://doi.org/10.1016/j.cub.2021.11.025 Fuentes R., Chebotarov D., Duitama J., Smith S., De La Hoz J., Mohiyuddin M., Wing R., McNally K., Tatarinova T., Grigoriev A., Mauleon R., and Alexandrov N., 2019, Structural variants in 3000 rice genomes, Genome Research, 29: 870-880. https://doi.org/10.1101/gr.241240.118
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