Rice Genomics and Genetics 2024, Vol.15, No.4, 153-163 http://cropscipublisher.com/index.php/rgg 161 With the continuous progress of sequencing technology and the continuous improvement of bioinformatics analysis methods, rice yield related genes and their regulatory networks can be further explored. The development of gene editing technology has provided more possibilities for precision breeding. Future research can explore how to use technologies such as CRISPR/Cas9 to accurately edit and improve key yield genes. In addition, the application of high-throughput phenotype technology and big data analysis in breeding will also become a future research hotspot. Molecular breeding of rice is a complex and intricate process that involves precise regulation and selection of specific genes. Therefore, future research should focus more on how genes respond to different environmental pressures, in order to further reveal the adaptive mechanisms of rice. With the continuous advancement of technology and innovation in methods, it is expected to discover more new yield related genes and regulatory mechanisms, providing a more solid scientific foundation for molecular breeding of rice. Acknowledgments Sincere thanks to the peer reviewers for their valuable guidance on the manuscript of this study. Their constructive suggestions have played an important role in improving the quality of the manuscript. Funding Science and Technology Project of State Administration of Science, Technology and Industry for National Defense, Seed Innovation and Industrialization Project of Fujian Province (zycxny2021003) Science and Technology Innovation Special Fund Project of Fujian Agriculture and Forestry University (KFB23198). Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Altaf A., Gull S., Shah A., Faheem M., Saeed A., Khan I., and Zhu M., 2021, Advanced genetic strategies for improving rice yield, Journal of Global Innovations in Agricultural Sciences, 9(4): 167-171. https://doi.org/10.22194/jgias/9.9520 Budhlakoti N., Kushwaha A., Rai A., Chaturvedi K., Kumar A., Pradhan A., Kumar U., Kumar R., Juliana P., Mishra D., and Kumar S., 2022, Genomic selection: a tool for accelerating the efficiency of molecular breeding for development of climate-resilient crops, Frontiers in Genetics, 13: 832153. https://doi.org/10.3389/fgene.2022.832153 Das G., Patra J., and Baek K., 2017, Insight into MAS: A molecular tool for development of stress resistant and quality of rice through gene stacking, Frontiers in Plant Science, 8: 985. https://doi.org/10.3389/fpls.2017.00985 Deng P., Jing W., Cao C., Sun M., Chi W., Zhao S., Dai J., Shi X., Wu Q., Zhang B., Jin Z., Guo C., Tian Q., Shen L., Yu J., Jiang L., Wang C., Chin J., Yuan J., Zhang Q., and Zhang W., 2022, Transcriptional repressor RST1 controls salt tolerance and grain yield in rice by regulating gene expression of asparagine synthetase, Proceedings of the National Academy of Sciences of the United States of America, 119(50): e2210338119. https://doi.org/10.1073/pnas.2210338119 González-Schain N., Dreni L., Lawas L., Galbiati M., Colombo L., Heuer S., Jagadish K., and Kater M., 2016, Genome-wide transcriptome analysis during anthesis reveals new insights into the molecular basis of heat stress responses in tolerant and sensitive rice varieties, Plant and Cell Physiology, 57(1): 57-68. https://doi.org/10.1093/pcp/pcv174 Grenier C., Cao T., Ospina Y., Quintero C., Chatel M., Tohme J., Courtois B., and Ahmadi N., 2015, Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding, PLoS One, 11(5): e0154976. https://doi.org/10.1371/journal.pone.0136594 Guo L., and Ye G., 2014, Use of major quantitative trait loci to improve grain yield of rice, Rice Science, 21: 65-82. https://doi.org/10.1016/S1672-6308(13)60174-2 Heffner E., Sorrells M., and Jannink J., 2009, Genomic selection for crop improvement, Crop Science, 49(1): 1-12. https://doi.org/10.2135/CROPSCI2008.08.0512 Huang L., Hua K., Xu R., Zeng D., Wang R., Dong G., Zhang G., Lu X., Fang N., Wang D., Duan P., Zhang B., Liu Z., Li N., Luo Y., Qian Q., Yao S., and Li Y., 2021, The LARGE2-APO1/APO2 regulatory module controls panicle size and grain number in rice, The Plant Cell, 33(4): 1212-1228. https://doi.org/10.1093/plcell/koab041
RkJQdWJsaXNoZXIy MjQ4ODYzNA==