Bioscience Methods 2024, Vol.15, No.6, 264-274 http://bioscipublisher.com/index.php/bm 272 information, can be analyzed using advanced statistical and machine learning techniques to identify optimal breeding strategies (Lin et al., 2007; Moreira et al., 2020). For example, polyploid genome-wide association studies (GWAS) have been used to dissect the genetic basis of complex traits, such as starch content, in sweet potatoes, identifying key genetic markers that can be targeted in breeding programs (Haque et al., 2023). Integrating these data-driven approaches with traditional breeding methods can enhance the efficiency and accuracy of selecting high-yield and high-starch sweet potato lines (Lin et al., 2007; Moreira et al., 2020; Haque et al., 2023). Moreover, the use of machine learning algorithms to analyze phenotypic and genotypic data can help predict the performance of breeding lines under different environmental conditions, further optimizing breeding strategies for improved yield and starch content (Lin et al., 2007; Moreira et al., 2020). Acknowledgments Thank you to Ms. T. Wang for assistance during the literature review and analysis process. Funding This research was funded by Municipal Agricultural Technology Chief Expert Studio Project (Innovation and Integration of Mini Sweet Potato Quality and Efficiency Enhancement Technology). 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. Reference Abdelrahman M., Al-Sadi A., Pour-Aboughadareh A., Burritt D., and Tran L., 2018, Genome editing using CRISPR/Cas9-targeted mutagenesis: an opportunity for yield improvements of crop plants grown under environmental stresses, Plant Physiology and Biochemistry, 131: 31-36. https://doi.org/10.1016/j.plaphy.2018.03.012 Acharjee A., Chibon P., Kloosterman B., America T., Renaut J., Maliepaard C., and Visser R., 2018, Genetical genomics of quality related traits in potato tubers using proteomics, BMC Plant Biology, 18: 1-10. https://doi.org/10.1186/s12870-018-1229-1 Andersson M., Turesson H., Olsson N., Fält A., Ohlsson P., González M., Samuelsson M., and Hofvander P., 2018, Genome editing in potato via CRISPR-Cas9 ribonucleoprotein delivery, Physiologia Plantarum, 164(4): 378-384. https://doi.org/10.1111/ppl.12731 Araus J., and Cairns J., 2014, Field high-throughput phenotyping: the new crop breeding frontier, Trends in Plant Science, 19(1): 52-61. https://doi.org/10.1016/j.tplants.2013.09.008 Babu R., Nair S., Prasanna B., and Gupta H., 2004, Integrating marker-assisted selection in crop breeding: prospects and challenges, Current Science, 87: 607-619. Beketova M., Chalaya N., Zoteyeva N., Gurina A., Kuznetsova M., Armstrong M., Hein I., Drobyazina P., Khavkin E., and Rogozina Е., 2021, Combination breeding and marker-assisted selection to develop late blight resistant potato cultivars, Agronomy, 11(11): 2192. https://doi.org/10.20944/preprints202110.0209.v1 Bortesi L., and Fischer R., 2015, The CRISPR/Cas9 system for plant genome editing and beyond, Biotechnology Advances, 33(1): 41-52. https://doi.org/10.1016/j.biotechadv.2014.12.006 Chen K., Wang Y., Zhang R., Zhang H., and Gao C., 2019, CRISPR/Cas genome editing and precision plant breeding in agriculture, Annual Review of Plant Biology, 70(1): 667-697. https://doi.org/10.1146/annurev-arplant-050718-100049 Collard B., and Mackill D., 2008, Marker-assisted selection: an approach for precision plant breeding in the twenty-first century, Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1491): 557-572. https://doi.org/10.1098/rstb.2007.2170 Fan W., Zhang Y., Wu Y., Zhou W., Yang J., Yuan L., Zhang P., and Wang H., 2021, The H+-pyrophosphatase IbVP1 regulates carbon flux to influence the starch metabolism and yield of sweet potato, Horticulture Research, 8: 20. https://doi.org/10.1038/s41438-020-00454-2 Francia E., Tacconi G., Crosatti C., Barabaschi D., Bulgarelli D., Dall’Aglio E., and Vale G., 2005, Marker assisted selection in crop plants, Plant Cell, Tissue and Organ Culture, 82: 317-342. https://doi.org/10.1007/s11240-005-2387-z Gemenet D., Pereira G., Boeck B., Wood J., Mollinari M., Olukolu B., Diaz F., Mosquera V., Ssali R., David M., Kitavi M., Burgos G., Felde T., Ghislain M., Carey E., Swanckaert J., Coin L., Fei Z., Hamilton J., Yada B., Yencho G., Zeng Z., Mwanga R., Khan A., Gruneberg W., and Buell C., 2019, Quantitative trait loci and differential gene expression analyses reveal the genetic basis for negatively associated β-carotene and starch content in hexaploid sweetpotato [Ipomoea batatas (L.) Lam.], Theoretical and Applied Genetics, 133: 23-36. https://doi.org/10.1007/s00122-019-03437-7
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