Field Crop 2024, Vol.7, No.1, 1-8 http://cropscipublisher.com/index.php/fc 7 resistance breeding. These results not only deepen our understanding of the genetic mechanism of crop resistance, but also provide a reliable basis for molecular marker-assisted breeding (MAS) and gene directed improvement. However, the application of GWAS in crop disease resistance breeding also faces some challenges. Due to the influence of population structure and linkage imbalance, GWAS may produce false positive results, affecting the accuracy of the results. Generally, GWAS can only identify large effect genes related to traits, while some small effect genes may be ignored. The biological interpretation and functional verification of GWAS results is also a complex process, which requires in-depth analysis with transcriptomic and proteomic data. With the continuous development of high-throughput sequencing technology and bioinformatics tools, the application of GWAS in crop disease resistance breeding will be more extensive and in-depth. Combined with multi-omics data and systems biology methods, GWAS will be able to reveal the complex genetic network of crop disease resistance in a more comprehensive way, and the combination of gene editing technology will also enable the disease resistance genes identified by GWAS to be quickly and accurately applied in breeding practice, improving the efficiency and accuracy of breeding. As a powerful genetic analysis tool, GWAS is playing an increasingly important role in crop disease resistance breeding. Future studies will further optimize GWAS analysis methods, combine multi-omics data and gene editing technology, and provide more accurate and efficient solutions for crop disease resistance breeding to meet the challenges faced by global agricultural production. References Belzile F., and Torkamaneh D., 2022, Designing a genome-wide association study: Main steps and critical decisions, In: Torkamaneh D., and Belzile F. (eds.), Genome-wide association studies, Methods in Molecular Biology, Humana, New York, USA, pp.3-12. https://doi.org/10.1007/978-1-0716-2237-7_1 Ciochetti N.P., Lugli-Moraes B., da Silva B.S., and Rovaris D.L., 2023, Genome-wide association studies: utility and limitations for research in physiology, The Journal of Physiology, 601(14): 2771-2799. https://doi.org/10.1113/JP284241 Heeney C., 2021, Problems and promises: How to tell the story of a genome wide association study? Stud. Hist. Philos. Sci., 89: 1-10. https://doi.org/10.1016/j.shpsa.2021.06.003 Lamichhane S., and Thapa S., 2022, Advances from conventional to modern plant breeding methodologies, Plant Breed. Biotech., 10: 1-14. https://doi.org/10.9787/PBB.2022.10.1.1 Laurie C.C., Doheny K.F., Mirel D.B., Pugh E.W., Bierut L.J., Bhangale T., Boehm F., Caporaso N.E., Cornelis M.C., Edenberg H.J., Gabriel S.B., Harris E.L., Hu F.B., Jacobs K.B., Kraft P., Landi M.T., Lumley T., Manolio T.A., McHugh C., Painter I., Paschall J., Rice J.P., Rice K.M., Zheng X., Weir B.S., and GENEVA Investigators, 2010, Quality control and quality assurance in genotypic data for genome-wide association studies, Genet. Epidemiol., 34(6): 591-602. https://doi.org/10.1002/gepi.20516 Marees A.T., de Kluiver H., Stringer S., Vorspan F., Curis E., Marie-Claire C., and Derks E.M., 2018, A tutorial on conducting genome-wide association studies: Quality control and statistical analysis, Int. J. Methods Psychiatr. Res., 27(2): e1608. https://doi.org/10.1002/mpr.1608 Mores A., Borrelli G.M., Laidò G., Petruzzino G., Pecchioni N., Amoroso L.G.M., Desiderio F., Mazzucotelli E., Mastrangelo A.M., and Marone D., 2021, Genomic approaches to identify molecular bases of crop resistance to diseases and to develop future breeding strategies, Int. J. Mol. Sci., 22(11): 5423. https://doi.org/10.3390/ijms22115423 Nerkar G., Devarumath S., Purankar M., Kumar A., Valarmathi R., Devarumath R., and Appunu C., 2022, Advances in crop breeding through precision genome editing, Front. Genet., 13: 880195. https://doi.org/10.3389/fgene.2022.880195 Normand R., and Yanai I., 2013, An introduction to high-throughput sequencing experiments: Design and bioinformatics analysis, In: Shomron N. (ed.), Deep sequencing data analysis, Methods in molecular biology, Humana Press, Totowa, USA, pp.1-26. https://doi.org/10.1007/978-1-62703-514-9_1 Osorio-Guarín J.A., Berdugo-Cely J.A., Coronado-Silva R.A., Baez E., Jaimes Y., and Yockteng R., 2020, Genome-wide association study reveals novel candidate genes associated with productivity and disease resistance to Moniliophthora spp. in cacao (Theobroma cacao L.), G3 Genes|Genomes|Genetics, 10(5): 1713-1725. https://doi.org/10.1534/g3.120.401153 Pazhamala L.T., Kudapa H., Weckwerth W., Millar A.H., and Varshney R.K., 2021, Systems biology for crop improvement, The Plant Genome, 14(2): e20098. https://doi.org/10.1002/tpg2.20098
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