Field Crop 2024, Vol.7, No.1, 1-8 http://cropscipublisher.com/index.php/fc 6 The development of bioinformatics tools supports the processing and analysis of high-throughput sequencing data. With the advent of the big data era, how to effectively manage, analyze and interpret massive biological information has become a challenge. Bioinformatics tools, including software and algorithms for sequence alignment, variation detection, gene annotation, and association analysis (Normand and Yanai, 2013), enable researchers to extract useful information from complex data. The application of machine learning and artificial intelligence technology also provides new methods for bioinformatics analysis, helping to reveal the complex genetic laws of crop disease resistance. 4.2 The application of multi-omics data integration and systems biology methods The application of multi-omics data integration and systems biology methods is an important development direction of genome-wide association study (GWAS) in crop disease resistance breeding in the future. With the progress of biotechnology, researchers can obtain the genome, transcriptomics, proteomics, metabolomics and other multi-omics data of crops, which provides comprehensive information on the physiological and molecular level of crops, and helps to deeply understand the complex mechanism of crop disease resistance. Multi-omics data integration refers to the comprehensive analysis of different levels of biological data to reveal a comprehensive picture of crop disease resistance. By integrating genomic and transcriptomic data, researchers can identify genes whose expression changes significantly during disease infection (Subramanian et al., 2020) and explore the role of these genes in disease resistance response. The integration of proteomic and metabolomic data helps to reveal changes in proteins and metabolites associated with disease resistance, providing deeper insight. The systems biology approach refers to the use of mathematical and computational models to analyze and explain complex biological systems. In crop disease resistance studies, systems biology approaches can be used to construct network models of disease resistance responses and reveal interactions between different genes, proteins, and metabolites (Pazhamala et al., 2021). This network model helps to identify the key regulatory factors and signaling pathways of disease resistance and provide targeted strategies for breeding. 4.3 The combination of precision breeding and gene editing The combination of precision breeding and gene editing technology is an important trend in contemporary crop improvement. Precision breeding, also known as molecular breeding, relies on molecular markers and genomic information to precisely select and aggregate genes associated with target traits through methods such as molecular marker-assisted selection (MAS). Gene editing technologies, such as the CRISPR/Cas9 system, can achieve precise modifications at specific sites in the crop genome to directly change the genetic characteristics of the crop (Nerkar et al., 2022), and the combination of these two technologies provides new strategies for crop disease resistance breeding. After using GWAS and other methods to identify the key genes or genetic markers related to disease resistance, these favorable genes can be aggregated into a variety through precision breeding technology to improve the disease resistance of crops. For some resistance traits that are difficult to obtain through traditional breeding methods, gene editing technology can directly introduce or modify specific resistance genes into the crop genome, thereby rapidly breeding new varieties with strong resistance to disease (Scheben and Edwards, 2017). The breeding strategy combined with precision breeding and gene editing technology can not only improve the efficiency and accuracy of breeding, but also expand the possibility of breeding, providing more choices and flexibility for crop disease resistance breeding. With the continuous development and improvement of these technologies, crop disease resistance breeding will be more efficient and accurate in the future, and is expected to make greater contributions to the sustainable development of agricultural production. 5Summary The application of genome-wide association study (GWAS) in crop disease resistance breeding has made remarkable achievements. Through GWAS, researchers can quickly and accurately identify genetic markers and genes related to disease resistance in the whole genome, providing a powerful molecular tool for crop disease
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