Plant Gene and Trait 2024, Vol.15, No.1, 15-22 http://genbreedpublisher.com/index.php/pgt 19 In 2017, Posthuma's research team published a study in Nature Communications using FUMA for functional mapping and annotation of genetic associations. FUMA can take GWAS summary statistics as input, prioritize functional SNPS and genes, and map genes based on function. This process includes localization mapping, eQTL mapping, and chromatin interaction mapping to identify key genes and biological processes associated with diseases or traits (Watanabe et al., 2017), and the application of this tool could improve researchers' understanding of the genetic basis of disease resistance in crops such as rice. 4.2 Biological interpretation and functional validation of GWAS results Genome-wide association study (GWAS) is a powerful tool to help identify genetic variants associated with complex traits such as crop disease resistance, but associations identified by GWAS alone do not fully explain the underlying biological mechanisms, so biological interpretation and functional validation of GWAS results are essential. Biological interpretation usually involves functional annotation and pathway analysis of GWAS-associated genes to understand the roles and interrelationships of these genes in biological processes, this can be achieved through Gene ontology analysis, pathway enrichment analysis, and protein interaction network analysis (Ritchie and Steen, 2018), which can reveal biological pathways and key regulators associated with crop disease resistance. Functional verification is a key step to confirm GWAS results, which usually includes gene knockout, gene expression analysis, protein interaction experiments, etc. (Gallagher and Chen-Plotkin, 2018). For example, gene editing technology (such as CRISPR-Cas9) can be used to knock out candidate genes identified by GWAS, observe the impact on crop disease resistance, and verify the expression pattern and level of candidate genes in the process of disease resistance through gene expression analysis. 4.3 Application of GWAS research in disease resistance breeding Genome-wide association study (GWAS) has made significant progress in disease resistance breeding, and by analyzing large-scale genotypic and phenotypic data, GWAS can help identify candidate genes and molecular markers associated with disease resistance. These genes and markers can be used as important genetic resources for crop disease resistance breeding (Gyawali et al., 2018), accelerating the process of crop disease resistance breeding. Traditional crop disease resistance breeding usually requires a long time of observation and selection, while GWAS can quickly identify disease-resistation-related genes, shorten the breeding cycle and improve breeding efficiency. GWAS can also improve the precision of breeding, helping researchers to more precisely understand the genetic basis of crop resistance and select more effective breeding methods and strategies. The disease-resistance-related molecular markers identified by GWAS can be used in marker-assisted selection (MAS) to help breeders more effectively select crop varieties with disease resistance genotypes and speed up the breeding process. It has a wide application prospect in disease resistance breeding, provides important scientific basis and technical support for crop disease resistance improvement, helps to breed new varieties with more disease resistance, and promotes the sustainable development of agricultural production. 5 Challenges and Future Directions 5.1 Limitations and challenges of GWAS research Although genome-wide association study has made remarkable progress in revealing the genetic basis of crop disease resistance, there are some limitations and challenges. GWAS requires the support of a large sample size to obtain statistically significant results. For complex traits such as crop disease resistance, larger samples may be needed to ensure the reliability of the results. GWAS results are also susceptible to factors such as population structure and kinship, and appropriate correction is needed to reduce false positive results (Uffelmann et al., 2021).
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