Molecular Plant Breeding 2024, Vol.15, No.2, 52-62 http://genbreedpublisher.com/index.php/mpb 60 Over the past few decades, genome-wide association studies (GWAS) have become an important tool to reveal the genetic mechanisms of crop stress resistance traits. GWAS has revealed the genetic basis of disease resistance and stress tolerance traits in many crops by searching genome-wide for markers associated with specific traits. Below, we discuss the contribution of GWAS in this field through specific examples. 4.3 Discuss the contribution of GWAS in revealing the genetic mechanism of stress resistance traits A clear example is the use of GWAS analysis to reveal genetic variation and candidate genes for traits related to salt tolerance in cotton (Gossypium hirsutum). By analyzing the genetic structure and linkage disequilibrium (LD) distribution of 217 cotton varieties, the researchers identified multiple significant association sites related to salt tolerance. This work detected 25 significant associations (-log10p>4) in the 2016 and 2017 datasets, covering 27 significant single nucleotide polymorphisms (SNPs) located on different chromosomes , the phenotypic variation rate (PVE) explained by each quantitative trait locus (QTL) ranges from 1.29% to 7.00%. These results not only provide molecular markers for the improvement of salt tolerance in cotton, but also reveal the complex genetic basis of this trait. In studies of maize (Zeamays), GWAS were used to identify candidate genes associated with disease resistance. A landmark study identifies 18 new genes associated with head rust resistance. In addition, for other disease resistance, such as sheath blight, gray leaf spot and Fumei virus resistance, important association loci and candidate genes were also identified through GWAS. These studies not only reveal the genetic mechanisms of crop disease resistance traits, but also provide valuable molecular markers for breeding programs (Xu et al., 2021). Rice (Oryza sativa) is the staple food of more than half of the world's population. For rice, fungal, bacterial and viral diseases are the main limiting factors. Using GWAS methods, researchers identified and verified genomic regions that are resistant to diseases such as bacterial stripe and rice blast. For example, an analysis of 56 important QTLs/genomic regions in different rice blast strains identified one region as the Pik allele, which showed resistance to all three strains. Through these examples, we can see the significant contribution of GWAS in revealing the genetic mechanisms of crop stress resistance traits. These studies not only increase our understanding of crop genetic diversity and resistance mechanisms, but also provide valuable resources for breeding and crop improvement. Although there are some challenges, such as the demand for data volume and quality, the difficulty of elucidating the genetic structure of complex traits, and the challenges of biological validation and functional analysis of the results. 5 Future Outlook In the past few years, high-throughput sequencing technology (HTS) and bioinformatics have made great progress in crop genetic improvement, especially in their application in genome-wide association studies (GWAS). These technologies not only improve the efficiency of genotype identification, but also promote research on improving crop stress resistance traits (Pavan et al., 2020). The development of GWAS allows us to link DNA variants to phenotypes of interest, thereby mapping genomic locations associated with economically important traits, including yield, resistance to biotic and abiotic stresses, and quality. In recent years, combined with high-density single nucleotide polymorphism (SNP) chips and DNA sequencing technology, the genotype space of multiple crops has been deeply explored, which has strengthened the role of GWAS in understanding the relationship between phenotype and genotype. importance. For example, the association of acid phosphatase activities of 280 mustard genotypes under different phosphorus levels was evaluated using a GWAS method, thus laying a solid foundation for using marker-assisted selection to improve the phosphorus efficiency of Indian mustard (Li and Marylyn, 2022). In addition, statistical methods that integrate GWAS results with functional genomic data (such as gene expression or chromatin activity profiles) are designed to identify cell types relevant to complex diseases, facilitating the translation from genomic discovery to functional understanding based on transcriptome breadth Association study
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