Molecular Plant Breeding 2024, Vol.15, No.2, 52-62 http://genbreedpublisher.com/index.php/mpb 58 First of all, it needs to be emphasized that the successful application of GWAS in improving crop stress resistance traits often requires large sample sizes and precise phenotypic definition of complex traits. For example, GWAS studies of Arabidopsis (Arabidopsis) have shown that although most analyzes used only a few hundred individuals, for some specific traits meaningful results can be obtained even with fewer than 100 cultivars, suggesting that these traits may be determined solely by A few loci control and explain a large amount of phenotypic variation. For crops, the application range of GWAS has been very wide, from cotton (Gossypium hirsutum), Japanese apricot (Prunus mume), corn (Zeamays), rape (Brassica napus), rice (Oryza sativa) to soybean (Glycine max). In many crops, GWAS has helped to analyze the genetic basis of their stress resistance traits. For example, the GWAS study of rice involved 163 479 association analyzes and 461 traits (Joshi et al., 2023). This shows that GWAS provides a large amount of genetic information and molecular markers in analyzing the genetic basis of stress resistance traits in rice. Synthetic associations due to correlation (Haile et al., 2023). These challenges need to be overcome through a comprehensive consideration of sample design, statistical analysis methods, and subsequent biological validation. In the future, with the development of high-throughput sequencing technology and the improvement of bioinformatics analysis methods, research on using GWAS strategies to accelerate the improvement of crop stress resistance traits will become more in-depth and precise. For example, data sets containing more complete genome sequences will allow researchers to more efficiently identify causal variants associated with traits, even if these variants are in linkage disequilibrium (LD) over long distances. In recent years, genome-wide association study (GWAS) strategies have made significant progress in improving crop stress resistance traits. The GWAS method reveals the resistance mechanisms of various crops to adverse stress by analyzing the association between genetic variation and phenotypic characteristics. This method is particularly suitable for genetic studies of complex traits because it can reveal the role of small-effect genes that are often difficult to detect in traditional breeding methods. 4.2 Analysis of successful cases Over the past few years, genome-wide association studies (GWAS) have significantly advanced our understanding of the genetic mechanisms of crop stress resistance traits. By combining modern genetics, bioinformatics and statistical methods, GWAS enables scientists to identify loci associated with specific traits, thereby accelerating the process of crop improvement. In a GWAS study on sesame (Sesamum indicum), researchers developed haplotype-based models and multi-locus models to overcome the limitations of single-locus model analysis. By considering associations between multiple genetic markers, these models allow studies to more accurately capture allelic diversity and optimize the use of high-density marker data. The results show that the multi-locus GWAS model is more effective than the single-locus model, and can discover multiple genes/loci controlling complex traits, reducing the false positive rate, and does not require Bonferroni correction for multiple tests, thus potentially avoiding Missing important points. This progress not only improves the quality and depth of association results, but also excels in the efficiency and ability to detect marker-trait associations in different plant species (Figure 3) (Berhe et al., 2021). Upland cotton (Gossypium hirsutum) reveals genetic variants and candidate genes associated with salt stress tolerance. The study considered the population structure and relative kinship matrix through a mixed linear model, conducted a GWAS analysis, and found 25 loci that were significantly associated with three salt tolerance-related traits in the 2016 and 2017 data sets. These loci included 27 significant SNPs, distributed on multiple chromosomes. Through these correlation analyses, the researchers discovered some genes that may be related to salt stress response, such as genes encoding protein kinases and genes encoding aquaporins. These genes may play an important role in salt stress tolerance in cotton. effect. In addition, this study also conducted preliminary functional verification of candidate genes through gene expression pattern analysis, providing important information for the future use of molecular breeding technology to improve cotton's salt stress tolerance (Xu et al., 2021).
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