Molecular Plant Breeding 2024, Vol.15, No.2, 52-62 http://genbreedpublisher.com/index.php/mpb 57 Figure 2 Application of GWAS in sesame (Adopted from Berhe et al., 2021) Image caption: a: Circos diagram summarizes the genetic findings of important agronomic traits of sesame; A: Pseudomolecule (LG); B: Gene density; C: QTL position; D: -log(p) of peak SNP; E: Pleiotropic QTL; b: Schematic showing potential candidate genes associated with important agronomic traits of sesame discovered to date (Adopted from Berhe et al., 2021) GWAS can exploit historical recombination events in populations and provide higher allele frequencies and comprehensive genome coverage, making it a powerful tool for exploring genetic diversity and the genetic basis of complex traits (Korte and Farlow, 2013). The power of GWAS is highly dependent on sample size. In some cases, even hundreds of samples may not be sufficient to discover small effect sites. Furthermore, genetic heterogeneity, whereby samples from different geographical locations may have different genetic variants that affect the trait due to local adaptation, reduces the ability to discover the association of each variant with the trait. Rare variants may be affected by strong or complete association with many nonpathogenic rare variants, such that one pathogenic locus may confer many synthetic associations. This demonstrates the importance of considering rare variants when conducting GWAS, but also exposes the limitations of identifying pathogenic variants through existing microarray datasets. Although traditional single-site model analysis is common in GWAS, it has limitations in addressing multiple testing, historical genotype effects, and pleiotropic effects. In contrast, multilocus models offer a more efficient approach that more accurately captures allelic diversity, optimizes the use of high-density marker data, and increases the power of phenotype-interaction discovery, thereby reducing multiplexing. Testing issues (Korte and Farlow, 2013). Selecting an appropriate genetic model is a challenge in GWAS studies. Single-locus and multi-locus models each have advantages and disadvantages, and which model to choose depends on the specific traits and goals of the study. Integrating single-locus and multi-locus models may improve the power and validity of complex trait association analysis (Korte and Farlow, 2013). 4 Current Status of Using GWAS to Accelerate the Improvement of Crop Stress Resistance Traits 4.1 Application of GWAS in improving crop stress resistance traits in recent years Genome-wide association study (GWAS) strategies in improving crop stress resistance traits has achieved remarkable results, especially in traits such as drought resistance and salt resistance. GWAS methods provide a powerful tool for understanding the genetic basis of traits by comprehensively scanning the genomes of large numbers of individuals to identify genetic variants associated with specific traits. The following is an analysis of some successful cases using GWAS strategies to improve crop stress resistance traits.
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