Genomics and Applied Biology 2024, Vol.15, No.4, 212-222 http://bioscipublisher.com/index.php/gab 215 developed to address these limitations by considering multiple traits and loci simultaneously. These methods increase the statistical power to detect associations and reduce false positives. For example, multi-locus GWAS methodologies such as mrMLM, ISIS EMBLASSO, and FASTmrMLM have been proposed to improve the detection of significant loci by leveraging the genetic architecture of complex traits (Zhang et al., 2019). Moreover, joint-GWAS approaches, which combine data from multiple studies, have been shown to enhance the power of GWAS by increasing sample sizes and capturing more genetic variation (Müller et al., 2018). Figure 1 GWAS results of the top significant SNPs associated with soybean seed protein (A), oil (B), and 100-seed weight (C) across different environments. The y-axis represents the value of the trait of interest, and the x-axis represents the genomic position of each SNP on the soybean genome (Adopted from Yoosefzadeh-Najafabadi et al., 2023)
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