Molecular Plant Breeding 2024, Vol.15, No.6, 371-378 http://genbreedpublisher.com/index.php/mpb 372 2 Methodological Overview of GWAS in Soybean 2.1 Fundamentals of GWAS in plant breeding Genome-Wide Association Studies (GWAS) have become a cornerstone in plant breeding, particularly for crops like soybean. GWAS involves scanning the genome of a diverse set of genotypes to identify genetic variations associated with specific traits. This method leverages natural genetic diversity and high-throughput genotyping to link phenotypic traits to genetic loci, facilitating the identification of quantitative trait loci (QTL) that can be used in marker-assisted selection (MAS) (Shook et al., 2021; Priyanatha et al., 2022; Rani et al., 2023). 2.2 Statistical models used in GWAS Several statistical models are employed in GWAS to account for the complex genetic architecture of traits. Mixed Linear Models (MLM) are commonly used due to their ability to control for population structure and relatedness among individuals, which reduces false positives (Yoosefzadeh-Najafabadi et al., 2021; Rani et al., 2023). Other models like Fixed and Random Model Circulating Probability Unification (FarmCPU) and machine learning algorithms such as Support Vector Regression (SVR) and Random Forest (RF) have also been utilized to enhance the detection power and accuracy of QTL identification (Yoosefzadeh-Najafabadi et al., 2023). Additionally, the environmental interactions for complex traits were not fully explored, which could affect the stability of the identified QTLs across different environments (Figure 1) (Yu et al., 2022). Figure 1 Statistics of QTLs in GWAS results under three models (Adopted from Yu et al., 2022) Image caption: (A) Statistics on the number of QTLs detected at different significance thresholds by different models or methods. (B)Venn diagram representing the number of unique and shared QTLs with six models. (C) Venn diagram representing the number of unique and shared QTLs with 3VmrMLM single-environment method and 3VmrMLM multiple-environment method. Finally determine the red line (A) represents the GWAS significance threshold of this study, both (B, C) are counted at this significance threshold. 3VmrMLM-S represents 3VmrMLM single-environment method, 3VmrMLM-M represents QTL detection of 3VmrMLM multiple-environment method, 3VmrMLM-QEI represents QEI detection of 3VmrMLM multiple-environment method (Adopted from Yu et al., 2022) 2.3 Genotyping techniques Genotyping in GWAS can be performed using various techniques, including Single Nucleotide Polymorphism (SNP) arrays and whole-genome sequencing. SNP arrays, such as the SoySNP50K iSelect BeadChip, provide a cost-effective and high-throughput method for genotyping large populations (Ayalew et al., 2022; Rani et al.,
RkJQdWJsaXNoZXIy MjQ4ODYzMg==