Genomics and Applied Biology 2024, Vol.15, No.4, 212-222 http://bioscipublisher.com/index.php/gab 217 nutritional traits in soybeans, such as protein content, amino acid composition, and micronutrient levels. These studies have revealed several loci that significantly influence these traits, providing valuable markers for breeding programs aimed at enhancing the nutritional quality of soybeans. The integration of these markers into genomic selection frameworks can accelerate the development of nutritionally superior soybean varieties (Susmitha et al., 2023) (Figure 2). 5.5 Collaborative initiatives and global research efforts The success of GWAS in Fabaceae is greatly enhanced by collaborative initiatives and global research efforts. Platforms like the GWAS Atlas provide curated resources of genome-wide variant-trait associations, facilitating data sharing and collaboration among researchers worldwide (Tian et al., 2019). Additionally, international consortia and breeding programs are increasingly adopting joint-GWAS approaches, which combine data from multiple studies to increase statistical power and validate findings across diverse populations (Müller et al., 2018). These collaborative efforts are essential for advancing our understanding of the genetic basis of complex traits and for translating GWAS findings into practical breeding applications. 6 Challenges and Considerations for Future GWAS Studies 6.1 Data quality and reproducibility issues Data quality and reproducibility are critical challenges in genome-wide association studies (GWAS). Ensuring high-quality data involves rigorous quality control measures to filter out errors and artifacts that can lead to false associations. For instance, the tutorial by Marees et al. (2018) emphasizes the importance of quality control and statistical analysis in GWAS, highlighting the need for dedicated genetics software to manage these tasks effectively. Additionally, reproducibility of results is a significant concern, as demonstrated by the MultiGWAS tool, which facilitates the replication of GWAS by testing different parameters and models to validate results (Garreta et al., 2020). This approach helps in identifying false associations and ensuring that findings are robust and reliable. Figure 2 Number of GWAS studies conducted in different leguminous crops from timeline 2012 to 2023 (Adopted from Susmitha et al., 2023) 6.2 Statistical challenges and advances in GWAS analysis Statistical challenges in GWAS include managing the vast amount of data and controlling for population structure to avoid spurious associations. Advances in statistical methods have been crucial in addressing these issues. For example, the development of mixed model frameworks has significantly reduced false positives compared to
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