Journal of Advances in Biology & Biotechnology

Traditional breeding methods have contributed significantly to enhancing traits such as yield, disease resistance, and stress tolerance. These approaches often require extensive time and resources to produce desired outcomes. Genome-Wide Association Studies (GWAS) have emerged as powerful tools for identifying genetic loci associated with important traits in plants, including agronomic performance, stress tolerance, disease resistance, and quality traits. The ability of GWAS to leverage natural genetic diversity across diverse populations provides high-resolution mapping, enabling the identification of quantitative trait loci (QTLs) contributing to complex traits. This paper aims to synthesize findings from recent studies that have utilized GWAS to unravel the genetic basis of these traits, offering valuable insights into breeding strategies for crop improvement. Recent advancements in GWAS have focused on integrating multi-omics approaches, including transcriptomics, metabolomics, and proteomics, to enhance trait prediction accuracy. The application of machine learning (ML) and artificial intelligence (AI) has further improved GWAS efficiency by refining predictive models and enabling the detection of minor-effect loci. Despite its success, GWAS faces significant challenges, such as false-positive associations due to population structure, genotype-environment interactions, and computational limitations. Incorporating pangenomes and structural variants, developing advanced statistical models, and expanding GWAS to orphan crops are essential for enhancing its accuracy and applicability. Integrating GWAS findings with marker-assisted selection (MAS) and genomic selection (GS) holds promise for accelerating crop improvement and developing climate-resilient varieties. Publicly available databases and global collaborative initiatives continue to facilitate GWAS research across various plant species. Expanding GWAS to understudied crops and integrating findings with breeding programs through marker-assisted selection (MAS) and genomic selection (GS) offer promising pathways for crop improvement. Future efforts should focus on improving computational frameworks, enhancing accessibility to genomic resources, and promoting the application of GWAS in underutilized crops. By addressing these challenges, GWAS has the potential to significantly contribute to sustainable agriculture, ensuring food security under changing environmental conditions.