Genomics and Applied Biology 2024, Vol.15, No.4, 212-222 http://bioscipublisher.com/index.php/gab 214 Soybean (Glycine max): Soybean is a crucial crop for its high protein and oil content. Numerous GWAS have been conducted to identify loci associated with agronomic traits, disease resistance, and seed quality (Shook et al., 2021; Kim et al., 2022; Yoosefzadeh-Najafabadi et al., 2023). Pea (Pisum sativum): Field pea is another important legume crop, with GWAS identifying loci related to agronomic traits, seed morphology, and seed quality (Gali et al., 2019). Common Bean (Phaseolus vulgaris): Common bean is widely cultivated for its nutritional value. GWAS in common bean have focused on yield and yield-contributing traits, leveraging the genetic diversity present in germplasm collections (Mir et al., 2021). Lentil (Lens culinaris): Although not explicitly covered in the provided data, lentils are another significant legume crop studied for various agronomic traits through GWAS. 3.3 Identification of trait-associated loci in fabaceae GWAS have been instrumental in identifying loci associated with various traits in Fabaceae crops. For instance, in soybean, significant associations have been found for traits such as days to flowering, seed coat color, and node number (Kim et al., 2022). Meta-GWAS approaches have further enhanced the detection of quantitative trait loci (QTL) by combining data from multiple studies, leading to the identification of hundreds of loci associated with traits like seed yield, plant height, and disease resistance (Shook et al., 2021). In field pea, GWAS have identified SNP markers associated with traits such as plant height, lodging resistance, and seed protein concentration (Gali et al., 2019). 3.4 Case study: GWAS for drought tolerance in soybean Drought tolerance is a critical trait for soybean, especially in the context of climate change. A study focusing on root traits in soybean landraces identified 112 significant SNP loci associated with seven root traits, which are crucial for drought tolerance (Kim et al., 2023). This study highlights the importance of understanding the genetic basis of root traits to develop drought-resistant soybean varieties. 3.5 Success stories: breeding applications from GWAS in fabaceae The application of GWAS findings in breeding programs has led to significant advancements in Fabaceae crop improvement. For example, the integration of GWAS and genotyping-by-sequencing (GBS) in soybean has facilitated the identification of mutation hotspots and the acceleration of genome evolution, which can be harnessed for breeding purposes (Kim et al., 2022). Additionally, the use of machine learning algorithms in GWAS has improved the detection of durable QTL associated with soybean seed quality traits, providing new tools for genomic-based breeding approaches (Yoosefzadeh-Najafabadi et al., 2023) (Figure 1). In common bean, the identification of stable and major marker-trait associations has paved the way for molecular breeding programs aimed at enhancing yield (Mir et al., 2021). 4 Advances in GWAS Technologies and Methods 4.1 High-throughput genotyping and sequencing technologies High-throughput genotyping and sequencing technologies have revolutionized genome-wide association studies (GWAS) by enabling the rapid and cost-effective generation of vast amounts of genomic data. These technologies facilitate the identification of genetic variants associated with complex traits by providing comprehensive genomic coverage. For instance, the integration of high-throughput phenotyping with GWAS has been shown to enhance the quality of trait data, thereby improving the accuracy of genetic association studies (Xiao et al., 2021). Additionally, the development of advanced sequencing platforms has allowed for the detailed examination of genetic variants across diverse populations, furthering our understanding of the genetic basis of complex traits (Cortes et al., 2021). 4.2 Multi-trait and multi-locus GWAS approaches Traditional GWAS methods often focus on single-marker associations, which can limit the detection of significant loci due to stringent multiple testing corrections. Multi-trait and multi-locus GWAS approaches have been
RkJQdWJsaXNoZXIy MjQ4ODYzMg==