Genomics and Applied Biology 2024, Vol.15, No.4, 212-222 http://bioscipublisher.com/index.php/gab 218 naïve methods (Cortes et al., 2021). Moreover, the use of joint-GWAS approaches, as seen in the study on Eucalyptus, has increased the power to detect significant associations by combining data from multiple populations (Müller et al., 2018). These advancements are essential for improving the accuracy and reliability of GWAS findings. 6.3 Ethical considerations and intellectual property rights Ethical considerations in GWAS encompass issues related to data privacy, informed consent, and the equitable representation of diverse populations. The importance of including ancestrally diverse populations in GWAS is highlighted by Peterson et al. (2019), which discusses the scientific and ethical imperatives of broadening the genetic research base. Additionally, intellectual property rights pose challenges in terms of data sharing and the commercialization of genetic findings. The NHGRI-EBI GWAS Catalog provides a high-quality curated collection of published GWAS, promoting transparency and accessibility while addressing some of these ethical concerns (Buniello et al., 2018). 6.4 Case study: overcoming population structure in GWAS of fabaceae crops Population structure can confound GWAS results, leading to false associations. Overcoming this challenge requires sophisticated statistical methods and careful study design. The study on Eucalyptus breeding populations serves as a relevant case study, where joint-GWAS approaches were used to combine data from different populations, thereby increasing the power to detect true associations (Müller et al., 2018). This method can be applied to Fabaceae crops to address population structure issues and improve the accuracy of GWAS findings. By leveraging joint-GWAS and other advanced statistical techniques, researchers can better understand the genetic architecture of Fabaceae crops and enhance breeding programs. 7 Applications of GWAS in Breeding Programs 7.1 Incorporating GWAS findings into marker-assisted selection Genome-wide association studies (GWAS) have become a cornerstone in modern plant breeding, particularly for marker-assisted selection (MAS). By identifying single nucleotide polymorphisms (SNPs) associated with desirable traits, GWAS enables breeders to select plants with favorable genetic profiles more efficiently. For instance, in soybean, SNP markers associated with yield, maturity, plant height, and seed weight have been identified, facilitating their use in MAS to improve these traits (Ravelombola et al., 2021). Similarly, in field pea, SNP markers linked to agronomic traits such as plant height, lodging resistance, and grain yield have been identified, which can be utilized in MAS to accelerate cultivar improvement (Gali et al., 2019). 7.2 Strategies for accelerating genetic gain through GWAS To maximize genetic gain, breeders can integrate GWAS findings with genomic selection (GS). This approach leverages the predictive power of SNP markers to estimate the breeding values of individuals, thereby accelerating the selection process. In soybean, the combination of GWAS and GS has shown high accuracy in selecting for yield and other agronomic traits, demonstrating the potential for rapid genetic improvement (Ravelombola et al., 2021). Additionally, the use of advanced statistical models such as ridge regression best linear unbiased prediction (rrBLUP) and Bayesian methods can further enhance the accuracy of GS, as seen in legume breeding programs (Susmitha et al., 2023). 7.3 Case study: GWAS-driven breeding for yield improvement in pea A notable example of GWAS application in breeding is the improvement of yield in field pea (Pisum sativumL.). A comprehensive GWAS involving 135 pea accessions from diverse breeding programs identified several SNP markers associated with key agronomic traits, including grain yield. These markers were consistent across multiple trials and locations, underscoring their reliability for use in breeding programs (Gali et al., 2019) (Figure 3). By incorporating these markers into MAS, breeders can more effectively select high-yielding pea varieties, thereby enhancing overall productivity.
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