Legume Genomics and Genetics 2024, Vol.15, No.6, 270-279 http://cropscipublisher.com/index.php/lgg 274 (StepLMM), which combine GWAS and GS in a single statistical framework. This model first estimates the variance components using genomic best linear unbiased prediction (GBLUP) and then selects the most significant SNPs through a linear mixed model transformation, improving both mapping precision and prediction accuracy (Li et al., 2017). Additionally, Bayesian models such as Bayesian Lasso (BL) and ridge regression Best Linear Unbiased Prediction (rrBLUP) can be used to incorporate GWAS-identified SNPs, enhancing the efficiency of GS pipelines (Qin et al., 2022). 5.3 Benefits of combining GWAS and GS for trait prediction and selection accuracy Combining GWAS and GS offers several benefits for trait prediction and selection accuracy. Firstly, it leverages the strengths of both approaches: GWAS provides high-resolution mapping of trait-associated loci, while GS offers robust prediction of breeding values using genome-wide markers. This synergy can lead to higher prediction accuracies for complex traits, as demonstrated in studies where the integration of GWAS findings into GS models resulted in improved prediction accuracy for traits such as yield, protein content, and disease resistance (Luo et al., 2021; Ravelombola et al., 2021; Qin et al., 2022). Moreover, the combined approach can reduce the rate of false positives in QTL mapping and enhance the precision of trait-associated loci identification, ultimately leading to more efficient and effective breeding programs (Li et al., 2017). The use of GWAS-informed GS models can also accelerate the breeding cycle by enabling earlier and more accurate selection of superior genotypes, thus increasing genetic gain over time (Matei et al., 2018; Stewart-Brown et al., 2019). 6 Case Studies 6.1 Yield improvement: integration of GWAS and GS to enhance yield traits The integration of Genome-Wide Association Studies (GWAS) and Genomic Selection (GS) has shown significant promise in enhancing yield traits in soybean breeding. For instance, a study on soybean breeding lines demonstrated the potential of GS for selecting quantitative traits such as yield, where traditional marker-assisted selection has often been less effective. The study utilized 483 elite breeding lines and achieved predictive abilities (rMP) of 0.26 for yield, indicating the potential of GS to improve yield traits through more accurate predictions and selection (Stewart-Brown et al., 2019). Additionally, the application of GS in other crops like wheat has shown that leveraging genomic information can significantly increase prediction accuracies, thereby accelerating the development of high-yielding varieties (Budhlakoti et al., 2022). 6.2 Disease resistance: examples of breeding for resistance to soybean pathogens Breeding for disease resistance in soybean has benefited from the integration of GWAS and GS. For example, the BREEDWHEAT project has successfully utilized these genomic tools to decipher traits of agronomical interest, including biotic resistance, which is crucial for developing disease-resistant varieties (Paux et al., 2022). Furthermore, the identification of specific loci associated with disease resistance through GWAS has enabled the development of soybean varieties with enhanced resistance to various pathogens. This approach not only helps in identifying candidate genes but also facilitates the implementation of genomic selection to improve disease resistance traits (Jannink et al., 2010). 6.3 Stress tolerance: breeding for abiotic stress (e.g., drought, salinity) using combined approaches The combined use of GWAS and GS has been instrumental in breeding for abiotic stress tolerance in soybean. For instance, a study identified a major salt-tolerance locus controlled by the E2 gene, which also influences flowering time and maturity. The loss of E2 function not only enhanced salt tolerance but also shortened flowering time, demonstrating the effectiveness of integrating GWAS findings into breeding programs for stress tolerance (Dong et al., 2022). Additionally, research on drought tolerance in soybean has identified several quantitative trait loci (QTLs) and candidate genes associated with drought resistance. The over-expression of the GmNFYB17 gene, identified through GWAS and linkage analysis, significantly improved drought resistance and yield accumulation in transgenic soybean plants (Sun et al., 2022) (Figure 2). These examples highlight the potential of integrating GWAS and GS to develop soybean varieties with improved tolerance to abiotic stresses such as drought and salinity (Valliyodan et al., 2016; Ouyang et al., 2022).
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