Legume Genomics and Genetics 2024, Vol.15, No.6, 270-279 http://cropscipublisher.com/index.php/lgg 273 protein, oil, and yield traits. The study achieved high predictive abilities (rMP) for protein (0.81) and oil (0.71) using the RR-BLUP model, indicating the effectiveness of GS in soybean breeding (Stewart-Brown et al., 2019). Another notable example is the integration of GS with Genome-Wide Association Studies (GWAS) to identify significant SNP markers associated with key agronomic traits such as maturity, plant height, and seed weight. This combined approach has led to the discovery of new loci and candidate genes that can be targeted for improving yield and other important traits in soybean (Ravelombola et al., 2021) (Figure 1). Overall, the successful implementation of GS in soybean breeding programs highlights its potential to accelerate genetic gains and improve the efficiency of breeding processes. By leveraging comprehensive genomic data and advanced prediction models, breeders can make more informed selection decisions and develop superior soybean varieties more rapidly (Crossa et al., 2017; Stewart-Brown et al., 2019; Ravelombola et al., 2021). Figure 1 Genomic selection accuracy for yield, maturity, plant height, and seed weight using training/testing sets from all 250 soybean accessions (all samples), samples derived from Q1, and samples from the Q2 subpopulation (Adopted from Ravelombola et al., 2021) 5 Integration of GWAS and Genomic Selection 5.1 How GWAS can inform and enhance GS models Genome-Wide Association Studies (GWAS) identify significant Single Nucleotide Polymorphisms (SNPs) associated with traits of interest, providing valuable insights into the genetic architecture of these traits. By pinpointing specific loci that contribute to phenotypic variation, GWAS can enhance Genomic Selection (GS) models by incorporating these significant SNPs, thereby improving the accuracy of trait prediction. For instance, the identification of SNPs associated with yield, maturity, plant height, and seed weight in soybean has been shown to enhance the predictive power of GS models (Ravelombola et al., 2021). Additionally, the integration of GWAS findings into GS models can help in selecting the most informative markers, which can reduce genotyping costs while maintaining or even improving prediction accuracy (Luo et al., 2021). 5.2 Methods for integrating GWAS findings into GS pipelines Several methods can be employed to integrate GWAS findings into GS pipelines. One approach is to use a subset of SNPs identified by GWAS for GS, which has been shown to improve prediction accuracy compared to using all available SNPs (Luo et al., 2021). Another method involves the use of stepwise linear regression mixed models
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