Legume Genomics and Genetics 2024, Vol.15, No.3, 126-139 http://cropscipublisher.com/index.php/lgg 133 Figure 3 Effects of population structure on prediction of oil content when utilizing the entire genomic selection dataset (EGSD) method (Adopted from Stewart-Brown et al., 2019) Image caption: Figure 3 shows the effects of population structure on the prediction of oil content when utilizing the entire genomic selection dataset (EGSD) method. Panel A displays the principal component analysis (PCA) of the genomic prediction population using all SNPs, while panel B shows the PCA using the 8th tag SNPs. Panel C presents the relationship between the average predicted genomic estimated breeding values (GEBV) and the observed best linear unbiased prediction (BLUP) values when using all SNPs, and panel D shows the similar relationship when using the 8th tag SNPs. Panel E illustrates the relationship between the average predicted GEBV and the observed BLUP values within Pop1-4 when using all SNPs, whereas panel F shows this relationship within Pop1-4 when using the 8th tag SNPs. The scatterplots in panels C-F display the correlation coefficients (Adapted from Stewart-Brown et al., 2019) 5.2 Achievements in marker-assisted selection (MAS) for disease resistance Marker-assisted selection (MAS) has played a crucial role in developing disease-resistant soybean varieties. Research has identified several quantitative trait loci (QTLs) associated with resistance to various soybean diseases, enabling breeders to incorporate these resistance genes more effectively. For instance, MAS has been used to introgress resistance genes for soybean cyst nematode (SCN) and Phytophthora root rot, significantly improving soybean resilience against these pathogens (Huang et al., 2021). Additionally, MAS has facilitated the pyramiding of multiple resistance genes, providing durable resistance to soybean rust and other major diseases (Liu et al., 2017).
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