Legume Genomics and Genetics 2024, Vol.15, No.3, 126-139 http://cropscipublisher.com/index.php/lgg 129 identified several QTLs associated with 100-seed weight, a critical factor for yield improvement. The study mapped 12 main effect QTLs and analyzed epistatic interactions between these loci, providing valuable insights for marker-assisted selection and breeding strategies. QTL mapping not only helps in understanding the genetic architecture of complex traits but also aids in the development of molecular markers for use in MAS. By incorporating QTL mapping results, breeders can more precisely target and select for beneficial traits, leading to the development of superior soybean varieties with enhanced performance and resilience. Figure 1 Manhattan plots and QQ-plots for tolerance indexes based on biomass reduction under SCN infestation (Adopted from Ravelombola et al., 2020) Image caption: Figure 1 displays Manhattan plots and QQ plots for tolerance indexes based on biomass reduction under SCN (soybean cyst nematode) infestation, using different statistical models (SMR, MLM_PCA, and MLM_PCA_K). In each Manhattan plot, the x-axis represents chromosome numbers, and the y-axis denotes the LOD value (-log10 (p-value)). Different colors represent different chromosomes. In the QQ plots, the x-axis represents the expected -log10 (p-value), and the y-axis shows the observed -log10 (p-value). Plot A shows the results from the single marker regression model (SMR), Plot B shows the results from the generalized linear model (GLM(PCA)), and Plot C shows the results from the mixed linear model (MLM (PCA+K)). The QQ plots are used to assess the deviation of p-value distributions, with a linear distribution indicating a better model fit (Adapted from Ravelombola et al., 2020) 3.5 Genotyping-by-sequencing (GBS) Genotyping-by-Sequencing (GBS) is a cost-effective and high-throughput method for generating large amounts of genetic data. This technique involves sequencing a subset of the genome, allowing for the identification of numerous genetic markers across the genome. GBS has been widely used in soybean breeding to identify SNPs and other genetic variations associated with important traits. For example, a study using GBS on a soybean population identified over 10 000 high-quality SNPs and mapped several genomic regions associated with traits such as yield, maturity, and seed weight. The study demonstrated the utility of GBS in enhancing the accuracy of genomic selection and marker-assisted selection in soybean breeding programs (Ravelombola et al., 2021). The high density of markers generated by GBS enables detailed genetic analyses and the identification of marker-trait associations, facilitating the rapid and efficient selection of superior soybean lines.
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