Integrated Approach of GWAS and GS Provides Cost-Effective Strategy for Breeding Negatively Correlated Traits in Soybean (<em>Glycine max</em> L.)

Oil content (OC) and protein content (PC) are two important traits determining the quality and yield in soybean. However, these traits are quantitative in nature, governed by polygenes possessing minor effects. In this study, we performed multi-locus genome-wide association studies (GWAS) to identify the quantitative trait nucleotides (QTNs) associated with OC and PC by using the 4404 multiparent F4 individuals genotyping with 20K SNP chip. A total of 83 and 110 QTNs significantly associated with OC and PC, respectively were detected by using six multi-locus GWAS methods. The identified QTNs as well as genome-wide SNPs (9,942 SNPs) were tested with training populations (TP) of different sizes for genomic selection (GS) analysis. Our results revealed that using the QTNs only has allowed to provide the higher prediction accuracy of 0.70 at reduced TP size of 10%; besides, using the QTNs-specific to each trait viz., OC and PC for GS selection minimizes the negative correlations among these traits. The present study, provided the detailed genetic architecture of PC and OC in soybean, besides provided a new method for developing soybean cultivars with both high PC and OC, which otherwise was the long-term unachievable goal of the soybean breeders.