A Quantitative Trait Nucleotide-Based Genomic Selection Strategy for Seed Oil and Protein Content in Soybean

In recent years, genomic selection (GS) has been widely adopted in plant breeding; however, its practical application is constrained by the high cost of genotyping large segregating populations. To address this issue, this study employed a Quantitative Trait Nucleotide (QTN)-assisted GS strategy to evaluate its efficiency in reducing genotyping costs for soybean seed oil content (OC) and protein content (PC). Based on six multi-parent F₄ populations (n = 4404) derived from seven elite soybean cultivars, which were genotyped using a 20K SNP chip, we identified 83 and 110 QTNs that were significantly associated with OC and PC, respectively. Among these loci, 37 and 62 QTNs were specific to OC and PC, respectively. Genomic prediction accuracies were evaluated across different training population (TP) sizes using three marker panels: genome-wide SNPs, all detected QTNs, and trait-specific QTNs. The panel consisting of all detected QTNs exhibited significantly higher prediction accuracy than the other two panels, except for PC when using 90% of the population as the training set. Phenotypic verification of the selected individuals showed that the PC-specific QTN panel yielded higher PC values and increased OC + PC values compared with the other marker panels. These results demonstrate that a small set of QTNs provides a cost-effective approach for genomic selection in practical soybean breeding programs.