Soybean (Glycine max) seeds are rich in amino acids, offering key nutritional and physiological benefits. In this study, 290 soybean accessions from the USDA Germplasm Collection based in Urbana, IL Information Network (GRIN) were analyzed. Four Genome-Wide Association Study (GWAS) models—Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), Mixed Linear Model (MLM), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Multi-Locus Mixed Model (MLMM)—identified two significant Single Nucleotide Polymorphisms (SNPs) associated with arginine content: Gm06_19014194_ss715593808 (LOD = 9.91, 3.91% variation) at 19,014,194 bp on chromosome 6 and Gm11_2054710_ss715609614 (LOD = 9.05, 19% variation) at 2,054,710 bp on chromosome 11. Two candidate genes, Glyma.06g203200 and Glyma.11G028600, were found in the two SNP marker regions, respectively. Genomic Prediction (GP) was performed for arginine content using several models: Bayes A (BA), Bayes B (BB), Bayesian LASSO (BL), Bayesian Ridge Regression (BRR), Ridge Regression Best Linear Unbiased Prediction (rrBLUP), Random Forest (RF), and Support Vector Machine (SVM). A high GP accuracy was observed in both across- and cross-populations, supporting Genomic Selection (GS) for breeding high-arginine soybean cultivars. This study holds significant commercial potential by providing valuable genetic resources and molecular tools for improving the nutritional quality and market value of soybean cultivars. Through the identification of SNP markers associated with high arginine content and the demonstration of high prediction accuracy using genomic selection, this research supports the development of soybean accessions with enhanced protein profiles. These advancements can better meet the demands of health-conscious consumers and serve high-value food and feed markets.

Genomic selection (GS) is a marker-based selection method used to improve the genetic gain of quantitative traits in plant breeding. A large number of breeding datasets are available in the soybean database, and the application of these public datasets in GS will improve breeding efficiency and reduce time and cost. However, the most important problem to be solved is how to improve the ability of across-population prediction. The objectives of this study were to perform genomic prediction (GP) and estimate the prediction ability (PA) for seed oil and protein contents in soybean using available public datasets to predict breeding populations in current, ongoing breeding programs. In this study, six public datasets of USDA GRIN soybean germplasm accessions with available phenotypic data of seed oil and protein contents from different experimental populations and their genotypic data of single-nucleotide polymorphisms (SNPs) were used to perform GP and to predict a bi-parent-derived breeding population in our experiment. The average PA was 0.55 and 0.50 for seed oil and protein contents within the bi-parents population according to the within-population prediction; and 0.45 for oil and 0.39 for protein content when the six USDA populations were combined and employed as training sets to predict the bi-parent-derived population. The results showed that four USDA-cultivated populations can be used as a training set individually or combined to predict oil and protein contents in GS when using 800 or more USDA germplasm accessions as a training set. The smaller the genetic distance between training population and testing population, the higher the PA. The PA increased as the population size increased. In across-population prediction, no significant difference was observed in PA for oil and protein content among different models. The PA increased as the SNP number increased until a marker set consisted of 10,000 SNPs. This study provides reasonable suggestions and methods for breeders to utilize public datasets for GS. It will aid breeders in developing GS-assisted breeding strategies to develop elite soybean cultivars with high oil and protein contents.

Soybean mosaic virus (SMV), a pathogen responsible for inducing leaf mosaic or necrosis symptoms, significantly compromises soybean seed yield and quality. According to the classification system in the United States, SMV is categorized into seven distinct strains (G1 to G7). In this study, we performed a genome-wide association study (GWAS) in GAPIT3 using four analytical models (MLM, MLMM, FarmCPU, and BLINK) on 218 soybean accessions. We identified 22 SNPs significantly associated with G1 resistance across chromosomes 1, 2, 3, 12, 13, 17, and 18. Notably, a major quantitative trait locus (QTL) spanning 873 kb (29.85–30.73 Mb) on chromosome 13 exhibited strong association with SMV G1 resistance, including the four key SNP markers: Gm13_29459954_ss715614803, Gm13_29751552_ss715614847, Gm13_30293949_ss715614951, and Gm13_30724301_ss715615024. Within this QTL, four candidate genes were identified: Glyma.13G194100, Glyma.13G184800, Glyma.13G184900, and Glyma.13G190800 (3Gg2). The genomic prediction (GP) accuracies ranged from 0.60 to 0.83 across three GWAS-derived SNP sets using five models, demonstrating the feasibility of GP for SMV-G1 resistance. These findings could provide a useful reference in soybean breeding targeting SMV-G1 resistance.

Soybean (Glycine max (L.) Merr.) is an important nutritional crop, not only as a rich source of protein and oil, but also for the production of isoflavones. There is a demand to breed soybean cultivars bearing consistently high protein, oil and isoflavone yields, yet this requires a clear heritable genetic relationship among isoflavone, protein and oil production. Here, two soybean genotypes contrasting in terms of protein, oil and isoflavone contents and their 185 F_8:10 recombinant inbred lines (RILs) were employed to characterize seed protein, oil and isoflavone contents over two years of field trials. In this population, protein, but not oil, was significantly correlated with isoflavone content. A high-density genetic linkage map containing 3943 SNP markers identified through genotyping-by-sequencing (GBS) technology was constructed for further genetic analysis, whereby a total of 25 integrated isoflavone loci were identified, including qISO1, qISO 6.1, qISO 6.3 and qISO 6.4, which are newly identified QTLs. Two major QTLs identified in this study, qISO5 and qISO6.2, were fine-mapped for production of daidzein and genistein derivatives, as well as for glycitein derivatives, in the sequences between nucleotide positions 41042159 and 42098680 on chromosome 5 and between 18449510 and 19395795 on chromosome 6, which, respectively, explain 9.3–20.4% and 7.8–24.8% of the phenotypic variation in these traits. Further combination of qISO5 and qISO6.2 resulted in additive impacts on isoflavone production. Among the 13 QTLs linked with seed protein content in this study, three also colocated with QTLs for isoflavone content, indicating that seed isoflavone and protein content may be coordinately inherited. These results contribute to understanding the relationships between isoflavone and protein or oil content in soybean seeds. This knowledge could be valuable for soybean breeding programs aiming to combine consistently high isoflavone production with high protein or oil content.

Soybean (Glycine max) seeds are rich in amino acids, offering key nutritional and physiological benefits. In this study, 290 soybean accessions from the USDA-ARS Germplasm Resources Information Network (GRIN) were analyzed. Four genome-wide association study (GWAS) models—Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), Mixed Linear Model (MLM), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Multi-Locus Mixed Model (MLMM)—identified two significant single nucleotide polymorphisms (SNPs) associated with arginine content: Gm06_19014194_ss715593808 (LOD = 9.91, 3.91% variation) at 19,014,194 bp on chromosome 6 and Gm11_2054710_ ss715609614 (LOD = 9.05, 19% variation) at 2,054,710 bp on chromosome 11. Two candidate genes Glyma.06g203200 and Glyma.11G028600 were founded in the two SNP marker regions, respectively. Genomic prediction was performed for arginine content using several models: Bayes A (BA), Bayes B (BB), Bayesian LASSO (BL), Bayesian Ridge Regression (BRR), Ridge Regression Best Linear Unbiased Prediction (rrBLUP), Random Forest (RF), and Support Vector Machine (SVM). High genomic prediction accuracy were observed in both across- and cross-populations, supporting genomic selection (GS) for breeding high-arginine soybean cultivars.

A high-throughput genotyping platform with customized flexibility, high genotyping accuracy and low cost is critical for marker-assisted selection and genetic mapping in soybean. Three assay panels were selected from the SoySNP50K, 40K, 20K and 10K, containing 41541, 20748 and 9670 SNP markers, respectively, for genotyping-by-target sequencing (GBTS). Fifteen representative accessions were used to assess the accuracy and consistency of the SNP alleles determined by the SNP panels and sequencing platform. SNP alleles were 99.87% identical between technical replications and 98.86% identical between the 40K SNP GBTS panel and 10× resequencing analysis. The GBTS method was also accurate because the genotypic dataset of the 15 representative accessions correctly revealed the pedigree of the accessions and the biparental progeny datasets correctly constructed the linkage maps of the SNPs. The 10K panel was also used to genotype two parent-derived populations and analyze QTLs controlling 100-seed weight, resulting in the identification of the stable associated genetic locus Locus_OSW_06 on chromosome 06. The markers flanking the QTL explained 7.05% and 9.83% of the phenotypic variation, respectively. Compared with GBS and DNA chips, 40K, 20K and 10K costs were reduced by 5.38% and 139.78%, 27.27% and 189.61%, and 55.56% and 253.97%, respectively. Low-cost genotyping panels could facilitate soybean germplasm assessment, genetic linkage map construction, QTL identification, and soybean genomic selection.