Identification of the quantitative trait locus (QTL) underlying salt tolerance is a prerequisite for marker-assisted selection in the salt-tolerant breeding process. In this study, the recombinant inbred lines derived from the salt-tolerant elite soybean cultivar ‘Jidou 12’ and the salt-sensitive elite cultivar ‘Ji NF 58’ were used to identify the QTL associated with salt tolerance, using both salt tolerance rating (STR) and leaf chlorophyll content (SPAD) as indicators. A major salt-tolerant QTL, which was flanked by SSR markers GMABAB and Barcsoyssr_03_1421 on chromosome 3, was identified based on single-marker regression, single trait composite interval mapping, and multiple interval mapping analysis. For STR, the LOD ranged from 19.8 to 20.1; R2 ranged from 44.3 to 44.7%; and the additive effect ranged from 0.876 to 0.885 among the three mapping methods. For SPAD, the LOD ranged from 10.6 to 11.0; R2 ranged from 27.0 to 27.6%; and the additive effect ranged from 1.634 to 1.679 among the three mapping methods. In this study, a major QTL conditioning salt tolerance on chromosome 3 was identified. The DNA markers closely associated with the QTLs might be useful in marker-assisted selection for soybean salt tolerance improvement in Huanghuaihai, China.

Developing high yielding cultivars with outstanding quality traits are perpetual objectives throughout crop breeding operations. Confoundingly, both of these breeding objectives typically involve working with complex quantitative traits that can be affected by genetic and environmental factors. Establishing correlations of these complex traits with more easily identifiable and highly heritable traits can simplify breeding processes. In this study, two parental soybean genotypes contrasting in seed hilum size, yield, and seed quality, as well as 175 F₉ recombinant inbred lines (RILs) derived from these parents, were grown in 3 years. The h²_b of four hilum size, two quality and two yield traits, ranged from 0.72 to 0.87. The four observed hilum size traits exhibited significant correlation (P < 0.05) with most of seed yield and quality traits, as indicated by correlation coefficients varying from -0.35 to 0.42, which suggests that hilum size could be considered as a proxy trait for soybean yield and quality. Interestingly, among 53 significant quantitative trait loci (QTLs) with logarithm of odds (LOD) values ranging from 2.51 to 6.69 and accounting for 6.40–16.10% of genetic variation, three loci encoding hilum size, qSH6.2, qSH8, and qSH10, colocated with QTLs for seed yield and quality traits, demonstrating that genes impacting seed hilum size colocalize in part with genes acting on soybean yield and quality. As a result of the breeding efforts and field observations described in this work, it is reasonable to conclude that optimizing hilum size through selection focused on a few QTLs may be useful for breeding new high yielding soybean varieties with favorable quality characteristics.

Introduction: Soybean is an important protein and oil crop, and improving yield has traditionally been a major breeding goal. However, salt stress is an important abiotic factor that can severely impair soybean yield by disrupting metabolic processes, inhibiting photosynthesis, and hindering plant growth, ultimately leading to a decrease in productivity.Methods: This study utilized phenotypic and genotypic data from 563 soybean germplasms sourced from over 20 countries. Employing four distinct models—we performed a genome-wide association study (GWAS) using four models, including MLM, MLMM, FarmCPU, and BLINK in GAPIT 3, we conducted a Genome-Wide Association Study (GWAS) to identify single nucleotide polymorphism (SNP) associated with salt tolerance in soybean. Subsequently, these identified SNP were further analyzed for candidate gene discovery. Using 34,181 SNPs for genomic prediction (GP) to assess prediction accuracy.Results: Our study identified 10 SNPs significantly associated with salt tolerance, located on chromosomes 1, 2, 3, 7, and 16. And we identified 11 genes within a 5 kb window upstream and downstream of the QTLs on chromosomes 1, 3, and 16. Utilizing the GWAS-derived SNP marker sets for genomic prediction (GP) yielded r-values greater than 0.35, indicating a higher level of accuracy. This suggests that genomic selection for salt tolerance is feasible.Discussion: The 10 identified SNP markers and candidate genes in this study provide a valuable reference for screening and developing salt-tolerant soybean germplasm resources.

Introduction: Genomic selection (GS) is a potential breeding approach for soybean improvement.Methods: In this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN.Results: Our results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log₁₀(P) greater than 1 were included.Discussion: This information will help improve GS efficiency and facilitate the application of GS.