The number of four-seed pods (NFSP) in soybean is an important yield trait and a quantitative trait regulated by multiple genes. Mapping quantitative trait nucleotide (QTN), mining major genes, and screening excellent breeding schemes for NFSP are of great significance for breeding high-yielding soybean varieties. In this study, a germplasm population (GP) containing 455 soybean varieties was planted in five environments to investigate NFSP. Single-nucleotide polymorphism (SNP) genotype data were obtained based on the Axiom_SoyaSNP 180K chip, and a genome-wide association study (GWAS) was used to locate QTNs, which were used to predict candidate genes and simulate breeding. The results showed that there was genetic variation in NFSP and different gene expression in various environments. The broad-sense heritability of NFSP over multiple environments was 72.7%. A total of 89 QTNs correlated with the number of four pods were identified on 20 chromosomes, including 34 stable QTNs repeatedly detected by multiple methods or in multiple environments and four QTNs with an additive by environment interaction effect. In the decay regions of 34 stable QTNs, three genes related to NFSP in soybean were screened and verified by haplotype analysis, namely, Glyma.13G105400, Glyma.04G063700, and Glyma.04G063100. Multiple regression analysis of 89 QTNs on NFSP was used to establish molecular-assisted selection models for five environments, which could explain 44.82%–55.06% of the phenotypic variation in NFSP. Based on this model, 153 breeding schemes were selected for five environments, which could achieve the breeding goals of NFSP over 43, 43, 25, 31, and 40 under 21AC, 21XY, 23WD, 23QQHE, and 23AC environments, respectively. These results laid the foundation for understanding the genetic mechanism of pod formation in soybean and molecular breeding of high-yielding varieties.

The protein content in soybean seeds serves as a crucial measure of soybean quality. Breeding high-protein varieties remains the most cost-effective and efficient approach to increasing soybean protein levels. Nevertheless, limited research has focused on identifying the genes responsible for high protein content among the diverse soybean cultivars. To address this gap, a genome-wide association study (GWAS) was conducted on 455 soybean varieties with varying protein content to predict and validate novel genes involved in regulating protein levels in soybean seeds. Protein content data were obtained from three distinct environments, along with three environmental variables derived from oil content, which is closely related to protein levels. Genotyping was performed using the SoySNP180k BeadChip, yielding genotype data for 63,306 non-redundant single nucleotide polymorphisms (SNPs). Five multi-locus GWAS methods were employed, resulting in the identification of 81 significant quantitative trait nucleotides (QTNs), of which 37 QTNs detected across different methods and environments were further analyzed. Moreover, the simulation platform Blib was used to conduct single-crossing simulation breeding on 81 QTN loci for actual breeding prediction. Haplotype analysis based on re-sequencing data confirmed 2 genes closely linked to protein synthesis, providing a theoretical basis for breeding high-protein soybean varieties and developing molecular breeding strategies.

Soybean (Glycine max L. Merr.) is a globally significant oilseed crop. The number of four-seeded pods in the lower part (FSPL) serves as a critical yield component under high-density planting. To date, numerous crop-specific traits have been investigated in multiple breeding studies of soybean; however, little attention has been paid to studies on FSPL. Hence, in this study, we investigated the genetic basis of FSPL using a recombinant inbred line population (RIL3613) across four environments. The segregated genetic mapping population was cultivated during the field experiments, and the collected phenotypic dataset of FSPL exhibited quantitative genetics and high broad-sense heritability (0.724), indicating stable genetic control. Further, we performed quantitative trait locus (QTL) mapping using raw means in each environment and identified 10 QTL, explaining phenotypic variations (PVE) ranging from 0.10% to 2.94%. Among the identified environmentally stable QTL, qFSPL-15-1 was consistently detected across all environments. Two candidate genes [Glyma.15G034100 (encoding lysophosphatidic acid acyltransferase 2) and Glyma.15G034200 (encoding an RNA-binding protein)] were predicted within the flanking genomic interval. The allele frequencies of haplotype combinations of Hap1: Pro2 + CDS1 for Glyma.15G034100 and Hap3: Pro3 + CDS1 for Glyma.15G034200 in wild soybeans (26.6–30.0%) were larger than improved cultivars (52.6–53.4%). We believe that our current findings elucidate the molecular mechanisms regulating lower-pod formation and provide precise genetic targets for marker-assisted selection in high-yield soybean breeding.

Soybeans are a primary source of plant-based protein, with seeds containing approximately 40% protein—a key quality trait. Selecting superior hybrid combinations and managing progeny effectively are crucial for developing high-protein soybean varieties. Using a recombinant inbred line population (RIL3613) derived from Dongnong L13 × Heihe 36 and its previously constructed high-density genetic linkage map, QTLs and QTL × environment interactions (QEIs) associated with seed protein content (SPC) were identified through the bi-parental population (BIP) model and multi-environment trials (MET) model in QTL IciMapping v4.2. Candidate genes were then predicted via sequence alignment and haplotype analysis between the parents. Finally, simulated breeding was conducted using the B4L function in the In Silico Breeding (ISB) module of the Blib platform to determine optimal breeding strategies across diverse environments. The analysis identified 19 QTLs associated with SPC and 97 QEIs linked to SPC. These QTLs collectively explained 84.442% of the phenotypic variance, with four QTLs exhibiting significant contributions. A key candidate gene, Glyma.12G231400, associated with soybean SPC, was predicted within the 38,995,090–39,293,825 bp interval on chromosome 12. Across 11 environments, three to six optimal breeding schemes were selected, all employing modified pedigree selection. These findings enhance our understanding of the genetic basis of soybean protein formation and provide technological support for molecular breeding for seed quality improvement.

Soybean frogeye leaf spot (FLS) disease has been reported globally and is caused by the fungus Cercospora sojina, which affects the growth, seed yield, and quality of soybean. Among the 15 physiological microspecies of C. sojina soybean in China, Race 7 is one of the main pathogenic microspecies. A few genes are involved in resistance to FLS, and they cannot meet the need to design molecular breeding methods for disease resistance. In this study, a soybean recombinant inbred line (RIL3613) population and a germplasm resource (GP) population were planted at two sites, Acheng (AC) and Xiangyang (XY). Phenotypic data on the percentage of leaf area diseased (PLAD) in soybean leaves were obtained via image recognition technology after the inoculation of seven physiological species and full onset at the R3 stage. Quantitative trait loci (QTLs) and quantitative trait nucleotides (QTNs) were mapped via linkage analysis and genome-wide association studies (GWASs), respectively. The resistance genes of FLS were subsequently predicted in the linkage disequilibrium region of the collocated QTN. We identified 114 QTLs and 18 QTNs in the RIL3613 and GP populations, respectively. A total of 14 QTN loci were colocalized in the two populations, six of which presented high phenotypic contributions. Through haplotype–phenotype association analysis and expression quantification, three genes (Glyma.06G300100, Glyma.06G300600, and Glyma.13G172300) located near molecular markers AX-90524088 and AX-90437152 (QTNs) are associated with FLS Chinese Race 7, identifying them as potential candidate resistance genes. These results provide a theoretical basis for the genetic mining of soybean antigray spot No. 7 physiological species. These findings also provide a theoretical basis for understanding the genetic mechanism underlying FLS resistance in soybeans.

Hundred-seed weight (HSW) is a critical determinant of soybean yield potential, yet its genetic dissection is often limited by the restricted allelic diversity of traditional biparental populations. In this study, we developed a four-way recombinant inbred line (FW-RIL) population comprising 144 lines derived from four founders with distinct HSW phenotypes to enhance mapping resolution and allelic richness. By integrating a high-density genetic linkage map with five multi-locus genome-wide association study (GWAS) models, we systemically analyzed the genetic basis of HSW across multiple environments. A total of 16 quantitative trait loci (QTLs) and 40 quantitative trait nucleotides (QTNs) were identified, among which three QTLs and two QTNs were consistently detected across environments and analytical approaches. Co-localization analysis, combined with linkage disequilibrium (LD) mapping, haplotype effect evaluation, and gene expression profiling, prioritized Glyma.02G047200 as the key candidate gene within the stable major-effect locus qHSW-2-1 . This gene encodes an oligopeptide transporter, and haplotype analyses demonstrated that elite alleles significantly increase seed weight across diverse genetic backgrounds. Furthermore, comparative evaluation of multiple genomic selection (GS) models revealed that the Light Gradient Boosting Machine (LightGBM) achieved the highest predictive accuracy for HSW. Using this model, breeding simulations identified several high-yielding hybrid combinations pyramiding multiple superior allelic variants. Overall, this study elucidates the genetic determinants of soybean HSW and highlights the effectiveness of integrating multi-parent populations, genomic selection, and molecular design breeding to accelerate yield improvement.