BACKGROUND:Improving breeding efficiency for superior soybean (Glycine max (L.) Merr.) germplasm used in natto and sprouts requires understanding how seed coat properties relate to quality and functional traits. METHODS:We measured seed weight, water uptake, and sprout length and thickness across various genotypes tested at different locations and years. For the 2023-2024 set, we also analyzed fresh sprout weight and the percentage of good sprouts. Seed coat percentage was determined as the seed coat weight relative to the total seed weight. Additionally, we established a robust and reproducible method using scanning electron microscopy (SEM) to directly measure seed coat thickness. RESULTS:Genotype was the dominant factor influencing nearly all traits, while location, year, and genotype × environment interactions were negligible - except for sprout length in one dataset. Seed coat percentage was unrelated to water uptake but inversely correlated with seed size, and did not consistently predict sprout thickness or length. Importantly, this study is the first to directly measure seed coat thickness and demonstrate its association with water uptake, offering a practical selection criterion for breeding programs targeting natto and sprout quality. SEM imaging further enables detailed analysis of seed coat layers and structural features, opening new opportunities for trait characterization. CONCLUSION:By introducing a direct, scalable approach to quantify seed coat traits, this work provides a foundation for more precise breeding strategies and highlights the role of structural seed attributes in improving specialty soybean products. Future research could integrate seed coat thickness into genomic selection models to accelerate breeding progress. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Soybean seed composition is governed by an inverse correlation between protein and oil, a relationship complicated by environmental factors. In Canada, western grown soybeans consistently have lower seed protein than eastern grown soybeans, a pattern driven by genotype by environment interactions that modulate the protein-oil balance. To uncover the transcriptional networks behind these differences, a multi-year Weighted Gene Co-Expression Network Analysis (WGCNA) was performed on leaf transcriptomes from ten soybean genotypes cultivated in contrasting eastern and western environments. The analysis identified oil-associated co-expression modules preserved across years and environments; their eigengenes showed significant, consistent correlations with seed oil content each year. A core set of recurring hub genes formed the backbone of this network, many of which co-localize with major seed composition Quantitative Trait Loci. Notably, a key hub lies adjacent to the protein-oil trade-off regulator on chromosome 20, while additional hubs map within a QTL on chromosome 10 linked to a more favorable protein-oil balance. The relationship between module expression and oil content displayed a genotype by environment interaction, providing a molecular basis for phenotypic differences between eastern and western regions. These hub transcription factors are prime candidates for breeding soybean cultivars with improved seed composition.

Germplasm characterization can enhance the management and utilization of plant germplasm conserved in genebanks worldwide. This study was conducted to characterize 774 diverse soybean [Glycine max (L.) Merr.] accessions, mainly conserved at Plant Gene Resources of Canada (PGRC), through a laboratory seedling vigor test under polyethylene glycol (PEG)-induced dehydration stress and 72 selected accessions through a greenhouse salinity test. The PEG-based test identified 95 accessions that showed vigorous seedling growth in Petri dishes containing 20% (w/v) PEG 6000 solution. The salinity test revealed 58 accessions that produced total seed yields per plant ranging from 0.03 g to 1.47 g under severe salinity stress (ECi 16.1 dS m-1). Six accessions originating from five countries displayed higher salt tolerance than the Canadian salt-tolerant cultivar OAC Ayton, but the latter still had the highest seed yield. One unique accession, CN29789, originating from China and named 'Hei Nung No.18', consistently showed high tolerance to both dehydration and salinity stresses and had vigorous root growth under severe salinity stress. These findings are significant, as they not only provide useful germplasm for soybean genetic improvement for abiotic stress tolerance but also demonstrate the value of characterizing plant germplasm conserved in a genebank for better utilization.

Soybean is an agronomically important crop due to protein- and oil-rich seeds. Soybeans in Western Canada have lower seed protein content by ∼1%-5% than those from Eastern Canada, as observed over the past two decades. To elucidate the environmental influence on seed protein accumulation, 10 soybean genotypes ranging in seed protein content were planted in four locations (one east, three west) from 2018 to 2021. Multi weighted gene co-expression network analysis (multiWGCNA) identified differentially co-expressed modules with protein × region trait association from two different networks from soybeans grown in 2019 and 2021. Two protein × region modules were assessed by intramodular connectivity, eigengene expression, and functional roles. The results identified differential co-expression of genes with key roles in epigenetic regulation of gene expression, translational modulation, photosynthetic capacity, seed protein storage vacuole filling, and carbon metabolism are environmentally regulated and likely influence seed composition. In this work, WGCNA and multiWGCNA untangled complex expression and trait data, providing valuable information on environmentally influenced genetic mechanisms influencing seed protein content.

Aboveground biomass (ABM) is a key determinant of soybean (Glycine max [L.] Merr.) yield and can be used to select for stress-resilient cultivars. The objective of our study was to develop a predictive model describing ABM in short-season soybean from vegetative cover (VC) and canopy height (CH). Over five growing seasons in Ottawa, Canada, actual ABM was measured weekly along with red, green, and blue and stereo-depth images. VC and CH, derived from these images, were used to develop a linear additive model for ABM with an R 2 of 0.90 and a root mean square error of 63 g m- 2. Model-predicted ABM at the beginning of seed development was significantly correlated with grain yield in a 5-year moisture-stress trial. The model was successfully applied to unmanned aerial vehicle-derived VC and CH data to predict ABM in the moisture-stress trial and a plant breeding trial selecting high-yielding natto soybean lines. The model provides a scalable approach for predicting ABM and may enhance soybean breeding by supporting the selection of cultivars with improved climatic resilience and yield potential.

Identification of marker trait associations (MTAs) for agronomic traits of soybean (Glycine max L. Merr.) can often be limited by confounding genotype by environment interactions. In this study, phenotypic data was derived from the calculation of genotypic principal component scores (gPCs) by GGEbiplot from a multiple year and location agronomic dataset to assess the validity and feasibility of using gPC scores in genome-wide association analysis (GWAS) in comparison with traditional phenotypes. Important quantitative trait loci (QTL) were discovered for maturity, seed oil content, yield, and plant height that were not detected using the traditional phenotypes. MTAs were detected by GWAS analysis with PC1, PC2, and PC4 phenotypes. QTL for maturity associated with the E1 and E3 soybean maturity loci demonstrate the validity of this approach by detecting these well studied regions. Epistatic analysis revealed QTL controlling both oil and protein content but did not uncover significant interactions associated with other traits. This result further contributes to the understanding of complex gene networks controlling pleiotropic traits such as seed oil and seed protein content. QTL for the studied traits are reported across six Glycine max chromosomes with 15 genes and one gene cluster proposed as candidates controlling agronomic traits.

Soybean is vital for global food security, necessitating understanding its response to environmental changes. We examined how growing season weather and rising atmospheric CO2 affect seed traits of historical short-season soybean cultivars in Eastern Canada, with particular focus on water-use efficiency and nitrogen fixation dynamics. Field trials data from 1993 to 2016 were analyzed for 14 cultivars spanning seven decades (1932–1992). Impacts of precipitation, mean maximum temperature (MTemp), mean maximum vapor pressure deficit (MVPD), and historical atmospheric CO2 (which increased by 47 ppm during the study period) on seed yield, protein and oil percentages, carbon isotope discrimination (Δ13C), and nitrogen isotopic composition (δ15N) were assessed using linear mixed-effect models and hierarchical partitioning analysis. Seed yield and Δ13C showed positive correlations with precipitation but negative correlations with MTemp and MVPD. Seed carbon percentage and Δ13C increased with atmospheric CO2, while seed protein and oil percentages, and δ15N decreased. Hierarchical partitioning highlighted yield vulnerability during early reproductive stages (R1-R3, July) and protein yield sensitivity during pod-formation and seed-filling (R4-R6, August). Historical cultivar selection favored seed and oil yields, but not protein yield, Δ13C, and δ15N. MVPD emerged as a better predictor of seed traits than temperature. Correlations between Δ13C, δ15N, and seed yield suggest selecting for higher yield may indirectly reduce water-use efficiency (indicated by higher Δ13C) and enhance biological nitrogen fixation (reflected by lower δ15N). These findings highlight the need to consider both seasonal weather variability and rising CO2 in soybean breeding programs.

Soybean seeds are rich in oil and protein; however, the seed composition is influenced by genotype and environment. For years, it has been observed that soybeans grown in western Canada have lower seed protein concentration (by ∼1%–5% total seed weight) than those grown in eastern Canada. In this study, soybean seeds harvested from five varieties were grown in four different locations in Canada (east and west growing regions) and analyzed using RNA-sequencing. Using gene ontology and biological pathway mapping, we identified a difference in cysteine and methionine metabolism between soybeans grown in eastern and western Canada that may attribute to the difference in seed protein concentration. Further, we identified differential gene expression within the oil biosynthesis pathway, specifically upregulation of lipoxygenases in western-grown soybeans, which may also influence seed composition and/or membrane fluidity. The information gained in this study is useful for marker assisted selection in soybean breeding programs across Canada and globally.

Bentazon is an effective post-emergence herbicide in soybean. A loss of function of cytochrome P450 hydroxylase encoded by a recessive gene, bzn-1 (Glyma.16G149300), is known to confer high sensitivity to bentazon, while there is natural variation causing moderate sensitivity to bentazon that cannot be accounted for by bzn-1. Here, we identified another recessive gene, bzn-2, that confers the moderate sensitivity to bentazon. The candidate region of bzn-2 was narrowed down to 92 kb on chromosome 11 by positional cloning using recombinant inbred lines from a cross between bentazon-tolerant and moderately sensitive varieties. Sequence comparison of these varieties for 12 genes located in the candidate region revealed that the moderately sensitive variety had a single-base substitution that caused a stop codon in the coding region of Glyma.11G138300. This gene encodes GRAS SCL14 (GRAS: GIBBERELLIN ACID INSENSITIVE, REPRESSOR of GA1, and SCARECROW; SCL: SCARECROW-like 14), a class II TGA transcription factor. Screening of germplasms by an amplification-refractory mutation system (ARMS) marker clearly distinguished the accessions that are moderately sensitive to bentazon. Three independent EMS mutants of Glyma.11G138300 were moderately sensitive to bentazon, strongly supporting Glyma.11G138300 as the causal gene for bzn-2. The low expression of mutated Glyma.11G138300 reduced expression of Glyma.16G149300, encoding the P450, resulting in a moderately sensitive phenotype. To the best of our knowledge, this is the first report demonstrating that natural variation in the GRAS gene family confers herbicide tolerance. Our findings are useful for understanding the mechanism of detoxification and for selection of bentazon tolerance through breeding.

AAC Springfield is a high protein soybean [ Glycine max (L.) Merr.] cultivar developed by the Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario. It is intended for production in 2400 to 2600 crop heat unit areas of Manitoba, Ontario, and Quebec. AAC Springfield has a unique combination of high protein, high 11S:7S ratio, and early maturity.

Molecular network analysis offers powerful insights for plant improvement by capturing complex regulatory interactions. However, translating omics data across species presents significant challenges. Non-model crops such as soybean and lupin often lack comprehensive genomic resources, which complicates network analysis. Model species (e.g., Arabidopsis thaliana) provide rich data but may lack legume-specific pathways. This review synthesizes these challenges and examines legume networks in soybean, lupin, and the model legume, Medicago truncatula. Strategies such as multi-omics integration and Artificial Intelligence (AI)-driven tools, combined with wet lab validation studies such as clustered regularly interspaced short palindromic repeats (CRISPR), are discussed to bridge the gap between discovery and application. Ultimately, we conclude that cross-species multi-omics integration, empowered by AI and validated by gene editing, will be pivotal for translating network discoveries into resilient legume crops. Strategic investments in under-researched non-model legumes and advanced molecular tools are essential to ensure sustainable agriculture and future crop resilience.

Soybean improvement has entered a new era with the advent of multi-omics strategies and bioinformatics innovations, enabling more precise and efficient breeding practices. This comprehensive review examines the application of multi-omics approaches in soybean—encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics. We first explore pre-breeding and genomic selection as tools that have laid the groundwork for advanced trait improvement. Subsequently, we dig into the specific contributions of each -omics field, highlighting how bioinformatics tools and resources have facilitated the generation and integration of multifaceted data. The review emphasizes the power of integrating multi-omics datasets to elucidate complex traits and drive the development of superior soybean cultivars. Emerging trends, including novel computational techniques and high-throughput technologies, are discussed in the context of their potential to revolutionize soybean breeding. Finally, we address the challenges associated with multi-omics integration and propose future directions to overcome these hurdles, aiming to accelerate the pace of soybean improvement. This review serves as a crucial resource for researchers and breeders seeking to leverage multi-omics strategies for enhanced soybean productivity and resilience.

Soybean breeding programs targeting tofu quality must evaluate their performance within zones of adaptation. A comprehensive study was carried out to examine soybean breeding lines from three maturity groups (MGs; MG0, MG00, and MG000) from 2018 to 2022. Several agronomic, chemical composition and tofu-related quality traits were evaluated, and the associations among traits were investigated. The results showed that genotypes in MG0 yielded higher and matured later, which confirmed that the selection of targeted genotypes for a specific maturity group was successful. Non-imbibed “stone seeds”, an important quality trait for tofu processors, were higher in MG000 lines. Tofu texture using both GDL and MgCl2 coagulants was positively associated, indicating one coagulant might be enough for screening purposes. The MG by traits biplot showed very clear MG clustering for all genotypes tested from 2018 to 2022, signifying that the MG has a more pronounced effect on the investigated traits than the environmental effects seen in different years, regardless of the MG. Most tofu-related traits were higher and showed stronger associations in MG0 lines compared to the lines in earlier MGs, indicating a need for future effort in shorter season MGs. Overall, this study provided useful information for selecting soybean lines for tofu end-use application targeting specific MGs.

Cold and excess moisture pose a serious threat to soybean production especially during seed germination in short-season environments. In this study, the effects of low temperature and excess moisture stress on seed germination were investigated in 187 soybean accessions originating from 18 countries. The experiment used a combination of three temperature conditions (i.e., 20 °C/14 °C, 14 °C/10 °C, and 10 °C/10 °C day/night) and two moisture levels (i.e., normal and excess). The seed germinability traits measured included germination rate (GR), germination index, germination time, germination uniformity, and coefficient of velocity of germination. Overall, GR was lowest in the 20 °C/14 °C + excess moisture and germination time was longest in the 10 °C/10 °C + excess moisture. When compared with 20 °C/14 °C + normal moisture treatment, GR at 10 °C/10 °C + excess moisture decreased by 38%; germination time increased by 20 days; seed viability decreased by 83%; germination uniformity decreased by 70%; germination speed decreased by 73%. Differences in GR, germination index, and germination velocity under different treatments were affected by temperature, moisture, and their interaction. Variation in germination time uniformity was determined by temperature, with no significant effects of moisture conditions and the interaction of temperature and moisture. It was shown that the temperature–excess moisture interaction led to a sharp decrease in seed germination. Two genotypes including PI 603147 and PI 507702 were identified with a GR over 90% at 10 °C/10 °C + excess moisture. This study generated new knowledge and data to further the understanding of genetic resistance to cold and excess moisture stress in soybean.

Soybean ( Glycine max L.) is the most important legume crop in the world and provides protein and oil for human consumption and animal feed. Cold and waterlogging or flooding are abiotic stress that are commonly encountered during soybean germination in short-season growing conditions in the Northern latitudes. Imbibition of cold water during the germination disrupts the cell membranes and increases leakage of their contents and makes seeds vulnerable to biotic stress. The cold tolerance is associated with the ability of cells to avoid or repair the damage to their membranes and organelles, restoring membrane function and metabolism, and managing the reactive oxygen species generated during the process. Excess moisture impedes aerobic respiration by oxygen deprivation and increases the likelihood of soil-borne diseases further reducing the germination rate. Tolerance to waterlogging is associated with mechanisms that slow down the rate of water uptake and help maintain efficient anaerobic metabolism. The quantitative trait loci mapping, transcriptomics, and proteomic studies have revealed several genes and pathways that likely play a role in seed response to cold and waterlogging stress. This review discusses the effects of cold and waterlogging on soybean seed germination at the physiological level, describes the molecular mechanisms involved, and provides an overview of soybean waterlogging and cold tolerance research. The methodologies commonly used to study the molecular mechanisms controlling tolerance to waterlogging and cold stress are also reviewed and discussed.

microRNAs (miRNAs) are small non-coding ribonucleic acids that post-transcriptionally regulate gene expression through the targeting of messenger RNA (mRNAs). Most miRNA target predictors have focused on animal species and prediction performance drops substantially when applied to plant species. Several rule-based miRNA target predictors have been developed in plant species, but they often fail to discover new miRNA targets with non-canonical miRNA-mRNA binding. Here, the recently published TarDB database of plant miRNA-mRNA data is leveraged to retrain the TarPmiR miRNA target predictor for application on plant species. Rigorous experiment design across four plant test species demonstrates that animal-trained predictors fail to sustain performance on plant species, and that the use of plant-specific training data improves accuracy depending on the quantity of plant training data used. Surprisingly, our results indicate that the complete exclusion of animal training data leads to the most accurate plant-specific miRNA target predictor indicating that animal-based data may detract from miRNA target prediction in plants. Our final plant-specific miRNA prediction method, dubbed P-TarPmiR, is freely available for use at http://ptarpmir.cu-bic.ca . The final P-TarPmiR method is used to predict targets for all miRNA within the soybean genome. Those ranked predictions, together with GO term enrichment, are shared with the research community.

The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection. Therefore, it is imperative that we identify additional mechanisms for SCN resistance. In this paper, we develop a bioinformatics pipeline to identify protein-protein interactions related to SCN resistance by data mining massive-scale datasets. The pipeline combines two leading sequence-based protein-protein interaction predictors, the Protein-protein Interaction Prediction Engine (PIPE), PIPE4, and Scoring PRotein INTeractions (SPRINT) to predict high-confidence interactomes. First, we predicted the top soy interacting protein partners of the Rhg1 and Rhg4 proteins. Both PIPE4 and SPRINT overlap in their predictions with 58 soybean interacting partners, 19 of which had GO terms related to defense. Beginning with the top predicted interactors of Rhg1 and Rhg4, we implement a "guilt by association" in silico proteome-wide approach to identify novel soybean genes that may be involved in SCN resistance. This pipeline identified 1,082 candidate genes whose local interactomes overlap significantly with the Rhg1 and Rhg4 interactomes. Using GO enrichment tools, we highlighted many important genes including five genes with GO terms related to response to the nematode (GO:0009624), namely, Glyma.18G029000, Glyma.11G228300, Glyma.08G120500, Glyma.17G152300, and Glyma.08G265700. This study is the first of its kind to predict interacting partners of known resistance proteins Rhg1 and Rhg4, forming an analysis pipeline that enables researchers to focus their search on high-confidence targets to identify novel SCN resistance genes in soybean.

Low seed protein content in soybeans [ Glycine max (L.) Merr.] grown in Western Canada can result in soybean meal that does not meet the 48% protein standard. The objectives of this study were to quantify seed composition, agronomic differences between Eastern and Western Canada-grown soybeans, and to determine the yield cost of raising Western soybean protein. Twenty high-to-low protein, including one non-nodulating, genotypes were grown at two locations in Eastern Canada, and eight locations in Western Canada from 2018 to 2021 to determine seed protein, seed composition, and agronomic traits. Over all environments, genotype seed protein ranged from 36.8% to 46.9% with 35.0% for the non-nodulating line. Average seed protein was significantly higher in Eastern Canada (41.6%) compared with Eastern Prairie (39.3%) and Prairie sites (39.7%). There are not separate east–west mega-environments for seed protein in Canada; a high protein genotype is high protein across Canada. With an increase of seed protein by 1%, seed yield dropped by 45.3 kg ha−1 in Eastern Canada, 53.1 kg ha−1 in the Eastern Prairie, and 78.4 kg ha−1 in Prairie sites. In Western Canada, plants were taller but lower yielding with fewer and smaller seeds, and produced lower fixed nitrogen protein yield compared with Eastern Canada. Seed protein quality, quantified with the 11S:7S ratio, was higher in Western Canada compared with Eastern Canada. Plant breeders and growers may need to select higher protein genotypes at the cost of lower yield, if the soybean industry is unable to exploit the protein quality advantage in Western Canada.

Soybean is an important global source of plant-based protein. A persistent trend has been observed over the past two decades that soybeans grown in western Canada have lower seed protein content than soybeans grown in eastern Canada. In this study, 10 soybean genotypes ranging in average seed protein content were grown in an eastern location (control) and three western locations (experimental) in Canada. Seed protein and oil contents were measured for all lines in each location. RNA-sequencing and differential gene expression analysis were used to identify differentially expressed genes that may account for relatively low protein content in western-grown soybeans. Differentially expressed genes were enriched for ontologies and pathways that included amino acid biosynthesis, circadian rhythm, starch metabolism, and lipid biosynthesis. Gene ontology, pathway mapping, and quantitative trait locus (QTL) mapping collectively provide a close inspection of mechanisms influencing nitrogen assimilation and amino acid biosynthesis between soybeans grown in the East and West. It was found that western-grown soybeans had persistent upregulation of asparaginase (an asparagine hydrolase) and persistent downregulation of asparagine synthetase across 30 individual differential expression datasets. This specific difference in asparagine metabolism between growing environments is almost certainly related to the observed differences in seed protein content because of the positive correlation between seed protein content at maturity and free asparagine in the developing seed. These results provided pointed information on seed protein-related genes influenced by environment. This information is valuable for breeding programs and genetic engineering of geographically optimized soybeans.

In Canada, the length of the frost-free season necessitates planting crops as early as possible to ensure that the plants have enough time to reach full maturity before they are harvested. Early planting carries inherent risks of cold water imbibition (specifically less than 4°C) affecting seed germination. A marker dataset developed for a previously identified Canadian soybean GWAS panel was leveraged to investigate the effect of cold water imbibition on germination. Seed from a panel of 137 soybean elite cultivars, grown in the field at Ottawa, ON, over three years, were placed on filter paper in petri dishes and allowed to imbibe water for 16 hours at either 4°C or 20°C prior to being transferred to a constant 20°C. Observations on seed germination, defined as the presence of a 1 cm radicle, were done from day two to seven. A three-parameter exponential rise to a maximum equation (3PERM) was fitted to estimate germination, time to the one-half maximum germination, and germination uniformity for each cultivar. Genotype-by-sequencing was used to identify SNPs in 137 soybean lines, and using genome-wide association studies (GWAS - rMVP R package, with GLM, MLM, and FarmCPU as methods), haplotype block analysis, and assumed linkage blocks of ±100 kbp, a threshold for significance was established using the qvalue package in R, and five significant SNPs were identified on chromosomes 1, 3, 4, 6, and 13 for maximum germination after cold water imbibition. Percent of phenotypic variance explained (PVE) and allele substitution effect (ASE) eliminated two of the five candidate SNPs, leaving three QTL regions on chromosomes 3, 6, and 13 (Chr3-3419152, Chr6-5098454, and Chr13-29649544). Based on the gene ontology (GO) enrichment analysis, 14 candidate genes whose function is predicted to include germination and cold tolerance related pathways were identified as candidate genes. The identified QTLs can be used to select future soybean cultivars tolerant to cold water imbibition and mitigate risks associated with early soybean planting.