Genome-wide Scan for Seed Composition Provides Insights into Soybean Quality Improvement and the Impacts of Domestication and Breeding

Soybean genome‑wide association study of seed weight, protein, and oil content in the southeastern USA

Soybean is a globally significant legume crop, providing essential protein and oil for human and livestock nutrition. Improving oil and protein content simultaneously without compromising yield has been challenging due to the quantitative nature of these traits and their interrelationships. This study aims to deepen our understanding of the molecular basis soybean of seed weight, protein, and oil content to facilitate marker-assisted breeding to enhance these traits. In this research, a Genome-Wide Association Study (GWAS) was conducted utilizing 285 diverse soybean accessions from maturity group V, employing genotyping through the SoySNP50K platform. These accessions were tested in three environmental conditions of the southeast US for three traits: 100-seed weight, protein, and oil content. The study identified 18, 23, and 26 SNPs significantly associated with 100-seed weight, seed oil, and protein content. Colocalized protein and oil content regions were discovered on chromosomes 15, 16, and 20. Chromosomes 15 and 20 are well documented to have pleiotropic but opposite effects on oil and protein content, but both regions contain genes that affect individual traits, such as FAD2-1 and nodulin MtN21. A 1.92 Mb region on chromosome 11 exhibits a target region to improve oil and seed weight without affecting protein content. This study highlights key genomic regions and candidate genes influencing seed weight, protein, and oil content, with some regions affecting multiple traits. Hence, these findings provide a valuable foundation for marker-assisted selection to optimize seed weight and simultaneously enhance oil and protein content in soybean breeding programs.

Large-scale metabolomic landscape of edible maize reveals convergent changes in metabolite differentiation and facilitates its breeding improvement

Edible maize is an important food crop that provides energy and nutrients to meet human health and nutritional requirements. However, how environmental pressures and human activity have shaped the metabolome of edible maize remains unclear. In this study, we collected 452 diverse edible maize accessions worldwide, including waxy, sweet, and field maize. A total of 3020 non-redundant metabolites, including 802 annotated metabolites, were identified using a two-step optimized approach, which generated the most comprehensive annotated metabolite dataset in plants to date. Although specific metabolite differentiation was detected between field and sweet maize and between field and waxy maize, convergent metabolite differentiation was the dominant pattern. We identified hub genes in all metabolite classes by hotspot analysis in a metabolite genome-wide association study. Seventeen and 15 hub genes were selected as the key differentiation genes for flavonoids and lipids, respectively. Surprisingly, almost all of these genes were under diversifying selection, suggesting that diversifying selection was the main genetic mechanism of convergent metabolic differentiation. Further genetic and molecular studies revealed the roles and genetic diversifying selection mechanisms of ZmGPAT11 in convergent metabolite differentiation in the lipid pathway. On the basis of our research, we established the first edible maize metabolome database, EMMDB (https://www.maizemdb.site/home/). We successfully used EMMDB for precision improvement of nutritional and flavor traits and bred the elite inbred line 6644_2, with greatly increased contents of flavonoids, lysophosphatidylcholines, lysophosphatidylethanolamines, and vitamins. Collectively, our study sheds light on the genetic mechanisms of metabolite differentiation in edible maize and provides a database for breeding improvement of flavor and nutritional traits in edible maize by metabolome precision design.

Nutritional and Functional Characterization of Flour from Seeds of Chañar (Geoffroea decorticans) to Promote Its Sustainable Use

Geoffroea decorticans (Gill. ex Hook. & Arn) Burk. is a native tree of the dry areas of Northwestern and Central Argentina. Its seeds are considered waste material. The flour of seeds was analyzed as a source of nutritional and bioactive compounds. It has a low carbohydrate content, containing about 9% protein and between 10 and 14% fat. Approximately 82-84% of the fatty acids were unsaturated (oleic and linoleic acids). A high polyphenol and dietary fiber content was detected. Flavonoids and condensed tannins were the dominant phenolics. Polyphenol-enriched extracts were obtained from seed flour. The HPLC-ESI-MS/MS analysis of these concentrated extracts allowed for the identification of six compounds including C-glycosyl flavones (vitexin and isovitexin), type A procyanidins (dimer and trimer), and epicatequin gallate. Polyphenolic extracts showed antioxidant capacity and were able to inhibit enzymes (α-glucosidase and α-amylase) related to carbohydrate metabolism and (lipoxygenase) pro-inflammatory enzymes and were not toxic. Flour and polyphenolic extract from chañar seeds could be considered as new alternative ingredients for the formulation of functional foods, nutraceuticals, or food supplements. The use of the seed flour in addition to the pulp of the fruit along with the rest of the plant would encourage the propagation of this species resistant to extreme arid environments for commercial and conservation purposes to boost the regional economies of vulnerable areas of South America.

Detection of genes associated with soybean protein content using a genome-wide association study

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.

Genomic and phenomic prediction for soybean seed yield, protein, and oil

Developments in genomics and phenomics have provided valuable tools for use in cultivar development. Genomic prediction (GP) has been used in commercial soybean [Glycine max L. (Merr.)] breeding programs to predict grain yield and seed composition traits. Phenomic prediction (PP) is a rapidly developing field that holds the potential to be used for the selection of genotypes early in the growing season. The objectives of this study were to compare the performance of GP and PP for predicting soybean seed yield, protein, and oil. We additionally conducted genome-wide association studies (GWAS) to identify significant single-nucleotide polymorphisms (SNPs) associated with the traits of interest. The GWAS panel of 292 diverse accessions was grown in six environments in replicated trials. Spectral data were collected at two time points during the growing season. A genomic best linear unbiased prediction (GBLUP) model was trained on 269 accessions, while three separate machine learning (ML) models were trained on vegetation indices (VIs) and canopy traits. We observed that PP had a higher correlation coefficient than GP for seed yield, while GP had higher correlation coefficients for seed protein and oil contents. VIs with high feature importance were used as covariates in a new GBLUP model, and a new random forest model was trained with the inclusion of selected SNPs. These models did not outperform the original GP and PP models. These results show the capability of using ML for in-season predictions for specific traits in soybean breeding and provide insights on PP and GP inclusions in breeding programs.

Network analysis combined with genome-wide association study helps identification of genes related to amino acid contents in soybean

BACKGROUND

Additional to total protein content, the amino acid (AA) profile is important to the nutritional value of soybean seed. The AA profile in soybean seed is a complex quantitative trait controlled by multiple interconnected genes and pathways controlling the accumulation of each AA. With a total of 621 soybean germplasm, we used three genome-wide association study (GWAS)-based approaches to investigate the genomic regions controlling the AA content and profile in soybean. Among those approaches, the GWAS network analysis we implemented takes advantage of the relationships between specific AAs to identify the genetic control of AA profile.

RESULTS

For Approach I, GWAS were performed for the content of 24 single AAs under all environments combined. Significant SNPs grouping into 16 linkage disequilibrium (LD) blocks from 18 traits were identified. For Approach II, the individual AAs were grouped by five families according to their metabolic pathways and were examined based on the sum, ratios, and interactions of AAs within the same biochemical family. Significant SNPs grouping into 35 LD blocks were identified, with SNPs associated with traits from the same biochemical family often positioned on the same LD blocks. Approach III, a correlation-based network analysis, was performed to assess the empirical relationships among AAs. Two groups were described by the network topology, Group 1: Ala, Gly, Lys, available Lys (Alys), and Thr and Group 2: Ile and Tyr. Significant SNPs associated with a ratio of connected AAs or a ratio of a single AA to its fully or partially connected metabolic groups were identified within 9 LD blocks for Group 1 and 2 LD blocks for Group 2. Among 40 identified QTL for AA or AA-derived traits, three genomic regions were novel in terms of seed composition traits (oil, protein, and AA content). An additional 24 regions had previously not been specifically associated with the AA content.

CONCLUSIONS

Our results confirmed loci identified from previous studies but also suggested that network approaches for studying AA contents in soybean seed are valuable. Three genomic regions (Chr 5: 41,754,397-41,893,109 bp, Chr 9: 1,537,829-1,806,586 bp, and Chr 20: 31,554,795-33,678,257 bp) were significantly identified by all three approaches. Yet, the majority of associations between a genomic region and an AA trait were approach- and/or environment-specific. Using a combination of approaches provides insights into the genetic control and pleiotropy among AA contents, which can be applied to mechanistic understanding of variation in AA content as well as tailored nutrition in cultivars developed from soybean breeding programs.

The amino acid permease SlAAP6 contributes to tomato growth and salt tolerance by mediating branched-chain amino acid transport

Branched-chain amino acids (BCAAs) are essential amino acids in tomato (Solanum lycopersicum) required for protein synthesis, which also modulate growth and abiotic stress responses. To date, little is known about their uptake and transport in tomato especially under abiotic stress. Here, the tomato amino acid permease 6 (SlAAP6) gene was identified as an amino acid transporter that restored mutant yeast cell growth on media with a variety of amino acids, including BCAAs. Overexpression of SlAAP6 (SlAAP6-OE) in tomato raised the BCAA content and elevated the fresh weight, while SlAAP6 knockouts (slaap6) showed reduced levels of neutral and basic amino acids in seedling tissues and lower total free amino acid distribution to shoots. In comparison to wild type and slaap6 mutants, SlAAP6-OE alleviated root limited growth by elevated BCAA transport and upregulated the expression of root-growth-related genes by increasing BCAAs in vivo. As SlAAP6 serves as a positive regulator for BCAA abundance, SlAAP6-OE lines showed greater salinity tolerance, while slaap6 mutants exhibited increased salt sensitivity. The salt tolerance of SlAAP6-OE plants was further enhanced by the application of exogenous BCAAs. In addition, BCAA supplementation reduced the accumulation of H2O2 in root under salt stress conditions. Based on these findings, SlAAP6-mediated uptake and transport of BCAAs facilitated growth and salt tolerance in tomato. By characterizing this key amino acid transporter, this study provides a novel approach to simultaneously enhance tomato nutritional quality, growth and development, and stress resistance through genetic improvement.

Genome-wide association study reveals the genetic basis of amino acids contents variations in Peanut (Arachis hypogaea L.)

Peanut is a significant source of protein for human consumption. One of the primary objectives in peanut breeding is the development of new cultivars with enhanced nutritional values. To further this goal, a genome-wide association study (GWAS) was conducted to analyze seed amino acids contents in 390 diverse peanut accessions collected worldwide, mainly from China, India, and the United States, in 2017 and 2018. These accessions were assessed for their content of 10 different amino acids. Variations in amino acids contents were observed, and arginine (Arg) was found to have the highest average value among all the amino acids quantified. The geographical distribution of the accessions also revealed variations in amino acids contents. High and positive correlation coefficients were observed among the amino acids, suggesting linked metabolic pathways or genetic regulation. A total of 88 single nucleotide polymorphisms (SNPs) spanning various chromosomes were identified, each associated with different amino acids. By using a combination of GWAS, expression anlaysis, and genomic polymorphisim comparisions, the Ahy_A09g041582 (LAC15) gene located on chromrosome A09 was identified as the key candidate which might be involved in plant growth and regulation and may alter amino acids levels. Expression analysis indicated that Ahy_A09g041582 has higher expressions in the shells and seeds than other genes located in the candidate region. This study may help with marker-based breeding of peanuts with higher nutritional value and offers fresh insights into the genetic basis of the amino acids contents of peanuts.

Identification of candidate genes and genomic prediction of soybean fatty acid components in two soybean populations

Soybean, a source of plant-derived lipids, contains an array of fatty acids essential for health. A comprehensive understanding of the fatty acid profiles in soybean is crucial for enhancing soybean cultivars and augmenting their qualitative attributes. Here, 180 F10 generation recombinant inbred lines (RILs), derived from the cross-breeding of the cultivated soybean variety 'Jidou 12' and the wild soybean 'Y9,' were used as primary experimental subjects. Using inclusive composite interval mapping (ICIM), this study undertook a quantitative trait locus (QTL) analysis on five distinct fatty acid components in the RIL population from 2019 to 2021. Concurrently, a genome-wide association study (GWAS) was conducted on 290 samples from a genetically diverse natural population to scrutinize the five fatty acid components during the same timeframe, thereby aiming to identify loci closely associated with fatty acid profiles. In addition, haplotype analysis and the Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed to predict candidate genes. The QTL analysis elucidated 23 stable QTLs intricately associated with the five fatty acid components, exhibiting phenotypic contribution rates ranging from 2.78% to 25.37%. In addition, GWAS of the natural population unveiled 102 significant loci associated with these fatty acid components. The haplotype analysis of the colocalized loci revealed that Glyma.06G221400 on chromosome 6 exhibited a significant correlation with stearic acid content, with Hap1 showing a markedly elevated stearic acid level compared with Hap2 and Hap3. Similarly, Glyma.12G075100 on chromosome 12 was significantly associated with the contents of oleic, linoleic, and linolenic acids, suggesting its involvement in fatty acid biosynthesis. In the natural population, candidate genes associated with the contents of palmitic and linolenic acids were predominantly from the fatty acid metabolic pathway, indicating their potential role as pivotal genes in the critical steps of fatty acid metabolism. Furthermore, genomic selection (GS) for fatty acid components was conducted using ridge regression best linear unbiased prediction based on both random single nucleotide polymorphisms (SNPs) and SNPs significantly associated with fatty acid components identified by GWAS. GS accuracy was contingent upon the SNP set used. Notably, GS efficiency was enhanced when using SNPs derived from QTL mapping analysis and GWAS compared with random SNPs, and reached a plateau when the number of SNP markers exceeded 3,000. This study thus indicates that Glyma.06G221400 and Glyma.12G075100 are genes integral to the synthesis and regulatory mechanisms of fatty acids. It provides insights into the complex biosynthesis and regulation of fatty acids, with significant implications for the directed improvement of soybean oil quality and the selection of superior soybean varieties. The SNP markers delineated in this study can be instrumental in establishing an efficacious pipeline for marker-assisted selection and GS aimed at improving soybean fatty acid components.

MutL homolog 1 participates in interference-sensitive meiotic crossover formation in soybean

MutL homolog 1 (MLH1), a member of the MutL homolog family, is required for normal recombination in most organisms. However, its role in soybean (Glycine max) remains unclear to date. Here, we characterized the Glycine max female and male sterility 1 (Gmfms1) mutation that reduces pollen grain viability and increases embryo sac abortion in soybean. Map-based cloning revealed that the causal gene of Gmfms1 is Glycine max MutL homolog 1 (GmMLH1), and CRISPR/Cas9 knockout approach further validated that disruption of GmMLH1 confers the female-male sterility phenotype in soybean. Loss of GmMLH1 function disrupted bivalent formation, leading to univalent mis-segregation during meiosis and ultimately to female-male sterility. The Gmmlh1 mutant showed about a 78.16% decrease in meiotic crossover frequency compared to the wild type. The residual chiasmata followed a Poisson distribution, suggesting that interference-sensitive crossover formation was affected in the Gmmlh1 mutant. Furthermore, GmMLH1 could interact with GmMLH3A and GmMLH3B both in vivo and in vitro. Overall, our work demonstrates that GmMLH1 participates in interference-sensitive crossover formation in soybean, and provides additional information about the conserved functions of MLH1 across plant species.

Across-environment seed protein stability and genetic architecture of seed components in soybean

The recent surge in the plant-based protein market has resulted in high demands for soybean genotypes with improved grain yield, seed protein and oil content, and essential amino acids (EAAs). Given the quantitative nature of these traits, complex interactions among seed components, as well as between seed components and environmental factors and management practices, add complexity to the development of desired genotypes. In this study, the across-environment seed protein stability of 449 genetically diverse plant introductions was assessed, revealing that genotypes may display varying sensitivities to such environmental stimuli. The EAAs valine, phenylalanine, and threonine showed the highest variable importance toward the variation in stability, while both seed protein and oil contents were among the explanatory variables with the lowest importance. In addition, 56 single nucleotide polymorphism (SNP) markers were significantly associated with various seed components. Despite the strong phenotypic Pearson's correlation observed among most seed components, many independent genomic regions associated with one or few seed components were identified. These findings provide insights for improving the seed concentration of specific EAAs and reducing the negative correlation between seed protein and oil contents.

Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content

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.

Integrating Genome-Wide Association Study, Transcriptome and Metabolome Reveal Novel QTL and Candidate Genes That Control Protein Content in Soybean

Protein content (PC) is crucial to the nutritional quality of soybean [Glycine max (L.) Merrill]. In this study, a total of 266 accessions were used to perform a genome-wide association study (GWAS) in three tested environments. A total of 23,131 high-quality SNP markers (MAF ≥ 0.02, missing data ≤ 10%) were identified. A total of 40 association signals were significantly associated with PC. Among them, five novel quantitative trait nucleotides (QTNs) were discovered, and another 32 QTNs were found to be overlapping with the genomic regions of known quantitative trait loci (QTL) related to soybean PC. Combined with GWAS, metabolome and transcriptome sequencing, 59 differentially expressed genes (DEGs) that might control the change in protein content were identified. Meantime, four commonly upregulated differentially abundant metabolites (DAMs) and 29 commonly downregulated DAMs were found. Remarkably, the soybean gene Glyma.08G136900, which is homologous with Arabidopsis hydroxyproline-rich glycoproteins (HRGPs), may play an important role in improving the PC. Additionally, Glyma.08G136900 was divided into two main haplotype in the tested accessions. The PC of haplotype 1 was significantly lower than that of haplotype 2. The results of this study provided insights into the genetic mechanisms regulating protein content in soybean.

Role of particle size-dependent copper bioaccumulation-mediated oxidative stress on Glycine max (L.) yield parameters with soil-applied copper oxide nanoparticles

Increased impetus on the application of nano-fertilizers to improve sustainable food production warrants understanding of nanophytotoxicity and its underlying mechanisms before its application could be fully realized. In this study, we evaluated the potential particle size-dependent effects of soil-applied copper oxide nanoparticles (nCuO) on crop yield and quality attributes (photosynthetic pigments, seed yield and nutrient quality, seed protein, and seed oil), including root and seed Cu bioaccumulation and a suite of oxidative stress biomarkers, in soybean (Glycine max L.) grown in field environment. We synthesized three distinct sized (25 nm = S [small], 50 nm = M [medium], and 250 nm = L [large]) nCuO with same surface charge and compared with soluble Cu2+ ions (CuCl2) and water-only controls. Results showed particle size-dependent effects of nCuO on the photosynthetic pigments (Chla and Chlb), seed yield, potassium and phosphorus accumulation in seed, and protein and oil yields, with nCuO-S showing higher inhibitory effects. Further, increased root and seed Cu bioaccumulation led to concomitant increase in oxidative stress (H2O2, MDA), and as a response, several antioxidants (SOD, CAT, POX, and APX) increased proportionally, with nCuO treatments including Cu2+ ion treatment. These results are corroborated with TEM ultrastructure analysis showing altered seed oil bodies and protein storage vacuoles with nCuO-S treatment compared to control. Taken together, we propose particle size-dependent Cu bioaccumulation-mediated oxidative stress as a mechanism of nCuO toxicity. Future research investigating the potential fate of varied size nCuO, with a focus on speciation at the soil-root interface, within the root, and edible parts such as seed, will guide health risk assessment of nCuO.

Genomic analysis and characterization of new loci associated with seed protein and oil content in soybeans

Breeding for increased protein without a reduction in oil content in soybeans [Glycine max (L.) Merr.] is a challenge for soybean breeders but an expected goal. Many efforts have been made to develop new soybean varieties with high yield in combination with desirable protein and/or oil traits. An elite line, R05-1415, was reported to be high yielding, high protein, and low oil. Several significant quantitative trait loci (QTL) for protein and oil were reported in this line, but many of them were unstable across environments or genetic backgrounds. Thus, a new study under multiple field environments using the Infinium BARCSoySNP6K BeadChips was conducted to detect and confirm stable genomic loci for these traits. Genetic analyses consistently detected a single major genomic locus conveying these two traits with remarkably high phenotypic variation explained (R2 ), varying between 24.2% and 43.5%. This new genomic locus is located between 25.0 and 26.7 Mb, distant from the previously reported QTL and did not overlap with other commonly reported QTL and the recently cloned gene Glyma.20G085100. Homolog analysis indicated that this QTL did not result from the paracentric chromosome inversion with an adjacent genomic fragment that harbors the reported QTL. The pleiotropic effect of this QTL could be a challenge for improving protein and oil simultaneously; however, a further study of four candidate genes with significant expressions in the seed developmental stages coupled with haplotype analysis may be able to pinpoint causative genes. The functionality and roles of these genes can be determined and characterized, which lay a solid foundation for the improvement of protein and oil content in soybeans.

A loss-of-function mutant allele of a glycosyl hydrolase gene has been co-opted for seed weight control during soybean domestication

The resultant DNA from loss-of-function mutation can be recruited in biological evolution and development. Here, we present such a rare and potential case of "to gain by loss" as a neomorphic mutation during soybean domestication for increasing seed weight. Using a population derived from a chromosome segment substitution line of Glycine max (SN14) and Glycine soja (ZYD06), a quantitative trait locus (QTL) of 100-seed weight (qHSW) was mapped on chromosome 11, corresponding to a truncated β-1, 3-glucosidase (βGlu) gene. The novel gene hsw results from a 14-bp deletion, causing a frameshift mutation and a premature stop codon in the βGlu. In contrast to HSW, the hsw completely lost βGlu activity and function but acquired a novel function to promote cell expansion, thus increasing seed weight. Overexpressing hsw instead of HSW produced large soybean seeds, and surprisingly, truncating hsw via gene editing further increased the seed size. We further found that the core 21-aa peptide of hsw and its variants acted as a promoter of seed size. Transcriptomic variation in these transgenic soybean lines substantiated the integration hsw into cell and seed size control. Moreover, the hsw allele underwent selection and expansion during soybean domestication and improvement. Our work cloned a likely domesticated QTL controlling soybean seed weight, revealed a novel genetic variation and mechanism in soybean domestication, and provided new insight into crop domestication and breeding, and plant evolution.

Development of a multiplex real-time PCR method for the detection of Pseudomonas savastanoi pv. glycinea and Curtobacterium flaccumfaciens pv. flaccumfaciens in soybean seeds

Multiplex real-time PCR with TaqMan® probes has been developed for the simultaneous detection of soybean pathogens Pseudomonas savastanoi pv. glycinea and Curtobacterium flaccumfaciens pv. flaccumfaciens. The method specificity has been confirmed using 25 strains of target bacteria and 18 strains of other bacteria common to soybean seeds as endophytes. The multiplex real-time PCR developed has been shown to have high sensitivity - a positive result was achieved at 0.01 ng/µl of DNA for both target organisms, and at 100 CFU/ml of bacteria in soybean seed homogenate. The robustness of the multiplex real-time PCR developed has been verified by the detection of the pathogens in 25 commercial seed stocks, in comparison with previously published PCR protocols. In all tests, three seed stocks were positive and 22 were negative. The multiplex real-time PCR can be applied in diagnostic practice for the simultaneous detection of two important pathogens of leguminous plants.

Differential gene expression provides leads to environmentally regulated soybean seed protein content

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.

Physiological and Agronomic Responses and Nutrient Uptake of Soybean Genotypes Cultivated Under Various Sowing Dates

Late or early sowing subjecting crop plants to stress conditions, this is simulating the climatic change effects. The global warming and climate change are critical issues in agriculture since progressive rise in temperature leads to exposure the crops to heat stress, hence low productivity. Since weather conditions are uncontrollable, it is impossible to modulate their negative impacts against crop growth and development. However, scientists should not be handcuffed about this serious problem. So, in open field conditions, the performance of some soybean genotypes was evaluated under different sowing dates. Along the two seasons of 2019 and 2020, field experiments were designed in a split-plot design using three replicates to evaluate the performance of four soybean genotypes (Giza-21, Giza-35, Giza-111, and Crawford) under four sowing dates (15th April, 30th April, 15th May, and 30th May). Various physiological and growth traits, yield attributes, seed nutrient contents, and oil and protein contents were estimated. Sowing Crawford (in both seasons) and Giza-35 (in the first season) on 15th April as well as Giza-111 either on 30th April or 15th May produced the highest catalase activity. In plots sown on 30th April, Crawford and Giza-21 (in the first season) and Giza-111 (in both seasons) exhibited the highest leaves area plant−1. Plots sown by Giza-111 on 30th April was the potent interaction for enhancing seed yield in both seasons. Under any sowing date in the second season and the sowing date of 30th April in the first season, Giza-111 was the effective genotype for recording the maximum seed oil content. For adopting a specific stress condition scenario, it is advisable to insert Giza-111 as an effective gene pool to improve soybean genotypes under unfavorable conditions, expressed in sowing dates.

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits

Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.

Genome-Wide Association Analysis for Resistance to Coniothyrium glycines Causing Red Leaf Blotch Disease in Soybean

Soybean is a high oil and protein-rich legume with several production constraints. Globally, several fungi, viruses, nematodes, and bacteria cause significant yield losses in soybean. Coniothyrium glycines (CG), the causal pathogen for red leaf blotch disease, is the least researched and causes severe damage to soybean. The identification of resistant soybean genotypes and mapping of genomic regions associated with resistance to CG is critical for developing improved cultivars for sustainable soybean production. This study used single nucleotide polymorphism (SNP) markers generated from a Diversity Arrays Technology (DArT) platform to conduct a genome-wide association (GWAS) analysis of resistance to CG using 279 soybean genotypes grown in three environments. A total of 6395 SNPs was used to perform the GWAS applying a multilocus model Fixed and random model Circulating Probability Unification (FarmCPU) with correction of the population structure and a statistical test p-value threshold of 5%. A total of 19 significant marker-trait associations for resistance to CG were identified on chromosomes 1, 5, 6, 9, 10, 12, 13, 15, 16, 17, 19, and 20. Approximately 113 putative genes associated with significant markers for resistance to red leaf blotch disease were identified across soybean genome. Positional candidate genes associated with significant SNP loci-encoding proteins involved in plant defense responses and that could be associated with soybean defenses against CG infection were identified. The results of this study provide valuable insight for further dissection of the genetic architecture of resistance to CG in soybean. They also highlight SNP variants and genes useful for genomics-informed selection decisions in the breeding process for improving resistance traits in soybean.

Genetic analysis of protein content and oil content in soybean by genome-wide association study

Soybean seed protein content (PC) and oil content (OC) have important economic value. Detecting the loci/gene related to PC and OC is important for the marker-assisted selection (MAS) breeding of soybean. To detect the stable and new loci for PC and OC, a total of 320 soybean accessions collected from the major soybean-growing countries were used to conduct a genome-wide association study (GWAS) by resequencing. The PC ranged from 37.8% to 46.5% with an average of 41.1% and the OC ranged from 16.7% to 22.6% with an average of 21.0%. In total, 23 and 29 loci were identified, explaining 3.4%-15.4% and 5.1%-16.3% of the phenotypic variations for PC and OC, respectively. Of these, eight and five loci for PC and OC, respectively, overlapped previously reported loci and the other 15 and 24 loci were newly identified. In addition, nine candidate genes were identified, which are known to be involved in protein and oil biosynthesis/metabolism, including lipid transport and metabolism, signal transduction, and plant development pathway. These results uncover the genetic basis of soybean protein and oil biosynthesis and could be used to accelerate the progress in enhancing soybean PC and OC.

Genome resequencing reveals genetic loci and genes conferring resistance to SMV-SC8 in soybean

KEY MESSAGE

A soybean natural population genotyped by resequencing and another RIL population genotyped by SoySNP6K were used to explore consistent genetic loci and genes under greenhouse- and field-conditions for SMV-SC8 resistance. Soybean mosaic virus (SMV) is a member of the genus Potyvirus that occurs in all soybean-growing areas of the world and causes serious losses of yield and seed quality. In this study, a natural population composed of 209 accessions resequenced at an average depth of 18.44 × and another RIL population containing 193 lines were used to explore genetic loci and genes conferring resistance to SMV-SC8. There were 3030 SNPs significantly associated with resistance to SC8 on chromosome 13 in the natural population, among which 327 SNPs were located within an ~ 0.14 Mb region (from 28.46 to 28.60 Mb) of the major QTL qRsc8F in the RIL population. Two genes among 21 candidate genes, GmMACPF1 and GmRad60, were identified in the region of consistent linkage and association. Compared to the mock control, the changes in the expression of these two genes after inoculation with SC8 differed between resistant and susceptible accessions. More importantly, GmMACPF1 was shown to confer resistance to SC8 by significantly decreasing virus content in soybean hairy roots overexpressing this gene. A functional marker, FMSC8, was developed based on the allelic variation of GmMACPF1, and a high coincidence rate of 80.19% between the disease index and marker genotype was identified in the 419 soybean accessions. The results provide valuable resources for studies on the molecular mechanism of SMV resistance and genetic improvement in soybean.

Genetic mapping and functional genomics of soybean seed protein

Soybean is an utterly important crop for high-quality meal protein and vegetative oil. Soybean seed protein content has become a key factor in nutrients for livestock feed as well as human dietary consumption. Genetic improvement of soybean seed protein is highly desired to meet the demands of rapidly growing world population. Molecular mapping and genomic analysis in soybean have identified many quantitative trait loci (QTL) underlying seed protein content control. Exploring the mechanisms of seed storage protein regulation will be helpful to achieve the improvement of protein content. However, the practice of breeding higher protein soybean is challenging because soybean seed protein is negatively correlated with seed oil content and yield. To overcome the limitation of such inverse relationship, deeper insights into the property and genetic control of seed protein are required. Recent advances of soybean genomics have strongly enhanced the understandings for molecular mechanisms of soybean with better seed quality. Here, we review the research progress in the genetic characteristics of soybean storage protein, and up-to-date advances of molecular mappings and genomics of soybean protein. The key factors underlying the mechanisms of the negative correlation between protein and oil in soybean seeds are elaborated. We also briefly discuss the future prospects of breaking the bottleneck of the negative correlation to develop high protein soybean without penalty of oil and yield.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01373-5.

Genetic regulatory networks of soybean seed size, oil and protein contents

As a leading oilseed crop that supplies plant oil and protein for daily human life, increasing yield and improving nutritional quality (high oil or protein) are the top two fundamental goals of soybean breeding. Seed size is one of the most critical factors determining soybean yield. Seed size, oil and protein contents are complex quantitative traits governed by genetic and environmental factors during seed development. The composition and quantity of seed storage reserves directly affect seed size. In general, oil and protein make up almost 60% of the total storage of soybean seed. Therefore, soybean's seed size, oil, or protein content are highly correlated agronomical traits. Increasing seed size helps increase soybean yield and probably improves seed quality. Similarly, rising oil and protein contents improves the soybean's nutritional quality and will likely increase soybean yield. Due to the importance of these three seed traits in soybean breeding, extensive studies have been conducted on their underlying quantitative trait locus (QTLs) or genes and the dissection of their molecular regulatory pathways. This review summarized the progress in functional genome controlling soybean seed size, oil and protein contents in recent decades, and presented the challenges and prospects for developing high-yield soybean cultivars with high oil or protein content. In the end, we hope this review will be helpful to the improvement of soybean yield and quality in the future breeding process.

Protective Properties of Copper-Loaded Chitosan Nanoparticles against Soybean Pathogens Pseudomonas savastanoi pv. glycinea and Curtobacterium flaccumfaciens pv. flaccumfaciens

Soybeans are a valuable food product, containing 40% protein and a large percentage of unsaturated fatty acids ranging from 17 to 23%. Pseudomonas savastanoi pv. glycinea (Psg) and Curtobacterium flaccumfaciens pv. flaccumfaciens (Cff) are harmful bacterial pathogens of soybean. The bacterial resistance of soybean pathogens to existing pesticides and environmental concerns requires new approaches to control bacterial diseases. Chitosan is a biodegradable, biocompatible and low-toxicity biopolymer with antimicrobial activity that is promising for use in agriculture. In this work, a chitosan hydrolysate and its nanoparticles with copper were obtained and characterized. The antimicrobial activity of the samples against Psg and Cff was studied using the agar diffusion method, and the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined. The samples of chitosan and copper-loaded chitosan nanoparticles (Cu²⁺ChiNPs) significantly inhibited bacterial growth and were not phytotoxic at the concentrations of the MIC and MBC values. The protective properties of chitosan hydrolysate and copper-loaded chitosan nanoparticles against soybean bacterial diseases were tested on plants in an artificial infection. It was demonstrated that the Cu²⁺ChiNPs were the most effective against Psg and Cff. Treatment of pre-infected leaves and seeds demonstrated that the biological efficiencies of (Cu²⁺ChiNPs) were 71% and 51% for Psg and Cff, respectively. Copper-loaded chitosan nanoparticles are promising as an alternative treatment for bacterial blight and bacterial tan spot and wilt in soybean.

Genome-Wide Detection of Quantitative Trait Loci and Prediction of Candidate Genes for Seed Sugar Composition in Early Mature Soybean

Seed sugar composition, mainly including fructose, glucose, sucrose, raffinose, and stachyose, is an important indicator of soybean [Glycine max (L.) Merr.] seed quality. However, research on soybean sugar composition is limited. To better understand the genetic architecture underlying the sugar composition in soybean seeds, we conducted a genome-wide association study (GWAS) using a population of 323 soybean germplasm accessions which were grown and evaluated under three different environments. A total of 31,245 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAFs) ≥ 5% and missing data ≤ 10% were selected and used in the GWAS. The analysis identified 72 quantitative trait loci (QTLs) associated with individual sugars and 14 with total sugar. Ten candidate genes within the 100 Kb flanking regions of the lead SNPs across six chromosomes were significantly associated with sugar contents. According to GO and KEGG classification, eight genes were involved in the sugar metabolism in soybean and showed similar functions in Arabidopsis. The other two, located in known QTL regions associated with sugar composition, may play a role in sugar metabolism in soybean. This study advances our understanding of the genetic basis of soybean sugar composition and facilitates the identification of genes controlling this trait. The identified candidate genes will help improve seed sugar composition in soybean.

MOTHER-OF-FT-AND-TFL1 regulates the seed oil and protein content in soybean

Soybean is a major crop that produces valuable seed oil and protein for global consumption. Seed oil and protein are regulated by complex quantitative trait loci (QTLs) and have undergone intensive selections during the domestication of soybean. It is essential to identify the major genetic components and understand their mechanism behind seed oil and protein in soybean. We report that MOTHER-OF-FT-AND-TFL1 (GmMFT) is the gene of a classical QTL that has been reported to regulate seed oil and protein content in many studies. Mutation of MFT decreased seeds oil content and weight in both Arabidopsis and soybean, whereas increased expression of GmMFT enhanced seeds oil content and weight. Haplotype analysis showed that GmMFT has undergone selection, which resulted in the extended haplotype homozygosity in the cultivated soybean and the enriching of the oil-favorable allele in modern soybean cultivars. This work unraveled the GmMFT-mediated mechanism regulating seed oil and protein content and seed weight, and revealed a previously unknown function of MFT that provides new insights into targeted soybean improvement and breeding.

Identification and Mapping of QTLs for Sulfur-Containing Amino Acids in Soybean (Glycine max L.)

Soybean is a major source of high-quality protein for humans and animals. The content of sulfur-containing amino acids (SAA) in soybean is insufficient, which has become the main factor limiting soybean nutrition. In this study, we used the high-density genetic maps derived from Guizao 1 and Brazil 13 to evaluate the quantitative trait loci of cysteine (Cys), methionine (Met), SAA, glycinin (7S), β-conglycinin (11S), ratio of glycinin to β-conglycinin (RGC), and protein content (PC). In genetic map linkage analysis, the major and stable 44 QTLs were detected, which shared nine bin intervals. Among them, the bin interval (bin157-bin160) on chromosome 5 was detected in multiple environments as a stable QTL, which was linked to 11S, 7S, RGC, and SSA. Based on the analysis of bioinformatics and RNA-sequencing data, 16 differentially expressed genes (DEGs) within these QTLs were selected as candidate genes. These results will help to elucidate the genetic mechanism of soybean SAA-related traits and provide the basis for the gene mining of sulfur-containing amino acids.

Genetic dissection reveals the complex architecture of amino acid composition in soybean seeds

Five loci related to soybean protein and amino acid contents were colocated by performing linkage mapping and GWAS. The haplotype analysis showed that Glyma.08G109100 may be useful to improve the soybean seed composition. Soybean (Glycine max (L.) Merr.) seeds are good protein sources. Although genetic variation is abundant, natural variation in seed amino acids and their derived traits is lacking across soybean accessions. Here, we determined the contents of protein and 17 amino acids, obtained 36 derived traits based on the protein and total amino acid contents, and derived 34 traits based on seven amino acid family groups. Furthermore, we performed a linkage analysis of the contents of 17 amino acids and 73 amino acid-derived traits based on the recombinant inbred line (RIL)-derived Kefeng No. 1 × Nannong 1138-2. Six hundred thirty-nine quantitative trait loci (QTLs) were identified, explaining 6.07-39.00% of the phenotypic variation. Among these loci, five were detected in diverse soybean accessions using a genome-wide association study. A network analysis revealed that some loci that were significantly associated with multiple amino acids were tightly linked on chromosome 8 based on linkage disequilibrium values, which also further confirmed the results of the correlation analysis among amino acid traits. Through a combination of a genome-wide association study, linkage analysis, qRT-PCR, and genomic polymorphism comparison, Glyma.08G109100 on chromosome 8, which may affect amino acid contents, was selected. The haplotype analysis showed that Hap-T of Glyma.08G109100 may be useful to improve the contents of protein and 16 amino acids in soybean. This study provides new insights into the genetic basis of the amino acid composition in soybean seeds and may facilitate marker-based breeding of soybean with improved nutritional value.

Enhancement of root sulfur metabolic pathway by overexpression of OAS-TL3 to increase total soybean seed protein content

Sulfur is essential for plant growth, and the uptake of sulfate by plant roots is the primary source of plant sulfur. Previous studies have shown that the OAS-TL gene is a key enzyme in the sulfur metabolic pathway and regulates cysteine (Cys) synthase production. However, the interaction mechanism of the glycine max OAS-TL3 Cys synthase (OAS-TL3) gene on soybean root morphology construction and seed protein accumulation is unclear. This study shows that mutant M18 has better root growth and development, higher seed protein content, and higher methionine (Met) content in sulfur-containing amino acids than wild-type JN18. By transcriptome sequencing, the differentially expressed OAS-TL3 gene was targeted in the mutant M18 root line. The relative expression of the OAS-TL3 gene in roots, stems, and leaves during the seedling, flowering, and bulking stages of the OAS-TL3 gene overexpression lines is higher than that of the recipient material. Compared to the recipient material JN74, the enzymatic activities, Cys, and GSH contents of OAS-TL are higher in the sulfur metabolic pathway of seedling roots. The receptor material JN74 is exogenously applied with different concentrations of reduced glutathione. The results demonstrate a positive correlation between reduced glutathione on total root length, projected area, surface area, root volume, total root tip number, total bifurcation number, and total crossing number. The Met and total protein contents of sulfur-containing amino acids in soybean seeds of the OAS-TL3 gene overexpression lines are higher than those of the recipient material JN74, while the gene-edited lines show the opposite results. In conclusion, the OAS-TL3 gene positively regulates soybean root growth, root activity, and the content of Met in the seeds through the OAS-TL-Cys-GSH pathway. It breaks the limitation of other amino acids and facilitates the increase of total seed protein content.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01348-y.

Using of Essential Oils and Plant Extracts against Pseudomonas savastanoi pv. glycinea and Curtobacterium flaccumfaciens pv. flaccumfaciens on Soybean

The bacteria Pseudomonas savastanoi pv. glycinea (Coerper, 1919; Gardan et al., 1992) (Psg) and Curtobacterium flaccumfaciens pv. flaccumfaciens (Hedges 1922) (Cff) are harmful pathogens of soybean (Glycine max). Presently, there are several strategies to control these bacteria, and the usage of environmentally friendly approaches is encouraged. In this work, purified essential oils (EOs) from 19 plant species and total aqueous and ethanolic plant extracts (PEs) from 19 plant species were tested in vitro to observe their antimicrobial activity against Psg and Cff (by agar diffusion and broth microdilution method). Tested EOs and PEs produced significant bacterial growth inhibition with technologically acceptable MIC and MBC values. Non-phytotoxic concentrations for Chinese cinnamon and Oregano essential oils and leather bergenia ethanolic extract, which previously showed the lowest MBC values, were determined. Testing of these substances with artificial infection of soybean plants has shown that the essential oils of Chinese cinnamon and oregano have the maximum efficiency against Psg and Cff. Treatment of leaves and seeds previously infected with phytopathogens with these essential oils showed that the biological effectiveness of leaf treatments was 80.6-77.5% and 86.9-54.6%, respectively, for Psg and Cff. GC-MS and GC-FID analyzes showed that the major compounds were 5-Methyl-3-methylenedihydro-2(3H)-furanone (20.32%) in leather bergenia ethanolic extract, cinnamaldehyde (84.25%) in Chinese cinnamon essential oil and carvacrol (62.32%) in oregano essential oil.

Using transcriptomic and metabolomic data to investigate the molecular mechanisms that determine protein and oil contents during seed development in soybean

Soybean [Glycine max (L.) Merri.] is one of the most valuable global crops. And vegetable soybean, as a special type of soybean, provides rich nutrition in people's life. In order to investigate the gene expression networks and molecular regulatory mechanisms that regulate soybean seed oil and protein contents during seed development, we performed transcriptomic and metabolomic analyses of soybean seeds during development in two soybean varieties that differ in protein and oil contents. We identified a total of 41,036 genes and 392 metabolites, of which 12,712 DEGs and 315 DAMs were identified. Analysis of KEGG enrichment demonstrated that DEGs were primarily enriched in phenylpropanoid biosynthesis, glycerolipid metabolism, carbon metabolism, plant hormone signal transduction, linoleic acid metabolism, and the biosynthesis of amino acids and secondary metabolites. K-means analysis divided the DEGs into 12 distinct clusters. We identified candidate gene sets that regulate the biosynthesis of protein and oil in soybean seeds, and present potential regulatory patterns that high seed-protein varieties may be more sensitive to desiccation, show earlier photomorphogenesis and delayed leaf senescence, and thus accumulate higher protein contents than high-oil varieties.

Identification of Loci Governing Agronomic Traits and Mutation Hotspots via a GBS-Based Genome-Wide Association Study in a Soybean Mutant Diversity Pool

In this study, we performed a genotyping-by-sequencing analysis and a genome-wide association study of a soybean mutant diversity pool previously constructed by gamma irradiation. A GWAS was conducted to detect significant associations between 37,249 SNPs, 11 agronomic traits, and 6 phytochemical traits. In the merged data set, 66 SNPs on 13 chromosomes were highly associated (FDR p < 0.05) with the following 4 agronomic traits: days of flowering (33 SNPs), flower color (16 SNPs), node number (6 SNPs), and seed coat color (11 SNPs). These results are consistent with the findings of earlier studies on other genetic features (e.g., natural accessions and recombinant inbred lines). Therefore, our observations suggest that the genomic changes in the mutants generated by gamma irradiation occurred at the same loci as the mutations in the natural soybean population. These findings are indicative of the existence of mutation hotspots, or the acceleration of genome evolution in response to high doses of radiation. Moreover, this study demonstrated that the integration of GBS and GWAS to investigate a mutant population derived from gamma irradiation is suitable for dissecting the molecular basis of complex traits in soybeans.

Identification of closely associated SNPs and candidate genes with seed size and shape via deep re-sequencing GWAS in soybean

KEY MESSAGE

A soybean natural population was genotyped by deep re-sequencing and phenotyped for six seed size- and shape-related traits under six environments to identify closely associated SNPs and candidate genes. Seed size and shape are important determining factors for soybean yield formation, while their genetic basis and molecular mechanism are still largely unknown, which seriously constrains the increasing of soybean yield at present. In view of this, a natural population was genotyped via the deep re-sequencing technique (~ 20 ×) and phenotyped for six related traits under six environments. In total, 154 SNPs were closely associated with seed length across diverse environments, and 323, 483, 565, 394 and 2038 SNPs were closely associated with seed width, seed diameter, seed circumference, seed area and ratio of length to width under multiple environments. Moreover, 98.70%, 96.28%, 48.24%, 85.13%, 97.21% and 98.58% of them were further demonstrated by the BLUP and mean values of the related traits. Furthermore, 218 genes flanking the associated SNPs on chromosomes 6 and 10 were analyzed for DNA mutations and RNA expressions through SNP alleles and transcriptome data, simultaneously. The candidate genes, Glyma.10G035200 (Sn1-specific diacylglycerol lipase), Glyma.10G035400 (transcription factor) and Glyma.10G058200 (phenylalanine ammonia-lyase), were discovered to relate with the seed size and shape for their different DNA sequences or differential RNA expressions among soybean varieties at five seed developmental stages. Thus, these closely associated SNPs and related genes provide novel insights and useful information for the seed size and shape genetic basis dissection and breeding improvement in soybean.

Identification of quantitative trait loci associated with seed quality traits between Canadian and Ukrainian mega-environments using genome-wide association study

KEY MESSAGE

Identifying QTL associated with soybean seed quality traits from a diverse GWAS panel cultivated in Canadian and Ukrainian mega-environments may facilitate future cultivar development for foreign markets. Understanding the complex genetic basis of seed quality traits for soybean in the mega-environments (MEs) is critical for developing a marker-assisted selection program that will lead to breeding superior cultivars adapted to specific regions. This study aimed to analyze the accumulation of 14 soybean seed quality traits in Canadian ME and two seed quality traits in Ukrainian ME and identify associated ME specific quantitative trait loci (QTLSP) and ME universal QTL (QTLU) for protein and oil using a genome-wide association study (GWAS) panel consisting of 184 soybean genotypes. The panel was planted in three locations in Canada and two locations in Ukraine in 2018 and 2019. Genotype plus genotype-by-environment biplot analysis was conducted to assess the accumulation of individual seed compounds across different locations. The protein accumulation was high in the Canadian ME and low in the Ukrainian ME, whereas the oil concentration showed the opposite trends between the two MEs. No QTLU were identified across the MEs for protein and oil concentrations. In contrast, nine Canadian QTLSP for protein were identified on various chromosomes, which were co-located with QTL controlling other traits identified in the Canadian ME. The lack of common QTLU for protein and oil suggests that it may be necessary to use QTLSP associated with these traits separately for the Canadian and Ukrainian ME. Additional Ukrainian data for seed compounds other than oil and protein are required to identify novel QTLSP and QTLU for such traits for the individual or combined Canadian and Ukrainian MEs.

Reference Genes and Expression Analysis of Seed Desaturases Genes in Soybean Mutant Accessions

Soybean oil is the second most-produced vegetable oil worldwide. To enhance the nutritional quality and oxidative stability of soybean oil, many soybean breeding programs are trying to increase oleic acid content and reduce linoleic and linolenic acid contents. The fatty acid profile of soybean oil is controlled by many genes, including those which code for omega-3 and omega-6 desaturases. Mutations in GmFAD2-1 and GmFAD3 genes are widely studied and their combinations can produce soybean oil with high oleic and low linoleic and linolenic content. However, few studies evaluate the effect of these mutations on gene expression. Therefore, the present study sought to identify reference genes, evaluate the expression of GmFAD2-1 and GmFAD3 seed desaturase genes in thirteen wild-type and mutated soybean accessions, and associate the expression patterns with fatty acid composition and with the GmFAD2-1 and GmFAD3 genotypes. GmCONS7 and GmUKN2 were identified as the best reference genes for combined use to normalize data. The GmFAD2-1A mutation of PI603452 accession was associated with a decrease in gene expression of GmFAD2-1A; however, downregulation may not be due to the truncated enzyme structure alone. These results suggested that there are factors other than GmFAD2-1A and GmFAD2-1B that have a considerable effect on oleic content, at least in soybeans with mutations in these two genes.

Identification of ST1 reveals a selection involving hitchhiking of seed morphology and oil content during soybean domestication

Seed morphology and quality of cultivated soybean (Glycine max) have changed dramatically during domestication from their wild relatives, but their relationship to selection is poorly understood. Here, we describe a semi-dominant locus, ST1 (Seed Thickness 1), affecting seed thickness and encoding a UDP-D-glucuronate 4-epimerase, which catalyses UDP-galacturonic acid production and promotes pectin biosynthesis. Interestingly, this morphological change concurrently boosted seed oil content, which, along with up-regulation of glycolysis biosynthesis modulated by ST1, enabled soybean to become a staple oil crop. Strikingly, ST1 and an inversion controlling seed coat colour formed part of a single selective sweep. Structural variation analysis of the region surrounding ST1 shows that the critical mutation in ST1 existed in earlier wild relatives of soybean and the region containing ST1 subsequently underwent an inversion, which was followed by successive selection for both traits through hitchhiking during selection for seed coat colour. Together, these results provide direct evidence that simultaneously variation for seed morphology and quality occurred earlier than variation for seed coat colour during soybean domestication. The identification of ST1 thus sheds light on a crucial phase of human empirical selection in soybeans and provides evidence that our ancestors improved soybean based on taste.

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.

Genome-Wide Association Study and Genomic Selection for Proteinogenic Methionine in Soybean Seeds

Soybean [Glycine max (L.) Merr.] seeds have an amino acid profile that provides excellent viability as a food and feed protein source. However, low concentrations of an essential amino acid, methionine, limit the nutritional utility of soybean protein. The objectives of this study were to identify genomic associations and evaluate the potential for genomic selection (GS) for methionine content in soybean seeds. We performed a genome-wide association study (GWAS) that utilized 311 soybean accessions from maturity groups IV and V grown in three locations in 2018 and 2019. A total of 35,570 single nucleotide polymorphisms (SNPs) were used to identify genomic associations with proteinogenic methionine content that was quantified by high-performance liquid chromatography (HPLC). Across four environments, 23 novel SNPs were identified as being associated with methionine content. The strongest associations were found on chromosomes 3 (ss715586112, ss715586120, ss715586126, ss715586203, and ss715586204), 8 (ss715599541 and ss715599547) and 16 (ss715625009). Several gene models were recognized within proximity to these SNPs, such as a leucine-rich repeat protein kinase and a serine/threonine protein kinase. Identification of these linked SNPs should help soybean breeders to improve protein quality in soybean seeds. GS was evaluated using k-fold cross validation within each environment with two SNP sets, the complete 35,570 set and a subset of 248 SNPs determined to be associated with methionine through GWAS. Average prediction accuracy (r ²) was highest using the SNP subset ranging from 0.45 to 0.62, which was a significant improvement from the complete set accuracy that ranged from 0.03 to 0.27. This indicated that GS utilizing a significant subset of SNPs may be a viable tool for soybean breeders seeking to improve methionine content.

Bacteriophage Control of Pseudomonas savastanoi pv. glycinea in Soybean

Bacterial viruses (bacteriophages) have been considered as potential agents for the biological control of bacterial phytopathogens due to their safety and host specificity. Pseudomonas savastanoi pv. glycinea (Psg) is a causative agent of the bacterial spotting of soybean (Glycine max Willd). The harm caused by this bacterium to crop production and the development of antibiotic resistance in Psg and other pathogenic microorganisms has led to the pursuit of alternative management strategies. In this study, three Psg-specific lytic bacteriophages were isolated from soybean field soil in geographically distant regions of Russia, and their potential for protective action on plants was assessed. Sequencing of phage genomes has revealed their close relatedness and attribution to the genus Ghunavirus, subfamily Studiervirinae, family Autographiviridae. Extensive testing of the biological properties of P421, the representative of the isolated phage group, has demonstrated a relatively broad host range covering closely related Pseudomonas species and stability over wide temperature (4-40 °C) and pH (pH 4-7) ranges, as well as stability under ultraviolet irradiation for 30 min. Application of the phages to prevent, and treat, Psg infection of soybean plants confirms that they are promising as biocontrol agents.

Progress in soybean functional genomics over the past decade

Soybean is one of the most important oilseed and fodder crops. Benefiting from the efforts of soybean breeders and the development of breeding technology, large number of germplasm has been generated over the last 100 years. Nevertheless, soybean breeding needs to be accelerated to meet the needs of a growing world population, to promote sustainable agriculture and to address future environmental changes. The acceleration is highly reliant on the discoveries in gene functional studies. The release of the reference soybean genome in 2010 has significantly facilitated the advance in soybean functional genomics. Here, we review the research progress in soybean omics (genomics, transcriptomics, epigenomics and proteomics), germplasm development (germplasm resources and databases), gene discovery (genes that are responsible for important soybean traits including yield, flowering and maturity, seed quality, stress resistance, nodulation and domestication) and transformation technology during the past decade. At the end, we also briefly discuss current challenges and future directions.

Resequencing 250 Soybean Accessions: New Insights into Genes Associated with Agronomic Traits and Genetic Networks

The limited knowledge of genomic diversity and functional genes associated with the traits of soybean varieties has resulted in slow progress in breeding. In this study, we sequenced the genomes of 250 soybean landraces and cultivars from China, America, and Europe, and investigated their population structure, genetic diversity and architecture, and the selective sweep regions of these accessions. Five novel agronomically important genes were identified, and the effects of functional mutations in respective genes were examined. The candidate genes GSTT1, GL3, and GSTL3 associated with the isoflavone content, CKX3 associated with yield traits, and CYP85A2 associated with both architecture and yield traits were found. The phenotype-gene network analysis revealed that hub nodes play a crucial role in complex phenotypic associations. This study describes novel agronomic trait-associated genes and a complex genetic network, providing a valuable resource for future soybean molecular breeding.

Integrating omics approaches to discover and prioritize candidate genes involved in oil biosynthesis in soybean

Soybean is a major source of edible protein and oil. Oil content is a quantitative trait that is significantly determined by genetic and environmental factors. Over the past 30 years, a large volume of soybean genetic, genomic, and transcriptomic data have been accumulated. Nevertheless, integrative analyses of such data remain scarce, in spite of their importance for crop improvement. We hypothesized that the co-occurrence of genomic regions for oil-related traits in different studies may reveal more stable regions encompassing important genetic determinants of oil content and quality in soybean. We integrated publicly available data, obtained with distinct techniques, to discover and prioritize candidate genes involved in oil biosynthesis and regulation in soybean. We detected key fatty acid biosynthesis genes (e.g., BCCP2 and ACCase, FADs, KAS family proteins) and several transcription factors, which are likely regulators of oil biosynthesis. In addition, we identified new candidates for seed oil accumulation and quality, such as Glyma.03G213300 and Glyma.19G160700, which encode a translocator protein homolog and a histone acetyltransferase, respectively. Further, oil and protein genomic hotspots are strongly associated with breeding and not with domestication, suggesting that soybean domestication prioritized other traits. The genes identified here are promising targets for breeding programs and for the development of soybean lines with increased oil content and quality.

Genome-Wide Association Study of Soybean Germplasm Derived From Canadian × Chinese Crosses to Mine for Novel Alleles to Improve Seed Yield and Seed Quality Traits

Genome-wide association study (GWAS) has emerged in the past decade as a viable tool for identifying beneficial alleles from a genomic diversity panel. In an ongoing effort to improve soybean [Glycine max (L.) Merr.], which is the third largest field crop in Canada, a GWAS was conducted to identify novel alleles underlying seed yield and seed quality and agronomic traits. The genomic panel consisted of 200 genotypes including lines derived from several generations of bi-parental crosses between modern Canadian × Chinese cultivars (CD-CH). The genomic diversity panel was field evaluated at two field locations in Ontario in 2019 and 2020. Genotyping-by-sequencing (GBS) was conducted and yielded almost 32 K high-quality SNPs. GWAS was conducted using Fixed and random model Circulating Probability Unification (FarmCPU) model on the following traits: seed yield, seed protein concentration, seed oil concentration, plant height, 100 seed weight, days to maturity, and lodging score that allowed to identify five QTL regions controlling seed yield and seed oil and protein content. A candidate gene search identified a putative gene for each of the three traits. The results of this GWAS study provide insight into potentially valuable genetic resources residing in Chinese modern cultivars that breeders may use to further improve soybean seed yield and seed quality traits.

Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline

Soybean is a primary meal protein for human consumption, poultry, and livestock feed. In this study, quantitative trait locus (QTL) controlling protein content was explored via genome-wide association studies (GWAS) and linkage mapping approaches based on 284 soybean accessions and 180 recombinant inbred lines (RILs), respectively, which were evaluated for protein content for 4 years. A total of 22 single nucleotide polymorphisms (SNPs) associated with protein content were detected using mixed linear model (MLM) and general linear model (GLM) methods in Tassel and 5 QTLs using Bayesian interval mapping (IM), single-trait multiple interval mapping (SMIM), single-trait composite interval mapping maximum likelihood estimation (SMLE), and single marker regression (SMR) models in Q-Gene and IciMapping. Major QTLs were detected on chromosomes 6 and 20 in both populations. The new QTL genomic region on chromosome 6 (Chr6_18844283-19315351) included 7 candidate genes and the Hap.X ^AA at the Chr6_19172961 position was associated with high protein content. Genomic selection (GS) of protein content was performed using Bayesian Lasso (BL) and ridge regression best linear unbiased prediction (rrBULP) based on all the SNPs and the SNPs significantly associated with protein content resulted from GWAS. The results showed that BL and rrBLUP performed similarly; GS accuracy was dependent on the SNP set and training population size. GS efficiency was higher for the SNPs derived from GWAS than random SNPs and reached a plateau when the number of markers was >2,000. The SNP markers identified in this study and other information were essential in establishing an efficient marker-assisted selection (MAS) and GS pipelines for improving soybean protein content.

Domestication and improvement genes reveal the differences of seed size- and oil-related traits in soybean domestication and improvement

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Due to reduced diversity, it is essential to map domesticated and improved genes.

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13 known and 442 candidate genes were mined for seed size- and oil-related traits.

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All the genes were used to explain trait changes in domestication and improvement.

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56 domesticated and 15 improved genes may be valuable for future soybean breeding.

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This study provides useful gene resources for future breeding and biology research.

To address domestication and improvement studies of soybean seed size- and oil-related traits, a series of domesticated and improved regions, loci, and candidate genes were identified in 286 soybean accessions using domestication and improvement analyses, genome-wide association studies, quantitative trait locus (QTL) mapping and bulked segregant analyses in this study. As a result, 534 candidate domestication regions (CDRs) and 458 candidate improvement regions (CIRs) were identified in this study and integrated with those in five and three previous studies, respectively, to obtain 952 CDRs and 538 CIRs; 1469 loci for soybean seed size- and oil-related traits were identified in this study and integrated with those in Soybase to obtain 433 QTL clusters. The two results were intersected to obtain 245 domestication and 221 improvement loci for the above traits. Around these trait-related domestication and improvement loci, 7 domestication and 7 improvement genes were found to be truly associated with these traits, and 372 candidate domestication and 87 candidate improvement genes were identified using gene expression, SNP variants in genome, miRNA binding, KEGG pathway, DNA methylation, and haplotype analysis. These genes were used to explain the trait changes in domestication and improvement. As a result, the trait changes can be explained by their frequencies of elite haplotypes, base mutations in coding region, and three factors affecting their expression levels. In addition, 56 domestication and 15 improvement genes may be valuable for future soybean breeding. This study can provide useful gene resources for future soybean breeding and molecular biology research.

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods

In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.

Genome-wide association study and population structure analysis of seed-bound amino acids and total protein in watermelon

BACKGROUND

Watermelon seeds are a powerhouse of value-added traits such as proteins, free amino acids, vitamins, and essential minerals, offering a paleo-friendly dietary option. Despite the availability of substantial genetic variation, there is no sufficient information on the natural variation in seed-bound amino acids or proteins across the watermelon germplasm. This study aimed to analyze the natural variation in watermelon seed amino acids and total protein and explore underpinning genetic loci by genome-wide association study (GWAS).

METHODS

The study evaluated the distribution of seed-bound free amino acids and total protein in 211 watermelon accessions of Citrullus spp, including 154 of Citrullus lanatus, 54 of Citrullus mucosospermus (egusi) and three of Citrullus amarus. We used the GWAS approach to associate seed phenotypes with 11,456 single nucleotide polymorphisms (SNPs) generated by genotyping-by-sequencing (GBS).

RESULTS

Our results demonstrate a significant natural variation in different free amino acids and total protein content across accessions and geographic regions. The accessions with high protein content and proportion of essential amino acids warrant its use for value-added benefits in the food and feed industries via biofortification. The GWAS analysis identified 188 SNPs coinciding with 167 candidate genes associated with watermelon seed-bound amino acids and total protein. Clustering of SNPs associated with individual amino acids found by principal component analysis was independent of the speciation or cultivar groups and was not selected during the domestication of sweet watermelon. The identified candidate genes were involved in metabolic pathways associated with amino acid metabolism, such as Argininosuccinate synthase, explaining 7% of the variation in arginine content, which validate their functional relevance and potential for marker-assisted analysis selection. This study provides a platform for exploring potential gene loci involved in seed-bound amino acids metabolism, useful in genetic analysis and development of watermelon varieties with superior seed nutritional values.

Genetic mapping high protein content QTL from soybean 'Nanxiadou 25' and candidate gene analysis

BACKGROUND

Soybean is a globally important legume crop that provides a primary source of high-quality vegetable protein and oil. Seed protein content (SPC) is a valuable quality trait controlled by multiple genes in soybean.

RESULTS

In this study, we performed quantitative trait loci (QTL) mapping, QTL-seq, and RNA sequencing (RNA-seq) to reveal the genes controlling protein content in the soybean by using the high protein content variety Nanxiadou 25. A total of 50 QTL for SPC distributed on 14 chromosomes except chromosomes 4, 12, 14, 17, 18, and 19 were identified by QTL mapping using 178 recombinant inbred lines (RILs). Among these QTL, the major QTL qSPC_20-1 and qSPC_20-2 on chromosome 20 were repeatedly detected across six tested environments, corresponding to the location of the major QTL detected using whole-genome sequencing-based QTL-seq. 329 candidate DEGs were obtained within the QTL region of qSPC_20-1 and qSPC_20-2 via gene expression profile analysis. Nine of which were associated with SPC, potentially representing candidate genes. Clone sequencing results showed that different single nucleotide polymorphisms (SNPs) and indels between high and low protein genotypes in Glyma.20G088000 and Glyma.16G066600 may be the cause of changes in this trait.

CONCLUSIONS

These results provide the basis for research on candidate genes and marker-assisted selection (MAS) in soybean breeding for seed protein content.