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Soybean is a major crop in the world and an essential source for minerals, including iron (Fe) and zin (Zn). Deficiency of Fe and Zn in soil and soybean plants result in yield loss and poor seed nutritional qualities. Information on genomic regions and candidate genes controlling seed Fe and Zn accumulation in soybean seed is limited. Therefore, The objective of this research was to identify genetic regions, known as quantitative trait loci (QTL), and candidate genes that control the accumulation of Fe and Zn in soybean mature seeds. A ‘Forrest’ by ‘Williams 82’ (F × W82) recombinant inbred line (RIL) population (n = 306) was used and genotyped using a total of 5405 single nucleotides polymorphism (SNP) markers using Infinium SNP6K BeadChips. A two-year experiment was conducted across two environments: North Carolina in 2018 (NC) and Illinois in 2020 (IL). Only QTL with LOD scores ≥ 2.5, as identified by the composite interval mapping (CIM) method, are reported here. In total, 6 QTL were identified for seed Fe; specifically, 3 QTL (qFe-01-[NC-2018], qFe-02-[NC-2018], and qFe-03-[NC-2018]) were located on chromosomes 1, 2, and 6, respectively, in the NC environment, and 3 QTL (qFe-01-[IL-2020], qFe-02-[IL-2020], and qFe-03-[IL-2020]) were positioned on chromosomes 1, 2, and 12, respectively, in the IL environment. A total of 6 QTL associated with seed Zn were also identified; 4 QTL (qZn-01-[NC-2018]; qZn-02-[NC-2018]; qZn-03-[NC-2018]; and qZn-04-[NC-2018]), respectively on Chr 2, 3, 7, and 19 in NC; and 2 QTL (qZn-01-[IL-2020] and qZn-02-[IL-2020]), respectively, on Chr 5 and 8 in IL. Several functional genes encode Fe- and Zn-proteins, transcription factors, proteins-zinc finger motifs (involved in DNA binding and transcriptional regulation; crosstalk between the regulatory pathways of Zn and Fe transporters) were identified and located within the QTL interval. To our knowledge, and based on the literature available, the QTL identified here on Chr 2 and Chr 6 are novel and were not previously identified. This current research provides a new knowledge of the genetic basis of seed Fe and Zn and the markers associated with QTL. The QTL identified here will contribute to efficient marker assisted selection for higher Fe and Zn content in soybean seeds. The candidate genes and metal-responsive transcription factors may coordinate the expression of both Zn and Fe transporters in response to changes in metal availability, providing new knowledge on minerals uptake and transport mechanisms, allowing for possible genetic engineering application.

Abstract Development of high yielding cowpea varieties coupled with good taste and rich in essential minerals can promote consumption and thus nutrition and profitability. The sweet taste of cowpea grain is determined by its sugar content, which comprises mainly sucrose and galacto-oligosaccharides (GOS) including raffinose and stachyose. However, GOS are indigestible and their fermentation in the colon can produce excess intestinal gas, causing undesirable bloating and flatulence. In this study, we aimed to examine variation in grain sugar and mineral concentrations, then map quantitative trait loci (QTLs) and estimate genomic-prediction (GP) accuracies for possible application in breeding. Grain samples were collected from a multi-parent advanced generation intercross (MAGIC) population grown in California during 2016–2017. Grain sugars were assayed using high-performance liquid chromatography. Grain minerals were determined by inductively coupled plasma–optical emission spectrometry and combustion. Considerable variation was observed for sucrose (0.6–6.9%) and stachyose (2.3–8.4%). Major QTLs for sucrose (QSuc.vu-1.1), stachyose (QSta.vu-7.1), copper (QCu.vu-1.1) and manganese (QMn.vu-5.1) were identified. Allelic effects of major sugar QTLs were validated using the MAGIC grain samples grown in West Africa in 2017. GP accuracies for minerals were moderate (0.4–0.58). These findings help guide future breeding efforts to develop mineral-rich cowpea varieties with desirable sugar content.

Abstract In addition to the challenge of meeting global demand for food production, there are increasing concerns about food safety and the need to protect consumer health from the negative effects of foodborne allergies. Certain bio‐molecules (usually proteins) present in food can act as allergens that trigger unusual immunological reactions, with potentially life‐threatening consequences. The relentless working lifestyles of the modern era often incorporate poor eating habits that include readymade prepackaged and processed foods, which contain additives such as peanuts, tree nuts, wheat, and soy‐based products, rather than traditional home cooking. Of the predominant allergenic foods (soybean, wheat, fish, peanut, shellfish, tree nuts, eggs, and milk), peanuts (Arachis hypogaea) are the best characterized source of allergens, followed by tree nuts (Juglans regia, Prunus amygdalus, Corylus avellana, Carya illinoinensis, Anacardium occidentale, Pistacia vera, Bertholletia excels), wheat (Triticum aestivum), soybeans (Glycine max), and kidney beans (Phaseolus vulgaris). The prevalence of food allergies has risen significantly in recent years including chance of accidental exposure to such foods. In contrast, the standards of detection, diagnosis, and cure have not kept pace and unfortunately are often suboptimal. In this review, we mainly focus on the prevalence of allergies associated with peanut, tree nuts, wheat, soybean, and kidney bean, highlighting their physiological properties and functions as well as considering research directions for tailoring allergen gene expression. In particular, we discuss how recent advances in molecular breeding, genetic engineering, and genome editing can be used to develop potential low allergen food crops that protect consumer health.

Abstract Soybean [Glycine max (L.) Merr.] is a globally important crop due to its valuable seed composition, versatile feed, food, and industrial end‐uses, and consistent genetic gain. Successful genetic gain in soybean has led to widespread adaptation and increased value for producers, processors, and consumers. Specific focus on the nutritional quality of soybean seed composition for food and feed has further elucidated genetic knowledge and bolstered breeding progress. Seed components are historical and current targets for soybean breeders seeking to improve nutritional quality of soybean. This article reviews genetic and genomic foundations for improvement of nutritionally important traits, such as protein and amino acids, oil and fatty acids, carbohydrates, and specific food‐grade considerations; discusses the application of advanced breeding technology such as CRISPR/Cas9 in creating seed composition variations; and provides future directions and breeding recommendations regarding soybean seed composition traits.

Abstract 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.

Abstract Complete, gapless telomere‐to‐telomere chromosome assemblies are a prerequisite for comprehensively investigating the architecture of complex regions, like centromeres or telomeres and removing uncertainties in the order, spacing, and orientation of genes. Using complementary genomics technologies and assembly algorithms, we developed highly contiguous, nearly gapless, genome assemblies for two economically important soybean [Glycine max (L.) Merr] cultivars (Williams 82 and Lee). The centromeres were distinctly annotated on all the chromosomes of both assemblies. We further found that the canonical telomeric repeats were present at the telomeres of all chromosomes of both Williams 82 and Lee genomes. A total of 10 chromosomes in Williams 82 and eight in Lee were entirely reconstructed in single contigs without any gap. Using the combination of ab initio prediction, protein homology, and transcriptome evidence, we identified 58,287 and 56,725 protein‐coding genes in Williams 82 and Lee, respectively. The genome assemblies and annotations will serve as a valuable resource for studying soybean genomics and genetics and accelerating soybean improvement.

The nutritional value of soybean [Glycine max (L.) Merr.] for animals is influenced by soluble carbohydrates, such as sucrose and stachyose. Although sucrose is nutritionally desirable, stachyose is an antinutrient causing diarrhea and flatulence in non-ruminant animals. We conducted a genome-wide association study of 220 soybean accessions using 21,317 single nucleotide polymorphisms (SNPs) from the SoySNP50K iSelect Beadchip data to identify significant SNPs associated with sucrose and stachyose content. Seven significant SNPs were identified for sucrose content across chromosomes (Chrs.) 2, 8, 12, 17, and 20, while thirteen significant SNPs were identified for stachyose content across Chrs. 2, 5, 8, 9, 10, 13, 14, and 15. Among those significant SNPs, three sucrose-related SNPs on Chrs. 8 and 17 were novel, while twelve stachyose-related SNPs on Chrs. 2, 5, 8, 9, 10, 13, 14, and 15 were novel. Based on Phytozome, STRING, and GO annotation, 17 and 24 candidate genes for sucrose and stachyose content, respectively, were highly associated with the carbohydrate metabolic pathway. Among these, the publicly available RNA-seq Atlas database highlighted four candidate genes associated with sucrose (Glyma.08g361200 and Glyma.17g258100) and stachyose (Glyma.05g025300 and Glyma.13g077900) content, which had higher gene expression levels in developing seed and multiple parts of the soybean plant. The results of this study will extend knowledge of the molecular mechanism and genetic basis underlying sucrose and stachyose content in soybean seed. Furthermore, the novel candidate genes and SNPs can be valuable genetic resources that soybean breeders may utilize to modify carbohydrate profiles for animal and human usage.

The adoption of dicamba-tolerant (DT) soybean in the United States resulted in extensive off-target dicamba damage to non-DT vegetation across soybean-producing states. Although soybeans are highly sensitive to dicamba, the intensity of observed symptoms and yield losses are affected by the genetic background of genotypes. Thus, the objective of this study was to detect novel marker-trait associations and expand on previously identified genomic regions related to soybean response to off-target dicamba. A total of 551 non-DT advanced breeding lines derived from 232 unique bi-parental populations were phenotyped for off-target dicamba across nine environments for three years. Breeding lines were genotyped using the Illumina Infinium BARCSoySNP6K BeadChip. Filtered SNPs were included as predictors in Random Forest (RF) and Support Vector Machine (SVM) models in a forward stepwise selection loop to identify the combination of SNPs yielding the highest classification accuracy. Both RF and SVM models yielded high classification accuracies (0.76 and 0.79, respectively) with minor extreme misclassifications (observed tolerant predicted as susceptible, and vice-versa). Eight genomic regions associated with off-target dicamba tolerance were identified on chromosomes 6 [Linkage Group (LG) C2], 8 (LG A2), 9 (LG K), 10 (LG O), and 19 (LG L). Although the genetic architecture of tolerance is complex, high classification accuracies were obtained when including the major effect SNP identified on chromosome 6 as the sole predictor. In addition, candidate genes with annotated functions associated with phases II (conjugation of hydroxylated herbicides to endogenous sugar molecules) and III (transportation of herbicide conjugates into the vacuole) of herbicide detoxification in plants were co-localized with significant markers within each genomic region. Genomic prediction models, as reported in this study, can greatly facilitate the identification of genotypes with superior tolerance to off-target dicamba.

Abstract Soybean (Glycine max) is a major crop that contributes more than half of global oilseed production. Much research has been directed towards improvement of the fatty acid profile of soybean seeds through marker assisted breeding. Recently published soybean pangenomes, based on thousands of soybean lines, provide an opportunity to identify new alleles that may be involved in fatty acid biosynthesis. In this study, we identify fatty acid biosynthesis genes in soybean pangenomes based on sequence identity with known genes and examine their sequence diversity across diverse soybean collections. We find three possible instances of a gene missing in wild soybean, including FAD8 and FAD2‐2D, which may be involved in oleic and linoleic acid desaturation, respectively, although we recommend follow‐up research to verify the absence of these genes. More than half of the 53 fatty acid biosynthesis genes identified contained missense variants, including one linked with a previously identified QTL for oil quality. These variants were present in multiple studies based on either short read mappings or alignment of reference grade genomes. Missense variants were found in previously characterized genes including FAD2‐1A and FAD2‐1B, both of which are involved in desaturation of oleic acid, as well as uncharacterized candidate fatty acid biosynthesis genes. We find that the frequency of missense alleles in fatty acid biosynthesis genes has been reduced significantly more than the global average frequency of missense mutations during domestication, and missense variation in some genes is near absent in modern cultivars. This could be due to the selection for fatty acid profiles in seed, though future work should be conducted towards understanding the phenotypic impacts of these variants.

Abstract Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.

Understanding the genetic basis of seed Ni and Mo is essential. Since soybean is a major crop in the world and a major source for nutrients, including Ni and Mo, the objective of the current research was to map genetic regions (quantitative trait loci, QTL) linked to Ni and Mo concentrations in soybean seed. A recombinant inbred line (RIL) population was derived from a cross between ‘Forrest’ and ‘Williams 82’ (F × W82). A total of 306 lines was used for genotyping using 5405 single nucleotides polymorphism (SNP) markers using Infinium SNP6K BeadChips. A two-year experiment was conducted and included the parents and the RIL population. One experiment was conducted in 2018 in North Carolina (NC), and the second experiment was conducted in Illinois in 2020 (IL). Logarithm of the odds (LOD) of ≥2.5 was set as a threshold to report identified QTL using the composite interval mapping (CIM) method. A wide range of Ni and Mo concentrations among RILs was observed. A total of four QTL (qNi-01, qNi-02, and qNi-03 on Chr 2, 8, and 9, respectively, in 2018, and qNi-01 on Chr 20 in 2020) was identified for seed Ni. All these QTL were significantly (LOD threshold > 2.5) associated with seed Ni, with LOD scores ranging between 2.71–3.44, and with phenotypic variance ranging from 4.48–6.97%. A total of three QTL for Mo (qMo-01, qMo-02, and qMo-03 on Chr 1, 3, 17, respectively) was identified in 2018, and four QTL (qMo-01, qMo-02, qMo-03, and qMo-04, on Chr 5, 11, 14, and 16, respectively) were identified in 2020. Some of the current QTL had high LOD and significantly contributed to the phenotypic variance for the trait. For example, in 2018, Mo QTL qMo-01 on Chr 1 had LOD of 7.8, explaining a phenotypic variance of 41.17%, and qMo-03 on Chr 17 had LOD of 5.33, with phenotypic variance explained of 41.49%. In addition, one Mo QTL (qMo-03 on Chr 14) had LOD of 9.77, explaining 51.57% of phenotypic variance related to the trait, and another Mo QTL (qMo-04 on Chr 16) had LOD of 7.62 and explained 49.95% of phenotypic variance. None of the QTL identified here were identified twice across locations/years. Based on a search of the available literature and of SoyBase, the four QTL for Ni, identified on Chr 2, 8, 9, and 20, and the five QTL associated with Mo, identified on Chr 1, 17, 11, 14, and 16, are novel and not previously reported. This research contributes new insights into the genetic mapping of Ni and Mo, and provides valuable QTL and molecular markers that can potentially assist in selecting Ni and Mo levels in soybean seeds.

Late embryogenesis abundant (LEA) proteins play important roles in regulating plant growth and responses to various abiotic stresses. In this research, a genome-wide survey was conducted to recognize the LEA genes in Glycine max. A total of 74 GmLEA was identified and classified into nine subfamilies based on their conserved domains and the phylogenetic analysis. Subcellular localization, the duplication of genes, gene structure, the conserved motif, and the prediction of cis-regulatory elements and tissue expression pattern were then conducted to characterize GmLEAs. The expression profile analysis indicated that the expression of several GmLEAs was a response to drought and salt stress. The co-expression-based gene network analysis suggested that soybean LEA proteins may exert regulatory effects through the metabolic pathways. We further explored GnLEA4_19 function in Arabidopsis and the results suggests that overexpressed GmLEA4_19 in Arabidopsis increased plant height under mild or serious drought stress. Moreover, the overexpressed GmLEA4_19 soybean also showed a drought tolerance phenotype. These results indicated that GmLEA4_19 plays an important role in the tolerance to drought and will contribute to the development of the soybean transgenic with enhanced drought tolerance and better yield. Taken together, this study provided insight for better understanding the biological roles of LEA genes in soybean.

Soybean seed sugars are among the most abundant beneficial compounds for human and animal consumption in soybean seeds. Higher seed sugars such as sucrose are desirable as they contribute to taste and flavor in soy-based food. Therefore, the objectives of this study were to use the ‘Forrest’ by ‘Williams 82’ (F × W82) recombinant inbred line (RIL) soybean population (n = 309) to identify quantitative trait loci (QTLs) and candidate genes that control seed sugar (sucrose, stachyose, and raffinose) contents in two environments (North Carolina and Illinois) over two years (2018 and 2020). A total of 26 QTLs that control seed sugar contents were identified and mapped on 16 soybean chromosomes (chrs.). Interestingly, five QTL regions were identified in both locations, Illinois and North Carolina, in this study on chrs. 2, 5, 13, 17, and 20. Amongst 57 candidate genes identified in this study, 16 were located within 10 Megabase (MB) of the identified QTLs. Amongst them, a cluster of four genes involved in the sugars’ pathway was collocated within 6 MB of two QTLs that were detected in this study on chr. 17. Further functional validation of the identified genes could be beneficial in breeding programs to produce soybean lines with high beneficial sucrose and low raffinose family oligosaccharides.

Nutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (DOF1-like zinc finger transcription factor, protein of unknown function, and C2H2 zinc finger protein). We also identified a putative negative regulator (gibberellin 20 oxidase 3) of the total number of lateral roots.

Abstract Improving seed yield is one of the main targets of soybean [Glycine max (L.) Merr.] breeding. Identification of loci that influence productivity and understanding their genetic mechanism will help marker‐assisted trait introgression. The present study evaluated a diverse panel of 541 soybean genotypes consisting of three maturity groups (MGs III–V) in four environments in Kansas, U.S. Data on seed yield, seed weight, shattering resistance, days to maturity, and plant height showed significant genotype, environmental, and genotype × environment interaction variations. Seed yield and shattering had moderate broad‐sense heritability (90%). The SoySNP50K iSelect BeadChip dataset was used to identify significantly associated loci via genome‐wide association studies (GWAS). A total of 19 single‐nucleotide polymorphisms (SNPs) were significantly associated with seed yield. Particularly, two stable seed yield quantitative trait loci (QTL) on chromosomes 9 and 17 were consistently detected in at least three out of four environments. Candidate gene analysis surrounding seed yield QTL on chromosome 9 showed that Glyma.09G048900, an oxygen binding protein, was the closest to the QTL peak. Similarly, Glyma.17G090200 and Glyma.17G090400 were within 20‐kb region of the seed yield QTL on chromosome 17. The candidate genes warrant further analysis to determine their functional mechanisms and develop markers for seed yield improvement.

The widespread adoption of genetically modified (GM) dicamba-tolerant (DT) soybean was followed by numerous reports of off-target dicamba damage and yield losses across most soybean-producing states. In this study, a subset of the USDA Soybean Germplasm Collection consisting of 382 genetically diverse soybean accessions originating from 15 countries was used to identify genomic regions associated with soybean response to off-target dicamba exposure. Accessions were genotyped with the SoySNP50K BeadChip and visually screened for damage in environments with prolonged exposure to off-target dicamba. Two models were implemented to detect significant marker-trait associations: the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a model that allows the inclusion of population structure in interaction with the environment (G×E) to account for variable patterns of genotype responses in different environments. Most accessions (84%) showed a moderate response, either moderately tolerant or moderately susceptible, with approximately 8% showing tolerance and susceptibility. No differences in off-target dicamba damage were observed across maturity groups and centers of origin. Both models identified significant associations in regions of chromosomes 10 and 19. The BLINK model identified additional significant marker-trait associations on chromosomes 11, 14, and 18, while the G×E model identified another significant marker-trait association on chromosome 15. The significant SNPs identified by both models are located within candidate genes possessing annotated functions involving different phases of herbicide detoxification in plants. These results entertain the possibility of developing non-GM soybean cultivars with improved tolerance to off-target dicamba exposure and potentially other synthetic auxin herbicides. Identification of genetic sources of tolerance and genomic regions conferring higher tolerance to off-target dicamba may sustain and improve the production of other non-DT herbicide soybean production systems, including the growing niche markets of organic and conventional soybean.

The J-protein family comprises molecular chaperones involved in plant growth, development, and stress responses. Little is known about this gene family in soybean. Hence, we characterized J-protein genes in soybean, with the most highly expressed and responsive during flower and seed development. We also revealed their phylogeny, structure, motif analysis, chromosome location, and expression. Based on their evolutionary links, we divided the 111 potential soybean J-proteins into 12 main clades (I–XII). Gene-structure estimation revealed that each clade had an exon-intron structure resembling or comparable to others. Most soybean J-protein genes lacked introns in Clades I, III, and XII. Moreover, transcriptome data obtained from a publicly accessible soybean database and RT-qPCR were used to examine the differential expression of DnaJ genes in various soybean tissues and organs. The expression level of DnaJ genes indicated that, among 14 tissues, at least one tissue expressed the 91 soybean genes. The findings suggest that J-protein genes could be involved in the soybean growth period and offer a baseline for further functional research into J-proteins' role in soybean. One important application is the identification of J-proteins that are highly expressed and responsive during flower and seed development in soybean. These genes likely play crucial roles in these processes, and their identification can contribute to breeding programs to improve soybean yield and quality.

The continuous evolution and spread of virulent forms of the soybean cyst nematode (SCN) driven by the environment and anthropogenic intervention is a serious threat to the soybean production worldwide, including China. Especially in China, the implemented measures to control SCN are insufficient for sustainable agricultural development yet. We summarized our knowledge about the spread and spatial distribution of SCN in China and the virulence diversity in the main soybean growing areas. To reveal the genetic relatedness and diversity of SCN populations, we re-sequenced 53 SCN genomes from the Huang-Huai Valleys, one of the two main soybean growing areas in China. We identified spreading patterns linked to the local agroecosystems and topographies. Moreover, we disclosed the first evidence for the selection of complex virulence in the field even under low selection pressure in an example from North Shanxi. SCN is present in all soybean growing areas in China but SCN susceptible cultivars are still largely grown indicating that SCN-related damage and financial loss have not received the attention they deserve yet. To prevent increasing yield losses and to improve the acceptance of resistant cultivars by the growers, we emphasized that it is time to accelerate SCN resistance breeding, planting resistant cultivars to a larger extent, and to support farmers to implement a wider crop rotation for sustainable development of the soybean production in China.

Southern root-knot nematode [SRKN, Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.

Measurement(s) genetic variation • SNP • Linkage Disequilibrium Technology Type(s) DNA sequencing • bioinformatics analysis • computational phylogenetic analysis Sample Characteristic - Organism Glycine soja • Glycine max Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13568552

Abstract The gene content of plants varies between individuals of the same species due to gene presence/absence variation, and selection can alter the frequency of specific genes in a population. Selection during domestication and breeding will modify the genomic landscape, though the nature of these modifications is only understood for specific genes or on a more general level (e.g., by a loss of genetic diversity). Here we have assembled and analyzed a soybean (Glycine spp.) pangenome representing more than 1,000 soybean accessions derived from the USDA Soybean Germplasm Collection, including both wild and cultivated lineages, to assess genomewide changes in gene and allele frequency during domestication and breeding. We identified 3,765 genes that are absent from the Lee reference genome assembly and assessed the presence/absence of all genes across this population. In addition to a loss of genetic diversity, we found a significant reduction in the average number of protein‐coding genes per individual during domestication and subsequent breeding, though with some genes and allelic variants increasing in frequency associated with selection for agronomic traits. This analysis provides a genomic perspective of domestication and breeding in this important oilseed crop.

Abstract In the last decade, more than 70 quantitative trait loci (QTL) related to soybean [Glycine max (L.) Merr.] partial resistance (PR) against Phytophthora sojae have been identified by genome‐wide association studies (GWAS). However, most of them have either a minor effect on the resistance level or are specific to a single phenotypic variable or one isolate, thereby limiting their use in breeding programs. In this study, we have used an analytical approach combining (a) the phenotypic characterization of a diverse panel of 357 soybean accessions for resistance to P. sojae captured through a single variable, corrected dry weight; (b) a new hydroponic assay allowing the inoculation of a combination of P. sojae isolates covering the spectrum of commercially relevant Rps genes; and (c) exhaustive genotyping through whole‐genome resequencing (WGS). This led to the identification of a novel P. sojae resistance QTL with a relatively major effect compared with the previously reported QTL. The QTL interval, spanning ∼500 kb on chromosome (Chr) 15, does not colocalize with previously reported QTL for P. sojae resistance. Plants carrying the favorable allele at this QTL were 60% more resistant. Eight genes were found to reside in the linkage disequilibrium (LD) block containing the peak single‐nucleotide polymorphism (SNP) including Glyma.15G217100, which encodes a major latex protein (MLP)‐like protein, with a functional annotation related to pathogen resistance. Expression analysis of Glyma.15G217100 indicated that it was nearly eight times more highly expressed in a group of plant introductions (PIs) carrying the resistant (R) allele compared with those carrying the susceptible (S) allele within a short period after inoculation. These results offer new and valuable options to develop improved soybean cultivars with broad resistance to P. sojae through marker‐assisted selection.

Abstract Since the first reported crop pangenome in 2014, advances in high‐throughput and cost‐effective DNA sequencing technologies facilitated multiple such studies including the pangenomes of oilseed rape (Brassica napus L.), soybean [Glycine max (L.) Merr.], rice (Oryza sativa L.), wheat (Triticum aestivum L.), and barley (Hordeum vulgare L.). Compared with single‐reference genomes, pangenomes provide a more accurate representation of the genetic variation present in a species. By combining the genomic data of multiple accessions, pangenomes allow for the detection and annotation of complex DNA polymorphisms such as structural variations (SVs), one of the major determinants of genetic diversity within a species. In this review we summarize the current literature on crop pangenomics, focusing on their application to find candidate SVs involved in traits of agronomic interest. We then highlight the potential of pangenomes in the discovery and functional characterization of noncoding regulatory sequences and their variations. We conclude with a summary and outlook on innovative data structures representing the complete content of plant pangenomes including annotations of coding and noncoding elements and outcomes of transcriptomic and epigenomic experiments.

Abstract Reniform nematode (RN, Rotylenchulus reniformis Linford & Oliveira) has emerged as one of the most important plant parasitic nematodes of soybean [Glycine max (L.) Merr.]. Planting resistant varieties is the most effective strategy for nematode management. The objective of this study was to identify quantitative trait loci (QTL) for RN resistance in an exotic soybean line, PI 438489B, using two linkage maps constructed from the Universal Soybean Linkage Panel (USLP 1.0) and next‐generation whole‐genome resequencing (WGRS) technology. Two QTL controlling RN resistance were identified—the soybean cyst nematode (SCN, Heterodera glycines) resistance gene GmSNAP18 at the rhg1 locus and its paralog GmSNAP11. Strong association between resistant phenotype and haplotypes of the GmSNAP11 and GmSNAP18 was observed. The results indicated that GmSNAP11 possibly could have epistatic effect on GmSNAP18, or vice versa, with the presence of a significant correlation in RN resistance of rhg1‐a GmSNAP18 vs. rhg1‐b GmSNAP18. Most importantly, our preliminary data suggested that GmSNAP18 and GmSNAP11 proteins physically interact in planta, suggesting that they belong to the same pathway for resistance. Unlike GmSNAP18, no indication of GmSNAP11 copy number variation was found. Moreover, gene‐based single nucleotide polymorphism (SNP) markers were developed for rapid detection of RN or SCN resistance at these loci. Our analysis substantiates synergic interaction between GmSNAP11 and GmSNAP18 genes and confirms their roles in RN as well as SCN resistance. These results could contribute to a better understanding of evolution and subfunctionalization of genes conferring resistance to multiple nematode species and provide a framework for further investigations.

Abstract Background Salinity is an abiotic stress that negatively affects soybean [Glycine max (L.) Merr.] seed yield. Although a major gene for salt tolerance was identified and consistently mapped to chromosome (Chr.) 3 by linkage mapping studies, it does not fully explain genetic variability for tolerance in soybean germplasm. In this study, a genome-wide association study (GWAS) was performed to map genomic regions for salt tolerance in a diverse panel of 305 soybean accessions using a single nucleotide polymorphism (SNP) dataset derived from the SoySNP50K iSelect BeadChip. A second GWAS was also conducted in a subset of 234 accessions using another 3.7 M SNP dataset derived from a whole-genome resequencing (WGRS) study. In addition, three gene-based markers (GBM) of the known gene, Glyma03g32900, on Chr. 3 were also integrated into the two datasets. Salt tolerance among soybean lines was evaluated by leaf scorch score (LSS), chlorophyll content ratio (CCR), leaf sodium content (LSC), and leaf chloride content (LCC). Results For both association studies, a major locus for salt tolerance on Chr. 3 was confirmed by a number of significant SNPs, of which three gene-based SNP markers, Salt-20, Salt14056 and Salt11655, had the highest association with all four traits studied. Also, additional genomic regions on Chrs. 1, 8, and 18 were found to be associated with various traits measured in the second GWAS using the WGRS-derived SNP dataset. Conclusions A region identified on Chr. 8 was identified to be associated with all four traits and predicted as a new minor locus for salt tolerance in soybean. The candidate genes harbored in this minor locus may help reveal the molecular mechanism involved in salt tolerance and to improve tolerance in soybean cultivars. The significant SNPs will be useful for marker-assisted selection for salt tolerance in soybean breeding programs.

Wild relatives of crop plants are invaluable germplasm for genetic improvement. Here, Xie et al. report a reference-grade wild soybean genome and show that it can be used to identify structural variation and refine quantitative trait loci.

Abstract Background Brassinosteroids (BRs) play a crucial role in plant vegetative growth and reproductive development. The transcription factors BZR1 and BES1/BZR2 are well characterized as downstream regulators of the BR signaling pathway in Arabidopsis and rice. Soybean contains four BZR1-like proteins (GmBZLs), and it was reported that GmBZL2 plays a conserved role in BR signaling regulation. However, the roles of other GmBZLs have not been thoroughly studied, and the targets of GmBZLs in soybean remain unclear. Results In this study, we first characterized GmBZL3 in soybean from gene expression patterns, conserved domains in coding sequences, and genomic replication times of four GmBZL orthologous. The results indicated that GmBZL3 might play conserved roles during soybean development. The overexpression of GmBZL3 P219L in the Arabidopsis BR-insensitive mutant bri1–5 partially rescued the phenotypic defects including BR-insensitivity, which provides further evidence that GmBZL3 functions are conserved between soybean and the homologous Arabidopsis genes. In addition, the identification of the GmBZL3 target genes through ChIP-seq technology revealed that BR has broad roles in soybean and regulates multiple pathways, including other hormone signaling, disease-related, and immunity response pathways. Moreover, the BR-regulated GmBZL3 target genes were further identified, and the results demonstrate that GmBZL3 is a major transcription factor responsible for BR-regulated gene expression and soybean growth. A comparison of GmBZL3 and AtBZR1/BES1 targets demonstrated that GmBZL3 might play conserved as well as specific roles in the soybean BR signaling network. Finally, the identification of two natural soybean varieties of the GmBZL3 mutantion by SNP analysis could facilitate the understanding of gene function during soybean development in the future. Conclusions We illustrate here that GmBZL3 orchestrates a genome-wide transcriptional response that underlies BR-mediated soybean early vegetative growth, and our results support that BRs play crucial regulatory roles in soybean morphology and gene expression levels.

Soybean seeds are rich in secondary metabolites which are beneficial for human health, including tocopherols. Tocopherols play an important role in human and animal nutrition thanks to their antioxidant activity. In this study, the ‘Forrest’ by ‘Williams 82’ (F×W82) recombinant inbred line (RIL) population (n = 306) was used to map quantitative trait loci (QTL) for seed α-tocopherol, β-tocopherol, δ -tocopherol, γ-tocopherol, and total tocopherol contents in Carbondale, IL over two years. Also, the identification of the candidate genes involved in soybean tocopherols biosynthetic pathway was performed. A total of 32 QTL controlling various seed tocopherol contents have been identified and mapped on Chrs. 1, 2, 5, 6, 7, 8, 9, 10, 12, 13, 16, 17, and 20. One major and novel QTL was identified on Chr. 6 with an R2 of 27.8, 9.9, and 6.9 for δ-tocopherol, α-tocopherol, and total tocopherol content, respectively. Reverse BLAST analysis of the genes that were identified in Arabidopsis allowed the identification of 37 genes involved in soybean tocopherol pathway, among which 11 were located close to the identified QTLs. The tocopherol cyclase gene (TC) Glyma.06G084100 is located close to the QTLs controlling δ-tocopherol (R2 = 27.8), α-tocopherol (R2 = 9.96), and total-tocopherol (R2 = 6.95). The geranylgeranyl diphosphate reductase (GGDR) Glyma.05G026200 gene is located close to a QTL controlling total tocopherol content in soybean (R2 = 4.42). The two methylphytylbenzoquinol methyltransferase (MPBQ-MT) candidate genes Glyma.02G002000 and Glyma.02G143700 are located close to a QTL controlling δ-tocopherol content (R2 = 3.57). The two γ-tocopherol methyltransferase (γ-TMT) genes, Glyma.12G014200 and Glyma.12G014300, are located close to QTLs controlling (γ+ß) tocopherol content (R2 = 8.86) and total tocopherol (R2 = 5.94). The identified tocopherol seed QTLs and candidate genes will be beneficial in breeding programs to develop soybean cultivars with high tocopherol contents.

Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. The flooding injury score (FIS) of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms. Aerial images were taken on the same day using a five-band multispectral and an infrared (IR) thermal camera at 20, 50, and 80 m above ground. Five image features, i.e., canopy temperature, normalized difference vegetation index, canopy area, width, and length, were extracted from the images at three flight heights. A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features. Results show that the image features were significantly different at three flight heights. The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS. The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.

The wide adoption of dicamba-tolerant (DT) soybean has led to numerous cases of off-target dicamba damage to non-DT soybean and dicot crops. This study aimed to develop a method to differentiate soybean response to dicamba using unmanned-aerial-vehicle-based imagery and machine learning models. Soybean lines were visually classified into three classes of injury, i.e., tolerant, moderate, and susceptible to off-target dicamba. A quadcopter with a built-in RGB camera was used to collect images of field plots at a height of 20 m above ground level. Seven image features were extracted for each plot, including canopy coverage, contrast, entropy, green leaf index, hue, saturation, and triangular greenness index. Classification models based on artificial neural network (ANN) and random forest (RF) algorithms were developed to differentiate the three classes of response to dicamba. Significant differences for each feature were observed among classes and no significant differences across fields were observed. The ANN and RF models were able to precisely distinguish tolerant and susceptible lines with an overall accuracy of 0.74 and 0.75, respectively. The imagery-based classification model can be implemented in a breeding program to effectively differentiate phenotypic dicamba response and identify soybean lines with tolerance to off-target dicamba damage.

Wild soybean species (Glycine soja Siebold & Zucc.) comprise a unique resource to widen the genetic base of cultivated soybean [Glycine max (L.) Merr.] for various agronomic traits. An inter-specific mapping population derived from a cross of cultivar Williams 82 and PI 483460B, a wild soybean accession, was utilized for genetic characterization of root architecture traits. The objectives of this study were to identify and characterize quantitative trait loci (QTL) for seedling shoot and root architecture traits, as well as to determine additive/epistatic interaction effects of identified QTLs. A total of 16,469 single nucleotide polymorphisms (SNPs) developed for the Illumina beadchip genotyping platform were used to construct a high resolution genetic linkage map. Among the 11 putative QTLs identified, two significant QTLs on chromosome 7 were determined to be associated with total root length (RL) and root surface area (RSA) with favorable alleles from the wild soybean parent. These seedling root traits, RL (BARC_020495_04641 ~ BARC_023101_03769) and RSA (SNP02285 ~ SNP18129_Magellan), could be potential targets for introgression into cultivated soybean background to improve both tap and lateral roots. The RL QTL region harbors four candidate genes with higher expression in root tissues: Phosphofructokinase (Glyma.07g126400), Snf7 protein (Glyma.07g127300), unknown functional gene (Glyma.07g127900), and Leucine Rich-Repeat protein (Glyma.07g127100). The novel alleles inherited from the wild soybean accession could be used as molecular markers to improve root system architecture and productivity in elite soybean lines.

Drought is the most prolific form of abiotic stress that legumes and cereal plants alike can endure, and the planting of an improper cultivar at the beginning of a season can cause unexpected losses up to fifty percent under water deficient conditions. Herein, a plant introduction (PI) of an exotic cultivar of soybean (Glycine max), PI 567731, which demonstrates a slow wilting (SW) canopy phenotype in maturity group III, was profiled under drought conditions in field trials in Missouri against a drought susceptible check cultivar, Pana. Metabolomic profiling was carried out on samples of leaves from each of these cultivars at V5 and R2 growth stages both while irrigated and while under drought stress for three weeks. PI 567731 was observed to have differential phytochemical content, and enhanced levels of chlorophyll (Chl) a/b and pheophytin (Pheo) were profiled by direct infusion electrospray Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Indicating drought induced changes of the photosystem and photosynthetic capabilities alongside water preservation strategies are important within the SW phenotype drought response. Subsequent multivariate analysis was able to form predictive models, encompassing the variance of growth and drought stress of the cultivar. Moreover, the existence of unique Chl-related metabolites (CRM) (m/z > 900) were confirmed through tandem mass spectrometry. The resultant coordination of fatty acids to the core of the porphyrin ring was observed and played an unknown role in the proliferation of the photosynthesis. However, the relative ratio of the most abundant CRM is undisturbed by drought stress in PI 567731, in contrast to the drought susceptible cultivar. These results provide key insights into drought related metabolic mechanisms.

Root-system architecture is vital for improving soybean (Glycine max L.) growth and nutrient uptake. We characterised root-system architecture and shoot traits of 30 soybean genotypes in a semi-hydroponic system 35 days after sowing (DAS) and validated eight genotypes with contrasting root-system architecture in 1.5 m-deep rhizoboxes at the flowering stage. Among them, two genotypes were selected for evaluation through to maturity. Abundant variation (coefficient of variation values ≥ 0.25) was observed in 11 of 13 measured roots and shoot traits during the early growth stage. After late growth stages, strong positive correlations were found between root traits and shoot traits, except for specific root length and diameter. Seed yield and yield traits at final harvest significantly differed between two contrasting soybean genotypes. The large-rooted genotype had a higher harvest index than the small-rooted genotype. Soybean genotypes with larger root systems had a long time to flowering than those with smaller root systems. Genotypes with large-root systems had 106% more leaf area, and 245% more shoot dry weight than those with small systems, presumably due to high canopy photosynthesis to supply the demand for carbon assimilates to roots. Total root length, and root: shoot ratio-traits data collected in the rhizobox study, strongly correlated with the same traits in the semi-hydroponic phenotyping system. We found genetic variation and phenotypic plasticity in other root and shoot traits such as taproot depth, root dry weight, specific root length, and average root diameter among the tested genotypes. Phenology, particularly time to flowering, was associated with root system size. Some root and shoot traits in the semi-hydroponic phenotyping system at the seedling stage produced similar rankings at the later phenological (flowering) stage when grown in the soil-filled rhizoboxes. The soybean genotypes characterised by vastly different root traits could be used for further glasshouse and field studies to improve adaptation to drought and other specific environments.

Phenotypic variation and correlations among root traits form the basis for selecting and breeding soybean varieties with efficient access to water and nutrients and better adaptation to abiotic stresses. Therefore, it is important to develop a simple and consistent system to study root traits in soybean. In this study, we adopted the semi-hydroponic system to investigate the variability in root morphological traits of 171 soybean genotypes popularized in the Yangtze and Huaihe River regions, eastern China. Highly diverse phenotypes were observed: shoot height (18.7–86.7 cm per plant with a median of 52.3 cm); total root length (208–1663 cm per plant with a median of 885 cm); and root mass (dry weight) (19.4–251 mg per plant with a median of 124 mg). Both total root length and root mass exhibited significant positive correlation with shoot mass (p ≤ 0.05), indicating their relationship with plant growth and adaptation strategies. The nine selected traits contributed to one of the two principal components (eigenvalues > 1), accounting for 78.9% of the total genotypic variation. Agglomerative hierarchical clustering analysis separated the 171 genotypes into five major groups based on these root traits. Three selected genotypes with contrasting root systems were validated in soil-filled rhizoboxes (1.5 m deep) until maturity. Consistent ranking of the genotypes in some important root traits at various growth stages between the two experiments indicates the reliability of the semi-hydroponic system in phenotyping root trait variability at the early growth stage in soybean germplasms.

Isoflavones are secondary metabolites that are abundant in soybean and other legume seeds providing health and nutrition benefits for both humans and animals. The objectives of this study were to construct a single nucleotide polymorphism (SNP)-based genetic linkage map using the ‘Forrest’ by ‘Williams 82’ (F×W82) recombinant inbred line (RIL) population (n = 306); map quantitative trait loci (QTL) for seed daidzein, genistein, glycitein, and total isoflavone contents in two environments over two years (NC-2018 and IL-2020); identify candidate genes for seed isoflavone. The FXW82 SNP-based map was composed of 2075 SNPs and covered 4029.9 cM. A total of 27 QTL that control various seed isoflavone traits have been identified and mapped on chromosomes (Chrs.) 2, 4, 5, 6, 10, 12, 15, 19, and 20 in both NC-2018 (13 QTL) and IL-2020 (14 QTL). The six QTL regions on Chrs. 2, 4, 5, 12, 15, and 19 are novel regions while the other 21 QTL have been identified by other studies using different biparental mapping populations or genome-wide association studies (GWAS). A total of 130 candidate genes involved in isoflavone biosynthetic pathways have been identified on all 20 Chrs. And among them 16 have been identified and located within or close to the QTL identified in this study. Moreover, transcripts from four genes (Glyma.10G058200, Glyma.06G143000, Glyma.06G137100, and Glyma.06G137300) were highly abundant in Forrest and Williams 82 seeds. The identified QTL and four candidate genes will be useful in breeding programs to develop soybean cultivars with high beneficial isoflavone contents.

Soybean is the second largest source of oil worldwide. Developing soybean varieties with high levels of oleic acid is a primary goal of the soybean breeders and industry. Edible oils containing high level of oleic acid and low level of linoleic acid are considered with higher oxidative stability and can be used as a natural antioxidant in food stability. All developed high oleic acid soybeans carry two alleles; GmFAD2-1A and GmFAD2-1B. However, when planted in cold soil, a possible reduction in seed germination was reported when high seed oleic acid derived from GmFAD2-1 alleles were used. Besides the soybean fatty acid desaturase (GmFAD2-1) subfamily, the GmFAD2-2 subfamily is composed of five members, including GmFAD2-2A, GmFAD2-2B, GmFAD2-2C, GmFAD2-2D, and GmFAD2-2E. Segmental duplication of GmFAD2-1A/GmFAD2-1B, GmFAD2-2A/GmFAD2-2C, GmFAD2-2A/GmFAD2-2D, and GmFAD2-2D/GmFAD2-2C have occurred about 10.65, 27.04, 100.81, and 106.55 Mya, respectively. Using TILLING-by-Sequencing+ technology, we successfully identified 12, 8, 10, 9, and 19 EMS mutants at the GmFAD2-2A, GmFAD2-2B, GmFAD2-2C, GmFAD2-2D, and GmFAD2-2E genes, respectively. Functional analyses of newly identified mutants revealed unprecedented role of the five GmFAD2-2A, GmFAD2-2B, GmFAD2-2C, GmFAD2-2D, and GmFAD2-2E members in controlling the seed oleic acid content. Most importantly, unlike GmFAD2-1 members, subcellular localization revealed that members of the GmFAD2-2 subfamily showed a cytoplasmic localization, which may suggest the presence of an alternative fatty acid desaturase pathway in soybean for converting oleic acid content without substantially altering the traditional plastidial/ER fatty acid production.

Alternative splicing (AS) is a common post-transcriptional regulatory mechanism that modulates gene expression to increase proteome diversity. Increasing evidence indicates that AS plays an important role in regulating plant stress responses. However, the mechanism by which AS coordinates with transcriptional regulation to regulate drought responses in soybean remains poorly understood. In this study, we performed a genome-wide analysis of AS events in soybean (Glycine max) roots grown under various drought conditions using the high-throughput RNA-sequencing method, identifying 385, 989, 1429, and 465 AS events that were significantly differentially spliced under very mild drought stress, mild drought stress, severe drought stress, and recovery after severe drought conditions, respectively. Among them, alternative 3′ splice sites and skipped exons were the major types of AS. Overall, 2120 genes that experienced significant AS regulation were identified from these drought-treated root samples. Gene Ontology term analysis indicated that the AS regulation of binding activity has vital roles in the drought response of soybean root. Notably, the genes encoding splicing regulatory factors in the spliceosome pathway and mRNA surveillance pathway were enriched according to the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Splicing regulatory factor-related genes in soybean root also responded to drought stress and were alternatively spliced under drought conditions. Taken together, our data suggest that drought-responsive AS acts as a direct or indirect mode to regulate drought response of soybean roots. With further in-depth research of the function and mechanism of AS in the process of abiotic stress, these results will provide a new strategy for enhancing stress tolerance of plants.

The root angle and fibrous root score determines soybean [ (L.) Merr.] productivity in water-limited environments. Studying root traits in the field is technically difficult to do nondestructively, and varying agroclimatic and edaphic factors create challenges, which could be circumvented using laboratory-based root studies with higher precision. In this study, we developed and validated a nondestructive root imaging platform and derived three-dimensional (3D) root features using algorithms that would predict the root response under field conditions. Nine soybean genotypes (five landrace accessions and four cultivars) were evaluated at seedling and reproductive stages in controlled environments and the field. Irrespective of the phenotyping platforms, soybean landraces showed higher phenotypic variation for different root architectural traits than cultivars. The plant introductions 200471 and 567731 had higher phenotypic values for root and shoot traits than cultivars Dunbar, Magellan, and Maverick. The seedling 3D root features—volume and surface area/volume ratio—were found to be the best predictors of field root response evaluated. This positive association is a promising step in using a nondestructive imaging system to screen soybean with a desired root system that matches different target environments. Thus, the selection based on accurate 3D root features will hasten the inclusion of root traits in soybean breeding programs specifically to improve productivity in harsh environments.

Wheat breeding nowadays must address producers and consumers' desire. According to the last FAO report, a dramatic decrease in wheat production is expected in the next decades mainly due to the upcoming climate change. The identification of the processes which are triggered by heat stress and how thermotolerance develops in wheat is an active research topic. Genomic approach may help wheat breeding since it allows direct study on the genotype and relationship with the phenotype. Here the isolation and characterization of four members of the chloroplast-localized small heat shock proteins (sHSP) encoded by the Hsp26 gene family is reported. Furthermore, two high throughput TILLING (Targeting Induced Local Lesions In Genomes) approaches in vivo and in silico were used for the identification of new alleles within this family. Small heat shock proteins are known to prevent the irreversible aggregation of misfolded proteins and contribute to the acquisition of thermotolerance. Chloroplast-localized sHSPs protect the photosynthetic machinery during episodes of high temperature stress. The modulation of the newly discovered genes within the sHsp26 family has been analyzed in vivo and by the ExpVIP platform widening the abiotic stress analysis; and their involvement in the heat stress response has been demonstrated. In addition, in this study a total of 50 TILLING mutant lines have been identified. A set of KASP (Kompetitive Allele Specific PCR) markers was also developed to follow the specific mutations in the ongoing backcrosses, applicable to high throughput genotyping approaches and usable in marker assisted selection breeding programs.

Drought provokes a number of physiological changes in plants including oxidative damage. Ascorbic acid (AsA), also known as vitamin C, is one of the most abundant water-soluble antioxidant compound present in plant tissues. However, little is known on the regulation of AsA biosynthesis under drought stress conditions. In the current work we analyze the effects of water deficit on the biosynthesis of AsA by measuring its content, in vivo biosynthesis and the expression level of genes in the Smirnoff-Wheeler pathway in one of the major legume crop, soybean (Glycine max L. Merr). Since the pathway has not been described in legumes, we first searched for the putative orthologous genes in the soybean genome. We observed a significant genetic redundancy, with multiple genes encoding each step in the pathway. Based on RNA-seq analysis, expression of the complete pathway was detected not only in leaves but also in root tissue. Putative paralogous genes presented differential expression patterns in response to drought, suggesting the existence of functional specialization mechanisms. We found a correlation between the levels of AsA and GalLDH biosynthetic rates in leaves of drought-stressed soybean plants. However, the levels of GalLDH transcripts did not show significant differences under water deficit conditions. Among the other known regulators of the pathway, only the expression of VTC1 genes correlated with the observed decline in AsA in leaves.

Much research has been conducted on the changes in gene expression of the model plant Arabidopsis to low-oxygen stress. Flooding results in a low oxygen environment in the root zone. However, there is ample evidence that tolerance to soil flooding is more than tolerance to low oxygen alone. In this study, we investigated the physiological response and differential expression of root-related transcription factors (TFs) associated with the tolerance of soybean plants to soil flooding. Differential responses of PI408105A and S99-2281 plants to ten days of soil flooding were evaluated at physiological, morphological and anatomical levels. Gene expression underlying the tolerance response was investigated using qRT-PCR of root-related TFs, known anaerobic genes, and housekeeping genes. Biomass of flood-sensitive S99-2281 roots remained unchanged during the entire 10 days of flooding. Flood-tolerant PI408105A plants exhibited recovery of root growth after 3 days of flooding. Flooding induced the development of aerenchyma and adventitious roots more rapidly in the flood-tolerant than the flood-sensitive genotype. Roots of tolerant plants also contained more ATP than roots of sensitive plants at the 7th and 10th days of flooding. Quantitative transcript analysis identified 132 genes differentially expressed between the two genotypes at one or more time points of flooding. Expression of genes related to the ethylene biosynthesis pathway and formation of adventitious roots was induced earlier and to higher levels in roots of the flood-tolerant genotype. Three potential flood-tolerance TFs which were differentially expressed between the two genotypes during the entire 10-day flooding duration were identified. This study confirmed the expression of anaerobic genes in response to soil flooding. Additionally, the differential expression of TFs associated with soil flooding tolerance was not qualitative but quantitative and temporal. Functional analyses of these genes will be necessary to reveal their potential to enhance flooding tolerance of soybean cultivars.

Flood has an important effect on plant growth by affecting their physiologic and biochemical properties. Soybean is one of the main cultivated crops in the world and the United States is one of the largest soybean producers. However, soybean plant is sensitive to flood stress that may cause slow growth, low yield, small crop production and result in significant economic loss. Therefore, it is critical to develop soybean cultivars that are tolerant to flood. One of the current bottlenecks in developing new crop cultivars is slow and inaccurate plant phenotyping that limits the genetic gain. This study aimed to develop a low-cost 3D imaging system to quantify the variation in the growth and biomass of soybean due to flood at its early growth stages. Two cultivars of soybeans, i.e. flood tolerant and flood sensitive, were planted in plant pots in a controlled greenhouse. A low-cost 3D imaging system was developed to take measurements of plant architecture including plant height, plant canopy width, petiole length, and petiole angle. It was found that the measurement error of the 3D imaging system was 5.8% in length and 5.0% in angle, which was sufficiently accurate and useful in plant phenotyping. Collected data were used to monitor the development of soybean after flood treatment. Dry biomass of soybean plant was measured at the end of the vegetative stage (two months after emergence). Results show that four groups had a significant difference in plant height, plant canopy width, petiole length, and petiole angle. Flood stress at early stages of soybean accelerated the growth of the flood-resistant plants in height and the petiole angle, however, restrained the development in plant canopy width and the petiole length of flood-sensitive plants. The dry biomass of flood-sensitive plants was near two to three times lower than that of resistant plants at the end of the vegetative stage. The results indicate that the developed low-cost 3D imaging system has the potential for accurate measurements in plant architecture and dry biomass that may be used to improve the accuracy of plant phenotyping.

Xyloglucan endotransglycosylases/hydrolases (XTHs) are a class of enzymes involved in the construction and remodeling of cellulose/xyloglucan crosslinks and play an important role in regulating cell wall extensibility. However, little is known about this class of enzymes in soybean. Here, 61 soybean XTH genes (GmXTHs) were identified and classified into three subgroups through comparative phylogenetic analysis. Genome duplication greatly contributed to the expansion of GmXTH genes in soybean. A conserved amino acid motif responsible for the catalytic activity was identified in all GmXTHs. Further expression analysis revealed that most GmXTHs exhibited a distinct organ-specific expression pattern, and the expression level of many GmXTH genes was significantly associated with ethylene and flooding stress. To illustrate a possible role of XTH genes in regulating stress responses, the Arabidopsis AtXTH31 gene was overexpressed in soybean. The generated transgenic plants exhibited improved tolerance to flooding stress, with a higher germination rate and longer roots/hypocotyls during the seedling stage and vegetative growth stages. In summary, our combined bioinformatics and gene expression pattern analyses suggest that GmXTH genes play a role in regulating soybean stress responses. The enhanced soybean flooding tolerance resulting from the expression of an Arabidopsis XTH also supports the role of XTH genes in regulating plant flooding stress responses.

The value of soybean [ (L.) Merr.] is determined in part by functionality of major seed components, extractable oil and high protein meal. Soybean meal is an important protein source for animal feeds, and use of soybean meal by the food industry is increasing. Food allergens have negative impacts for both foods and feeds. The major soybean allergen, Gly m Bd 30K, was identified as the P34 protein, a low abundance but highly conserved seed protein. Recently, two soybean germplasm accessions (PI 567476 and PI 603570A) were identified as having reduced P34 content without other collateral changes in seed protein composition. In order to develop molecular markers to enhance breeding for low P34, our objective was to characterize the P34 gene region in wild-type and low-P34 soybean lines and exploit any sequence differences to allow direct selection for mutant P34 alleles. The results indicated the two low-P34 germplasm accessions contain a four-basepair insertion at the P34 start codon. The mutant allele may inhibit efficient translation initiation of the P34 protein. Inheritance of the mutant P34 alleles completely associated with the low-P34 phenotype. Molecular markers were developed that were based on the mutant allele of the P34 gene.

The potential of association mapping (AM) and genomic selection (GS) has not yet been explored for investigating resistance to soybean cyst nematode (SCN), the most destructive pest affecting soybean. We genotyped 282 representative accessions from the University of Minnesota soybean breeding program using a genome-wide panel of 1536 single nucleotide polymorphism (SNP) markers and evaluated plant responses to SCN HG type 0. After adjusting for population structure, AM detected significant signals at two loci corresponding to and plus a third locus located at the opposite end of chromosome 18. Our analysis also identified a discontinuous long-range haplotype of over 600 kb around locus associated with resistance to SCN HG type 0. The same phenotypic and genotypic datasets were then used to access GS accuracy for prediction of SCN resistance in the presence of major genes through a sixfold cross-validation study. Genomic selection using the full marker set produced average prediction accuracy ranging from 0.59 to 0.67 for SCN resistance, significantly more accurate than marker-assisted selection (MAS) strategies using two -associated DNA makers. Reducing the number of markers to 288 SNPs in the GS training population had little effect on genomic prediction accuracy. This study demonstrates that AM can be an effective genomic tool for identifying genes of interest in diverse germplasm. The results also indicate that improved MAS and GS can enhance breeding efficiency for SCN resistance in existing soybean improvement programs.

Soybean production is greatly influenced by abiotic stresses imposed by environmental factors such as drought, water submergence, salt, and heavy metals. A thorough understanding of plant response to abiotic stress at the molecular level is a prerequisite for its effective management. The molecular mechanism of stress tolerance is complex and requires information at the omic level to understand it effectively. In this regard, enormous progress has been made in the omics field in the areas of genomics, transcriptomics, and proteomics. The emerging field of ionomics is also being employed for investigating abiotic stress tolerance in soybean. Omic approaches generate a huge amount of data, and adequate advancements in computational tools have been achieved for effective analysis. However, the integration of omic-scale information to address complex genetics and physiological questions is still a challenge. In this review, we have described advances in omic tools in the view of conventional and modern approaches being used to dissect abiotic stress tolerance in soybean. Emphasis was given to approaches such as quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), and genomic selection (GS). Comparative genomics and candidate gene approaches are also discussed considering identification of potential genomic loci, genes, and biochemical pathways involved in stress tolerance mechanism in soybean. This review also provides a comprehensive catalog of available online omic resources for soybean and its effective utilization. We have also addressed the significance of phenomics in the integrated approaches and recognized high-throughput multi-dimensional phenotyping as a major limiting factor for the improvement of abiotic stress tolerance in soybean.