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Abstract Background Soybean is a major oil crop; the nutritional components of soybean oil are mainly controlled by unsaturated fatty acids (FA). Unsaturated FAs mainly include oleic acid (OA, 18:1), linoleic acid (LLA, 18:2), and linolenic acid (LNA, 18:3). The genetic architecture of unsaturated FAs in soybean seeds has not been fully elucidated, although many independent studies have been conducted. A 3 V multi-locus random single nucleotide polymorphism (SNP)-effect mixed linear model (3VmrMLM) was established to identify quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits. Results In this study, 194 soybean accessions with 36,981 SNPs were calculated using the 3VmrMLM model. As a result, 94 quantitative trait nucleotides (QTNs) and 19 QEIs were detected using single-environment (QTN) and multi-environment (QEI) methods. Three significant QEIs, namely rs4633292, rs39216169, and rs14264702, overlapped with a significant single-environment QTN. Conclusions For QTNs and QEIs, further haplotype analysis of candidate genes revealed that the Glyma.03G040400 and Glyma.17G236700 genes were beneficial haplotypes that may be associated with unsaturated FAs. This result provides ideas for the identification of soybean lipid-related genes and provides insights for breeding high oil soybean varieties in the future.

Background: Soybean mosaic virus (SMV) is a globally prevalent and detrimental virus that belongs to the Potyvirus genus. Pathogenic viruses of this genus are typically linear in shape, with dimensions ranging between 630 and 750 nm, and are composed of single-stranded RNA and proteins. We have developed an SMV-resistant soybean line, Dongnong 93-046, which has no significant changes in disease resistance identification in the adult plants and has neat grains with no obvious brown or black markings. To explore the defense mechanisms of soybean against SMV, we performed comparative transcriptomic sequencing of the leaves between the Dongnong 93-046 inoculated with SMV at 8 h (T) and the non-inoculated control (C) on the HiSeq2000 platform. In addition, we performed non-targeted metabolomic sequencing of leaves from the treatment and control groups. Results: We identified a total of 41,189 differentially expressed genes (DEGs). A total of 9809 differentially expressed genes (DEGs) met the criteria of |Log2FC (Fold Change)| ≥ 1 and adjusted p-value ≤ 0.001. Among the 41,189 DEGs identified, 9196 exhibited FPKM values greater than 10. KEGG pathway enrichment analysis of the 9809 DEGs revealed significant enrichment of genes involved in resistance-related pathways such as plant–pathogen interaction, linoleic acid metabolism, mitogen-activated protein kinase (MAPK) signaling pathway, and plant hormone signaling transduction. Functional analysis using MapMan software identified multiple DEGs that were associated with pathways such as jasmonate synthesis and phenylpropanoid biosynthesis. Weighted gene co-expression network analysis (WGCNA) using the differential metabolites and the 9196 DEGs revealed a strong correlation between gene clusters within the Turquoise module and the content of jasmonate-related metabolites. Further functional enrichment analysis of the 894 genes within the gene clusters showed a significant and repeated enrichment of pathways related to plant–pathogen interaction, linoleic acid metabolism, and plant hormone signaling transduction. Subsequent focused pathway analysis identified key genes involved in plant hormone signaling transduction pathways, such as the jasmonate ZIM domain protein Glyma.16G010000, the gene Glyma.01G235600 encoding the essential diterpene reductase required for jasmonate synthesis in the jasmonate biosynthesis pathway, and the transcription factor Glyma.02G232600 involved in the plant–pathogen interaction pathway, among others. This study provides a theoretical framework for understanding the resistance mechanism of soybean cultivar Dongnong 93-046 against the SMV N1 strain, offers potential gene resources for breeding soybean varieties with resistance to SMV, and paves the way for new strategies to control SMV infection, enhance resistance, and improve crop yield and quality.

Soybean (Glycine max (L.) Merr.) is an important crop for both food and feed, playing a significant role in agricultural production and the human diet. During long-term storage, soybean seeds often exhibit reduced quality, decreased germination, and lower seedling vigor, ultimately leading to significant yield reductions in soybean crops. Seed storage tolerance is a complex quantitative trait controlled by multiple genes and is also influenced by environmental factors during seed formation, harvest, and storage. This study aimed to evaluate soybean germplasms for their storage tolerance, identify quantitative trait nucleotides (QTNs) associated with seed storage tolerance traits, and screen for candidate genes. The storage tolerance of 168 soybean germplasms was evaluated, and 23,156 high-quality single nucleotide polymorphism (SNP) markers were screened and analyzed through a genome-wide association study (GWAS). Ultimately, 14 QTNs were identified as being associated with seed storage tolerance and were distributed across the eight chromosomes of soybean, with five QTNs (rs25887810, rs27941858, rs33981296, rs44713950, and rs18610980) being newly reported loci in this study. In the linkage disequilibrium regions of these SNPs, 256 genes were identified. By combining GWAS and weighted gene co-expression network analysis (WGCNA), eight hub genes (Glyma.03G058300, Glyma.04G1921100, Glyma.04G192600, Glyma.04G192900, Glyma.07G002000, Glyma.08G329400, Glyma.16G074600, Glyma.16G091400) were jointly identified. Through the analysis of expression patterns, two candidate genes (Glyma.03G058300, Glyma.16G074600) potentially involved in seed storage tolerance were ultimately identified. Additionally, haplotype analysis revealed that natural variations in Glyma.03G058300 could affect seed storage tolerance. The findings of this research provide a theoretical foundation for understanding the regulatory mechanism underlying soybean storage.

The soybean cyst nematode (SCN), Heterodera glycines Ichinohe, 1952, is one of the most destructive plant-parasitic nematodes in soybean production worldwide. The use of resistant soybean is the most effective alternative for its management. However, SCN-resistant soybean cultivars with increased yield and favorable agronomic traits remain limited in the market. Here, we developed a new SCN-resistant soybean cultivar Nongqing 28 from the cross of the female parent cultivar An 02-318 and a male parent line F2 (Hei 99-980 × America Xiaoheidou). Resistance evaluation suggested that Nongqing 28 displayed stable resistance to SCN race 3 in pot assays and the 5-year field experiments, including inhibition of SCN development and reduction in female and cyst numbers. The average yields of Nongqing 28 were 2593 kg/ha and 2660 kg/ha in the 2-year regional trails and the 1-year production trials, with a yield increase of 6.2% and 8.1% compared with the local cultivar Nengfeng 18, respectively. The average seed fat contents in Nongqing 28 reached 21.26%. Additionally, RNA-seq analysis revealed that the resistance of Nongqing 28 to SCN infection is involved in pathogen perception and defense activation, such as reactive oxygen species burst, calcium-mediated defense signaling, hormonal signaling, the MAPK signaling cascade, and phenylpropanoid biosynthesis. In summary, this study provides a detailed characterization of a novel SCN-resistant soybean cultivar with high oil and yield potential.

The utilization of saline land is a global challenge, and cultivating salt-tolerant soybean varieties is beneficial for improving the efficiency of saline land utilization. Exploring the genetic basis of salt-tolerant soybean varieties and developing salt-tolerant molecular markers can effectively promote the process of soybean salt-tolerant breeding. In the study, the membership function method was used to evaluate seven traits related to salt tolerance and comprehensive salt tolerance at the soybean seedling stage; genome-wide association analysis (GWAS) was performed in a natural population containing 200 soybean materials; and linkage analysis was performed in 112 recombinant inbred lines (RIL) population to detect quantitative trait loci (QTLs) of salt tolerance. In the GWAS, 147 SNPs were mapped, explaining 5.28–17.16% of phenotypic variation. In the linkage analysis, 10 QTLs were identified, which could explain 6.9–16.16% of phenotypic variation. And it was found that there were two co-located regions between the natural population and the RIL population, containing seven candidate genes of salt tolerance in soybean. In addition, one colocalization interval was found to contain qZJS-15-1, rs47665107, and rs4793412, all of which could explain more than 10% of phenotypic variation rates, making it suitable for molecular marker development. The physical positions of rs47665107 and rs47934112 were included in qZJS-15-1. Therefore, a KASP marker was designed and developed using Chr. 15:47907445, which was closely linked to the qZJS-15-1. This marker could accurately and clearly cluster the materials of salt-tolerant genotypes in the heterozygous population tested. The QTLs and KASP markers found in the study provide a theoretical and technical basis for accelerating the salt-tolerant breeding of soybean.

Soybean saponin is a natural antioxidant and is anti-inflammatory. Hyperspectral analysis technology was applied to detect soybean saponin content rapidly and nondestructively in this paper. Firstly, spectral preprocessing methods were studied, and standard normal variable (SNV) was used to remove noise information. Secondly, a two-step hybrid variable selection approach based on synergy interval partial least squares (SiPLS) and iteratively retains informative variables (IRIV) was proposed to extract characteristic variables. Then, the ensemble learning model was constructed by back propagation neural network (BPNN), deep forest (DF), partial least squares regression (PLSR), and extreme gradient boosting (EXG). Finally, image information was combined into spectral data to improve model accuracy. The prediction coefficient of determination (R2) of the final model reached 0.9216. It can provide rapid, nondestructive, and accurate detection technology of soybean saponin content. A combination of spectral and image information will provide a new idea for application of hyperspectral.

Abstract Background Soybean (Glycine max (L.) Merr) is an important source of human food, animal feed, and bio-energy. Although the genetic network of lipid metabolism is clear in Arabidopsis, the understanding of lipid metabolism in soybean is limited. Results In this study, 30 soybean varieties were subjected to transcriptome and metabolome analysis. In total, 98 lipid-related metabolites were identified, including glycerophospholipid, alpha-linolenic acid, linoleic acid, glycolysis, pyruvate, and the sphingolipid pathway. Of these, glycerophospholipid pathway metabolites accounted for the majority of total lipids. Combining the transcriptomic and metabolomic analyses, we found that 33 lipid-related metabolites and 83 lipid-related genes, 14 lipid-related metabolites and 17 lipid-related genes, and 12 lipid-related metabolites and 25 lipid-related genes were significantly correlated in FHO (five high-oil varieties) vs. FLO (five low-oil varieties), THO (10 high-oil varieties) vs. TLO (10 low-oil varieties), and HO (15 high-oil varieties) vs. LO (15 low-oil varieties), respectively. Conclusions The GmGAPDH and GmGPAT genes were significantly correlated with lipid metabolism genes, and the result revealed the regulatory relationship between glycolysis and oil synthesis. These results improve our understanding of the regulatory mechanism of soybean seed oil improvement.

Soybean vegetable oil is an important source of the human diet. However, the analysis of the genetic mechanism leading to changes in soybean oil content is still incomplete. In this study, a total of 227 soybean materials were applied and analyzed by a genome-wide association study (GWAS). There are 44 quantitative trait nucleotides (QTNs) that were identified as associated with oil content. A total of six, four, and 34 significant QTN loci were identified in Xiangyang, Hulan, and Acheng, respectively. Of those, 26 QTNs overlapped with or were near the known oil content quantitative trait locus (QTL), and 18 new QTNs related to oil content were identified. A total of 594 genes were located near the peak single nucleotide polymorphism (SNP) from three tested environments. These candidate genes exhibited significant enrichment in tropane, piperidine, and pyridine alkaloid biosynthesiss (ko00960), ABC transporters (ko02010), photosynthesis-antenna proteins (ko00196), and betalain biosynthesis (ko00965). Combined with the GWAS and weighted gene co-expression network analysis (WGCNA), four candidate genes (Glyma.18G300100, Glyma.11G221100, Glyma.13G343300, and Glyma.02G166100) that may regulate oil content were identified. In addition, Glyma.18G300100 was divided into two main haplotypes in the studied accessions. The oil content of haplotype 1 is significantly lower than that of haplotype 2. Our research findings provide a theoretical basis for improving the regulatory mechanism of soybean oil content.

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.

IntroductionGlyceraldehyde-3-phosphate dehydrogenase (GAPDH) is an essential key enzyme in the glycolytic pathway and plays an important role in stress responses. Although GAPDH family genes have been found in different plant species, the determination of their gene family analysis and their functional roles in soybean are still unknown. MethodsIn this study, gene sequence and expression data were obtained using online tools, and systematic evolution, expression profile analysis, and qRT-PCR analysis were conducted.Results and DiscussionHere a total of 16 GmGAPDH genes were identified on nine chromosomes, which were classified into three clusters. Additionally, all GmGAPDH genes harbor two highly conserved domains, including Gp_dh_N (PF00044) and Gp_dh_C (PF02800). The qRTPCR analysis also showed that most GmGAPDH genes significantly responded to multiple abiotic stresses, including NaHCO3, polyethylene glycol, cold, and salt. Among them, GmGAPDH14 was extraordinarily induced by salt stress. The GmGAPDH14 gene was cloned and overexpressed through soybean hair roots. The overexpressed transgenic soybean plants of the GmGAPDH14 gene have also shown better growth than that of control plants. Moreover, the overexpressed transgenic plants of GmGAPDH14 gene had higher activities of superoxide dismutase but lower malonaldehyde (MDA) content than those of control plants under salt stress. Meanwhile, a total of four haplotypes were found for the GmGAPDH14 gene, and haplotypes 2, 3, and 4 were beneficial for the tolerance of soybean to salt stress. These results suggest that the GmGAPDH14 gene might be involved in the process of soybean tolerance to salt stress. The results of this study will be valuable in understanding the role of GAPDH genes in the abiotic stress response of soybean.

IntroductionSoybean cyst nematode (SCN, Heterodera glycines Ichinohe) is an important disease affecting soybean yield in the world. Potential SCN-related QTLs and QTL-by-environment interactions (QEIs) have been used in SCN-resistant breeding.MethodsIn this study, a compressed variance component mixed model, 3VmrMLM, in genome-wide association studies was used to detect QTLs and QEIs for resistance to SCN HG Type 0 and HG Type 1.2.3.5.7 in 156 different soybean cultivars materials.Results and discussionThe results showed that 53 QTLs were detected in single environment analysis; 36 QTLs and 9 QEIs were detected in multi-environment analysis. Based on the statistical screening of the obtained QTLs, we obtained 10 novel QTLs and one QEI which were different from the previous studies. Based on previous studies, we identified 101 known genes around the significant/suggested QTLs and QEIs. Furthermore, used the transcriptome data of SCN-resistant (Dongnong L-10) and SCN-susceptible (Suinong 14) cultivars, 10 candidate genes related to SCN resistance were identified and verified by Quantitative real time polymerase chain reaction (qRT-PCR) analysis. Haplotype difference analysis showed that Glyma.03G005600 was associated with SCN HG Type 0 and HG Type 1.2.3.5.7 resistance and had a haplotype beneficial to multi-SCN-race resistance. These results provide a new idea for accelerating SCN disease resistance breeding.

IntroductionThe soybean cyst nematode (SCN) is a major disease in soybean production thatseriously affects soybean yield. At present, there are no studies on weighted geneco-expression network analysis (WGCNA) related to SCN resistance.MethodsHere, transcriptome data from 36 soybean roots under SCN HG Type 0 (race 3) stresswere used in WGCNA to identify significant modules.Results and DiscussionA total of 10,000 differentially expressed genes and 21 modules were identified, of which the module most related to SCN was turquoise. In addition, the hub gene GmHg1 with high connectivity was selected, and its function was verified. GmHg1 encodes serine/threonine protein kinase (PK), and the expression of GmHg1 in SCN-resistant cultivars (‘Dongnong L-204’) and SCN-susceptible cultivars (‘Heinong 37’) increased significantly after HG Type 0 stress. Soybean plants transformed with GmHg1-OX had significantly increased SCN resistance. In contrast, the GmHg1-RNAi transgenic soybean plants significantly reduced SCN resistance. In transgenic materials, the expression patterns of 11 genes with the same expression trend as the GmHg1 gene in the ‘turquoise module’ were analyzed. Analysis showed that 11genes were co-expressed with GmHg1, which may be involved in the process of soybean resistance to SCN. Our work provides a new direction for studying the Molecular mechanism of soybean resistance to SCN.

Germination of soybean seed is the imminent vital process after sowing. The status of plumular axis and radicle determine whether soybean seed can emerge normally. Epicotyl, an organ between cotyledons and first functional leaves, is essential for soybean seed germination, seedling growth and early morphogenesis. Epicotyl length (EL) is a quantitative trait controlled by multiple genes/QTLs. Here, the present study analyzes the phenotypic diversity and genetic basis of EL using 951 soybean improved cultivars and landraces from Asia, America, Europe and Africa. 3VmrMLM was used to analyze the associations between EL in 2016 and 2020 and 1,639,846 SNPs for the identification of QTNs and QTN-by-environment interactions (QEIs)”.A total of 180 QTNs and QEIs associated with EL were detected. Among them, 74 QTNs (ELS_Q) and 16 QEIs (ELS_QE) were identified to be associated with ELS (epicotyl length of single plant emergence), and 60 QTNs (ELT_Q) and 30 QEIs (ELT_QE) were identified to be associated with ELT (epicotyl length of three seedlings). Based on transcript abundance analysis, GO (Gene Ontology) enrichment and haplotype analysis, ten candidate genes were predicted within nine genic SNPs located in introns, upstream or downstream, which were supposed to be directly or indirectly involved in the process of seed germination and seedling development., Of 10 candidate genes, two of them (Glyma.04G122400 and Glyma.18G183600) could possibly affect epicotyl length elongation. These results indicate the genetic basis of EL and provides a valuable basis for specific functional studies of epicotyl traits.

Genome-wide association studies (GWAS) is an efficient method to detect quantitative trait locus (QTL), and has dissected many complex traits in soybean [Glycine max (L.) Merr.]. Although these results have undoubtedly played a far-reaching role in the study of soybean biology, environmental interactions for complex traits in traditional GWAS models are frequently overlooked. Recently, a new GWAS model, 3VmrMLM, was established to identify QTLs and QTL-by-environment interactions (QEIs) for complex traits. In this study, the GLM, MLM, CMLM, FarmCPU, BLINK, and 3VmrMLM models were used to identify QTLs and QEIs for tocopherol (Toc) content in soybean seed, including δ‐Tocotrienol (δ‐Toc) content, γ‐Tocotrienol (γ‐Toc) content, α‐Tocopherol (α‐Toc) content, and total Tocopherol (T-Toc) content. As a result, 101 QTLs were detected by the above methods in single-environment analysis, and 57 QTLs and 13 QEIs were detected by 3VmrMLM in multi-environment analysis. Among these QTLs, some QTLs (Group I) were repeatedly detected three times or by at least two models, and some QTLs (Group II) were repeatedly detected only by 3VmrMLM. In the two Groups, 3VmrMLM was able to correctly detect all known QTLs in group I, while good results were achieved in Group II, for example, 8 novel QTLs were detected in Group II. In addition, comparative genomic analysis revealed that the proportion of Glyma_max specific genes near QEIs was higher, in other words, these QEIs nearby genes are more susceptible to environmental influences. Finally, around the 8 novel QTLs, 11 important candidate genes were identified using haplotype, and validated by RNA-Seq data and qRT-PCR analysis. In summary, we used phenotypic data of Toc content in soybean, and tested the accuracy and reliability of 3VmrMLM, and then revealed novel QTLs, QEIs and candidate genes for these traits. Hence, the 3VmrMLM model has broad prospects and potential for analyzing the genetic structure of complex quantitative traits in soybean.

Soluble sugar is a major indicator of the intrinsic quality of vegetable soybean [Glycine max (L.) Merr. ]. The improvement of soluble sugar content in soybean is very important due to its healthcare functions for humans. The genetic mechanism of soluble sugar in soybean is unclear. In this study, 278 diverse soybean accessions were utilized to identify the quantitative trait nucleotides (QTNs) for total soluble sugar content in soybean seeds based on a genome-wide association study (GWAS). A total of 25,921 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAFs) ≥ 5% and missing data ≤ 10% were selected for GWAS. Totally, thirteen QTNs associated with total soluble sugar content were identified, which were distributed on ten chromosomes. One hundred and fifteen genes near the 200-kb flanking region of these identified QTNs were considered candidate genes associated with total soluble sugar content in soybean seed. Gene-based association analysis and haplotype analysis were utilized to further identify the effect of candidate genes on total soluble sugar content. Totally, 84 SNPs from seventeen genes across four chromosomes were significantly associated with the total soluble sugar content. Among them, three SNPs from Glyma.02G292900 were identified at two locations, and other eighty-one SNPs from sixteen genes were detected at three locations. Furthermore, expression level analysis of candidate genes revealed that Glyma.02G293200 and Glyma.02G294900 were significantly positively associated with soluble sugar content and Glyma.02G294000 was significantly negatively associated with soluble sugar content. Six genes (i.e., Glyma.02G292600, Glyma.02G292700, Glyma.02G294000, Glyma.02G294300, Glyma.02G294400, and Glyma.15G264200) identified by GWAS were also detected by the analysis of differential expression genes based on soybean germplasms with higher and lower soluble sugar content. Among them, Glyma.02G294000 is the only gene that was identified by gene-based association analysis with total soluble sugar content and was considered an important candidate gene for soluble sugar content. These candidate genes and beneficial alleles would be useful for improving the soluble sugar content of soybean.

Soybean frogeye leaf spot (FLS) is a worldwide fungal disease. Its higher occurrence frequency and wider distribution range always led to severe yield losses of soybean, therefore, breeding new cultivars with FLS resistance has been an important breeding goal for soybean breeders. In this study, an association panel of 183 representative soybean accessions was used to evaluate their resistance to FLS race 1, and to identify quantitative trait nucleotides (QTNs) and candidate genes based on genome-wide association study (GWAS) and high-throughput single-nucleotide polymorphisms (SNPs). A total of 23,156 high-quality SNPs were developed using the specific locus-amplified fragment sequencing (SLAF-seq) approach. Finally, 13 novel association signals associated with FLS race 1 resistance were identified by the compressed mixed linear model (CMLM). In addition, 119 candidate genes were found within the 200-kb flanking genomic region of these 13 peak SNPs. Based on the gene-based association analysis, haplotype analysis, expression pattern analysis, and virus-induced gene silencing (VIGS) systems, four genes (Glyma.05G121100, Glyma.17G228300, Glyma.19G006900, and Glyma.19G008700) were preliminarily proved to play an important role in the soybean resistance to FLS race 1.

Isoflavones, one of the most important secondary metabolites produced by soybeans (Glycine max (L.) Merr.), are important for a variety of biological processes, and are beneficial for human health. To identify genetic loci underlying soybean isoflavone content, a mapping population containing 119 F5:18 recombinant inbred lines, derived by crossing soybean cultivar “Zhongdou27” with “Dongong8004,” was used. We identified 15 QTLs associated with isoflavone contents. A novel loci, qISO19-1, was mapped onto soybean chromosome 19 and was fine-mapped to a 62.8 kb region using a BC2F2 population. We considered GmMT1 as a candidate gene for the qISO19-1 locus due to the significant positive correlation recovered between its expression level and isoflavone content in the seeds of 43 soybean germplasms. Overexpression of GmMT1 in Arabidopsis and soybean cultivars increased isoflavone contents. Transgenic soybeans overexpressing GmMT1 also exhibited improved resistance to pathogenic infection, while transgenic Arabidopsis resisted salt and drought stress.

Abstract Background As a photoperiod-sensitive and self-pollinated species, the growth periods traits play important roles in the adaptability and yield of soybean. To examine the genetic architecture of soybean growth periods, we performed a genome-wide association study (GWAS) using a panel of 278 soybean accessions and 34,710 single nucleotide polymorphisms (SNPs) with minor allele frequencies (MAF) higher than 0.04 detected by the specific-locus amplified fragment sequencing (SLAF-seq) with a 6.14-fold average sequencing depth. GWAS was conducted by a compressed mixed linear model (CMLM) involving in both relative kinship and population structure. Results GWAS revealed that 37 significant SNP peaks associated with soybean flowering time or other growth periods related traits including full bloom, beginning pod, full pod, beginning seed, and full seed in two or more environments at -log10(P) > 3.75 or -log10(P) > 4.44 were distributed on 14 chromosomes, including chromosome 1, 2, 3, 5, 6, 9, 11, 12, 13, 14, 15, 17, 18, 19. Fourteen SNPs were novel loci and 23 SNPs were located within known QTLs or 75 kb near the known SNPs. Five candidate genes (Glyma.05G101800, Glyma.11G140100, Glyma.11G142900, Glyma.19G099700, Glyma.19G100900) in a 90 kb genomic region of each side of four significant SNPs (Gm5_27111367, Gm11_10629613, Gm11_10950924, Gm19_34768458) based on the average LD decay were homologs of Arabidopsis flowering time genes of AT5G48385.1, AT3G46510.1, AT5G59780.3, AT1G28050.1, and AT3G26790.1. These genes encoding FRI (FRIGIDA), PUB13 (plant U-box 13), MYB59, CONSTANS, and FUS3 proteins respectively might play important roles in controlling soybean growth periods. Conclusions This study identified putative SNP markers associated with soybean growth period traits, which could be used for the marker-assisted selection of soybean growth period traits. Furthermore, the possible candidate genes involved in the control of soybean flowering time were predicted.

Abstract Background The hundred seed weight (HSW) is one of the yield components of soybean [Glycine max (L.) Merrill] and is especially critical for various soybean food types. In this study, a representative sample consisting of 185 accessions was selected from Northeast China and analysed in three tested environments to determine the quantitative trait nucleotide (QTN) of HSW through a genome-wide association study (GWAS). Result A total of 24,180 single nucleotide polymorphisms (SNPs) with minor allele frequencies greater than 0.2 and missing data less than 3% were utilized to estimate linkage disequilibrium (LD) levels in the tested association panel. Thirty-four association signals were identified as associated with HSW via GWAS. Among them, nineteen QTNs were novel, and another fifteen QTNs were overlapped or located near the genomic regions of known HSW QTL. A total of 237 genes, derived from 31 QTNs and located near peak SNPs from the three tested environments in 2015 and 2016, were considered candidate genes, were related to plant growth regulation, hormone metabolism, cell, RNA, protein metabolism, development, starch accumulation, secondary metabolism, signalling, and the TCA cycle, some of which have been found to participate in the regulation of HSW. A total of 106 SNPs from 16 candidate genes were significantly associated with HSW in soybean. Conclusions The identified loci with beneficial alleles and candidate genes might be valuable for the molecular network and MAS of HSW.

Abstract Background The nutritional value of soybean oil is largely influenced by the proportions of unsaturated fatty acids (FAs), including oleic acid (OA, 18:1), linoleic acid (LLA, 18:2), and linolenic acid (LNA, 18:3). Genome-wide association (GWAS) studies along with gene expression studies in soybean [Glycine max (L.) Merr.] were leveraged to dissect the genetics of unsaturated FAs. Results A association panel of 194 diverse soybean accessions were phenotyped in 2013, 2014 and 2015 to identify Single Nucleotide Polymorphisms (SNPs) associated with OA, LLA, and LNA content, and determine putative candidate genes responsible for regulating unsaturated FAs composition. 149 SNPs that represented 73 genomic regions were found to be associated with the unsaturated FA contents in soybean seeds according to the results of GWAS. Twelve novel genes were predicted to be involved in unsaturated FA synthesis in soybean. The relationship between expression pattern of the candidate genes and the accumulation of unsaturated FAs revealed that multiple genes might be involved in unsaturated FAs regulation simultaneously but work in very different ways: Glyma.07G046200 and Glyma.20G245500 promote the OA accumulation in soybean seed in all the tested accessions; Glyma.13G68600 and Glyma.16G200200 promote the OA accumulation only in high OA germplasms; Glyma.07G151300 promotes OA accumulation in higher OA germplasms and suppresses that in lower OA germplasms; Glyma.16G003500 has the effect of increasing LLA accumulation in higher LA germplasms; Glyma.07G254500 suppresses the accumulation of LNA in lower OA germplasms; Glyma.14G194300 might be involved in the accumulation of LNA content in lower LNA germplasms. Conclusions The beneficial alleles and candidate genes identified might be valuable for improving marker-assisted breeding efficiency and exploring the molecular mechanisms underlying unsaturated fatty acid of soybean.

Long-chain acyl-CoA synthases (LACSs) are a key factor in the formation of acyl-CoA after fatty acid hydrolysis and play an important role in plant stress resistance. This gene family has not been research in soybeans. In this study, the soybean (Glycine max (L.) Merr.) whole genome was identified, the LACS family genes of soybean were screened, and the bioinformatics, tissue expression, abiotic stress, drought stress and co-expression of transcription factors of the gene family were analyzed to preliminarily clarify the function of the LACS family of soybean. A total of 17 LACS genes were screened from soybean genome sequencing data. A bioinformatics analysis of the GmLACS gene was carried out from the aspects of phylogeny, gene structure, conserved sequence and promoter homeopathic element. The transcription spectra of GmLACSs in different organs and abiotic stresses were used by qRT-PCR. The GmLACS genes, which co-expresses the significant response of the analysis of drought stress and transcription factors. The results showed that all soybean LACS have highly conserved AMP-binding domains, and all soybean LACS genes were divided into 6 subfamilies. Transcriptome analysis indicated that the gene-encoding expression profiles under alkali, low temperature, and drought stress. The expression of GmLACS9/15/17 were significantly upregulated under alkali, low temperature and drought stress. Co-expression analysis showed that there was a close correlation between transcription factors and genes that significantly responded to LACS under drought stress. These results provide a theoretical and empirical basis for clarifying the function of LACS family genes and abiotic stress response mechanism of soybean.

Abstract The sucrose concentration in soybean seed significantly affects the flavor of soybean‐derived products. In this study, an association panel of 178 elite accessions and 33,149 single‐nucleotide polymorphisms (SNPs) was utilized to identify quantitative trait nucleotides (QTNs) of sucrose concentration in soybean seeds by genome‐wide association study (GWAS). Five QTNs (rs2688589, rs29026218, rs5926884, rs6886889, and rs10299216) distributed across five genomic regions in five chromosomes were identified in two or more locations by GWAS. A total of 60 candidate genes near the 200‐kb flanking region of these five identified loci were identified. Three of these genes (Glyma.04G032600, Glyma.04G034600, and Glyma.11G092100) have been reported to be involved in the process of sugar biosynthesis. Based on gene‐based association and haplotype analyses, a total of 35 SNPs from 10 genes associated with sucrose concentration were identified. Of them, Glyma.04G032600 was the only gene that has been reported to be related to sucrose content; the other nine genes were novel and may be associated with sucrose content. These beneficial alleles and candidate genes may be of great value in improving sucrose content in soybean seeds.

Tocopherol (Toc) occurs in soybean seeds and is extracted together with the soybean oil. Toc is utilized as an antioxidant in food and an additive in animal feed. A total of 180 representative accessions and 144 recombinant inbred lines (RILs) from the cross of ‘Hefeng 25’ and ‘OAC Bayfield’ were selected to evaluate individuals and total Toc concentrations in soybean seeds. The 180 soybean samples were sequenced by the approach of Specific Locus Amplified Fragment Sequencing (SLAF-seq). A total of 22,611 single nucleotide polymorphisms (SNPs) were developed. Nineteen quantitative trait nucleotides (QTNs) were identified associated with individual or total-Toc based on genome-wide association analysis (GWAS). Among them, three QTNs located near known QTLs, and 16 were novel. Eighteen QTLs and nine eQTLs were also detected by linkage mapping. The QTN rs9337368 on Chr.02 was colocalized according to the linkage mapping of the RILs and genome-wide association analysis and regarded as a stable locus for mining the candidate genes in association with Toc. A total of 42 candidate genes near the 200 kbp flanking region of this identified locus were found. Upon a gene-based association, 11 SNPs from five genes out of the 42 candidates were detected. Expression level analysis of five candidate genes revealed that two genes were significantly related to Toc content. The identified loci, along with the candidate genes, might be valuable for increasing the Toc concentration in soybean seeds and improving the nutritional value of soybean oil.

Sclerotinia stem rot (SSR) is a devastating fungal disease that causes severe yield losses of soybean worldwide. In the present study, a representative population of 185 soybean accessions was selected and utilized to identify the quantitative trait nucleotide (QTN) of partial resistance to soybean SSR via a genome-wide association study (GWAS). A total of 22,048 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAF) > 5% and missing data < 3% were used to assess linkage disequilibrium (LD) levels. Association signals associated with SSR partial resistance were identified by two models, including compressed mixed linear model (CMLM) and multi-locus random-SNP-effect mixed linear model (mrMLM). Finally, seven QTNs with major effects (a known locus and six novel loci) via CMLM and nine novel QTNs with minor effects via mrMLM were detected in relation to partial resistance to SSR, respectively. One of all the novel loci (Gm05:14834789 on Chr.05), which was co-located by these two methods, might be a stable one that showed high significance in SSR partial resistance. Additionally, a total of 71 major and 85 minor candidate genes located in the 200-kb genomic region of each peak SNP detected by CMLM and mrMLM were found, respectively. By using a gene-based association, a total of six SNPs from three major effects genes and eight SNPs from four minor effects genes were identified. Of them, Glyma.18G012200 has been characterized as a significant element in controlling fungal disease in plants.

Abstract Background Soybean (Glycine max L. Merr.) cyst nematode (SCN, Heterodera glycines I,) is a major pest of soybean worldwide. The most effective strategy to control this pest involves the use of resistant cultivars. The aim of the present study was to investigate the genome-wide genetic architecture of resistance to SCN HG Type 2.5.7 (race 1) in landrace and elite cultivated soybeans. Results A total of 200 diverse soybean accessions were screened for resistance to SCN HG Type 2.5.7 and genotyped through sequencing using the Specific Locus Amplified Fragment Sequencing (SLAF-seq) approach with a 6.14-fold average sequencing depth. A total of 33,194 SNPs were identified with minor allele frequencies (MAF) over 4%, covering 97% of all the genotypes. Genome-wide association mapping (GWAS) revealed thirteen SNPs associated with resistance to SCN HG Type 2.5.7. These SNPs were distributed on five chromosomes (Chr), including Chr7, 8, 14, 15 and 18. Four SNPs were novel resistance loci and nine SNPs were located near known QTL. A total of 30 genes were identified as candidate genes underlying SCN resistance. Conclusions A total of sixteen novel soybean accessions were identified with significant resistance to HG Type 2.5.7. The beneficial alleles and candidate genes identified by GWAS might be valuable for improving marker-assisted breeding efficiency and exploring the molecular mechanisms underlying SCN resistance.

Seed weight per plant (SWPP) of soybean (Glycine max (L.) Merr.), a complicated quantitative trait controlled by multiple genes, was positively associated with soybean seed yields. In the present study, a natural soybean population containing 185 diverse accessions primarily from China was used to analyze the genetic basis of SWPP via genome-wide association analysis (GWAS) based on high-throughput single-nucleotide polymorphisms (SNPs) generated by the Specific Locus Amplified Fragment Sequencing (SLAF-seq) method. A total of 33,149 SNPs were finally identified with minor allele frequencies (MAF) > 5% which were present in 97% of all the genotypes. Twenty association signals associated with SWPP were detected via GWAS. Among these signals, eight SNPs were novel loci, and the other twelve SNPs were overlapped or located in the linked genomic regions of the reported QTL from SoyBase database. Several genes belonging to the categories of hormone pathways, RNA regulation of transcription in plant development, ubiquitin, transporting systems, and other metabolisms were considered as candidate genes associated with SWPP. Furthermore, nine genes from the flanking region of Gm07:19488264, Gm08:15768591, Gm08:15768603, or Gm18:23052511 were significantly associated with SWPP and were stable among multiple environments. Nine out of 18 haplotypes from nine genes showed the effect of increasing SWPP. The identified loci along with the beneficial alleles and candidate genes could be of great value for studying the molecular mechanisms underlying SWPP and for improving the potential seed yield of soybean in the future.

Soybean (Glycine max (L.) Merr.) isoflavone is important for human health and plant defense system. To identify novel quantitative trait loci (QTL) and epistatic QTL underlying isoflavone content in soybean, F5:6, F5:7 and F5:8 populations of 130 recombinant inbred (RI) lines, derived from the cross of soybean cultivar 'Zhong Dou 27' (high isoflavone) and 'Jiu Nong 20' (low isoflavone), were analyzed with 95 new SSR markers. A new linkage map including 194 SSR markers and covering 2,312 cM with mean distance of about 12 cM between markers was constructed. Thirty four QTL for both individual and total seed isoflavone contents of soybean were identified. Six, seven, ten and eleven QTL were associated with daidzein (DZ), glycitein (GC), genistein (GT) and total isoflavone (TI), respectively. Of them 23 QTL were newly identified. The qTIF_1 between Satt423 and Satt569 shared the same marker Satt569 with qDZF_2, qGTF_1 and qTIF_2. The qGTD2_1 between Satt186 and Satt226 was detected in four environments and explained 3.41%-10.98% of the phenotypic variation. The qGTA2_1, overlapped with qGCA2_1 and detected in four environments, was close to the previously identified major QTL for GT, which were responsible for large a effects. QTL (qDZF_2, qGTF_1 and qTIF_2) between Satt144-Satt569 were either clustered or pleiotropic. The qGCM_1, qGTM_1 and qTIM_1 between Satt540-Sat_244 explained 2.02%-9.12% of the phenotypic variation over six environments. Moreover, the qGCE_1 overlapped with qGTE_1 and qTIE_1, the qTIH_2 overlapped with qGTH_1, qGCI_1 overlapped with qDZI_1, qTIL_1 overlapped with qGTL_1, and qTIO_1 overlapped with qGTO_1. In this study, some of unstable QTL were detected in different environments, which were due to weak expression of QTL, QTL by environment interaction in the opposite direction to a effects, and/or epistasis. The markers identified in multi-environments in this study could be applied in the selection of soybean cultivars for higher isoflavone content and in the map-based gene cloning.