大豆自然群体GWAS定位

Plant height of soybean is associated with a haplotype block on chromosome 19, which classified 211 soybean accessions into five distinct groups showing significant differences for the target trait. Genetic variation is pivotal for crop improvement. Natural populations are precious genetic resources. However, efficient strategies for the targeted utilization of these resources for quantitative traits, such as plant height (PH), are scarce. Being an important agronomic trait associated with soybean yield and quality, it is imperative to unravel the genetic mechanisms underlying PH in soybean. Here, a genome-wide association study (GWAS) was performed to identify single nucleotide polymorphisms (SNPs) significantly associated with PH in a natural population of 211 cultivated soybeans, which was genotyped with NJAU 355 K Soy SNP Array and evaluated across six environments. A total of 128 SNPs distributed across 17 chromosomes were found to be significantly associated with PH across six environments and a combined environment. Three significant SNPs were consistently identified in at least three environments on Chr.02 (AX-93958260), Chr.17 (AX-94154834), and Chr.19 (AX-93897200). Genomic regions of ~ 130 kb flanking these three consistent SNPs were considered as stable QTLs, which included 169 genes. Of these, 22 genes (including Dt1) were prioritized and defined as putative candidates controlling PH. The genomic region flanking 12 most significant SNPs was in strong linkage disequilibrium (LD). These SNPs formed a single haplotype block containing five haplotypes for PH, namely Hap-A, Hap-B, Hap-C, Hap-D, and Hap-E. Deployment of such superior haplotypes in breeding programs will enable development of improved soybean varieties with desirable plant height.

Soybean [Glycine max(L.)Merr.] is a leading oil-bearing crop and cultivated globally over a vast scale. The agricultural landscape in China faces a formidable challenge with drought significantly impacting soybean production. In this study, we treated a natural population of 264 Chinese soybean accessions using 15% PEG-6000 and used GR, GE, GI, RGR, RGE, RGI and ASFV as evaluation index. Using the ASFV, we screened 17 strong drought-tolerant soybean germplasm in the germination stage. Leveraging 2,597,425 high-density SNP markers, we conducted Genome-Wide Association Studies (GWAS) and identified 92 SNPs and 9 candidate genes significantly associated with drought tolerance. Furthermore, we developed two KASP markers for S14_5147797 and S18_53902767, which closely linked to drought tolerance. This research not only enriches the pool of soybean germplasm resources but also establishes a robust foundation for the molecular breeding of drought tolerance soybean varieties.

No abstract available

Shade has a direct impact on photosynthesis and production of plants. Exposure to shade significantly reduces crops yields. Identifying shade-tolerant genomic loci and soybean varieties is crucial for improving soybean yields. In this study, we applied a shade treatment (30% light reduction) to a natural soybean population consisting of 264 accessions, and measured several traits, including the first pod height, plant height, pod number per plant, grain weight per plant, branch number, and main stem node number. Additionally, we performed GWAS on these six traits with and without shade treatment, as well as on the shade tolerance coefficients (STCs) of the six traits. As a result, we identified five shade-tolerance varieties, 733 SNPs and four candidate genes over two years. Furthermore, we developed four kompetitive allele-specific PCR (KASP) makers for the STC of S18_1766721, S09_48870909, S19_49517336, S18_3429732. This study provides valuable genetic resources for breeding soybean shade tolerance and offers new insights into the theoretical research on soybean shade tolerance.

Introduction Isoflavones are the secondary metabolites synthesized by the phenylpropanoid biosynthesis pathway in soybean that benefits human and plant health. Methods In this study, we have profiled seed isoflavone content by HPLC in 1551 soybean accessions grown in Beijing and Hainan for two consecutive years (2017 and 2018) and in Anhui for one year (2017). Results A broad range of phenotypic variations was observed for individual and total isoflavone (TIF) content. The TIF content ranged from 677.25 to 5823.29 µg g-1 in the soybean natural population. Using a genome-wide association study (GWAS) based on 6,149,599 single nucleotide polymorphisms (SNPs), we identified 11,704 SNPs significantly associated with isoflavone contents; 75% of them were located within previously reported QTL regions for isoflavone. Two significant regions on chromosomes 5 and 11 were associated with TIF and malonylglycitin across more than 3 environments. Furthermore, the WGCNA identified eight key modules: black, blue, brown, green, magenta, pink, purple, and turquoise. Of the eight co-expressed modules, brown (r = 0.68***), magenta (r = 0.64***), and green (r = 0.51**) showed a significant positive association with TIF, as well as with individual isoflavone contents. By combining the gene significance, functional annotation, and enrichment analysis information, four hub genes Glyma.11G108100, Glyma.11G107100, Glyma.11G106900, and Glyma.11G109100 encoding, basic-leucine zipper (bZIP) transcription factor, MYB4 transcription factor, early responsive to dehydration, and PLATZ transcription factor respectively were identified in brown and green modules. The allelic variation in Glyma.11G108100 significantly influenced individual and TIF accumulation. Discussion The present study demonstrated that the GWAS approach, combined with WGCNA, could efficiently identify isoflavone candidate genes in the natural soybean population.

Objective Seed weight is a key factor in soybean yield and value, but its genetic basis and environmental stability are not fully understood. Despite many QTL studies, there’s a lack of integration between bi-parental linkage mapping and diverse germplasm association analysis. We hypothesized that combining high-resolution QTL mapping in recombinant inbred lines with GWAS in natural populations could identify both population-specific and broadly segregating seed weight loci, aiding in candidate gene discovery for breeding. Methods We integrated biparental QTL mapping with genome-wide association studies (GWAS) to comprehensively dissect the genetics of hundred-seed weight (HSW). A recombinant inbred line population of 325 F2:5 lines from Qihuang 34 × Dongsheng 16 was phenotyped across three environments and genotyped using SLAF-seq, generating a high-density genetic map with 6,297 SNP markers spanning 2,945.26 cM (0.47 cM resolution). Simultaneously, 348 diverse soybean accessions underwent whole-genome resequencing (10× coverage), yielding 1,882,531 SNPs for association analysis across two years. Results QTL mapping identified 11 significant loci explaining 2.47-8.59% of phenotypic variance, with broad-sense heritability of 0.78. The major-effect QTL qHSW-19-4 (44.84-44.85 Mb, LOD = 9.72) demonstrated unprecedented 11.4 kb mapping precision. GWAS independently detected six genome-wide significant associations (P < 1 × 10–8), including a stable chromosome 19 peak at 45.28 Mb (P = 2.06 × 10–²³) explaining 15.3-18.7% of variance. Remarkably, this GWAS signal co-localized within 580 kb of qHSW-19-4, providing robust cross-population validation of chromosome 19 as a major seed weight regulatory region. Functional analysis of 44 candidate genes, validated by quantitative RT-PCR across seed developmental stages, identified four high-priority candidates: Glyma.19G195400 (cell wall invertase, 2.7-fold upregulation in large-seeded parent, r = 0.68 with HSW), Glyma.19G194300 (PEBP/Dt1 family protein), Glyma.19G193400 (bZIP transcription factor), and Glyma.06G095100 (Myb DNA-binding domain). Novelty and conclusions This first integrated QTL-GWAS analysis for soybean seed weight reveals both major-effect loci and polygenic architecture, providing validated molecular markers and candidate genes for breeding programs targeting yield improvement.

No abstract available

Soybean (Glycine max) is a crop with high demand for molybdenum (Mo) and typically requires Mo fertilization to achieve maximum yield potential. However, the genetic basis underlying the natural variation of Mo concentration in soybean and its impact on soybean agronomic performance is still poorly understood. Here, we performed a genome-wide association study (GWAS) to identify GmMOT1.1 and GmMOT1.2 that drive the natural variation of soybean Mo concentration and confer agronomic traits by affecting auxin synthesis. The soybean population exhibits five haplotypes of the two genes, with the haplotype 5 demonstrating the highest expression of GmMOT1.1 and GmMOT1.2, as well as the highest transport activities of their proteins. Further studies showed that GmMOT1.1 and GmMOT1.2 improve soybean yield, especially when cultivated in acidic or slightly acidic soil. Surprisingly, these two genes contribute to soybean growth by enhancing the activity of indole-3-acetaldehyde (IAAld) aldehyde oxidase (AO), leading to increased indole-3-acetic acid (IAA) synthesis, rather than being involved in symbiotic nitrogen fixation or nitrogen assimilation. Furthermore, the geographical distribution of five haplotypes in China and their correlation with soil pH suggest the potential significance of GmMOT1.1 and GmMOT1.2 in soybean breeding strategies.

Soybean is an important crop worldwide that provides ~ 50% oil for humans. Salinity is a major abiotic stress that inhibits soybean growth and yield. Dissecting the genetic basis of salt tolerance is an effective way for soybean plants to combat salt-related yield losses. In this study, the variety salt tolerance index (STIv) of a natural population of 140 soybean germplasms was calculated in terms of plant height (PH), leaf area (LA), shoot fresh weight (SFW) and shoot dry weight (SDW), which were measured under normal condition and in a 1.50% NaCl solution. GWAS analysis was subsequently conducted on the basis of STIv and 150 K SNP markers of “Zhongdouxin-1”. The results revealed that 365 significant SNPs located on 19 chromosomes (excluding Gm03) were associated with STIv. Among them, 108 SNPs were associated with LA-STIv, 71 SNPs associated with PH-STIv, 95 SNPs associated with SDW-STIv and 91 SNPs associated with SFW-STIv. A total of 333 genes were identified according to the flanking region (150 kb) of the significant SNPs. 333 genes were identified. Based on gene functional annotations, SNP mutations, and RNA expressions, nine causal genes responsible for soybean salt tolerance were identified. Thus, the significantly associated SNPs and candidate genes detected in this study might provide novel insights into soybean salt tolerance in breeding programs.

No abstract available

Salt stress poses a significant challenge to crop productivity, and understanding the genetic basis of salt tolerance is paramount for breeding resilient soybean varieties. In this study, a soybean natural population was evaluated for salt tolerance during the germination stage, focusing on key germination traits, including germination rate (GR), germination energy (GE), and germination index (GI). It was seen that under salt stress, obvious inhibitions were found on these traits, with GR, GE, and GI diminishing by 32% to 54% when compared to normal conditions. These traits displayed a coefficient of variation (31.81% to 50.6%) and a substantial generalized heritability (63.87% to 86.48%). Through GWAS, a total of 1841 significant single-nucleotide polymorphisms (SNPs) were identified to be associated with these traits, distributed across chromosome 2, 5, 6, and 20. Leveraging these significant association loci, 12 candidate genes were identified to be associated with essential functions in coordinating cellular responses, regulating osmotic stress, mitigating oxidative stress, clearing reactive oxygen species (ROS), and facilitating heavy metal ion transport - all of which are pivotal for plant development and stress tolerance. To validate the candidate genes, quantitative real-time polymerase chain reaction (qRT-PCR) analysis was conducted, revealing three highly expressed genes (Glyma.02G067700, Glyma.02G068900, and Glyma.02G070000) that play pivotal roles in plant growth, development, and osmoregulation. In addition, based on these SNPs related with salt tolerance, KASP (Kompetitive Allele-Specific PCR)markers were successfully designed to genotype soybean accessions. These findings provide insight into the genetic base of soybean salt tolerance and candidate genes for enhancing soybean breeding programs in this study.

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

The colorations of stem pubescence and hilum are crucial criteria for discerning diverse soybean germplasms, governed by multiple genes that substantially influence the seed’s outward appearance quality and the resistance to abiotic stresses. This comprehensive study delved into the stem pubescence and hilum color traits across a natural population of 264 accessions during 2021 and 2022. The phenotypes of these two traits within our population were analyzed for the investigation of population genetics and evaluation of germplasm resources in the future. Numerous noteworthy SNPs associated with both traits were detected through a genome-wide association study (GWAS), with the most significant signals for 2021 and 2022 localized on chromosome 6. Seven candidate genes regulating stem pubescence color and four genes influencing hilum color were identified by analyzing the expression patterns, cold stress responses, and regulatory pathways of genes within the LD decay distance of SNPs. This study not only underscores the applicability of GWAS in unraveling the genetic basis of quality traits, but also contributes novel genetic reservoirs and research paradigms to the explorations of the soybean plant and seed color. These results provide foundational insights into the breeding improvement of seeds’ outward appearance quality and a comprehensive evaluation of soybean germplasm.

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.

Summary Soybean is a short‐day plant that typically flowers earlier when exposed to short‐day conditions. However, the identification of genes associated with earlier flowering time but without a yield penalty is rare. In this study, we conducted genome‐wide association studies (GWAS) using two re‐sequencing datasets that included 113 wild soybeans (G. soja) and 1192 cultivated soybeans (G. max), respectively, and simultaneously identified a candidate flowering gene, qFT13‐3, which encodes a protein homologous to the pseudo‐response regulator (PRR) transcription factor. We identified four major haplotypes of qFT13‐3 in the natural population, with haplotype H4 (qFT13‐3 H4 ) being lost during domestication, while qFT13‐3 H1 underwent natural and artificial selection, increasing in proportion from 4.5% in G. soja to 43.8% in landrace and to 81.9% in improve cultivars. Notably, most cultivars harbouring qFT13‐3 H1 were located in high‐latitude regions. Knockout of qFT13‐3 accelerated flowering and maturity time under long‐day conditions, indicating that qFT13‐3 functions as a flowering inhibitor. Our results also showed that qFT13‐3 directly downregulates the expression of GmELF3b‐2 which is a component of the circadian clock evening complex. Field trials revealed that the qft13‐3 mutants shorten the maturity period by 11 days without a concomitant penalty on yield. Collectively, qFT13‐3 can be utilized for the breeding of high‐yield cultivars with a short maturity time suitable for high latitudes.

Soybean fat contains five principal fatty acids, and its fatty acid composition and nutritional value depend on the type of soybean oil, storage duration, and conditions. Among the fat contents, polyunsaturated fatty acids, such as linoleic acid and linolenic acid, play an essential role in maintaining human life activities; thus, increasing the proportions of the linoleic acid and linolenic acid contents can help improve the nutritional value of soybean oil. Our laboratory completed SLAF-seq whole genome sequencing of the natural population (292 soybean varieties) in the previous growth period. In this study, genome-wide association analysis (GWAS) was performed based on the natural population genotypic data and three-year phenotypic data of soybean linoleic acid and linolenic acid contents, and a significant single nucleotide polymorphisms (SNPs) locus (Gm13_10009679) associated with soybean oleic acid content was repeatedly detected over a span of 3 years using the GLM model and MLM model. Additionally, another significant SNP locus (Gm19_41366844) correlated with soybean linolenic acid was identified through the same models. Genes within the 100 Kb interval upstream and downstream of the SNP loci were scanned and analyzed for their functional annotation and enrichment, and one gene related to soybean linoleic acid synthesis (Glyma.13G035600) and one gene related to linolenic acid synthesis (Glyma.19G147400) were screened. The expressions of the candidate genes were verified using qRT-PCR, and based on the verification results, it was hypothesized that Glyma.13G035600 and Glyma.19G147400 positively regulate linoleic acid and linolenic acid synthesis and accumulation, respectively. The above study lays the foundation for further validating gene functions, and analyzing the regulatory mechanisms of linoleic acid and linolenic acid synthesis and accumulation in soybean.

Summary Soybean is one of the most economically important crops worldwide and an important source of unsaturated fatty acids and protein for the human diet. Consumer demand for healthy fats and oils is increasing, and the global demand for vegetable oil is expected to double by 2050. Identification of key genes that regulate seed fatty acid content can facilitate molecular breeding of high‐quality soybean varieties with enhanced fatty acid profiles. Here, we analysed the genetic architecture underlying variations in soybean seed fatty acid content using 547 accessions, including mainly landraces and cultivars from northeastern China. Through fatty acid profiling, genome re‐sequencing, population genomics analyses, and GWAS, we identified a SEIPIN homologue at the FA9 locus as an important contributor to seed fatty acid content. Transgenic and multiomics analyses confirmed that FA9 was a key regulator of seed fatty acid content with pleiotropic effects on seed protein and seed size. We identified two major FA9 haplotypes in 1295 resequenced soybean accessions and assessed their phenotypic effects in a field planting of 424 accessions. Soybean accessions carrying FA9 H2 had significantly higher total fatty acid contents and lower protein contents than those carrying FA9 H1 . FA9 H2 was absent in wild soybeans but present in 13% of landraces and 26% of cultivars, suggesting that it may have been selected during soybean post‐domestication improvement. FA9 therefore represents a useful genetic resource for molecular breeding of high‐quality soybean varieties with specific seed storage profiles.

The production of soybean [Glycine max (L.) Merr.] is seriously threatened by various leaf-feeding insects, and wild soybean [Glycine soja Sieb. & Zucc.] has a greater resistance capacity and genetic diversity. In this study, a natural population consisting of 121 wild soybean accessions was used for detecting insect resistance genes. The larval weight (LW) of the common cutworm (CCW), the resistance level (RL) and the index of damaged leaf (IDL) were evaluated as resistance indicators to herbivores. An association synonymous SNP AX-94083016 located in the coding region of the respiratory burst oxidase gene GsRbohA1 was identified by genome-wide association study (GWAS) analyses. The overexpression of GsRbohA1 in soybean hairy roots enhanced resistance to CCW. One SNP in the promoter region cosegregated with AX-94083016 contributing to soybean resistance to CCW by altering GsRbohA1 gene expression and reactive oxygen species (ROS) accumulation. Two major haplotypes, GsRbohA1A and GsRbohA1G, were identified based on the SNP. The resistant haplotype GsRbohA1A predominates in wild soybeans, although it has been gradually lost in landraces and cultivars. The nucleotide diversity around GsRbohA1 is much lower in landraces and cultivars than in its ancestors. In conclusion, a new resistant haplotype, GsRbohA1A, was identified in wild soybean, which will be a valuable gene resource for soybean insect resistance breeding through introducing into improvement lines, and it offers a strategy for exploring resistance gene resources from its wild relatives.

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

Isoflavone, a secondary metabolite produced by soybeans (Glycine max (L.) Merr.), is valuable for human and plant health. However, the genetic architecture of soybean isoflavone content remains unclear, despite several mapping studies. We generated genomic data for 200 soybean cultivars and 150 recombinant inbred lines (RILs) to localize putative loci associated with soybean seed isoflavone content. Using a genome-wide association study (GWAS), we identified 87 single nucleotide polymorphisms (SNPs) that were significantly associated with isoflavone concentration. Using linkage mapping, we identified 37 QTLs underlying four isoflavone content in the RILs. A major locus on Chromosome 8 (qISO8-1) was co-located by both the GWAS and linkage mapping. qISO8-1 was fine mapped to a 99.5-kilobase region, flanked by SSR_08_1651 and SSR_08_1656, in a BC2 F5 population. GmMPK1, encoding a mitogen-activated protein kinase, was identified as the causal gene in qISO8-1, and two natural GmMPK1 polymorphisms were significantly associated with isoflavone content. Overexpression of GmMPK1 in soybean hairy roots resulted in increased isoflavone concentrations. Overexpressing GmMPK1 in transgenic soybeans had greater resistance to Phytophthora root rot, suggesting that GmMPK1 might increase soybean resistance to biotic stress by influencing isoflavone content. Our results not only increase our understanding of the genetic architecture of soybean seed isoflavone content, but also provide a framework for the future marker-assisted breeding of high-isoflavone content in soybean cultivars.

Phosphorus (P) is an essential element in maintaining high biomass and yield in crops. Soybean [Glycine max (L.) Merr.] requires a large amount of P during growth and development. Improvement of P efficiency and identification of P efficiency genes are important strategies for increasing soybean yield. Genome-wide association analysis (GWAS) with NJAU 355 K SoySNP array was performed to identify single nucleotide polymorphisms (SNPs) significantly associated with three shoot P efficiency-related traits of a natural population of 211 cultivated soybeans and relative values of these traits under normal P (+P) condition and P deficiency (−P) condition. A total of 155 SNPs were identified significantly associated with P efficiency-related traits. SNPs that were significantly associated with shoot dry weight formed a SNP cluster on chromosome 11, while SNPs that were significantly associated with shoot P concentration formed a SNP cluster on chromosome 10. Thirteen haplotypes were identified based on 12 SNPs, and Hap9 was considered as the optimal haplotype. Four SNPs (AX-93636685, AX-93636692, AX-93932863, and AX-93932874) located on chromosome 10 were identified to be significantly associated with shoot P concentration under +P condition in two hydroponic experiments. Among these four SNPs, two of them (AX-93636685 and AX-93932874) were also significantly associated with the relative values of shoot P concentration under two P conditions. One SNP AX-93932874 was detected within 5′-untranslated region of Glyma.10 g018800, which contained SPX and RING domains and was named as GmSPX-RING1. Furthermore, the function research of GmSPX-RING1 was carried out in soybean hairy root transformation. Compared with their respective controls, P concentration in GmSPX-RING1 overexpressing transgenic hairy roots was significantly reduced by 32.75% under +P condition; In contrast, P concentration in RNA interference of GmSPX-RING1 transgenic hairy roots was increased by 38.90 and 14.51% under +P and -P conditions, respectively. This study shows that the candidate gene GmSPX-RING1 affects soybean phosphorus efficiency by negatively regulating soybean phosphorus concentration in soybean hairy roots. The SNPs and candidate genes identified should be potential for improvement of P efficiency in future soybean breeding programs.

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.

A worldwide food shortage has been projected as a result of the current increase in global population and climate change. In order to provide sufficient food to feed more people, we must develop crops that can produce higher yields. Plant early vigor traits, early growth rate (EGR), early plant height (EPH), inter-node length, and node count are important traits that are related to crop yield. Glycine soja, the wild counterpart to cultivated soybean, Glycine max, harbors much higher genetic diversity and can grow in diverse environments. It can also cross easily with cultivated soybean. Thus, it holds a great potential in developing soybean cultivars with beneficial agronomic traits. In this study, we used 225 wild soybean accessions originally from diverse environments across its geographic distribution in East Asia. We quantified the natural variation of several early vigor traits, investigated the relationships among them, and dissected the genetic basis of these traits by applying a Genome-Wide Association Study (GWAS) with genome-wide single nucleotide polymorphism (SNP) data. Our results showed positive correlation between all early vigor traits studied. A total of 12 SNPs significantly associated with EPH were identified with 4 shared with EGR. We also identified two candidate genes, Glyma.07G055800.1 and Glyma.07G055900.1, playing important roles in influencing trait variation in both EGR and EPH in G. soja.

No abstract available

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

No abstract available

The determination of the soybean branch number plays a pivotal role in plant morphogenesis and yield components. This polygenic trait is subject to environmental influences, and despite its significance, the genetic mechanisms governing the soybean branching number remain incompletely understood. To unravel these mechanisms, we conducted a comprehensive investigation employing a genome-wide association study (GWAS) and bulked sample analysis (BSA). The GWAS revealed 18 SNPs associated with the soybean branch number, among which qGBN3 on chromosome 2 emerged as a consistently detected locus across two years, utilizing different models. In parallel, a BSA was executed using an F2 population derived from contrasting cultivars, Wandou35 (low branching number) and Ruidou1 (high branching number). The BSA results pinpointed a significant quantitative trait locus (QTL), designated as qBBN1, located on chromosome 2 by four distinct methods. Importantly, both the GWAS and BSA methods concurred in co-locating qGBN3 and qBBN1. In the co-located region, 15 candidate genes were identified. Through gene annotation and RT-qPCR analysis, we predicted that Glyma.02G125200 and Glyma.02G125600 are candidate genes regulating the soybean branch number. These findings significantly enhance our comprehension of the genetic intricacies regulating the branch number in soybeans, offering promising candidate genes and materials for subsequent investigations aimed at augmenting the soybean yield. This research represents a crucial step toward unlocking the full potential of soybean cultivation through targeted genetic interventions.

Isoflavone is a secondary metabolite of the soybean phenylpropyl biosynthesis pathway with physiological activity and is beneficial to human health. In this study, the isoflavone content of 205 soybean germplasm resources from 3 locations in 2020 showed wide phenotypic variation. A joint genome-wide association study (GWAS) and weighted gene coexpression network analysis (WGCNA) identified 33 single-nucleotide polymorphisms and 11 key genes associated with soybean isoflavone content. Gene ontology enrichment analysis, gene coexpression, and haplotype analysis revealed natural variations in the Glyma.12G109800 (GmOMT7) gene and promoter region, with Hap1 being the elite haplotype. Transient overexpression and knockout of GmOMT7 increased and decreased the isoflavone content, respectively, in hairy roots. The combination of GWAS and WGCNA effectively revealed the genetic basis of soybean isoflavone and identified potential genes affecting isoflavone synthesis and accumulation in soybean, providing a valuable basis for the functional study of soybean isoflavone.

Seed size/weight plays an important role in determining crop yield, yet only few genes controlling seed size have been characterized in soybean. Here, we perform a genome-wide association study and identify a major quantitative trait locus (QTL), named GmSW17 (Seed Width 17), on chromosome 17 that determine soybean seed width/weight in natural population. GmSW17 encodes a ubiquitin-specific protease, an ortholog to UBP22, belonging to the ubiquitin-specific protease (USPs/UBPs) family. Further functional investigations reveal that GmSW17 interacts with GmSGF11 and GmENY2 to form a deubiquitinase (DUB) module, which influences H2Bub levels and negatively regulates the expression of GmDP-E2F-1, thereby inhibiting the G1-to-S transition. Population analysis demonstrates that GmSW17 undergo artificial selection during soybean domestication but has not been fixed in modern breeding. In summary, our study identifies a predominant gene related to soybean seed weight, providing potential advantages for high-yield breeding in soybean. Seed size plays an important role in determining soybean yield. Here, the authors report GmSW17, encoding a homolog of Arabidopsis UBP22 that plays a role in deubiquitination, as a positive regulator of soybean seed width and seed weight through inhibition of the G1-to-S transition by interacting with GmSGF11 and GmENY2.

SUMMARY Improving the efficiency of crop photosynthesis has the potential to increase yields. Genetic manipulation showed photosynthesis can be improved by speeding up the relaxation of photoprotective mechanisms during sun‐to‐shade transitions. However, it is unclear if natural variation in the relaxation of non‐photochemical quenching (NPQ) can be exploited in crop breeding programs. To address this issue, we measured six NPQ parameters in the 40 founder lines and common parent of a Soybean Nested Association Mapping (SoyNAM) panel over two field seasons in Illinois. Leaf disks were sampled from plants grown in the field, and induction and relaxation of NPQ were measured under controlled conditions. NPQ parameters did not show consistently variable trends throughout development, and variation between sampling days suggests environmental impacts on NPQ dynamics. Seventeen genotypes were found to show small but consistent differences in NPQ relaxation kinetics relative to a reference line, providing a basis for future mapping studies. Finally, a soybean canopy model predicted available phenotypic variation could result in a 1.6% difference in carbon assimilation when comparing the fastest and slowest relaxing NPQ values. No correlation could be found between yield and rates of NPQ relaxation, but a full test will require an analysis of isogenic lines.

No abstract available

Seedling diseases and root rot, caused by species of Fusarium, can limit soybean (Glycine max L.) production in the United States. Currently, there are few commercially available cultivars resistant to Fusarium. This study was conducted to assess the resistance of soybean maturity group (MG) accessions from 0 and I to Fusarium proliferatum, F. sporotrichioides, and F. subglutinans, as well as to identify common quantitative trait loci (QTL) for resistance to these pathogens, in addition to F. graminearum, using a genome-wide association study (GWAS). A total of 155, 91, and 48 accessions from the USDA soybean germplasm collection from maturity groups 0 and I were screened with a single isolate each of F. proliferatum, F. sporotrichioides, and F. subglutinans, respectively, using the inoculum layer inoculation method in the greenhouse. The disease severity was assessed 21 days post-inoculation and analyzed using non-parametric statistics to determine the relative treatment effects (RTE). Eleven and seven accessions showed significantly lower RTEs when inoculated with F. proliferatum and F. subglutinans, respectively, compared to the susceptible cultivar 'Williams 82'. One accession was significantly less susceptible to both F. proliferatum and F. subglutinans. The GWAS conducted with 41,985 single-nucleotide markers identified one QTL associated with resistance to both F. proliferatum and F. sporotrichioides, as well as another QTL for resistance to both F. subglutinans and F. graminearum. However, no common QTLs were identified for the four pathogens. The USDA accessions and QTLs identified in this study can be utilized to selectively breed resistance to multiple species of Fusarium.

Soybean (Glycine max) productivity is significantly reduced by drought stress. Breeders are aiming to improve soybean grain yields both under well-watered (WW) and drought-stressed (DS) conditions, however, little is known about the genetic architecture of yield-related traits. Here, a panel of 188 soybean germplasm was used in a genome wide association study (GWAS) to identify single nucleotide polymorphism (SNP) markers linked to yield-related traits including pod number per plant (PN), biomass per plant (BM) and seed weight per plant (SW). The SLAF-seq genotyping was conducted on the population and three phenotype traits were examined in WW and DS conditions in four environments. Based on best linear unbiased prediction (BLUP) data and individual environmental analyses, 39 SNPs were significantly associated with three soybean traits under two conditions, which were tagged to 26 genomic regions by linkage disequilibrium (LD) analysis. Of these, six QTLs qPN-WW19.1, qPN-DS8.8, qBM-WW1, qBM-DS17.4, qSW-WW4 and qSW-DS8 were identified controlling PN, BM and SW of soybean. There were larger proportions of favorable haplotypes for locus qPN-WW19.1 and qSW-WW4 rather than qBM-WW1, qBM-DS17.4, qPN-DS8.8 and qSW-DS8 in both landraces and improved cultivars. In addition, several putative candidate genes such as Glyma.19G211300, Glyma.17G057100 and Glyma.04G124800, encoding E3 ubiquitin-protein ligase BAH1, WRKY transcription factor 11 and protein zinc induced facilitator-like 1, respectively, were predicted. We propose that the further exploration of these locus will facilitate accelerating breeding for high-yield soybean cultivars.

Phytophthora sojae, an oomycete pathogen of soybean, causes stem and root rot, resulting in annual economic loss up to $2 billion worldwide. Varieties with P. sojae resistance are environmental friendly to effectively reduce disease damages. In order to improve the resistance of P. sojae and broaden the genetic diversity in Southern soybean cultivars and germplasm in the U.S., we established a P. sojae resistance gene pool that has high genetic diversity, and explored genomic regions underlying the host resistance to P. sojae races 1, 3, 7, 17 and 25. A soybean germplasm panel from maturity groups (MGs) IV and V including 189 accessions originated from 10 countries were used in this study. The panel had a high genetic diversity compared to the 6,749 accessions from MGs IV and V in USDA Soybean Germplasm Collection. Based on disease evaluation dataset of these accessions inoculated with P. sojae races 1, 3, 7, 17 and 25, which are publically available, five accessions in this panel were resistant to all races. Genome-wide association analysis identified a total of 32 significant SNPs, which were clustered in resistance-associated genomic regions, among those, ss715619920 was only 3kb away from the gene Glyma.14g087500, a subtilisin protease. Gene expression analysis showed that the gene was down-regulated more than 4 fold (log2 fold > 2.2) in response to P. sojae infection. The identified molecular markers and genomic regions that are associated with the disease resistance in this gene pool will greatly assist the U.S. Southern soybean breeders in developing elite varieties with broad genetic background and P. sojae resistance.

Bacterial leaf pustule (BLP), caused by Xanthornonas axonopodis pv. glycines (Xag), is a worldwide disease of soybean, particularly in warm and humid regions. To date, little is known about the underlying molecular mechanisms of BLP resistance. The only single recessive resistance gene rxp has not been functionally identified yet, even though the genotypes carrying the gene have been widely used for BLP resistance breeding. Using a linkage mapping in a recombinant inbred line (RIL) population against the Xag strain Chinese C5, we identified that quantitative trait locus (QTL) qrxp–17–2 accounted for 74.33% of the total phenotypic variations. We also identified two minor QTLs, qrxp–05–1 and qrxp–17–1, that accounted for 7.26% and 22.26% of the total phenotypic variations, respectively, for the first time. Using a genome-wide association study (GWAS) in 476 cultivars of a soybean breeding germplasm population, we identified a total of 38 quantitative trait nucleotides (QTNs) on chromosomes (Chr) 5, 7, 8, 9,15, 17, 19, and 20 under artificial infection with C5, and 34 QTNs on Chr 4, 5, 6, 9, 13, 16, 17, 18, and 20 under natural morbidity condition. Taken together, three QTLs and 11 stable QTNs were detected in both linkage mapping and GWAS analysis, and located in three genomic regions with the major genomic region containing qrxp_17_2. Real-time RT-PCR analysis of the relative expression levels of five potential candidate genes in the resistant soybean cultivar W82 following Xag treatment showed that of Glyma.17G086300, which is located in qrxp–17–2, significantly increased in W82 at 24 and 72 h post-inoculation (hpi) when compared to that in the susceptible cultivar Jack. These results indicate that Glyma.17G086300 is a potential candidate gene for rxp and the QTLs and QTNs identified in this study will be useful for marker development for the breeding of Xag-resistant soybean cultivars.

No abstract available

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). 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. 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.

No abstract available

Phytophthora root and stem rot (PRR) caused by Phytophthora sojae is one of the most serious diseases affecting soybean (Glycine max (L.) Merr.) production all over the world. The most economical and environmentally-friendly way to control the disease is the exploration and utilization of resistant varieties. We screened a soybean mini core collection composed of 224 germplasm accessions for resistance against eleven P. sojae isolates. Soybean accessions from the Southern and Huanghuai regions, especially the Hubei, Jiangsu, Sichuan and Fujian provinces, had the most varied and broadest spectrum of resistance. Based on gene postulation, Rps1b, Rps1c, Rps4, Rps7 and novel resistance genes were identified in resistant accessions. Consequently, association mapping of resistance to each isolate was performed with 1,645 single nucleotide polymorphism (SNP) markers. A total of 14 marker-trait associations for Phytophthora resistance were identified. Among them, four were located in known PRR resistance loci intervals, five were located in other disease resistance quantitative trait locus (QTL) regions, and five associations unmasked novel loci for PRR resistance. In addition, we also identified candidate genes related to resistance. This is the first P. sojae resistance evaluation conducted using the Chinese soybean mini core collection, which is a representative sample of Chinese soybean cultivars. The resistance reaction analyses provided an excellent database of resistant resources and genetic variations for future breeding programs. The SNP markers associated with resistance will facilitate marker-assisted selection (MAS) in breeding programs for resistance to PRR, and the candidate genes may be useful for exploring the mechanism underlying P. sojae resistance.

The soybean yield is a complex quantitative trait that is significantly influenced by environmental factors. G × E interaction (GEI), which derives the performance of soybean genotypes differentially in various environmental conditions, is one of the main obstacles to increasing the net production. The primary goal of this study is to identify the outperforming genotypes in different latitudes, which can then be used in future breeding programs. A total of 96 soybean genotypes were examined in two different ecological regions: Faisalabad and Tando Jam in Pakistan. The evaluation of genotypes in different environmental conditions showed a substantial amount of genetic diversity for grain yield. We identified 13 environment-specific genotypes showing their maximum grain yield in each environment. Genotype G69 was found to be an ideal genotype with higher grain yield than other genotypes tested in this study and is broadly adapted for environments E1 and E2 and also included in top-yielding genotypes in E3, E4, and E5. G92 is another genotype that is broadly adapted in E1, E3, and E4. In the case of environments, E3 is suggested to be a more ideal environment as it is plotted near the concentric circle and is very informative for the selection of genotypes with high yield. Despite the presence of GEI, advances in DNA technology provided very useful tools to investigate the insight of advanced genotypes. Association mapping is a useful method for swiftly and efficiently investigating the genetic basis of significant plant traits. A total of 26 marker–trait associations were found for six agronomic traits in five environments, with the highest significance (p-value = 2.48 × 10–08) for plant height and the lowest significance (1.03 × 10–03) for hundred-grain weight. Soybean genotypes identified in the present study could be a valuable source for future breeding programs as they are adaptable to a wide range of environments. Genetic selection of genotypes with the best yields can be used for gross grain production in a wide range of climatic conditions, and it would give an essential reference in terms of soybean variety selection.

Target spot caused by Corynespora cassiicola is a problematic disease in tropical and subtropical soybean (Glycine max) growing regions. Although resistant soybean genotypes have been identified, the genetic mechanisms underlying target spot resistance has not yet been studied. To address this knowledge gap, this is the first genome-wide association study (GWAS) conducted using the SoySNP50K array on a panel of 246 soybean accessions, aiming to unravel the genetic architecture of resistance. The results revealed significant associations of 14 and 33 loci with resistance to LIM01 and SSTA C. cassiicola isolates, respectively, with six loci demonstrating consistent associations across both isolates. To identify potential candidate genes within GWAS-identified loci, dynamic transcriptome profiling was conducted through RNA-Seq analysis. The analysis involved comparing gene expression patterns between resistant and susceptible genotypes, utilizing leaf tissue collected at different time points after inoculation. Integrating results of GWAS and RNA-Seq analyses identified 238 differentially expressed genes within a 200 kb region encompassing significant quantitative trait loci (QTLs) for disease severity ratings. These genes were involved in defense response to pathogen, innate immune response, chitinase activity, histone H3-K9 methylation, salicylic acid mediated signaling pathway, kinase activity, and biosynthesis of flavonoid, jasmonic acid, phenylpropanoid, and wax. In addition, when combining results from this study with previous GWAS research, 11 colocalized regions associated with disease resistance were identified for biotic and abiotic stress. This finding provides valuable insight into the genetic resources that can be harnessed for future breeding programs aiming to enhance soybean resistance against target spot and other diseases simultaneously.

No abstract available

Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2400 nm) leaf reflectance spectroscopy to build partial least squares regression (PLSR) models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate and maximum electron transport. In total, eleven models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and RMSE approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the Soybean Nested Association Mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

Soybean (Glycine max) peptide lunasin exhibits significant cancer-preventive, antioxidant, and hypocholesterolemic effects. This study aimed to identify quantitative trait nucleotides (QTNs) associated with lunasin content and to annotate the candidate genes in the soybean genome. The mapping panel of 144 accessions was gathered from the USDA Soybean Germplasm Collection, encompassing diverse geographical origins and genetic backgrounds, and was genotyped using SoySNP50K iSelect Beadchips. The lunasin content in soybean seeds was measured using the enzyme-linked immunosorbent assay (ELISA) method, with lipid-adjusted soybean flour prepared from seeds obtained from the Germplasm Resource Information Network (GRIN) of USDA-ARS in 2003 and from North Carolina in 2021, respectively. QTNs significantly related to lunasin content in soybean seeds were detected on 15 chromosomes, with LOD scores greater than 3.0, explaining various phenotypic variations identified using the R package mrMLM (v4.0). Significant QTNs on chromosomes 3, 13, 16, 18, and 20 were consistently identified across multiple models as being significantly associated with soybean lunasin content, based on assessment data from two years. Twenty-nine candidate genes were found, with 12 identified in seeds from 2003 and 17 from 2021. Our study is an important effort to understand the genetic basis and functional genes for lunasin production in soybean seeds.

Soybean (Glycine max [L.] Merr.) is one of the most significant crops in the world in terms of oil and protein. Owing to the rising demand for soybean products, there is an increasing need for improved varieties for more productive farming. However, complex correlation patterns among quantitative traits along with genetic interactions pose a challenge for soybean breeding. Association studies play an important role in the identification of accession with useful alleles by locating genomic sites associated with the phenotype in germplasm collections. In the present study, a genome-wide association study was carried out for seven agronomic and yield-related traits. A field experiment was conducted in 2015/2016 at two locations that include 155 diverse soybean germplasm. These germplasms were genotyped using SoySNP50K Illumina Infinium Bead-Chip. A total of 51 markers were identified for node number, plant height, pods per plant, seeds per plant, seed weight per plant, hundred-grain weight, and total yield using a multi-locus linear mixed model (MLMM) in FarmCPU. Among these significant SNPs, 18 were putative novel QTNs, while 33 co-localized with previously reported QTLs. A total of 2,356 genes were found in 250 kb upstream and downstream of significant SNPs, of which 17 genes were functional and the rest were hypothetical proteins. These 17 candidate genes were located in the region of 14 QTNs, of which ss715580365, ss715608427, ss715632502, and ss715620131 are novel QTNs for PH, PPP, SDPP, and TY respectively. Four candidate genes, Glyma.01g199200, Glyma.10g065700, Glyma.18g297900, and Glyma.14g009900, were identified in the vicinity of these novel QTNs, which encode lsd one like 1, Ergosterol biosynthesis ERG4/ERG24 family, HEAT repeat-containing protein, and RbcX2, respectively. Although further experimental validation of these candidate genes is required, several appear to be involved in growth and developmental processes related to the respective agronomic traits when compared with their homologs in Arabidopsis thaliana. This study supports the usefulness of association studies and provides valuable data for functional markers and investigating candidate genes within a diverse germplasm collection in future breeding programs.

Wild soybean (Glycine soja Siebold & Zucc.) has valuable genetic diversity for improved disease resistance, stress tolerance, seed protein content and seed sulfur-containing amino acid concentrations. Many studies have reported loci controlling seed composition traits based on cultivated soybean populations, but wild soybean has been largely overlooked. In this study, a nested association mapping (NAM) population consisting of 10 families and 1107 recombinant inbred lines was developed by crossing 10 wild accessions with the common cultivar NC-Raleigh. Seed composition of the F6 generation grown at two locations was phenotyped, and genetic markers were identified for each line. The average number of recombination events in the wild soybean-derived population was significantly higher than that in the cultivated soybean-derived population, which resulted in a higher resolution for QTL mapping. Segregation bias in almost all NAM families was significantly biased toward the alleles of the wild soybean parent. Through single-family linkage mapping and association analysis of the entire NAM population, new QTLs with positive allele effects were identified from wild parents, including 5, 6, 18, 9, 16, 17 and 20 for protein content, oil content, total protein and oil content, methionine content, cysteine content, lysine content and threonine content, respectively. Candidate genes associated with these traits were identified based on gene annotations and gene expression levels in different tissues. This is the first study to reveal the genetic characteristics of wild soybean-derived populations, landscapes and the extent of effects of QTLs and candidate genes controlling traits from different wild soybean parents.

Alkaline stress is one of the major abiotic constraints that limits plant growth and development. However, the genetic basis underlying alkaline tolerance in soybean [Glycine max (L.) Merr.] remains largely unexplored. In this study, an integrated genomic analysis approach was employed to elucidate the genetic architecture of alkaline tolerance in a diverse panel of 326 soybean cultivars. Through association mapping, we detected 28 single nucleotide polymorphisms (SNPs) significantly associated with alkaline tolerance. By examining the genomic distances around these significant SNPs, five genomic regions were characterized as stable quantitative trait loci (QTLs), which were designated as qAT1, qAT4, qAT14, qAT18, and qAT20. These QTLs are reported here for the first time in soybean. Seventeen putative candidate genes were identified within the physical intervals of these QTLs. Haplotype analysis indicated that four of these candidate genes exhibited significant allele variation associated with alkaline tolerance-related traits, and the haplotype alleles for these four genes varied in number from two to four. The findings of this study may have important implications for soybean breeding programs aimed at enhancing alkaline tolerance.

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). 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. The identified loci with beneficial alleles and candidate genes might be valuable for the molecular network and MAS of HSW.

The growth period traits are important traits that affect soybean yield. The insights into the genetic basis of growth period traits can provide theoretical basis for cultivated area division, rational distribution, and molecular breeding for soybean varieties. In this study, genome-wide association analysis (GWAS) was exploited to detect the quantitative trait loci (QTL) for number of days to flowering (ETF), number of days from flowering to maturity (FTM), and number of days to maturity (ETM) using 4032 single nucleotide polymorphism (SNP) markers with 146 cultivars mainly from Northeast China. Results showed that abundant phenotypic variation was presented in the population, and variation explained by genotype, environment, and genotype by environment interaction were all significant for each trait. The whole accessions could be clearly clustered into two subpopulations based on their genetic relatedness, and accessions in the same group were almost from the same province. GWAS based on the unified mixed model identified 19 significant SNPs distributed on 11 soybean chromosomes, 12 of which can be consistently detected in both planting densities, and 5 of which were pleotropic QTL. Of 19 SNPs, 7 SNPs located in or close to the previously reported QTL or genes controlling growth period traits. The QTL identified with high resolution in this study will enrich our genomic understanding of growth period traits and could then be explored as genetic markers to be used in genomic applications in soybean breeding.

Seed oil content (SOC) and seed protein content (SPC) are the crucial traits determining the economic importance of soybeans. However, the molecular mechanism underlying the high SOC and low SPC of Northeast China soybeans is still limited. To address this, we elucidated the genetic basis of SOC and SPC in soybean germplasm adapted to Northeast China by employing an integrated genomic analysis. The genome-wide association study (GWAS) detected 105 and 59 significant SNPs associated with the SOC and SPC, respectively across four environments plus combined environment (CE). The haplotype allele number in the 15 identified haplotype blocks varied from 2–4 regulating the SOC and SPC in the range of 16.68-21.15% and 38.63-42.69%, respectively. Five quantitative trait loci (QTLs) among the total 17 identified QTLs were novel that include qSOC1, qSPC1, qSOC9, qSOC_SPC15.1 and qSOC_SPC15.2 associated with SOC or/and SPC. Based on the in-silico, variant annotation and haplotype analysis, the 80 genes were prioritized as potential candidates. The haplotype alleles of these genes varied from 2–8 regulating SOC and SPC in the range of 15.98-21.23% and 37.69%-43.30%, respectively. Twelve of 80 genes showed distinct selection signatures between the two populations, suggesting their key roles in shaping the specific seed quality profiles of soybean germplasm in Northeast China. Hence, the current study provides novel insights on divergent breeding influencing the local adaptation and seed quality difference between different regional soybean populations. Besides, the stable QTLs, superior haplotypes and candidate genes identified can be used for soybean improvement.

Enhancing plant adaptation to challenging climates through breeding techniques requires studying plant systems with diverse genetic architectures. Comprehensive understanding of the genetic architecture of root traits is crucial for analyzing overall plant development and incorporating these insights into crop breeding programs on challenging climate adaptation. To dissect genetic architecture of root traits in soybean, we applied genome-wide association study (GWAS) in soybean germplasm population. Phenotyping of six root-related traits was performed at two plant growth stages, V1 (two-weeks growth stage) and V2 (three-weeks growth stage), under hydroponic culture and GWAS was performed to identify key SNPs and genes associated with root traits. Total 58 single nucleotide polymorphisms (SNPs) associated with six root-related traits were detected for two growth stages using three GWAS models, Mixed Linear Model (MLM), the Fixed and Random Model Circulating Probability Unification (FarmCPU) and 3 V multi-locus random-SNP-effect Mixed Linear Model (3VmrMLM). A total of 35 SNPs were detected for six root traits at V1 stage, while 23 SNPs were detected for the same traits at V2 stage. Quantitative trait locus (QTL) qRoot10.1 represented by three significant SNPs, was identified for primary root length (PRL) at V1 and V2 stage, and for root tips (RT) at V2 stage. Further, QTL qRoot10.1 was validated for PRL and total root length (TRL) in a separate set of soybean population. Candidate gene analysis in genomic regions of 58 SNPs identified 63 candidate genes, with annotations associated with various pathways of root development. Differential gene expression analysis of the candidate gene Glyma.10g273000 at qRoot10.1 revealed a significant difference in expression between long-rooting and short-rooting genotypes. In this study, we offer new insights into the root architecture of soybean, identifying key SNPs and genes that could be instrumental in future breeding programs aimed at developing highly efficient root systems in soybean.

In this investigation, a GWAS analysis was performed for four water logging tolerance traits in a panel of 265 soybean germplasm accessions with 66719 SNPs. A total of 29 SNPs and some candidate genes associated with different water logging tolerance traits were identified. This study also identified some soybean accessions, having superior water logging tolerance ability at vegetative and reproductive stages, which may be used as potential donors in soybean improvement programs.

A genome-wide association study (GWAS) was conducted for Charcoal rot resistance by using 267 soybean germplasm lines. GWAS analysis revealed two most significant SNPs at chromosome 3 (S3_8215775 and S3_8309181) for multiple traits associated with adult plant resistance. Candidate genes related to calcium ion signaling, response to nematode infestation, abscisic acid stimulus and defense response to bacteria were identified within the flanking regions of the significant SNP positions. Among all, one most important gene (Glyma.03G057100) involved in jasmonic acid biosynthesis was identified. Two SNPs each on chromosome 8 (S8_22467783 and S8_22467802) and chromosome 9 (S9_2923863 and S9_2935518), associated with seedlingresistance have also been identified.

No abstract available

Understanding the genetic architecture of soybean seed fatty acid (FA) compositions to enhance oil quality is crucial for nutritional value and industrial applications. This study elucidates the genomic determinants of seed FA composition in soybean (Glycine max [L.] Merr.) through comprehensive genome-wide association study (GWAS) analysis utilizing 1,550 diverse soybean accessions evaluated across five distinct environmental conditions. The phenotypic evaluation revealed significant genetic variability and environmental influences on the biosynthetic process of five essential FAs: palmitic (PA), stearic (SA), oleic (OA), linoleic (LA), and linolenic acid (LNA). High-throughput genomic association mapping identified 110,964 significant SNP-trait associations encompassing 18,841 putative genes. Notable genetic loci included chromosome 5 and 17 harboring GmFATB1A and GmFATB1B for PA biosynthesis; chromosome 2 and 8 containing Glyma.02G161200 and Glyma.08G279700 associated with SA regulation; chromosomes 10, 13, and 20 with GmKCS21, GmKAS2, and GmFAD2 affecting OA concentration; chromosomes 10 and 13 with GmKCS21 and GmKAS2 influencing LA content; and chromosome 14 containing GmFAD3 controlling LNA biosynthesis. Functional annotation through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed significant overrepresentation of lipid metabolic processes, particularly glycerolipid metabolic pathways. The haplotype characterization of three key regulatory genes GmKCS21, GmFAD2, and GmFAD3 revealed distinct geographic distribution patterns across the northern region, Huang-Huai-Hai region, and southern ecoregions of China, with varying allelic frequencies between improved cultivars and landraces, reflecting adaptive evolution and selection pressure during domestication and enhancement. This study provides a comprehensive genetic resource of 110,964 SNP-trait associations and functionally characterized haplotypes of key regulatory genes (GmKCS21, GmFAD2, and GmFAD3) that demonstrate ecoregion-specific allele frequency patterns, enabling marker-assisted selection strategies tailored to those soybean production ecoregions.

Flooding has become a major threat to soybean [Glycine max (L.) Merr.] production as the frequency and intensity of extreme precipitations have been increasing due to climate change. While advances have been made in identifying soybean genetic resources and genomic regions associated with mid‐season flood tolerance, there is limited understanding of early season flood tolerance at the vegetative growth stages V2/V4. This study aimed to identify genomic regions associated with early season flood tolerance using a diverse panel of 254 soybean accessions, as well as investigate the viability of implementing genomic prediction models for flood tolerance. Field trials were conducted over 2 years, with flooding imposed at the V2/V4 vegetative growth stages. Genome‐wide association studies were performed using the Bayesian‐information and linkage‐disequilibrium iteratively nested keyway and the multiple locus mixed linear model. Forward stepwise genomic prediction models using random forest (RF) were developed to identify the set of single nucleotide polymorphisms (SNPs) yielding the highest prediction accuracy while assessing the negative impacts of multicollinearity and overfitting on prediction accuracy. Genomic regions on chromosomes 4, 17, and 20 associated with early season flood tolerance were identified, all distinct from regions previously identified for mid‐season tolerance. The RF model achieved a prediction accuracy of 0.64 with 29 selected SNPs, significantly improving over RF and ridge regression best linear unbiased prediction models with higher SNP counts. These findings provide genomic tools for improving the efficiency of breeding for early season flood tolerance, supporting the need to develop season‐long flood‐tolerant soybean genotypes.

Soybean is challenged with a problem of poor seed longevity, a complex trait and key target for breeding. Therefore, understanding the genetic basis of seed longevity is of great significance for mining favorable genes and prolonging the seed life. A genome-wide association study (GWAS) was conducted to understand the genetic background of seed longevity over two ageing methods (natural ageing and accelerated ageing) and seasons. This study evaluated seed longevity traits in a panel of sixty diverse soybean genotypes with different seed coat colors, seed sizes, and agro-morphological traits under natural and accelerated ageing across two seasons. Seed longevity related traits such as seed germination, seedling vigor index-I (SVI-I), seedling vigor index-II (RVI-II), reduction in vigor index-I (RVI-I), and reduction in vigor index-II (RVI-II) were recorded after 12 and 14 months after natural ageing and accelerated ageing during the year 2019 and 2021. Seed germination under natural aging averaged 45.2% (range: 4.8–100%) after 12 months and 38.1% (range: 0.0–90%) after 14 months. Accelerated ageing resulted in germination rates ranging from 0.0 to 80.6% with means of 43.38% and 40.7% in 2019 and 2021, respectively. Significant variability in SVI-I, SVI-II, RVI-I, and RVI-II was observed across genotypes and conditions. GWAS using 29,955 Genotyping-by-sequencing (GBS)-single nucleotide polymorphism (SNP) markers identified 71 significant SNPs linked to seed longevity traits. Chromosomes 1, 4, and 8 harboured common SNPs for seed germination, SVI-I, SVI-II, and RVI-I. QTL hotspots were detected on chromosomes 2 and 8, encompassing multiple SNPs within 189 bp and 19 bp, respectively. Twenty-eight candidate genes were identified, including Glyma20g27840 (encoding a LEA hydroxyproline-rich glycoprotein family) on chromosome 20 for germination and SVI-I, and Glyma08g24630 (encoding ATP-dependent RNA helicase A) on chromosome 8 for germination, SVI-II, and RVI-II. Several candidate genes involved in ROS scavenging, cell regulation, ATP production, metabolism, stress response, and seed development were also associated with seed longevity traits. The SNPs associated with many longevity related traits and genes in the present study can be used for soybean breeding and functional studies of seed longevity in soybean after validation using linkage mapping or diverse population.

Tocopherol plays an important role as a powerful antioxidant in human beings and in plants. This study investigated the genetic basis of tocopherol content in soybean. A RIL population of 192 lines derived from 2 cultivars, ZH13 and ZH35, was evaluated for tocopherol content across 3 environments. QTL mapping identified 38 QTL for tocopherol, with stable QTL identified on Chromosomes 5 and 12. Ninety polymorphic genes were identified from these regions. Further SNP variation of a natural population identified 47 SNPs, with missense variants in 19 genes, including the heat shock transcription factor gene (GmHSFA8) and gamma-tocopherol methyltransferase (GmVTE4), potentially related to tocopherol accumulation in soybean. Haplotype analysis revealed significant variations in these missense variants in the natural population. This study provides insights into the genetic mechanisms underlying tocopherol content in soybean, which is important for breeding high tocopherol soybean cultivars.

No abstract available

No abstract available

Summary The cultivated [Glycine max (L) Merr.] and wild [Glycine soja Siebold & Zucc.] soybean species comprise wide variation in seed composition traits. Compared to wild soybean, cultivated soybean contains low protein, high oil, and high sucrose. In this study, an interspecific population was derived from a cross between G. max (Williams 82) and G. soja (PI 483460B). This recombinant inbred line (RIL) population of 188 lines was sequenced at 0.3× depth. Based on 91 342 single nucleotide polymorphisms (SNPs), recombination events in RILs were defined, and a high‐resolution bin map was developed (4070 bins). In addition to bin mapping, quantitative trait loci (QTL) analysis for protein, oil, and sucrose was performed using 3343 polymorphic SNPs (3K‐SNP), derived from Illumina Infinium BeadChip sequencing platform. The QTL regions from both platforms were compared, and a significant concordance was observed between bin and 3K‐SNP markers. Importantly, the bin map derived from next‐generation sequencing technology enhanced mapping resolution (from 1325 to 50 Kb). A total of five, nine, and four QTLs were identified for protein, oil, and sucrose content, respectively, and some of the QTLs coincided with soybean domestication‐related genomic loci. The major QTL for protein and oil were mapped on Chr. 20 (qPro_20) and suggested negative correlation between oil and protein. In terms of sucrose content, a novel and major QTL were identified on Chr. 8 (qSuc_08) and harbours putative genes involved in sugar transport. In addition, genome‐wide association using 91 342 SNPs confirmed the genomic loci derived from QTL mapping. A QTL‐based haplotype using whole‐genome resequencing of 106 diverse soybean lines identified unique allelic variation in wild soybean that could be utilized to widen the genetic base in cultivated soybean.

Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum (Lib.) de Bary, is an important cause of yield loss in soybean. Although many papers have reported different loci contributing to partial resistance, few of these were proved to reproduce the same phenotypic impact in different populations. In this study, we identified a major quantitative trait loci (QTL) associated with resistance to SSR progression on the main stem by using a genome-wide association mapping (GWAM). A population of 127 soybean accessions was genotyped with 1.5 M SNPs derived from genotyping-by-sequencing (GBS) and whole-genome sequencing (WGS) ensuring an extensive genome coverage and phenotyped for SSR resistance. SNP-trait association led to discovery of a new QTL on chromosome 1 (Chr01) where resistant lines had shorter lesions on the stem by 29 mm. A single gene (Glyma.01 g048000) resided in the same LD block as the peak SNP, but it is of unknown function. The impact of this QTL was even more significant in the descendants of a cross between two lines carrying contrasted alleles for Chr01. Individuals carrying the resistance allele developed lesions almost 50% shorter than those bearing the sensitivity allele. These results suggest that the new region on chromosome 1 harbors a promising resistance QTL to SSR that can be used in soybean breeding program.

Plant height is an important target for soybean breeding. It is a typical quantitative trait controlled by multiple genes and is susceptible to environmental influences. Here, we carried out phenotypic analysis of 156 recombinant inbred lines derived from “Dongnong L13” and “Henong 60” in nine environments at four locations over 6 years using interval mapping and inclusive composite interval mapping methods. We performed quantitative trait locus (QTL) analysis by applying pre-built simple-sequence repeat maps. We detected 48 QTLs, including nine significant QTLs detected by multiple methods and in multiple environments. Meanwhile, genotyping of all lines using the SoySNP660k BeadChip produced 54,836 non-redundant single-nucleotide polymorphism (SNP) genotypes. We used five multi-locus genome-wide association analysis methods to locate 10 quantitative trait nucleotides (QTNs), four of which overlap with previously located QTLs. Five candidate genes related to plant height are predicted to lie within 200 kb of these four QTNs. We identified 19 homologous genes in Arabidopsis, two of which may be associated with plant height. These findings further our understanding of the multi-gene regulatory network and genetic determinants of soybean plant height, which will be important for breeding high-yielding soybean.

Quantitative trait locus (QTL) mapping often yields associations with dissimilar loci/genes as a consequence of diverse factors. One trait for which very limited agreement between mapping studies has been observed is resistance to white mold in soybean. To explore whether different approaches applied to a single data set could lead to more consistent results, haplotype-trait association and epistasis interaction effects were explored as a complement to a more conventional marker-trait analysis. At least 10 genomic regions were significantly associated with Sclerotinia sclerotiorum resistance in soybean, which have not been previously reported. At a significance level of α = 0.05, haplotype-trait association showed that the most prominent signal originated from a haplotype with 4-SNP (single nucleotide polymorphism) on chromosome 17, and single SNP-trait analysis located a nucleotide polymorphism at position rs34387780 on chromosome 3. All of the peak-SNPs (p-value < 0.05) of each chromosome also appeared in their respective haplotypes. Samples with extreme phenotypes were singled-out for association studies, 25–30% from each end of the phenotypic spectrum appeared in the present investigation to be the most appropriate sample size. Some key genes were identified by epistasis interaction analysis. By combining information on the nearest positional genes indicated that most loci have not been previously reported. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses suggest potential candidate genes underlying callose deposition in the cell wall and mitogen-activated protein kinase (MAPK) signaling pathway-plant, as well as plant-pathogen interaction pathway, were activated. Integration of multi-method genome-wide association study (GWAS) revealed novel genomic regions and promising candidate genes in novel regions, which include Glyma.01g048500, Glyma.03g129100, Glyma.17g072200, and the Dishevelled (Dvl) family of proteins on chromosomes 1, 3, 17, and 20, respectively.

BackgroundBi-parental mapping populations have been commonly utilized to identify and characterize quantitative trait loci (QTL) controlling resistance to soybean cyst nematode (SCN, Heterodera glycines Ichinohe). Although this approach successfully mapped a large number of SCN resistance QTL, it captures only limited allelic diversity that exists in parental lines, and it also has limitations for genomic resolution. In this study, a genome-wide association study (GWAS) was performed using a diverse set of 553 soybean plant introductions (PIs) belonging to maturity groups from III to V to detect QTL/genes associated with SCN resistance to HG Type 0.ResultsOver 45,000 single nucleotide polymorphism (SNP) markers generated by the SoySNP50K iSelect BeadChip (http//www.soybase.org) were utilized for analysis. GWAS identified 14 loci distributed over different chromosomes comprising 60 SNPs significantly associated with SCN resistance. Results also confirmed six QTL that were previously mapped using bi-parental populations, including the rhg1 and Rhg4 loci. GWAS identified eight novel QTL, including QTL on chromosome 10, which we have previously mapped by using a bi-parental population. In addition to the known loci for four simple traits, such as seed coat color, flower color, pubescence color, and stem growth habit, two traits, like lodging and pod shattering, having moderately complex inheritance have been confirmed with great precision by GWAS.ConclusionsThe study showed that GWAS can be employed as an effective strategy for identifying complex traits in soybean and for narrowing GWAS-defined genomic regions, which facilitates positional cloning of the causal gene(s).

Seed oil represents a key trait in soybeans, which holds substantial economic significance, contributing to roughly 60% of global oilseed production. This research employed genome-wide association mapping to identify genetic loci associated with oil content in soybean seeds. A panel comprising 341 soybean accessions, primarily sourced from Northeast China, was assessed for seed oil content at Heilongjiang Province in three replications over two growing seasons (2021 and 2023) and underwent genotyping via whole-genome resequencing, resulting in 1,048,576 high-quality SNP markers. Phenotypic analysis indicated notable variation in oil content, ranging from 11.00% to 21.77%, with an average increase of 1.73% to 2.28% across all growing regions between 2021 and 2023. A genome-wide association study (GWAS) analysis revealed 119 significant single-nucleotide polymorphism (SNP) loci associated with oil content, with a prominent cluster of 77 SNPs located on chromosome 8. Candidate gene analysis identified four key genes potentially implicated in oil content regulation, selected based on proximity to significant SNPs (≤10 kb) and functional annotation related to lipid metabolism and signal transduction. Notably, Glyma.08G123500, encoding a receptor-like kinase involved in signal transduction, contained multiple significant SNPs with PROVEAN scores ranging from deleterious (−1.633) to neutral (0.933), indicating complex functional impacts on protein function. Additional candidate genes include Glyma.08G110000 (hydroxycinnamoyl-CoA transferase), Glyma.08G117400 (PPR repeat protein), and Glyma.08G117600 (WD40 repeat protein), each showing distinct expression patterns and functional roles. Some SNP clusters were associated with increased oil content, while others correlated with decreased oil content, indicating complex genetic regulation of this trait. The findings provide molecular markers with potential for marker-assisted selection (MAS) in breeding programs aimed at increasing soybean oil content and enhancing our understanding of the genetic architecture governing this critical agricultural trait.

No abstract available

The hypocotyl length and elongation is an important characteristic that affect the soybean seedling emergence and photosynthesis. However, the basic genetic mechanism of this feature remains incompletely understood. In this study, the hypocotyl length of four-day germinated soybean seedlings was evaluated before and after 24 h cultivation to assess hypocotyl elongation (HE) in 330 soybean accessions. Five quantitative trait loci (QTLs) that significantly associated with HE trait were detected by genome-wide association study (GWAS) in two models, and they are located on chromosome (Chr.) 2, 3, 11, 15, and 17, respectively. A total of 84 gene models have been found in HE QTLs candidate regions, and with a large proportion enriched in the biological processes of photosynthesis and cell differentiation. A CCCH zinc finger protein gene of GmZFP1 (Glyma.15G262900) was identified as the candidate in the major locus qHE_8 through the analysis of linkage disequilibrium (LD) blocks, gene expression patterns, and natural variation. Three SNPs substantially associated with HE in the GmZFP1 area resulted in 12 haplotypes (Hap 1–12) and four haplotype groups (Hap Ⅰ-Ⅳ). Soybean accessions carrying superior Hap Ⅲ showed significantly higher HE than the soybean lines containing Hap I, and the Hap Ⅲ made up 13.4% of the G. soja subpopulation and 61.98% of the G. max subpopulation, respectively. In genetic diversity and molecular evolution analysis, the GmZFP1 was also located in the genome selective sweep region during soybean domestication. Five QTLs were mapped by GWAS in both EMMAX and TASSEL models that significantly associated with soybean hypocotyl elongation (HE). The major candidate gene GmZFP1 underlying the qHE_8 locus was identified, and the superior haplotypes and selective sweep signals were also detected in the GmZFP1 region. The QTLs and GmZFP1 discovered in this study provided potential genetic resources for the soybean molecular breeding in the future.

No abstract available

Drought is the major abiotic stress threatening soybean production globally. However, the genetic basis of soybean drought resistance at the germination stage remains largely unknown. In this study, the drought-tolerance phenotypes of a diverse panel of 207 soybean accessions were examined. Leveraging 95,043 high-density single-nucleotide polymorphism (SNP) markers, a total of 58 quantitative trait loci (QTLs) were detected using the restricted two-stage multi-locus genome-wide association study (RTM-GWAS) method, and 10 of these QTLs were considered as large-contribution QTLs that have larger phenotype variation. Furthermore, RNA-sequencing was performed on the roots of two soybean accessions with contrasting drought tolerance. A total of 1,183, 1,354, and 1,581 differentially expressed genes (DEGs) between two soybean accessions after 0h, 12h, and 24h of drought treatment were separately obtained, and 4,012 and 4,586 genes responsive to drought stress were identified at 12h and 24h, respectively. By utilizing these DEGs, a weighted gene co-expression network analysis (WGCNA) was constructed, and 13 distinct modules were obtained, among which four modules were considered as key modules. Subsequently, 40 hub genes were identified in these four modules. In addition, by combining RTM-GWAS and transcriptome analysis, 22 candidate genes underlying large-contribution QTLs were identified. Based on the functional annotations, Glyma.12G141700, Glyma.15G040000, Glyma.05G049300, Glyma.14G105900, and Glyma.15G041100 were regarded as the most possible candidate genes that regulate soybean drought tolerance at the germination stage. The QTLs, key modules, and hub genes discovered in this study will provide a new understanding of the genetic basis of soybean drought resistance at the germination stage and lay a theoretical foundation for the improvement and innovation of high-quality soybean germplasm.

No abstract available

Soybean [Glycine max (L.) Merr.] is an excellent source of protein. Understanding the genetic basis of protein content (PC) will accelerate breeding efforts to increase soybean quality. In the present study, a genome-wide association study (GWAS) was applied to detect quantitative trait loci (QTL) for PC in soybean using 264 re-sequenced soybean accessions and a high-quality single nucleotide polymorphism (SNP) map. Eleven QTL were identified as associated with PC. The QTL qPC-14 was detected by GWAS in both environments and was shown to have undergone strong selection during soybean improvement. Fifteen candidate genes were identified in qPC-14, and three candidate genes showed differential expression between a high-PC and a low-PC variety during the seed development stage. The QTL identified here will be of significant use in molecular breeding efforts, and the candidate genes will play essential roles in exploring the mechanisms of protein biosynthesis.

Bacterial blight caused by Pseudomonas syringae pv. glycines (Psg) is a widespread foliar disease. Although four Resistance to Pseudomonas syringae pv. glycinea (Rpg) 1 ~ 4 (Rpg1~4) genes that have been observed to segregate in a Mendelian pattern have been reported to confer resistance to Psg in soybean, the genetic basis of quantitative resistance to bacterial blight in soybean remains unclear. In the present study, the Psg resistance of two soybean association panels consisting of 573 and 213 lines, respectively, were phenotyped in multiple environments in 2014 - 2016. Genome-wide association study (GWAS) were performed using 2 models FarmCPU and BLINK to identify Psg resistance loci. A total of 40 soybean varieties with high level of Psg resistance were identified, and 14 quantitative trait loci (QTLs) were detected on 12 soybean chromosomes. These QTLs were identified for the first time. The majority of the QTLs were only detected in one or the other association panels, while qRPG-18-1 was detected in both association panels for at least one growing season. A total of 46 candidate Psg resistance genes were identified from the qRpg_13_1, qRPG-15-1, and qRPG-18-1 loci based on gene function annotation. In addition, we found the genomic region covering rpg1-b and rpg1-r harbored the synteny with a genomic region on chromosome 15, and identified 16 nucleotide binding site - leucine-rich repeat (NBS-LRR) genes as the candidate Psg resistance genes from the synteny blocks. This study provides new information for dissecting the genetic control of Psg resistance in soybean.

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

Seed size traits, including seed length (SL), seed width (SW), and seed thickness (ST), are crucial appearance parameters that determine soybean seed weight, yield, and ultimate utilization. However, there is still a large gap in the understanding of the genetic mechanism of these traits. Here, 281 soybeans were utilized to analyze the genetic architecture of seed size traits in different years through multiple (single-locus and multi-locus) genome-wide association study (GWAS) models, and candidate genes were predicted by integrating information on gene function and transcriptome sequencing data. As a result, two, seven, and three stable quantitative trait nucleotides (QTNs) controlling SL, SW, and ST were detected in multiple environments using the single-locus GWAS model, and concurrently detected by the results of the multi-locus GWAS models. These stable QTNs are located on 10 linkage disequilibrium blocks, with single genome regions ranging in size from 20 to 440 kb, and can serve as the major loci controlling soybean seed size. Furthermore, by combining gene functional annotation and transcriptome sequencing data of seeds at different developmental stages from two extreme soybean accessions, nine candidate genes, including Glyma.05G038000, Glyma.05G244100, Glyma.05G246900, Glyma.07G070200, Glyma.11G010000, Glyma.11G012400, Glyma.17G165500, Glyma.17G166500, and Glyma.20G012600 within the major loci that may regulate soybean seed size, were mined. Overall, these findings offer valuable insights for molecular improvement breeding as well as gene functional studies to unravel the mechanism of soybean seed size.

Soybean (Glycine max [L.] Merr.) is one of the primary sources of plant protein and oil for human foods, animal feed, and industrial processing. The seed number per pod generally varies from one to four and is an important component of seed number per unit area and seed yield. We used natural variation in 264 landraces and improved cultivars or lines to identify candidate genes involved in the regulation of seed number per pod in soybean. Genome-wide association tests revealed 65 loci that are associated with seed number per pod trait. Among them, 11 could be detected in multiple environments. Candidate genes were identified for seed number per pod phenotype from the most significantly associated loci, including a gene encoding protein argonaute 4, a gene encoding histone acetyltransferase of the MYST family 1, a gene encoding chromosome segregation protein SMC-1 and a gene encoding exocyst complex component EXO84A. In addition, plant hormones were found to be involved in ovule and seed development and the regulation of seed number per pod in soybean. This study facilitates the dissection of genetic networks underlying seed number per pod in soybean, which will be useful for the genetic improvement of seed yield in soybean.

The duration of flowering and maturity is an important agricultural trait determining the suitability of a variety for cultivation in the target region. In the present study, we used genome-wide association analysis (GWAS) to search for loci associated with soybean flowering and maturity in the Central and West Siberian regions of Russia. A field experiment was conducted in 2021/2022 at two locations (Orel and Novosibirsk). A germplasm collection of 180 accessions was genotyped using SoySNP50K Illumina Infinium Bead-Chip. From the initial collection, we selected 129 unrelated accessions and conducted GWAS on this dataset using two multi-locus models: FarmCPU and BLINK. As a result, we identified 13 loci previously reported to be associated with duration of soybean development, and 17 new loci. 33 candidate genes were detected in these loci using analysis of co-expression, gene ontology, and literature data, with the best candidates being Glyma.03G177500, Glyma.13G177400, and Glyma.06G213100. These candidate genes code the Arabidopis orthologs TOE1 (TARGET OF EAT 1), SPL3 (SQUAMOSA PROMOTER BINDING PROTEIN LIKE 3), the DELLA protein, respectively. In these three genes, we found haplotypes which may be associated with the length of soybean flowering and maturity, providing soybean adaptation to a northern latitudes.

Seed weight is a complex yield-related trait with a lot of quantitative trait loci (QTL) reported through linkage mapping studies. Integration of QTL from linkage mapping into breeding program is challenging due to numerous limitations, therefore, Genome-wide association study (GWAS) provides more precise location of QTL due to higher resolution and diverse genetic diversity in un-related individuals. The present study utilized 573 breeding lines population with 61,166 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and candidate genes for seed weight in Chinese summer-sowing soybean. GWAS was conducted with two single-locus models (SLMs) and six multi-locus models (MLMs). Thirty-nine SNPs were detected by the two SLMs while 209 SNPs were detected by the six MLMs. In all, two hundred and thirty-one QTNs were found to be associated with seed weight in YHSBLP with various effects. Out of these, seventy SNPs were concurrently detected by both SLMs and MLMs on 8 chromosomes. Ninety-four QTNs co-localized with previously reported QTL/QTN by linkage/association mapping studies. A total of 36 candidate genes were predicted. Out of these candidate genes, four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280 and Glyma19g28070) were identified by the integration of co-expression network. Among them, three were relatively expressed higher in the high HSW genotypes at R5 stage compared with low HSW genotypes except Glyma12g33280. Our results show that using more models especially MLMs are effective to find important QTNs, and the identified HSW QTNs/genes could be utilized in molecular breeding work for soybean seed weight and yield. Application of two single-locus plus six multi-locus models of GWAS identified 231 QTNs. Four hub genes (Glyma06g44510,Glyma08g06420,Glyma12g33280 & Glyma19g28070) detected via integration of co-expression network among the predicted candidate genes.

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

Fusarium virguliforme is a soil borne root pathogen that causes sudden death syndrome (SDS) in soybean [Glycine max (L.) Merrill]. Once the fungus invades the root xylem tissues, the pathogen secretes toxins that cause chlorosis and necrosis in foliar tissues leading to defoliation, flower and pod drop and eventually death of plants. Resistance to F. virguliforme in soybean is partial and governed by over 80 quantitative trait loci (QTL). We have conducted genome-wide association study (GWAS) for a group of 254 plant introductions lines using a panel of approximately 30,000 SNPs and identified 19 single nucleotide polymorphic loci (SNPL) that are associated with 14 genomic regions encoding foliar SDS and eight SNPL associated with seven genomic regions for root rot resistance. Of the identified 27 SNPL, six SNPL for foliar SDS resistance and two SNPL for root rot resistance co-mapped to previously identified QTL for SDS resistance. This study identified 13 SNPL associated with eight novel genomic regions containing foliar SDS resistance genes and six SNPL with five novel regions for root-rot resistance. This study identified five genes carrying nonsynonymous mutations: (i) three of which mapped to previously identified QTL for foliar SDS resistance and (ii) two mapped to two novel regions containing root rot resistance genes. Of the three genes mapped to QTL for foliar SDS resistance genes, two encode LRR-receptors and third one encodes a novel protein with unknown function. Of the two genes governing root rot resistance, Glyma.01g222900.1 encodes a soybean-specific LEA protein and Glyma.10g058700.1 encodes a heparan-alpha-glucosaminide N-acetyltransferase. In the LEA protein, a conserved serine residue was substituted with asparagine; and in the heparan-alpha-glucosaminide N-acetyltransferase, a conserved histidine residue was substituted with an arginine residue. Such changes are expected to alter functions of these two proteins regulated through phosphorylation. The five genes with nonsynonymous mutations could be considered candidate SDS resistance genes and should be suitable molecular markers for breeding SDS resistance in soybean. The study also reports desirable plant introduction lines and novel genomic regions for enhancing SDS resistance in soybean.

Powdery mildew (PMD), caused by the pathogen Microsphaera diffusa, leads to substantial yield decreases in susceptible soybean under favorable environmental conditions. Effective prevention of soybean PMD damage can be achieved by identifying resistance genes and developing resistant cultivars. In this study, we genotyped 331 soybean germplasm accessions, primarily from Northeast China, using the SoySNP50K BeadChip, and evaluated their resistance to PMD in a greenhouse setting. To identify marker-trait associations while effectively controlling for population structure, we conducted genome-wide association studies utilizing factored spectrally transformed linear mixed models, mixed linear models, efficient mixed-model association eXpedited, and compressed mixed linear models. The results revealed seven single nucleotide polymorphism (SNP) loci strongly associated with PMD resistance in soybean. Among these, one SNP was localized on chromosome (Chr) 14, and six SNPs with low linkage disequilibrium were localized near or in the region of previously mapped genes on Chr 16. In the reference genome of Williams82, we discovered 96 genes within the candidate region, including 17 resistance (R)-like genes, which were identified as potential candidate genes for PMD resistance. In addition, we performed quantitative real-time reverse transcription polymerase chain reaction analysis to evaluate the gene expression levels in highly resistant and susceptible genotypes, focusing on leaf tissues collected at different times after M. diffusa inoculation. Among the examined genes, three R-like genes, including Glyma.16G210800, Glyma.16G212300, and Glyma.16G213900, were identified as strong candidates associated with PMD resistance. This discovery can significantly enhance our understanding of soybean resistance to PMD. Furthermore, the significant SNPs strongly associated with resistance can serve as valuable markers for genetic improvement in breeding M. diffusa-resistant soybean cultivars.

Soybean is a globally important industrial, food, and cash crop. Despite its importance in present and future economies, its production is severely hampered by bruchids (Callosobruchus chinensis), a destructive storage insect pest, causing considerable yield losses. Therefore, the identification of genomic regions and candidate genes associated with bruchid resistance in soybean is crucial as it helps breeders to develop new soybean varieties with improved resistance and quality. In this study, 6 multi-locus methods of the mrMLM model for genome-wide association study were used to dissect the genetic architecture of bruchid resistance on 4traits: percentage adult bruchid emergence (PBE), percentage weight loss (PWL), median development period (MDP), and Dobie susceptibility index (DSI) on 100 diverse soybean genotypes, genotyped with 14,469 single-nucleotide polymorphism (SNP) markers. Using the best linear unbiased predictors (BLUPs), 13 quantitative trait nucleotides (QTNs) were identified by the mrMLM model, of which rs16_14976250 was associated with more than 1 bruchid resistance traits. As a result, the identified QTNs linked with resistance traits can be employed in marker-assisted breeding for the accurate and rapid screening of soybean genotypes for resistance to bruchids. Moreover, a gene search on the Phytozome soybean reference genome identified 27 potential candidate genes located within a window of 478.45 kb upstream and downstream of the most reliable QTNs. These candidate genes exhibit molecular and biological functionalities associated with various soybean resistance mechanisms and, therefore, could be incorporated into the farmers’ preferred soybean varieties that are susceptible to bruchids.

Common cutworm (CCW) is an omnivorous insect causing severe yield losses in soybean crops. The seedling-stage mini-tray identification system with the damaged leaf percentage (DLP) as an indicator was used to evaluate antixenosis against CCW in the Chinese soybean landrace population (CSLRP) under three environments. Using the innovative restricted two-stage multi-locus genome-wide association study procedure (RTM-GWAS), 86 DLP QTLs with 243 alleles (2–11/QTL) were identified, including 66 main-effect loci with 203 alleles and 57 QTL-environment interaction loci with 172 alleles. Among the main-effect loci, 12 large-contribution loci (R2 ≥ 1%) explained 25.45% of the phenotypic variation (PV), and 54 small-contribution loci (R2 < 1%) explained 16.55% of the PV. This indicates that the CSLRP can be characterized with a DLP QTL-allele system complex that has not been found before, except for a few individual QTLs without alleles involved. From the DLP QTL-allele matrix, the recombination potentials expressed in the 25th percentile of the DLP of all possible crosses were predicted to be reduced by 41.5% as the maximum improvement and 14.2% as the maximum transgression, indicating great breeding potential in the antixenosis of the CSLRP. From the QTLs, 62 candidate genes were annotated, which were involved in eight biological function categories as a gene network of the DLP. Changing from susceptible to moderate plus resistant varieties in the CSLRP, 26 QTLs had 32 alleles involved, in which 19 genes were annotated from 25 QTL-alleles, including eight increased negative alleles on seven loci and 11 decreased positive alleles on 11 loci, showing the major genetic constitution changes for the antixenosis enhancement at the seedling stage in the CSLRP.

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Ideal plant architecture is essential for enhancing crop yields. Ideal soybean (Glycine max) architecture encompasses an appropriate plant height, increased node number, moderate seed weight, and compact architecture with smaller branch angles for growth under high-density planting. However, the functional genes regulating plant architecture are far not fully understood in soybean. In this study, we investigated the genetic basis of 12 agronomic traits in a panel of 496 soybean accessions with a wide geographical distribution in China. Analysis of phenotypic changes in 148 historical elite soybean varieties indicated that seed-related traits have mainly been improved over the past 60 years, with targeting plant architecture traits having the potential to further improve yields in future soybean breeding programs. In a genome-wide association study (GWAS) of 12 traits, we detected 169 significantly associated loci, of which 61 overlapped with previously reported loci and 108 new loci. By integrating the GWAS loci for different traits, we constructed a genetic association network and identified 90 loci that were associated with a single trait and 79 loci with pleiotropic effects. Of these 79 loci, 7 hub-nodes were strongly linked to at least three related agronomic traits. qHub_5, containing the previously characterized Determinate 1 (Dt1) locus, was associated not only with plant height and node number (as determined previously), but also with internode length and pod range. Furthermore, we identified qHub_7, which controls three branch angle-related traits; the candidate genes in this locus may be beneficial for breeding soybean with compact architecture. These findings provide insights into the genetic relationships among 12 important agronomic traits in soybean. In addition, these studies uncover valuable loci for further functional gene studies and will facilitate molecular design breeding of soybean architecture.

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Water and nutrient acquisition is a critical function of plant root systems. Root system architecture (RSA) traits are often complex and controlled by many genes. This is the first genome-wide association study reporting genetic loci for RSA traits for field-grown soybean (Glycine max). A collection of 289 soybean genotypes was grown in three environments, root crowns were excavated, and 12 RSA traits assessed. The first two components of a principal component analysis of these 12 traits were used as additional aggregate traits for a total of 14 traits. Marker–trait association for RSA traits were identified using 31,807 single-nucleotide polymorphisms (SNPs) by a genome-wide association analysis. In total, 283 (non-unique) SNPs were significantly associated with one or more of the 14 root traits. Of these, 246 were unique SNPs and 215 SNPs were associated with a single root trait, while 26, four, and one SNPs were associated with two, three, and four root traits, respectively. The 246 SNPs marked 67 loci associated with at least one of the 14 root traits. Seventeen loci on 13 chromosomes were identified by SNPs associated with more than one root trait. Several genes with annotation related to processes that could affect root architecture were identified near these 67 loci. Additional follow-up studies will be needed to confirm the markers and candidate genes identified for RSA traits and to examine the importance of the different root characteristics for soybean productivity under a range of soil and environmental conditions.

Genomic regions associated with seed protein, oil and amino acid contents were identified by genome-wide association analyses. Geographic distributions of haplotypes indicate scope of improvement of these traits. Soybean [Glycine max (L.) Merr.] protein and oil are used worldwide in feed, food and industrial materials. Increasing seed protein and oil contents is important; however, protein content is generally negatively correlated with oil content. We conducted a genome-wide association study using phenotypic data collected from five environments for 621 accessions in maturity groups I–IV and 34,014 markers to identify quantitative trait loci (QTL) for seed content of protein, oil and several essential amino acids. Three and five genomic regions were associated with seed protein and oil contents, respectively. One, three, one and four genomic regions were associated with cysteine, methionine, lysine and threonine content (g kg−1 crude protein), respectively. As previously shown, QTL on chromosomes 15 and 20 were associated with seed protein and oil contents, with both exhibiting opposite effects on the two traits, and the chromosome 20 QTL having the most significant effect. A multi-trait mixed model identified trait-specific QTL. A QTL on chromosome 5 increased oil with no effect on protein content, and a QTL on chromosome 10 increased protein content with little effect on oil content. The chromosome 10 QTL co-localized with maturity gene E2/GmGIa. Identification of trait-specific QTL indicates feasibility to reduce the negative correlation between protein and oil contents. Haplotype blocks were defined at the QTL identified on chromosomes 5, 10, 15 and 20. Frequencies of positive effect haplotypes varied across maturity groups and geographic regions, providing guidance on which alleles have potential to contribute to soybean improvement for specific regions.

A genome-wide association study (GWAS) identifies trait-associated loci, but identifying the causal genes can be a bottleneck, due in part to slow decay of linkage disequilibrium (LD). A transcriptome-wide association study (TWAS) addresses this issue by identifying gene expression–phenotype associations or integrating gene expression quantitative trait loci with GWAS results. Here, we used self-pollinated soybean (Glycine max [L.] Merr.) as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay. We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29 286 soybean genes. Different TWAS solutions were less affected by LD and were robust to the source of expression, identifing known genes related to traits from different tissues and developmental stages. The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing. By introducing a new exon proportion feature, we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing. As a result, the genes identified through our TWAS approach exhibited a diverse range of causal variations, including SNPs, insertions or deletions, gene fusion, copy number variations, and alternative splicing. Using this approach, we identified genes associated with flowering time, including both previously known genes and novel genes that had not previously been linked to this trait, providing insights complementary to those from GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.

Sclerotinia stem rot (SSR) is a disease of soybean [Glycine max (L.) Merr] that causes severe yield losses. We studied 185 representative soybean accessions to evaluate partial SSR resistance and sequenced these by the specific-locus amplified fragment sequencing method. In total, 22,048 single-nucleotide polymorphisms (SNPs), with minor allele frequencies (MAF) ≥5% and missing data <3%, were developed and applied to genome-wide association study of SSR responsiveness and assess linkage disequilibrium (LD) level for candidate gene selection. We identified 18 association signals related to SSR partial resistance. Among them, six overlapped the regions of previous quantitative trait loci, and twelve were novel. We identified 243 candidate genes located in the 200 kb genomic region of these peak SNPs. Based on quantitative real-time polymerase chain reaction and haplotype analysis, Glyma.03G196000 and Glyma.20G095100, encoding pentatricopeptide repeat proteins, might be important factors in the resistance response of soybean to SSR.

The ability of soybean [Glycine max (L.) Merr.] to adapt to different latitudes is attributed to genetic variation in major E genes and quantitative trait loci (QTLs) determining flowering time (R1), maturity (R8), and reproductive length (RL). Fully revealing the genetic basis of R1, R8, and RL in soybeans is necessary to enhance genetic gains in soybean yield improvement. Here, we performed a genome-wide association analysis (GWA) with 31,689 single nucleotide polymorphisms (SNPs) to detect novel loci for R1, R8, and RL using a soybean panel of 329 accessions with the same genotype for three major E genes (e1-as/E2/E3). The studied accessions were grown in nine environments and observed for R1, R8 and RL in all environments. This study identified two stable peaks on Chr 4, simultaneously controlling R8 and RL. In addition, we identified a third peak on Chr 10 controlling R1. Association peaks overlap with previously reported QTLs for R1, R8, and RL. Considering the alternative alleles, significant SNPs caused RL to be two days shorter, R1 two days later and R8 two days earlier, respectively. We identified association peaks acting independently over R1 and R8, suggesting that trait-specific minor effect loci are also involved in controlling R1 and R8. From the 111 genes highly associated with the three peaks detected in this study, we selected six candidate genes as the most likely cause of R1, R8, and RL variation. High correspondence was observed between a modifying variant SNP at position 04:39294836 in GmFulb and an association peak on Chr 4. Further studies using map-based cloning and fine mapping are necessary to elucidate the role of the candidates we identified for soybean maturity and adaptation to different latitudes and to be effectively used in the marker-assisted breeding of cultivars with optimal yield-related traits.

We report a meta-Genome Wide Association Study involving 73 published studies in soybean (Glycine max L. [Merr.]) covering 17,556 unique accessions, with improved statistical power for robust detection of loci associated with a broad range of traits. De novo GWAS and meta-analysis were conducted for composition traits including fatty acid and amino acid composition traits, disease resistance traits, and agronomic traits including seed yield, plant height, stem lodging, seed weight, seed mottling, seed quality, flowering timing, and pod shattering. To examine differences in detectability and test statistical power between single- and multi-environment GWAS, comparison of meta-GWAS results to those from the constituent experiments were performed. Using meta-GWAS analysis and the analysis of individual studies, we report 483 quantitative trait loci (QTL) at 393 unique loci. Using stringent criteria to detect significant marker trait associations, 66 candidate genes were identified, including 17 candidate genes for agronomic traits, 19 for seed related traits, and 33 for disease reaction traits. This study identified potentially valuable candidate genes that affect multiple traits. The success in narrowing down the genomic region for some loci through overlapping mapping results of multiple studies is a promising avenue for community-based studies and plant breeding applications.

Mapping quantitative trait loci through the use of linkage disequilibrium (LD) in populations of unrelated individuals provides a valuable approach for dissecting the genetic basis of complex traits in soybean (Glycine max). The haplotype-based genome-wide association study (GWAS) has now been proposed as a complementary approach to intensify benefits from LD, which enable to assess the genetic determinants of agronomic traits. In this study a GWAS was undertaken to identify genomic regions that control 100-seed weight (SW), plant height (PH) and seed yield (SY) in a soybean association mapping panel using single nucleotide polymorphism (SNP) markers and haplotype information. The soybean cultivars (N = 169) were field-evaluated across four locations of southern Brazil. The genome-wide haplotype association analysis (941 haplotypes) identified eleven, seventeen and fifty-nine SNP-based haplotypes significantly associated with SY, SW and PH, respectively. Although most marker-trait associations were environment and trait specific, stable haplotype associations were identified for SY and SW across environments (i.e., haplotypes Gm12_Hap12). The haplotype block 42 on Chr19 (Gm19_Hap42) was confirmed to be associated with PH in two environments. These findings enable us to refine the breeding strategy for tropical soybean, which confirm that haplotype-based GWAS can provide new insights on the genetic determinants that are not captured by the single-marker approach.

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.

Soybean oil is the most widely produced vegetable oil in the world and its content in soybean seed is an important quality trait in breeding programs. More than 100 quantitative trait loci (QTLs) for soybean oil content have been identified. However, most of them are genotype specific and/or environment sensitive. Here, we used both a linkage and association mapping methodology to dissect the genetic basis of seed oil content of Chinese soybean cultivars in various environments in the Jiang-Huai River Valley. One recombinant inbred line (RIL) population (NJMN-RIL), with 104 lines developed from a cross between M8108 and NN1138-2, was planted in five environments to investigate phenotypic data, and a new genetic map with 2,062 specific-locus amplified fragment markers was constructed to map oil content QTLs. A derived F2 population between MN-5 (a line of NJMN-RIL) and NN1138-2 was also developed to confirm one major QTL. A soybean breeding germplasm population (279 lines) was established to perform a genome-wide association study (GWAS) using 59,845 high-quality single nucleotide polymorphism markers. In the NJMN-RIL population, 8 QTLs were found that explained a range of phenotypic variance from 6.3 to 26.3% in certain planting environments. Among them, qOil-5-1, qOil-10-1, and qOil-14-1 were detected in different environments, and qOil-5-1 was further confirmed using the secondary F2 population. Three loci located on chromosomes 5 and 20 were detected in a 2-year long GWAS, and one locus that overlapped with qOil-5-1 was found repeatedly and treated as the same locus. qOil-5-1 was further localized to a linkage disequilibrium block region of approximately 440 kb. These results will not only increase our understanding of the genetic control of seed oil content in soybean, but will also be helpful in marker-assisted selection for breeding high seed oil content soybean and gene cloning to elucidate the mechanisms of seed oil content.

BackgroundCultivated soybean (Glycine max) is a major agricultural crop that provides a crucial source of edible protein and oil. Decreased amounts of saturated palmitic acid and increased amounts of unsaturated oleic acid in soybean oil are considered optimal for human cardiovascular health and therefore there has considerable interest by breeders in discovering genes affecting the relative concentrations of these fatty acids. Using a genome-wide association (GWA) approach with nearly 30,000 single nucleotide polymorphisms (SNPs), we investigated the genetic basis of protein, oil and all five fatty acid levels in seeds from a sample of 570 wild soybeans (Glycine soja), the progenitor of domesticated soybean, to identify quantitative trait loci (QTLs) affecting these seed composition traits.ResultsWe discovered 29 SNPs located on ten different chromosomes that are significantly associated with the seven seed composition traits in our wild soybean sample. Eight SNPs co-localized with QTLs previously uncovered in linkage or association mapping studies conducted with cultivated soybean samples, while the remaining SNPs appeared to be in novel locations. Twenty-four of the SNPs significantly associated with fatty acid variation, with the majority located on chromosomes 14 (6 SNPs) and seven (8 SNPs). Two SNPs were common for two or more fatty acids, suggesting loci with pleiotropic effects. We also identified some candidate genes that are involved in fatty acid metabolism and regulation. For each of the seven traits, most of the SNPs produced differences between the average phenotypic values of the two homozygotes of about one-half standard deviation and contributed over 3% of their total variability.ConclusionsThis is the first GWA study conducted on seed composition traits solely in wild soybean populations, and a number of QTLs were found that have not been previously discovered. Some of these may be useful to breeders who select for increased protein/oil content or altered fatty acid ratios in the seeds. The results also provide additional insight into the genetic architecture of these traits in a large sample of wild soybean, and suggest some new candidate genes whose molecular effects on these traits need to be further studied.

Long read sequencing has been widely used to detect structure variations that are not captured by short read sequencing in plant genomic research. In this study, we described an analysis of whole genome re-sequencing of 29 soybean varieties using nanopore long-read sequencing. The compiled germplasm reflects diverse applications of food-grade soybeans, including soy milk and tofu production, as well as consumption of natto, sprout, and edamame (vegetable soybean). We have identified 365,497 structural variations in these newly re-sequenced genomes and found that the newly identified structural variations are associated with important agronomic traits. These traits include seed weight, flowering time, plant height, oleic acid content, methionine content, and Kunitz trypsin inhibitor content, all of which significantly impact soybean production, quality, and market value. Experimental validation supports the roles of predicted candidate genes and structural variants in these biological processes. Our research provides a new source for rapid marker discovery in soybean and other crop genomes using structural variation and whole genome sequencing.

Soybean (Glycine max [L.] Merr.) is a crop characterized by rich content of oil and protein in seeds, enhancing both yield and quality is considered a pressing challenge in current soybean research and production. Soybean yield is determined by individual traits, including seed number per plant, seed weight per plant, pod number per plant, pod weight per plant and 100−seed weight. Here, 338 resequenced soybean varieties (or lines) were evaluated under two planting densities for five pod−related traits. Substantial variation was detected among the 338 accessions under both densities, and all phenotypic traits followed a normal distribution. A total of 47 and 56 significant SNPs were identified respectively under high and low planting densities through genome−wide association studies (GWAS). Among them, eight SNPs were repeatedly detected across at least two planting densities or environments, and were significantly associated with the seed number per plant (SNPP), seed weight per plant (SWPP) and 100−seed weight (HSW). Based on linkage disequilibrium (LD) analysis, haplotype analysis, gene functional annotation, and qRT−PCR validation, Glyma.20G116200 and Glyma.13G162800 were identified as key genes associated with HSW and SNPP, respectively. Based on this, a KASP marker, S20_35808042 (G/C), was developed and successfully validated in 97 soybean accessions. In summary, these findings hold substantial value for soybean improvement, providing new insights into the genetic architecture of pod−related traits and establishing a conceptual foundation for marker−based selection in breeding programs.

Soybean (Glycine max (L.) Merr.), as a crucial source of oil and protein globally, is widely cultivated in many countries. Low-temperature stress has become one of the major environmental factors affecting soybean production, especially in colder regions, making the improvement of cold tolerance traits in soybean a key breeding objective. This study integrates Genome-Wide Association Studies (GWAS) and Marker-Assisted Selection (MAS) to enhance the predictive performance of soybean cold tolerance traits. First, three GWAS methods—Fast3VmrMLM, fastGWA, and FarmCPU—were used to analyze soybean cold tolerance traits, and significant SNP markers were identified. Principal Component Analysis (PCA) was employed to reveal genetic differences among various soybean germplasm. Then, based on the identified SNP markers, multiple Genomic Selection (GS) models, such as GBLUP, BayesA, BayesB, BayesC, BL, and BRR, were used for prediction to evaluate the contribution of genetic effects to phenotypic variation. The results showed that the markers selected through GWAS significantly improved the prediction accuracy of genomic selection, especially with the Fast3VmrMLM and FarmCPU methods in larger datasets. Finally, Gene Ontology (GO) analysis was performed to further identify candidate genes associated with cold tolerance traits and their biological functions, providing theoretical support for molecular breeding of cold-tolerant soybean varieties.

Understanding the genetic architecture of complex traits is crucial for crop improvement and molecular breeding. We developed a mutagenized soybean population using carbon ion beam irradiation and conducted genome-wide association studies (GWAS) to identify variants controlling key agronomic traits. Whole-genome resequencing of 199 M4 lines revealed 1.48 million SNPs, predominantly C→T transitions, with population structure analysis identifying three distinct genetic groups. GWAS across five traits revealed striking differences in genetic architecture: the podding habit showed extreme polygenic control with 87,029 significant associations of small effect, while pubescence color exhibited oligogenic inheritance with only 122 variants. Hundred-seed weight displayed moderate complexity (4637 associations) with the largest effect sizes (−3.74 to 5.03) and major QTLs on chromosomes 4, 7, and 15–20. Growth habit involved 12,136 SNPs, including a strong chromosome 3 association (−log10(p-value) > 50). Flower color showed 2662 associations clustered on chromosome 15. Functional analysis of 18,542 candidate genes revealed trait-specific pathway enrichments: flavonoid biosynthesis for flower color, phloem transport for seed weight, auxin signaling for growth habit, and amino acid transport for podding habit. This study demonstrates how mutagenesis-induced variation, combined with association mapping, reveals evolutionary constraints that shape genetic architectures, providing insights for genetics-assisted breeding strategies.

Understanding trait relationships is fundamental in soybean breeding because the goal is to maximize simultaneous gains. Standard multi-trait genome-wide association studies (MT-GWAS) identify variants linked to multiple traits but fail to capture phenotypic structures or interrelations. Structural Equation Models (SEM) account for covariances and recursion, enabling the decomposition of single nucleotide polymorphism (SNP) effects into direct or indirect components and identifying pleiotropic regions. We applied SEM to analyze morphology (pod thickness, PT) and yield traits (number of pods, NP; number of grains, NG; hundred-grain weight, HGW). The dataset comprised 96 soybean individuals genotyped with 4070 SNP markers. The phenotypic network was constructed using the hill-climbing algorithm, a class of score-based methods commonly applied to learn the structure of Bayesian networks, and structural coefficients were estimated with SEM. According to coefficient signs, we identified negative interrelationships between NG and HGW, and positive ones between NP and NG, and HGW and PT. NG, HGW, and PT showed indirect SNP effects. We also found loci jointly controlling traits. In total, 46 candidate genes were identified: 7 associated exclusively with NP and 4 associated with NG. An additional 15 genes were common to NP and NG, 3 were common to NP and HGW, 6 were common to NG and HGW, and 11 were common to NP, NG, and HGW. In summary, SEM-GWAS revealed novel relationships among soybean traits, including PT, supporting breeding programs.

Genome-wide association studies (GWAS) are powerful statistical methods that detect associations between genotype and phenotype at genome scale. Despite their power, GWAS frequently fail to pinpoint the causal variant or the gene controlling a trait at a given locus in crop species. Assessing genetic variants beyond single-nucleotide polymorphisms (SNPs) could alleviate this problem, for example by including structural variants (SVs). In this study, we tested the potential of SV-and k-mer-based GWAS in soybean by applying these methods to 13 traits. We also performed conventional GWAS analysis based on SNPs and small indels for comparison. We assessed the performance of each GWAS approach based on results at loci for which the causal genes or variants were known from previous genetic studies. We found that k-mer-based GWAS was the most versatile approach and the best at pinpointing causal variants or candidate genes based on the most significantly associated k-mers. Moreover, k-mer-based analyses identified promising candidate genes for loci related to pod color, pubescence form, and resistance to the oomycete Phytophthora sojae. In our dataset, SV-based GWAS did not add value compared to k-mer-based GWAS and may not be worth the time and computational resources required to genotype SVs at population scale. Despite promising results, significant challenges remain regarding the downstream analysis of k-mer-based GWAS. Notably, better methods are needed to associate significant k-mers with sequence variation. Together, our results suggest that coupling k-mer-and SNP/indel-based GWAS is a powerful approach for discovering candidate genes in crop species.

Japanese soybeans are traditionally bred to produce soy foods such as tofu, miso and boiled soybeans. Here, to investigate their distinctive genomic features, including genomic structural variations (SVs), we constructed 11 nanopore-based genome references for Japanese and other soybean lines. Our assembly-based comparative method, designated ‘Asm2sv’, identified gene-level SVs comprehensively, enabling pangenome analysis of 462 worldwide cultivars and varieties. Based on these, we identified selective sweeps between Japanese and US soybeans, one of which was the pod-shattering resistance gene PDH1. Genome-wide association studies further identified several quantitative trait loci that accounted for large-seed phenotypes of Japanese soybean lines, some of which were also close to regions of the selective sweeps, including PDH1. Notably, specific combinations of alleles, including SVs, were found to increase the seed size of some Japanese landraces. In addition to the differences in cultivation environments, distinct food processing usages might result in changes in Japanese soybean genomes. Long-read genome assemblies for seven Japanese, three North American and one primitive Glycine max cultivars highlight gene-level structural variation underlying distinct seed morphology phenotypes.

Regional differences in soybean seed protein and amino acid content in Canada present significant challenges for crop improvement and the market value of high‐protein livestock feed. This study employed genome‐wide association studies (GWAS) using a novel panel of 206 cultivars to investigate the genetic basis of regional variations. Field trials were conducted across six site years in Eastern and Western Canada in 2021 and 2022. Phenotypic analysis revealed lower protein and amino acid content in Western regions, with an average decrease of 0.9% in protein compared with Eastern regions. Using 31,362 SNPs, we identified 370 significant marker trait associations (MTAs), consolidated into 175 quantitative trait loci (QTL), 27 of which are novel. Differences in reporting methodology for amino acid content, whether on a dry matter or protein basis, resulted in different results in phenotypic correlation and detected MTAs. Gene ontology analysis of novel QTL revealed pathways related to amino acid metabolism, cold stress response, and auxin biosynthesis. Previously reported QTL on Chromosomes 14, 15, and 20 were validated through detection in this panel. Stable critical amino acid values (CAAVs) across regions and only one detected MTA suggest that an amino acid–specific and not CAAV‐targeted approach should be used in breeding strategies. The novel association panel assembled here will be a resource for crop improvement efforts. This study provides valuable insights into the genetic architecture of regional protein and amino acid variation in Canadian soybean and identifies promising targets for breeding programs aimed at improving seed protein content and amino acid profiles in specific growing regions.

Soybean (Glycine max) is a major source of protein and edible oil worldwide and is cultivated in a wide range of latitudes. However, it is extremely sensitive to photoperiod, which influences flowering time, maturity and yield, and severely limits soybean latitude adaptation. In this study, a genome-wide association study (GWAS) identified a novel locus in accessions harboring the E1 allele, called Time of flowering 8 (Tof8), which promotes flowering and enhances adaptation to high latitude in cultivated soybean. Gene functional analyses showed that Tof8 is an ortholog of Arabidopsis FKF1. We identified two FKF1 homologs in the soybean genome. Both FKF1 homologs are genetically dependent on E1 by binding to E1 promoter to activate E1 transcription, thus repressing FLOWERING LOCUS T 2a (FT2a) and FT5a transcription, which modulate flowering and maturity through the E1 pathway. We also demonstrate that the natural allele FKF1bH3 facilitated adaptation of soybean to high-latitude environments and was selected during domestication and improvement, leading to its rapid expansion in cultivated soybean. These findings provide novel insights into the roles of FKF1 in controlling flowering time and maturity in soybean and offer new means to fine tune adaptation to high latitudes and increase grain yield.

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.

No abstract available

No abstract available

The major soy protein QTL, cqProt-003, was analysed for haplotype diversity and global distribution, and results indicate 304 bp deletion and variable tandem repeats in protein coding regions are likely causal candidates. Here, we present association and linkage analysis of 985 wild, landrace and cultivar soybean accessions in a pan genomic dataset to characterize the major high-protein/low-oil associated locus cqProt-003 located on chromosome 20. A significant trait-associated region within a 173 kb linkage block was identified, and variants in the region were characterized, identifying 34 high confidence SNPs, 4 insertions, 1 deletion and a larger 304 bp structural variant in the high-protein haplotype. Trinucleotide tandem repeats of variable length present in the second exon of gene Glyma.20G085100 are strongly correlated with the high-protein phenotype and likely represent causal variation. Structural variation has previously been found in the same gene, for which we report the global distribution of the 304 bp deletion and have identified additional nested variation present in high-protein individuals. Mapping variation at the cqProt-003 locus across demographic groups suggests that the high-protein haplotype is common in wild accessions (94.7%), rare in landraces (10.6%) and near absent in cultivated breeding pools (4.1%), suggesting its decrease in frequency primarily correlates with domestication and continued during subsequent improvement. However, the variation that has persisted in under-utilized wild and landrace populations holds high breeding potential for breeders willing to forego seed oil to maximize protein content. The results of this study include the identification of distinct haplotype structures within the high-protein population, and a broad characterization of the genomic context and linkage patterns of cqProt-003 across global populations, supporting future functional characterization and modification.

Soybean [Glycinemax (L.) Merr.] maturity determines the growing region of a given soybean variety and is a primary factor in yield and other agronomic traits. The objectives of this research were to identify the quantitative trait loci (QTL) associated with maturity groups (MGs) and determine the genetic control of soybean maturity in each MG. Using data from 16,879 soybean accessions, genome‐wide association (GWA) analyses were conducted for each paired MG and across MGs 000 through IX. Genome‐wide association analyses were also performed using 184 genotypes (MGs V–IX) with days to flowering (DTF) and maturity (DTM) collected in the field. A total of 58 QTL were identified to be significantly associated with MGs in individual GWAs, which included 12 reported maturity loci and two stem termination genes. Genome‐wide associations across MGs 000–IX detected a total of 103 QTL and confirmed 54 QTL identified in the individual GWAs. Of significant loci identified, qMG‐5.2 had effects on the highest number (9) of MGs, followed by E2, E3, Dt2, qMG‐15.5, E1, qMG‐13.1, qMG‐7.1, and qMG‐16.1, which affected five to seven MGs. A high number of genetic loci (8–25) that affected MGs 0–V were observed. Stem termination genes Dt1 and Dt2 mainly had significant allele variation in MGs II–V. Genome‐wide associations for DTF, DTM, and reproductive period (RP) in the diversity panel confirmed 15 QTL, of which seven were observed in MGs V–IX. The results generated can help soybean breeders manipulate the maturity loci for genetic improvement of soybean yield.

BackgroundSoybean (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.ResultsA 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.ConclusionsA 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.

Shade tolerance is a critical trait for soybean [Glycine max (L.) Merr.] adaptation to intercropping systems. This study investigated the genetic architecture of shade response in soybean through genome‐wide association analysis of 461 diverse accessions evaluated under both normal and shade conditions across two distinct environments in China (Heilongjiang and Inner Mongolia) in the 2022 season. Plant height (PH), main stem node number (MSNN), and pod number per plant (PODn) were assessed to characterize morphological responses to shade stress. Principal component analysis revealed that geographic location was the primary driver of phenotypic variation, explaining 57.4% of total variance. Shade treatment significantly increased PH while reducing MSNN and PODn across both locations, demonstrating classic shade avoidance syndrome traits. Genome‐wide association study using 82,208 high‐quality single‐nucleotide polymorphisms (SNPs) identified 31 significant marker‐trait associations (p < 6.0E‐4) distributed across 14 chromosomes. Six SNPs were associated with MSNN, 20 with PODn, and five with PH. Notable stable associations included SNP Gm16_9640074 for pod number under normal conditions and Gm08_2576632 for PH under shade stress across both environments. The identification of both environment‐specific and stable genetic loci demonstrates the complex genetic architecture underlying shade response in soybean. These findings provide valuable insights into the molecular mechanisms of shade tolerance and identify promising genetic markers for developing soybean varieties better adapted to intercropping systems, potentially enhancing sustainable agricultural practices in diverse agroecological zones.

Anti-nutritional factors (ANFs) can reduce nutrient bioavailability for monogastric animals. Therefore, this study aimed to understand the genetic architecture underlying ANF accumulation in soybean. Diversity arrays technology and a spectrophotometric method were employed to generate genotypic and phenotypic data, respectively, and gene mining was performed within 100-kb genomic window. A significant difference was found regarding ANFs content in the genotypes (p < 0.001). Significant SNP markers for phytate were identified on chromosomes 3, 4, 13, and 20 by FarmCPU, and for total trypsin inhibitors (TTI) on 6, 12, and 14 by CMLM models, whereas mrMLM model detected markers on chromosome 3, 12 and 15 for phytate, 4, 9, 13, 17 and 18 for TTI. Genes associated with phytate content include Glyma.03G001600, Glyma.04G194600, Glyma.13G128200, Glyma.20G118700, Glyma.14G213400, and Glyma.16G126400. For TTI, the genes are Glyma.06G074700, Glyma.12G241600, Glyma.14G176700, Glyma.13G052700, and Glyma.18G050400. These genes are primarily linked to plant defense and substrate interactions. Most promising SNP markers for marker-assisted selection aimed at reducing phytate levels include Soy_3_218818 (218,818 bp), Soy_3_241209 (241,209 bp), Soy_4_45462019 (45,462,019 bp), Soy_14_48672982 (48,672,982 bp), and Soy_6_5695090 (5,695,090 bp). For TTI, key markers include Soy_14_43649238 (43,649,238 bp), Soy_12_41339023 (41,339,023 bp), Soy_18_4301721 (4,301,721 bp), and Soy_13_14029215 (14,029,215 bp). These findings offer a valuable foundation for marker-assisted breeding aimed at improving soybean nutritional quality.

Digital imagery can help to quantify seasonal changes in desirable crop phenotypes that can be treated as quantitative traits. Because limitations in precise and functional phenotyping restrain genetic improvement in the postgenomic era, imagery-based phenomics could become the next breakthrough to accelerate genetic gains in field crops. Whereas many phenomic studies focus on exploratory analysis of spectral data without obvious interpretative value, we used field images to directly measure soybean canopy development from phenological stage V2 to R5. Over 3 years, we collected imagery using ground and aerial platforms of a large and diverse nested association panel comprising 5555 lines. Genome-wide association analysis of canopy coverage across sampling dates detected a large quantitative trait locus (QTL) on soybean (Glycine max, L. Merr.) chromosome 19. This QTL provided an increase in yield of 47.3 kg ha−1. Variance component analysis indicated that a parameter, described as average canopy coverage, is a highly heritable trait (h2 = 0.77) with a promising genetic correlation with grain yield (0.87), enabling indirect selection of yield via canopy development parameters. Our findings indicate that fast canopy coverage is an early season trait that is inexpensive to measure and has great potential for application in breeding programs focused on yield improvement. We recommend using the average canopy coverage in multiple trait schemes, especially for the early stages of the breeding pipeline (including progeny rows and preliminary yield trials), in which the large number of field plots makes collection of grain yield data challenging.

Soybean (Glycine max [L.] Merril) is a photoperiod-sensitive crop, with traits like days to flowering, days to maturity playing crucial roles in its adaptability and yield. These traits are regulated by genetic networks controlling flowering time and environmental adaptation, making their genetic basis as an essential knowledge for breeders aiming to improve yield and adaptability. In this study, a Genome-Wide Association Study (GWAS) was conducted for Days to flowering (DTF) and days to maturity (DTM) by using FarmCPU, BLINK and MLM models on 254 diverse soybean genotypes over four consecutive years (2019–2022) to dissect genetic architecture for flowering and maturity traits in an Indian Environment. In this study, GWAS identified 20 significant loci for days to flowering and maturity, among them 12 are new and 8 were previously reported loci. Among the 12 newly identified loci, a significant locus, Lee.Gm03-3 on chromosome 03, is associated with days to flowering and linked with SNP markers S3_46108324 and S3_46108342. Key candidate genes for Lee.Gm03-3, include Glyma.03G227300 (circadian rhythm and photomorphogenesis, Phytochrome region), Glyma.03G225000 (circadian rhythm, gibberellic acid signaling, red/far-red light signaling), Glyma.03G219100 (cytokinin signaling, embryo sac development), and Glyma.03G226000 (meristem initiation). These genes are vital for light-response and developmental pathways. In addition, we also validated eight previously known genes E2, E4, E9, E11, E10/FT4, PRR7/Tof12, Dt1, and Dt2 that influence flowering and maturity in Indian environment. This study advances understanding of the genetic basis underlying photoperiod sensitivity related genes for circadian rhythm and photomorphogenesis, gibberellic acid signaling, red/far-red light signaling in soybean and highlights potential targets for genetic improvement of flowering, maturity duration and adaptability of soybean under Indian environment.

Soybean (Glycine max L.) is a major crop valued for both food and industrial applications. The genetic diversity preserved within landrace accessions serves as a critical resource for improving agronomic traits and enhancing adaptation to climatic variability. Korean landrace soybeans, shaped by long-term cultivation across diverse local environments, provide an ideal population for dissecting genetic variation underlying key traits and regional adaptation. A genome-wide association study (GWAS) was performed on 1,693 Korean landrace soybean accessions genotyped with 67,222 SNPs from the 180 K Axiom® Soya SNP array. Population structure and genetic diversity were assessed using model-based stratification (K = 15), principal component analysis (PCA), linkage disequilibrium (LD) decay analysis (r² = 0.2 at 309 kb), and FST-based differentiation. GWAS using the MLMM, FarmCPU, and BLINK models identified 38 significant SNPs associated with flowering date (FD), maturity date (MD), number of seeds per pod (NOSPP), and 100-seed weight (SW), including pleiotropic loci. Candidate genes within LD blocks were annotated and subjected to Gene Ontology (GO) enrichment, revealing biological processes such as signaling, transcriptional regulation, sterol metabolism, and cell division. Haplotype analysis supported phenotypic differentiation among allelic groups. For SNPs located outside of LD blocks, variation in allele frequencies and trait values across regions indicated evidence of local adaptation. These findings provide new insights into the genetic architecture of agronomic traits in Korean landrace soybeans. The identified loci, functionally annotated candidate genes, and region-specific alleles constitute a valuable genomic resource for breeding programs aiming to develop climate-resilient and regionally adapted soybean cultivars.

No abstract available

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. 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. 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.

Soybean [Glycine max (L.) Merr.], an important oilseed crop, is a low-cost source of protein and oil. In Southeast Asia and Africa, soybeans are widely cultivated for use as traditional food and feed and industrial purposes. Given the ongoing changes in global climate, developing crops that are resistant to climatic extremes and produce viable yields under predicted climatic conditions will be essential in the coming decades. To develop such crops, it will be necessary to gain a thorough understanding of the genetic basis of agronomic and plant root traits. As plant roots generally lie beneath the soil surface, detailed observations and phenotyping throughout plant development present several challenges, and thus the associated traits have tended to be ignored in genomics studies. In this study, we phenotyped 357 soybean landraces at the early vegetative (V2) growth stages and used a 180 K single-nucleotide polymorphism (SNP) soybean array in a genome-wide association study (GWAS) conducted to determine the phenotypic relationships among root traits, elucidate the genetic bases, and identify significant SNPs associated with root trait-controlling genomic regions/loci. A total of 112 significant SNP loci/regions were detected for seven root traits, and we identified 55 putative candidate genes considered to be the most promising. Our findings in this study indicate that a combined approach based on SNP array and GWAS analyses can be applied to unravel the genetic basis of complex root traits in soybean, and may provide an alternative high-resolution marker strategy to traditional bi-parental mapping. In addition, the identified SNPs, candidate genes, and diverse variations in the root traits of soybean landraces will serve as a valuable basis for further application in genetic studies and the breeding of climate-resilient soybeans characterized by improved root traits.

Iron (Fe) is an essential micronutrient for plant growth and development. Iron deficiency chlorosis (IDC), caused by calcareous soils or high soil pH, can limit iron availability, negatively affecting soybean (Glycine max) yield. This study leverages genome-wide association study (GWAS) and a genome-wide epistatic study (GWES) with previous gene expression studies to identify regions of the soybean genome important in iron deficiency tolerance. A GWAS and a GWES were performed using 460 diverse soybean PI lines from 27 countries, in field and hydroponic iron stress conditions, using more than 36,000 single nucleotide polymorphism (SNP) markers. Combining this approach with available RNA-sequencing data identified significant markers, genomic regions, and novel genes associated with or responding to iron deficiency. Sixty-nine genomic regions associated with IDC tolerance were identified across 19 chromosomes via the GWAS, including the major-effect quantitative trait locus (QTL) on chromosome Gm03. Cluster analysis of significant SNPs in this region deconstructed this historically prominent QTL into four distinct linkage blocks, enabling the identification of multiple candidate genes for iron chlorosis tolerance. The complementary GWES identified SNPs in this region interacting with nine other genomic regions, providing the first evidence of epistatic interactions impacting iron deficiency tolerance. This study demonstrates that integrating cutting edge genome wide association (GWA), genome wide epistasis (GWE), and gene expression studies is a powerful strategy to identify novel iron tolerance QTL and candidate loci from diverse germplasm. Crops, unlike model species, have undergone selection for thousands of years, constraining and/or enhancing stress responses. Leveraging genomics-enabled approaches to study these adaptations is essential for future crop improvement.

No abstract available

100-seed weight (100-SW) in soybeans is a yield component trait and controlled by multiple genes with different effects, but limited information is available for its quantitative trait nucleotides (QTNs) and candidate genes. To better understand the genetic architecture underlying the trait and improve the precision of marker-assisted selection, a total of 43,834 single nucleotide polymorphisms (SNPs) in 250 soybean accessions were used to identify significant QTNs for 100-SW in four environments and their BLUP values using six multi-locus and one single-locus genome-wide association study methods. As a result, a total of 218 significant QTNs were detected using multi-locus methods, whereas eight QTNs were identified by a single-locus method. Among 43 QTNs or QTN clusters identified repeatedly across various environments and/or approaches, all of them exhibited significant trait differences between their corresponding alleles, 33 were found in the genomic region of previously reported QTLs, 10 were identified as new QTNs, and three (qHSW-4-1, qcHSW-7-3, and qcHSW-10-4) were detected in all the four environments. The number of seed weight (SW) increasing alleles for each accession ranged from 8 (18.6%) to 36 (83.72%), and three accessions (Yixingwuhuangdou, Nannong 95C-5, and Yafanzaodou) had more than 35 SW increasing alleles. Among 36 homologous seed-weight genes in Arabidopsis underlying the above 43 stable QTNs, more importantly, Glyma05g34120, GmCRY1, and GmCPK11 had known seed-size/weight-related genes in soybean, and Glyma07g07850, Glyma10g03440, and Glyma10g36070 were candidate genes identified in this study. These results provide useful information for genetic foundation, marker-assisted selection, genomic prediction, and functional genomics of 100-SW.

No abstract available

Seed-flooding stress is one of the major abiotic constraints severely affecting soybean yield and quality. Understanding the molecular mechanism and genetic basis underlying seed-flooding tolerance will be of greatly importance in soybean breeding. However, very limited information is available about the genetic basis of seed-flooding tolerance in soybean. The present study performed Genome-Wide Association Study (GWAS) to identify the quantitative trait nucleotides (QTNs) associated with three seed-flooding tolerance related traits, viz., germination rate (GR), normal seedling rate (NSR) and electric conductivity (EC), using a panel of 347 soybean lines and the genotypic data of 60,109 SNPs with MAF > 0.05. A total of 25 and 21 QTNs associated with all three traits were identified via mixed linear model (MLM) and multi-locus random-SNP-effect mixed linear model (mrMLM) in three different environments (JP14, HY15, and Combined). Among these QTNs, three major QTNs, viz., QTN13, qNSR-10 and qEC-7-2, were identified through both methods MLM and mrMLM. Interestingly, QTN13 located on Chr.13 has been consistently identified to be associated with all three studied traits in both methods and multiple environments. Within the 1.0 Mb physical interval surrounding the QTN13, nine candidate genes were screened for their involvement in seed-flooding tolerance based on gene annotation information and available literature. Based on the qRT-PCR and sequence analysis, only one gene designated as GmSFT (Glyma.13g248000) displayed significantly higher expression level in all tolerant genotypes compared to sensitive ones under flooding treatment, as well as revealed nonsynonymous mutation in tolerant genotypes, leading to amino acid change in the protein. Additionally, subcellular localization showed that GmSFT was localized in the nucleus and cell membrane. Hence, GmSFT was considered as the most likely candidate gene for seed-flooding tolerance in soybean. In conclusion, the findings of the present study not only increase our knowledge of the genetic control of seed-flooding tolerance in soybean, but will also be of great utility in marker-assisted selection and gene cloning to elucidate the mechanisms of seed-flooding tolerance.

High-density planting is crucial for maximizing the genetic potential of soybean cultivars to achieve higher yields. However, increasing the planting density can lead to the risk of plant lodging. Therefore, the identification of gene loci associated with lodging resistance is considered critical for the development of high-yielding, lodging-resistant soybean cultivars. In this study, 338 natural soybean accessions from the similar latitude were used to identify candidate genes associated with lodging resistance. Based on 9,400,987 SNPs, the soybean population was classified into three subpopulations. Genome-wide association analysis revealed that under planting densities of 300,000 and 150,000 plants/ha, a total of 20 significant SNPs were repeatedly detected under both planting densities, distributed across 14 chromosomes of soybeans. A hotspot region was identified on chromosome 19, from which seven candidate genes were detected. Based on haplotype and gene expression analyses, Glyma.19g212800 (SUS3) and Glyma.19g212700 (GH9B13) were found to be associated with significant phenotypic variations and were identified as candidate genes. RNA-seq analysis showed that DEGs were primarily enriched in the starch and sucrose metabolism pathways. The differential expression of Glyma.19g212800 among soybean haplotypes was further validated by qRT-PCR. By participating in sucrose decomposition and polysaccharide metabolism processes, it regulated cellulose content, thereby affecting the soybean plant lodging. This study facilitated the dissection of genetic networks underlying lodging traits in soybean, which benefits the genetic improvement of high-yield soybean with dense planting.

Powdery mildew disease (PMD) is caused by the obligate biotrophic fungus Microsphaera diffusa Cooke & Peck (M. diffusa) and results in significant yield losses in soybean (Glycine max (L.) Merr.) crops. By identifying disease-resistant genes and breeding soybean accessions with enhanced resistance, we can effectively mitigate the detrimental impact of PMD on soybeans. We analyzed PMD resistance in a diversity panel of 315 soybean accessions in two locations over 3 years, and candidate genes associated with PMD resistance were identified through genome-wide association studies (GWAS), haplotype analysis, qRT-PCR, and EMS mutant analysis. Based on the GWAS approach, we identified a region on chromosome 16 (Chr16) in which 21 genes form a gene cluster that is highly correlated with PMD resistance. In order to validate and refine these findings, we conducted haplotype analysis of 21 candidate genes and indicated there are single nucleotide polymorphisms (SNPs) and insertion-deletions (InDels) variations of Glyma.16G214000, Glyma.16G214200, Glyma.16G215100 and Glyma.16G215300 within the coding and promoter regions that exhibit a strong association with resistance against PMD. Subsequent structural analysis of candidate genes within this cluster revealed that in 315 accessions, the majority of accessions exhibited resistance to PMD when Glyma.16G214300, Glyma.16G214800 and Glyma.16G215000 were complete; however, they demonstrated susceptibility to PMD when these genes were incomplete. Quantitative real-time PCR assays (qRT-PCR) of possible candidate genes showed that 14 candidate genes (Glyma.16G213700, Glyma.16G213800, Glyma.16G213900, Glyma.16G214000, Glyma.16G214200, Glyma.16G214300, Glyma.16G214500, Glyma.16G214585, Glyma.16G214669, Glyma.16G214700, Glyma.16G214800, Glyma.16G215000, Glyma.16G215100 and Glyma.16G215300) were involved in PMD resistance. Finally, we evaluated the PMD resistance of mutant lines from the Williams 82 EMS mutations library, which revealed that mutants of Glyma.16G214000, Glyma.16G214200, Glyma.16G214300, Glyma.16G214800, Glyma.16G215000, Glyma.16G215100 and Glyma.16G215300, exhibited sensitivity to PMD. Combined with the analysis results of GWAS, haplotypes, qRT-PCR and mutants, the genes Glyma.16G214000, Glyma.16G214200, Glyma.16G214300, Glyma.16G214800, Glyma.16G215000, Glyma.16G215100 and Glyma.16G215300 were identified as highly correlated with PMD resistance. The candidate genes identified above are all NLR family genes, and these discoveries deepen our understanding of the molecular basis of PMD resistance in soybeans and will be useful for guiding breeding strategies.

No abstract available

Soybean is a crucial crop globally, serving as a significant source of unsaturated fatty acids and protein in the human diet. However, further enhancements are required for the related genes that regulate soybean oil synthesis. In this study, 155 soybean germplasms were cultivated under three different environmental conditions, followed by phenotypic identification and genome-wide association analysis using simplified sequencing data. Genome-wide association analysis was performed using SLAF-seq data. A total of 36 QTLs were significantly associated with oil content (−log10(p) > 3). Out of the 36 QTLs associated with oil content, 27 exhibited genetic overlap with previously reported QTLs related to oil traits. Further transcriptome sequencing was performed on extreme high–low oil soybean varieties. Combined with transcriptome expression data, 22 candidate genes were identified (|log2FC| ≥ 3). Further haplotype analysis of the potential candidate genes showed that three potential candidate genes had excellent haplotypes, including Glyma.03G186200, Glyma.09G099500, and Glyma.18G248900. The identified loci harboring beneficial alleles and candidate genes likely contribute significantly to the molecular network’s underlying marker-assisted selection (MAS) and oil content.

Soybean (Glycine max) is a globally important grain and oil crop, but its yield and quality are severely limited by soybean cyst nematode (SCN, Heterodera glycines Ichinohe), a devastating soil-borne pathogen. Here, we evaluated SCN race 3 resistance in 306 soybean germplasms and combined a genome-wide association study (GWAS) with transcriptome analysis to identify key resistance-related genes. GWAS using 30× resequencing data (632,540 SNPs) revealed 77 significant quantitative trait loci (QTLs) associated with SCN resistance, while transcriptome comparison between the extreme resistant accession Dongnong L10 and susceptible Heinong 37 identified 4185 upregulated and 3195 downregulated genes. Integrating these results, we characterized the GmRF2-like gene as a candidate resistance gene. Subcellular localization showed GmRF2-like encodes a nuclear-localized protein. Functional validation via soybean hairy root transformation demonstrated that overexpression of GmRF2-like significantly inhibits SCN race 3 infection. Collectively, our findings confirm that GmRF2-like plays a positive role in soybean resistance to SCN race 3, providing critical insights for dissecting the molecular mechanism of SCN resistance and facilitating the development of resistant soybean varieties.

Drought stress significantly limits soybean yield, especially if it occurs during flowering and early pod development stages. To better understand the genetic mechanisms of drought tolerance in legume soybean, we conducted genome-wide association studies (GWAS) for (i) leaf-flipping and (ii) transpiration traits. A short list of seven candidate drought tolerance genes was generated from 67 GWAS-discovered genes by determining if (i) mutations alter structure and function of candidate genes, (ii) the genes are drought responsive due to mutations in putative cis-acting elements, and (iii) they were shown to contribute towards drought tolerance. We used rainout shelters to ensure drought stress and wearable plant sensors to measure leaf-surface humidity and temperature to determine transpiration rates. From GWAS of 240 soybean accessions for the leaf-flipping trait, we identified three candidate drought tolerance genes: (i) a thaumatin-like protein gene, the tea homologue of which regulates the root hair development and drought tolerance in Arabidopsis, (ii) a chloroplast isopropyl malate synthase gene that plays an important role in root development for drought tolerance; (iii) transcriptionally regulated glycinol 2-dimethyltransferase gene. Investigation of 47 accessions for transpiration rates revealed two candidate transcriptionally regulated drought-responsive genes encoding α-tubulin and phosphoenolpyruvate carboxykinase (PCK). The α-tubulin was shown to control stomatal opening, while PCK improves water retention by closing stomata during drought stress. An uncharacterized DUF1118 containing protein and HAT5 homeodomain-leucine zipper protein could also regulate transpiration during drought stress. In this study, we have demonstrated that short-read sequences and transcriptomic data facilitate identification of strong candidate drought tolerance genes.

Soybean, a globally important crop, is a typical short-day and thermophilic plant. Continuous efforts are necessary to elucidate the genetic basis of its essential traits. In this study, we assembled a collection of 203 soybean varieties, all of which are well suited for cultivation in the northeastern region of China. We assessed 15 agronomic traits under three distinct environments, noting substantial phenotypic variations in the panel and stable correlations among traits. The population structure analysis, based on genotyping-by-sequencing (GBS) data, revealed seven subpopulations within the panel and significant gene flows among these subpopulations. Through genome-wide association studies (GWASs), we identified 64 significantly associated loci (SALs) for 15 traits and unveiled the genetic interconnections between yield and related traits. Additionally, we highlighted a few candidate genes within SALs for yield and related traits. Finally, we evaluated the genomic prediction performances of four distinct methods across the three environments, revealing the significant influence of environmental factors on predictive accuracies. We found that rrBLUP is suitable for most traits, though specific traits may benefit from more complex machine learning models. Our findings establish a foundation for the future research of genetic mechanisms of soybean agronomic traits and the application of genomic selection in soybean breeding.

Seed size is an economically important trait that directly determines the seed yield in soybean. In the current investigation, we used an integrated strategy of linkage mapping, association mapping, haplotype analysis and candidate gene analysis to determine the genetic makeup of four seed size-related traits viz., 100-seed weight (HSW), seed area (SA), seed length (SL), and seed width (SW) in soybean. Linkage mapping identified a total of 23 quantitative trait loci (QTL) associated with four seed size-related traits in the F2 population; among them, 17 were detected as novel QTLs, whereas the remaining six viz., qHSW3-1, qHSW4-1, qHSW18-1, qHSW19-1, qSL4-1 and qSW6-1 have been previously identified. Six out of 23 QTLs were major possessing phenotypic variation explained (PVE) ≥ 10%. Besides, the four QTL Clusters/QTL Hotspots harboring multiple QTLs for different seed size-related traits were identified on Chr.04, Chr.16, Chr.19 and Chr.20. Genome-wide association study (GWAS) identified a total of 62 SNPs significantly associated with the four seed size-related traits. Interestingly, the QTL viz., qHSW18-1 was identified by both linkage mapping and GWAS, and was regarded as the most stable loci regulating HSW in soybean. In-silico, sequencing and qRT-PCR analysis identified the Glyma.18G242400 as the most potential candidate gene underlying the qHSW18-1 for regulating HSW. Moreover, three haplotype blocks viz., Hap2, Hap6A and Hap6B were identified for the SW trait, and one haplotype was identified within the Glyma.18G242400 for the HSW. These four haplotypes harbor three to seven haplotype alleles across the association mapping panel of 350 soybean accessions, regulating the seed size from lowest to highest through intermediate phenotypes. Hence, the outcome of the current investigation can be utilized as a potential genetic and genomic resource for breeding the improved seed size in soybean.

Soybean (Glycine max (L.) Merrill) oil is a complex mixture of five fatty acids (palmitic, stearic, oleic, linoleic, and linolenic). The high content of linoleic acid (LA) contributes to the oil having poor oxidative stability. Therefore, soybean seed with a lower LA content is desirable. To investigate the genetic architecture of LA, we performed a genome-wide association study (GWAS) using 510 soybean cultivars collected from China. The phenotypic identification results showed that the content of LA varied from 36.22% to 72.18%. The GWAS analysis showed that there were 37 genes related to oleic acid content, with a contribution rate of 7%. The candidate gene Glyma.04G116500.1 (GmWRI14) on chromosome 4 was detected in three consecutive years. The GmWRI14 showed a negative correlation with the LA content and the correlation coefficient was −0.912. To test whether GmWRI14 can lead to a lower LA content in soybean, we introduced GmWRI14 into the soybean genome. Matrix-assisted laser desorption/ionization time-of-flight imaging mass spectrometry (MALDI-TOF IMS) showed that the overexpression of GmWRI14 leads to a lower LA content in soybean seeds. Meanwhile, RNA-seq verified that GmWRI14-overexpressed soybean lines showed a lower accumulation of GmFAD2-1A and GmFAD2-1B than control lines. Our results indicate that the down-regulation of the FAD2 gene triggered by the transcription factor GmWRI14 is the underlying mechanism reducing the LA level of seed. Our results provide novel insights into the genetic architecture of LA and pinpoint potential candidate genes for further in-depth studies.

No abstract available

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

Background Soybean oil is a complex mixture of five fatty acids (palmitic, stearic, oleic, linoleic, and linolenic). Soybean oil with a high oleic acid content is desirable because this monounsaturated fatty acid improves the oxidative stability of the oil. To investigate the genetic architecture of oleic acid in soybean seeds, 260 soybean germplasms from Northeast China were collected as natural populations. A genome-wide association study (GWAS) was conducted on a panel of 260 germplasm resources. Results Phenotypic identification results showed that the oleic acid content varied from 8.2 to 35.0%. A total of 2,311,337 single-nucleotide polymorphism (SNP) markers were obtained. GWAS analysis showed that there were many genes related to oleic acid content with a contribution rate of 7%. The candidate genes Glyma.11G229600.1 on chromosome 11 and Glyma.04G102900.1 on chromosome 4 were detected in a 2-year-long GWAS. The candidate gene Glyma.11G229600.1 showed a positive correlation with the oleic acid content, and the correlation coefficient was 0.980, while Glyma.04G102900.1 showed a negative correlation, with a coefficient of − 0.964. Conclusions Glyma.04G102900.1 on chromosome 4 and Glyma.11G229600.1 on chromosome 11 were detected in both analyses (2018 and 2019). Glyma.04G102900.1 and Glyma.11G229600.1 are new key candidate genes related to oleic acid in soybean seeds. These results will be useful for high-oleic soybean breeding.

No abstract available

The cultivated soybean (Glycine max (L.) Merrill) is domesticated from wild soybean (Glycine soja) and has heavier seeds with a higher oil content than the wild soybean. In this study, we identified a novel candidate gene associated with SW using a genome-wide association study (GWAS). The candidate gene GmWRI14-like was detected by GWAS analysis in three consecutive years. By constructing transgenic soybeans overexpressing the GmWRI14-like gene and gmwri14-like soybean mutants, we found that overexpression of GmWRI14-like increased the SW and increased total fatty acid content. We then used RNA-seq and qRT-PCR to identify the target genes directly or indirectly regulated by GmWRI14-like. Transgenic soyabeans overexpressing GmWRI14-like showed increased accumulation of GmCYP78A50 and GmCYP78A69 than non-transgenic soybean lines. Interestingly, we also found that GmWRI14-like proteins could interact with GmCYP78A69/GmCYP78A50 using yeast two-hybrid and bimolecular fluorescence complementation. Our results not only shed light on the genetic architecture of cultivated soybean SW, but also lays a theoretical foundation for improving the SW and oil content of soybeans.

No abstract available

Flowering time and active accumulated temperature (AAT) are two key factors that limit the expanded production especially for soybean across different regions. Wild soybean provides an important germplasm for functional genomics study in cultivar soybean. However, the studies on genetic basis underlying flowering time in response to AAT especially in wild soybean were rarely reported. In this study, we used 294 wild soybean accessions derived from major soybean production region characterized by different AAT in Northeast of China. Based on genome-wide association study (GWAS), we identified 96 SNPs corresponded to 342 candidate genes that significantly associated with flowering time recorded in two-year experiments. Gene Ontology enrichment analysis suggests that the pathways of photosynthesis light reaction and actin filament binding were significantly enriched. We found three lead SNPs with -log 10 ( p -value) > 32 across the two-year experiments, i.e., Chr02:9490318, Chr04:8545910 and Chr09:49553555. Linkage disequilibrium block analysis shows 28 candidate genes within the genomic region centered on the lead SNPs. Among them, expression levels of three genes (aspartic peptidase 1, serine/threonine-protein kinase and protein SCAR2-like) were significantly differed between two subgroups possessing contrasting flowering time distributed at chromosome 2, 4 and 9, respectively. There are 6, 7 and 3 haplotypes classified on the coding regions of the three genes, respectively. Collectively, accessions with late flowering time phenotype are typically derived from AAT zone 1, which is associated with the haplotypic distribution and expression levels of the three genes. This study provides an insight into a potential mechanism responsible for flowering time in response to AAT in wild soybean, which could promote the understanding of genetic basis for other major crops.

Vegetable soybeans are one of the most important vegetable types in East Asia. The yield of vegetable soybeans is considerably influenced by the size of their pods. To facilitate the understanding of the genetic basis of the pod length and width in vegetable soybeans, we conducted a genome-wide association study (GWAS) and transcriptome sequencing. Four quantitative trait loci, namely, qGPoL1, qGPoL2, qGPoW1, and qGPoW2, were mapped via GWAS analysis. Through the integration of gene function annotation, transcriptome sequencing, and expression pattern analysis, we identified Glyma.06G255000 and Glyma.13G007000 as the key determinants of the pod length and width in vegetable soybeans, respectively. Furthermore, two kompetitive allele-specific polymerase chain reaction (KASP) markers, namely, S06-42138365 (A/T) and S13_628331 (A/T), were developed and effectively validated in 27 vegetable soybean accessions. Overall, our research identified genes that regulate the pod length and width and determined KASP markers for molecular marker-assisted selection breeding. These findings have crucial implications for the improvement of soybean crops and can contribute to the development of efficient breeding strategies.

No abstract available

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.

Drought is one of the most important factors affecting plant growth and productivity. The previous results on drought tolerance (DT) genetic system in soybean indicated a complex of genes not only few ones were involved in the trait. This study is featured with a relatively thorough identification of QTL-allele/candidate-gene system using an efficient restricted two-stage multi-locus multi-allele genome-wide association study, on two comprehensive DT indicators, membership index values of relative plant weight (MPW) and height (MPH), instead of a single biological characteristic, in a large sample (564 accessions) of the Chinese cultivated soybean population (CCSP). Based on 24,694 multi-allele markers, 75 and 64 QTL with 261 and 207 alleles (2–12/locus) were detected for MPW and MPH, explaining 54.7% and 47.1% of phenotypic variance, respectively. The detected QTL-alleles were organized into a QTL-allele matrix for each indicator, indicating DT is a super-trait conferred by two (even more) QTL-allele systems of sub-traits. Each CCSP matrix was separated into landrace (LR) and released cultivar (RC) sub-matrices, which showed significant differentiation in QTL-allele constitutions, with 58 LR alleles excluded and 16 new ones emerged in RC. Using the matrices, optimal crosses with great DT transgressive recombinants were predicted. From the detected QTL, 177 candidate genes were annotated and validated with quantitative Real-time PCR, and grouped into nine categories, with ABA and stress responders as the major parts. The key point of the above results is the establishment of relatively full QTL-allele matrices composed of numerous gene functions jointly conferring DT, therefore, demonstrates the complexity of DT genetic system and potential of CCSP in DT breeding.

Branch number is one of the main factors affecting the yield of soybean (Glycine max (L.)). In this study, we conducted a genome-wide association study combined with linkage analysis for the identification of a candidate gene controlling soybean branching. Five quantitative trait nucleotides (QTNs) were associated with branch numbers in a soybean core collection. Among these QTNs, a linkage disequilibrium (LD) block qtnBR6-1 spanning 20 genes was found to overlap a previously identified major quantitative trait locus qBR6-1. To validate and narrow down qtnBR6-1, we developed a set of near-isogenic lines (NILs) harboring high-branching (HB) and low-branching (LB) alleles of qBR6-1, with 99.96% isogenicity and different branch numbers. A cluster of single nucleotide polymorphisms (SNPs) segregating between NIL-HB and NIL-LB was located within the qtnBR6-1 LD block. Among the five genes showing differential expression between NIL-HB and NIL-LB, BRANCHED1 (BRC1; Glyma.06G210600) was down-regulated in the shoot apex of NIL-HB, and one missense mutation and two SNPs upstream of BRC1 were associated with branch numbers in 59 additional soybean accessions. BRC1 encodes TEOSINTE-BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTORS 1 and 2 transcription factor and functions as a regulatory repressor of branching. On the basis of these results, we propose BRC1 as a candidate gene for branching in soybean.

The elemental content of a soybean seed is a determined by both genetic and environmental factors and is an important component of its nutritional value. The elemental content is chemically stable, making the samples stored in germplasm repositories an intriguing source of experimental material. To test the efficacy of using samples from germplasm banks for gene discovery, we analyzed the elemental profile of seeds from 1653 lines in the USDA Soybean Germplasm Collection. We observed large differences in the elemental profiles based on where the lines were grown, which lead us to break up the genetic analysis into multiple small experiments. Despite these challenges, we were able to identify candidate SNPs controlling elemental accumulation as well as lines with extreme elemental accumulation phenotypes. Our results suggest that elemental analysis of germplasm samples can identify SNPs in linkage disequilibrium to genes, which can be leveraged to assist in crop improvement efforts.

Identifying genetic loci associated with yield stability has helped plant breeders and geneticists begin to understand the role and influence of genotype by environment (GxE) interactions in soybean [Glycine max (L.) Merr.] productivity, as well as other crops. Quantifying a genotype’s range of performance across testing locations has been developed over decades with dozens of methodologies available. This includes directly modeling GxE interactions as part of an overall model for yield, as well as methods which generate overall yield “stability” values from multi-environment trial data. Correspondence between these methods as it pertains to the outcomes of genome wide association studies (GWAS) has not been well defined. In this study, the GWAS results for yield and yield stability were compared in 213 soybean lines across 11 environments to determine their utility and potential intersection. Both univariate and multivariate conventional stability estimates were considered alongside a mixed model for yield that fit marker by environment interactions as a random effect. One-hundred and six total QTL were discovered across all mapping results, however, genetic loci that were significant in the mixed model for grain yield that fit marker by environment interactions were completely distinct from those that were significant when mapping using traditional stability measures as a phenotype. Furthermore, 73.21% of QTL discovered in the mixed model were determined to cause a crossover interaction effect which cause genotype rank changes between environments. Overall, the QTL discovered via explicitly mapping GxE interactions also explained more yield variance that those QTL associated with differences in traditional stability estimates making their theoretical impact on selection greater. A lack of intersecting results between mapping approaches highlights the importance of examining stability in multiple contexts when attempting to manipulate GxE interactions in soybean.

No abstract available

Soybean is a major legume crop cultivated globally due to the high quality and quantity of its seed protein and oil. However, drought stress is the most significant factor that decreases soybean yield, and more than 90% of US soybean acreage is dependent on rainfall. Water use efficiency (WUE) is positively correlated with the carbon isotopic ratio 13C/12C (C13 ratio) and selecting soybean varieties for high C13 ratio may enhance WUE and help improve tolerance to drought. Our study objective was to identify genetic loci associated with C13 ratio using a diverse set of 205 soybean maturity group IV accessions, and to examine the genomic prediction accuracy of C13 ratio across a range of environments. An accession panel was grown and assessed across seven distinct combinations of site, year and treatment, with five site-years under irrigation and two site-years under drought stress. Genome-wide association mapping (GWAM) analysis identified 103 significant single nucleotide polymorphisms (SNPs) representing 93 loci associated with alterations to C13 ratio. Out of these 93 loci, 62 loci coincided with previous studies, and 31 were novel. Regions tagged by 96 significant SNPs overlapped with 550 candidate genes involved in plant stress responses. These confirmed genomic loci could serve as a valuable resource for marker-assisted selection to enhance WUE and drought tolerance in soybean. This study also demonstrated that genomic prediction can accurately predict C13 ratio across different genotypes and environments and by examining only significant SNPs identified by GWAM analysis, higher prediction accuracies (P ≤ 0.05; 0.51 ≤ r ≤ 0.65) were observed. We generated genomic estimated breeding values for each genotype in the entire USDA-GRIN germplasm collection for which there was marker data. This information was used to identify the top ten extreme genotypes for each soybean maturity group, which could serve as valuable genetic and physiological resources for future breeding and physiological studies.

Given the narrow genetic base of North American soybean germplasm, which originates from approximately 35 ancestral lines, discovering and introducing useful diversity for key traits in exotic germplasm could potentially enhance diversity in the current elite gene pool. This study explores the potential of exotic germplasm to enhance yield and agronomic traits in the University of Guelph soybean germplasm. We utilized a nested association mapping (NAM) design to develop a population (n = 294) composed of crosses of high-yielding Canadian elite cultivar, OAC Bruton, with four high-yielding exotic lines developed at USDA (Urbana, IL), and we mapped the genetic architecture of agronomic and seed composition traits using association mapping methods. The analysis across three Southwestern Ontario environments revealed seven unique genomic regions underlying agronomic traits and four for seed composition traits, with both desirable and undesirable alleles from the exotic parents. Notably, a region on chromosome 10, co-locating to the E2 maturity locus, was found to be associated with seed yield and maturity. The allele that increased yield by 166 kg/ha was contributed by all exotic parents and was absent in the Canadian-adapted parent. The study underscores the potential of using exotic germplasm to introduce novel genetic diversity into the Canadian elite soybean breeding pool. By identifying exotic-derived beneficial alleles, our findings offer a pathway for enhancing agronomic traits in Canadian soybeans with novel exotic diversity.

Background Hundred-seed weight (HSW) is a critical yield component in soybean that directly influences productivity and seed quality. Despite its agronomic importance, the genetic architecture underlying natural variation in seed weight remains incompletely understood. Methods We conducted a comprehensive genome-wide association study (GWAS) using 554 globally diverse soybean accessions, comprising 453 Chinese varieties (81.8%) and 101 international accessions (18.2%) from 15 countries. Accessions were evaluated across three consecutive years (2022-2024) and genotyped with 78,050 high-quality single-nucleotide polymorphisms (SNPs). Results Mixed linear model (MLM) analysis revealed a major QTL on Chr.20 that consistently explained the largest proportion of phenotypic variation across all environments. This QTL demonstrated exceptional temporal stability, maintaining genome-wide significance with peak -log10(P) values of 13.4, 12.1, and 10.2 across the three evaluation years. Fine mapping narrowed the critical interval to 493.69 kb containing 25 annotated genes. The lead SNP within Glyma.20G223200 explained 8-12% of phenotypic variance, while multi-SNP models incorporating five high-priority candidates cumulatively explained 14-18% of variance. Expression analysis of candidate genes revealed differential patterns between large-seeded and small-seeded varieties during seed development, with up to 32-fold expression differences. Conclusions The environmentally stable Chr. 20 QTL provides immediate opportunities for marker-assisted selection (MAS) in soybean breeding programs. Genomic prediction modeling suggests 35% greater genetic gain compared to phenotypic selection alone, supporting broad applicability for global soybean improvement programs.

Nitrogen (N) plays a key role in plants because it is a major component of RuBisCO and chlorophyll. Hence, N is central to both the dark and light reactions of photosynthesis. Genotypic variation in canopy greenness provides insights into the variation of N and chlorophyll concentration, photosynthesis rates, and N2 fixation in legumes. The objective of this study was to identify significant loci associated with the intensity of greenness of the soybean [Glycine max (L.) Merr.] canopy as determined by the Dark Green Color Index (DGCI). A panel of 200 maturity group IV accessions was phenotyped for canopy greenness using DGCI in three environments. Association mapping identified 45 SNPs that were significantly (P ≤ 0.0003) associated with DGCI in three environments, and 16 significant SNPs associated with DGCI averaged across all environments. These SNPs likely tagged 43 putative loci. Out of these 45 SNPs, eight were present in more than one environment. Among the identified loci, 21 were located in regions previously reported for N traits and ureide concentration. Putative loci that were coincident with previously reported genomic regions may be important resources for pyramiding favorable alleles for improved N and chlorophyll concentrations, photosynthesis rates, and N2 fixation in soybean.

No abstract available

No abstract available

Soybean (Glycine max (L.) Merr.) is a short day plant. Its flowering and maturity time are controlled by genetic and environmental factors, as well the interaction between the two factors. Previous studies have shown that both genetic and environmental factors, mainly photoperiod and temperature, control flowering time of soybean. Additionally, these studies have reported gene × gene and gene × environment interactions on flowering time. However, the effects of quantitative trait loci (QTL) in response to photoperiod and temperature have not been well evaluated. The objectives of the current study were to identify the effects of loci associated with flowering time under different photo-thermal conditions and to understand the effects of interaction between loci and environment on soybean flowering. Different photoperiod and temperature combinations were obtained by adjusting sowing dates (spring sowing and summer sowing) or day-length (12 h, 16 h). Association mapping was performed on 91 soybean cultivars from different maturity groups (MG000-VIII) using 172 SSR markers and 5107 SNPs from the Illumina SoySNP6K iSelectBeadChip. The effects of the interaction between QTL and environments on flowering time were also analysed using the QTXNetwork. Large-effect loci were detected on Gm 11, Gm 16 and Gm 20 as in previous reports. Most loci associated with flowering time are sensitive to photo-thermal conditions. Number of loci associated with flowering time was more under the long day (LD) than under the short day (SD) condition. The variation of flowering time among the soybean cultivars mostly resulted from the epistasis × environment and additive × environment interactions. Among the three candidate loci, i.e. Gm04_4497001 (near GmCOL3a), Gm16_30766209 (near GmFT2a and GmFT2b) and Gm19_47514601 (E3 or GmPhyA3), the Gm04_4497001 may be the key locus interacting with other loci for controlling soybean flowering time. The effects of loci associated with the flowering time of soybean were dependent upon the photo-thermal conditions. This study facilitates the understanding of the genetic mechanism of soybean flowering and molecular breeding for the improvement of soybean adaptability to specific and/or broad regions.

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.

As a domestication trait, pod dehiscence has a pleiotropic effect on agronomic traits and significantly contributes to yield loss in soybean. Population studies are still required to comprehend the genetic basis of dehiscence and to develop pod dehiscence-resistant cultivars with the optimal haplotype, thereby improving soybean production. We collected data for one wild (Glycine soja) (G. soja) and four cultivated (Glycine max) (G. max) populations from the USDA database. The G. max populations were evaluated in multi-environment conditions and used for genome-wide association study (GWAS) and selection. GWAS captured 86 quantitative trait loci (QTLs). Seventy-four new QTLs were colocalized in two different G. max populations, and 12 QTLs were closely mapped with previously reported QTLs. Eight out of 86 QTLs were associated with the domestication of pod dehiscence. We implemented marker-assisted selection (MAS) and genomic selection (GS) approaches to select pod dehiscence-resistant accessions with the best haplotype and lowest genomic breeding value (GBV), respectively. While our findings could be utilized for biology, genetics, and plant breeding, selecting pod dehiscence-resistant cultivars with the optimal haplotype will need further studies to confirm additional QTLs and assess advanced GS models.

Soybean seed coat exists in a range of colors from yellow, green, brown, black, to bicolor. Classical genetic analysis suggested that soybean seed color was a moderately complex trait controlled by multi-loci. However, only a couple of loci could be detected using a single biparental segregating population. In this study, a combination of association mapping and bulk segregation analysis was employed to identify genes/loci governing this trait in soybean. A total of 14 loci, including nine novel and five previously reported ones, were identified using 176,065 coding SNPs selected from entire SNP dataset among 56 soybean accessions. Four of these loci were confirmed and further mapped using a biparental population developed from the cross between ZP95-5383 (yellow seed color) and NY279 (brown seed color), in which different seed coat colors were further dissected into simple trait pairs (green/yellow, green/black, green/brown, yellow/black, yellow/brown, and black/brown) by continuously developing residual heterozygous lines. By genotyping entire F2 population using flanking markers located in fine-mapping regions, the genetic basis of seed coat color was fully dissected and these four loci could explain all variations of seed colors in this population. These findings will be useful for map-based cloning of genes as well as marker-assisted breeding in soybean. This work also provides an alternative strategy for systematically isolating genes controlling relative complex trait by association analysis followed by biparental mapping.

Drought causes significant soybean [Glycine max (L.) Merr.] yield losses each year in rain-fed production systems of many regions. Genetic improvement of soybean for drought tolerance is a cost-effective approach to stabilize yield under rain-fed management. The objectives of this study were to confirm previously reported soybean loci and to identify novel loci associated with canopy wilting (CW) using a panel of 200 diverse maturity group (MG) IV accessions. These 200 accessions along with six checks were planted at six site-years using an augmented incomplete block design with three replications under irrigated and rain-fed treatments. Association mapping, using 34,680 single nucleotide polymorphisms (SNPs), identified 188 significant SNPs associated with CW that likely tagged 152 loci. This includes 87 SNPs coincident with previous studies that likely tagged 68 loci and 101 novel SNPs that likely tagged 84 loci. We also determined the ability of genomic estimated breeding values (GEBVs) from previous research studies to predict CW in different genotypes and environments. A positive relationship (P ≤ 0.05;0.37 ≤ r ≤ 0.5) was found between observed CW and GEBVs. In the vicinity of 188 significant SNPs, 183 candidate genes were identified for both coincident SNPs and novel SNPs. Among these 183 candidate genes, 57 SNPs were present within genes coding for proteins with biological functions involved in plant stress responses. These genes may be directly or indirectly associated with transpiration or water conservation. The confirmed genomic regions may be an important resource for pyramiding favorable alleles and, as candidates for genomic selection, enhancing soybean drought tolerance.