Seed quality QTL in a prominent soybean population

Genome-Wide Association and RNA-Seq Analyses Reveal a Potential Candidate Gene Related to Oil Content in Soybean Seeds

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.

GWAS and WGCNA Analysis Uncover Candidate Genes Associated with Oil Content in Soybean

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.

Identification of Quantitative Trait Loci (QTL) for Sucrose and Protein Content in Soybean Seed

Protein and sugar content are important seed quality traits in soybean because they improve the value and sustainability of soy food and feed products. Thus, identifying Quantitative Trait Loci (QTL) for soybean seed protein and sugar content can benefit plant breeders and the soybean market by accelerating the breeding process via marker-assisted selection. For this study, a population of recombinant inbred lines (RILs) was developed from a cross between R08-3221 (high protein and low sucrose) and R07-2000 (high sucrose and low protein). Phenotypic data for protein content were taken from the F2:4 and F2:5 generations. The DA7250 NIR analyzer and HPLC instruments were used to analyze total seed protein and sucrose content. Genotypic data were generated using analysis via the SoySNP6k chip. A total of four QTLs were identified in this study. Two QTLs for protein content were located on chromosomes 11 and 20, and two QTLs associated with sucrose content were located on chromosomes 14 and. 11, the latter of which co-localized with detected QTLs for protein, explaining 10% of the phenotypic variation for protein and sucrose content in soybean seed within the study population. Soybean breeding programs can use the results to improve soybean seed quality.

Identification of quantitative trait loci controlling soybean seed protein and oil content

Soybean is a major source of seed protein and oil globally with an average composition of 40% protein and 20% oil in the seed. The goal of this study was to identify quantitative trait loci (QTL) conferring seed protein and oil content utilizing a population constructed by crossing an above average protein content line, PI 399084 to another line that had a low protein content value, PI 507429, both from the USDA soybean germplasm collection. The recombinant inbred line (RIL) population, PI 507429 x PI 399084, was evaluated in two replications over four years (2018-2021); the seeds were analyzed for seed protein and oil content using near-infrared reflectance spectroscopy. The recombinant inbred lines and the two parents were re-sequenced using genotyping by sequencing. A total of 12,761 molecular markers, which came from genotyping by sequencing, the SoySNP6k BeadChip and selected simple sequence repeat (SSR) markers from known protein QTL chromosomal regions were used for mapping. One QTL was identified on chromosome 2 explaining up to 56.8% of the variation for seed protein content and up to 43% for seed oil content. Another QTL identified on chromosome 15 explained up to 27.2% of the variation for seed protein and up to 41% of the variation for seed oil content. The protein and oil QTLs of this study and their associated molecular markers will be useful in breeding to improve nutritional quality in soybean.

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

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

Major genetic locus with pleiotropism determined seed-related traits in cultivated and wild soybeans

KEY MESSAGE

Here, a novel pleiotropic QTL qSS14 simultaneously regulating four seed size traits and two consistently detected QTLs qSW17 and qSLW02 were identified across multiple years. Seed-related traits were the key agronomic traits that have been artificially selected during the domestication of wild soybean. Identifying the genetic loci and genes that regulate seed size could clarify the genetic variations in seed-related traits and provide novel insights into high-yield soybean breeding. In this study, we used a high-density genetic map constructed by F₁₀ RIL populations from a cross between Glycine max and Glycine soja to detect additive QTLs for seven seed-related traits over the last three years. As a result, we identified one novel pleiotropic QTL, qSS14, that simultaneously controlled four seed size traits (100-seed weight, seed length, seed width, and seed thickness) and two consistently detected QTLs, qSW17, and qSLW02, in multiple years of phenotypic data. Furthermore, we predicted two, two and three candidate genes within these three critical loci based on the parental resequencing data and gene function annotations. And the relative expression of four candidate genes GLYMA_14G155100, GLYMA_17G061000, GLYMA_02G273100, and GLYMA_02G273300 showed significant differences among parents and the extreme materials through qRT-PCR analysis. These findings could facilitate the determination of beneficial genes in wild soybean and contribute to our understanding of the soybean domestication process.

Genome-wide survey identified superior and rare haplotypes for plant height in the north-eastern soybean germplasm of China

The proper and efficient utilization of natural genetic diversity can significantly impact crop improvements. Plant height is a quantitative trait governing the plant type as well as the yield and quality of soybean. Here, we used a combined approach including a genome-wide association study (GWAS) and haplotype and candidate gene analyses to explore the genetic basis of plant height in diverse natural soybean populations. For the GWAS analysis, we used the whole-genome resequencing data of 196 diverse soybean cultivars collected from different accumulated temperature zones of north-eastern China to detect the significant single-nucleotide polymorphisms (SNPs) associated with plant height across three environments (E1, E2, and E3). A total of 33 SNPs distributed on four chromosomes, viz., Chr.02, Chr.04, Chr.06, and Chr.19, were identified to be significantly associated with plant height across the three environments. Among them, 23 were consistently detected in two or more environments and the remaining 10 were identified in only one environment. Interestingly, all the significant SNPs detected on the respective chromosomes fell within the physical interval of linkage disequilibrium (LD) decay (± 38.9 kb). Hence, these genomic regions were considered to be four quantitative trait loci (QTLs), viz., qPH2, qPH4, qPH6, and qPH19, regulating plant height. Moreover, the genomic region flanking all significant SNPs on four chromosomes exhibited strong LD. These significant SNPs thus formed four haplotype blocks, viz., Hap-2, Hap-4, Hap-6, and Hap-19. The number of haplotype alleles underlying each block varied from four to six, and these alleles regulate the different phenotypes of plant height ranging from dwarf to extra-tall heights. Nine candidate genes were identified within the four haplotype blocks, and these genes were considered putative candidates regulating soybean plant height. Hence, these stable QTLs, superior haplotypes, and candidate genes (after proper validation) can be deployed for the development of soybean cultivars with desirable plant heights.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01363-7.

Genetic mapping and functional genomics of soybean seed protein

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

Supplementary Information

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

Identification of major QTLs for soybean seed size and seed weight traits using a RIL population in different environments

INTRODUCTION

The seed weight of soybean [Glycine max (L.) Merr.] is one of the major traits that determine soybean yield and is closely related to seed size. However, the genetic basis of the synergistic regulation of traits related to soybean yield is unclear.

METHODS

To understand the molecular genetic basis for the formation of soybean yield traits, the present study focused on QTLs mapping for seed size and weight traits in different environments and target genes mining.

RESULTS

A total of 85 QTLs associated with seed size and weight traits were identified using a recombinant inbred line (RIL) population developed from Guizao1×B13 (GB13). We also detected 18 environmentally stable QTLs. Of these, qSL-3-1 was a novel QTL with a stable main effect associated with seed length. It was detected in all environments, three of which explained more than 10% of phenotypic variance (PV), with a maximum of 15.91%. In addition, qSW-20-3 was a novel QTL with a stable main effect associated with seed width, which was identified in four environments. And the amount of phenotypic variance explained (PVE) varied from 9.22 to 21.93%. Five QTL clusters associated with both seed size and seed weight were summarized by QTL cluster identification. Fifteen candidate genes that may be involved in regulating soybean seed size and weight were also screened based on gene function annotation and GO enrichment analysis.

DISCUSSION

The results provide a biologically basic reference for understanding the formation of soybean seed size and weight traits.

Variability in Maturity, Oil and Protein Concentration, and Genetic Distinctness among Soybean Accessions Conserved at Plant Gene Resources of Canada

Soybean (Glycine max (L.) Merr.) is one of the important crops in Canada and has the potential to expand its production further north into the Canadian Prairies. Such expansion, however, requires the search for adapted soybean germplasm useful for the development of productive cultivars with earlier maturity and increased protein concentration. We initiated several research activities to characterize 848 accessions of the soybean collection conserved at Plant Gene Resources of Canada (PGRC) for maturity, oil and protein concentration, and genetic distinctness. The characterization revealed a wide range of variations present in each assessed trait among the PGRC soybean accessions. The trait variabilities allowed for the identification of four core subsets of 35 PGRC soybean accessions, each specifically targeted for early maturity for growing in Saskatoon and Ottawa, and for high oil and protein concentration. The two early maturity core subsets for Saskatoon and Ottawa displayed days to maturity ranging from 103 to 126 days and 94 to 102 days, respectively. The two core subsets for high oil and protein concentration showed the highest oil and protein concentration from 25.0 to 22.7% and from 52.8 to 46.7%, respectively. However, these core subsets did not differ significantly in genetic distinctness (as measured with 19,898 SNP markers across 20 soybean chromosomes) from the whole PGRC soybean collection. These findings are useful, particularly for the management and utilization of the conserved soybean germplasm.

Identification of Drought-Tolerance Genes in the Germination Stage of Soybean

Drought stress influences the vigor of plant seeds and inhibits seed germination, making it one of the primary environmental factors adversely affecting food security. The seed germination stage is critical to ensuring the growth and productivity of soybeans in soils prone to drought conditions. We here examined the genetic diversity and drought-tolerance phenotypes of 410 accessions of a germplasm diversity panel for soybean and conducted quantitative genetics analyses to identify loci associated with drought tolerance of seed germination. We uncovered significant differences among the diverse genotypes for four growth indices and five drought-tolerance indices, which revealed abundant variation among genotypes, upon drought stress, and for genotype × treatment effects. We also used 158,327 SNP markers and performed GWAS for the drought-related traits. Our data met the conditions (PCA + K) for using a mixed linear model in TASSEL, and we thus identified 26 SNPs associated with drought tolerance indices for germination stage distributed across 10 chromosomes. Nine SNP sites, including, for example, Gm20_34956219 and Gm20_36902659, were associated with two or more phenotypic indices, and there were nine SNP markers located in or adjacent to (within 500 kb) previously reported drought tolerance QTLs. These SNPs led to our identification of 41 candidate genes related to drought tolerance in the germination stage. The results of our study contribute to a deeper understanding of the genetic mechanisms underlying drought tolerance in soybeans at the germination stage, thereby providing a molecular basis for identifying useful soybean germplasm for breeding new drought-tolerant varieties.

Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and 'Omics' Tools

Grain legumes are a rich source of dietary protein for millions of people globally and thus a key driver for securing global food security. Legume plant-based 'dietary protein' biofortification is an economic strategy for alleviating the menace of rising malnutrition-related problems and hidden hunger. Malnutrition from protein deficiency is predominant in human populations with an insufficient daily intake of animal protein/dietary protein due to economic limitations, especially in developing countries. Therefore, enhancing grain legume protein content will help eradicate protein-related malnutrition problems in low-income and underprivileged countries. Here, we review the exploitable genetic variability for grain protein content in various major grain legumes for improving the protein content of high-yielding, low-protein genotypes. We highlight classical genetics-based inheritance of protein content in various legumes and discuss advances in molecular marker technology that have enabled us to underpin various quantitative trait loci controlling seed protein content (SPC) in biparental-based mapping populations and genome-wide association studies. We also review the progress of functional genomics in deciphering the underlying candidate gene(s) controlling SPC in various grain legumes and the role of proteomics and metabolomics in shedding light on the accumulation of various novel proteins and metabolites in high-protein legume genotypes. Lastly, we detail the scope of genomic selection, high-throughput phenotyping, emerging genome editing tools, and speed breeding protocols for enhancing SPC in grain legumes to achieve legume-based dietary protein security and thus reduce the global hunger risk.

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

KEY MESSAGE

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

Identification of major genomic regions for soybean seed weight by genome-wide association study

The hundred-seed weight (HSW) is an important yield component and one of the principal breeding traits in soybean. More than 250 quantitative trait loci (QTL) for soybean HSW have been identified. However, most of them have a large genomic region or are environmentally sensitive, which provide limited information for improving the phenotype in marker-assisted selection (MAS) and identifying the candidate genes. Here, we utilized 281 soybean accessions with 58,112 single nucleotide polymorphisms (SNPs) to dissect the genetic basis of HSW in across years in the northern Shaanxi province of China through one single-locus (SL) and three multi-locus (ML) genome-wide association study (GWAS) models. As a result, one hundred and fifty-four SNPs were detected to be significantly associated with HSW in at least one environment via SL-GWAS model, and 27 of these 154 SNPs were detected in all (three) environments and located within 7 linkage disequilibrium (LD) block regions with the distance of each block ranged from 40 to 610 Kb. A total of 15 quantitative trait nucleotides (QTNs) were identified by three ML-GWAS models. Combined with the results of different GWAS models, the 7 LD block regions associated with HSW detected by SL-GWAS model could be verified directly or indirectly by the results of ML-GWAS models. Eleven candidate genes underlying the stable loci that may regulate seed weight in soybean were predicted. The significantly associated SNPs and the stable loci as well as predicted candidate genes may be of great importance for marker-assisted breeding, polymerization breeding, and gene discovery for HSW in soybean.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01310-y.

Construction of Chromosome Segment Substitution Lines and Inheritance of Seed-Pod Characteristics in Wild Soybean

Genetic populations provide the basis for genetic and genomic research, and chromosome segment substitution lines (CSSLs) are a powerful tool for the fine mapping of quantitative traits, new gene mining, and marker-assisted breeding. In this study, 213 CSSLs were obtained by self-crossing, backcrossing, and marker-assisted selection between cultivated soybean (Glycine max [L.] Merr.) variety Suinong14 (SN14) and wild soybean (Glycine soja Sieb. et Zucc.) ZYD00006. The genomes of these 213 CSSLs were resequenced and 580,524 single-nucleotide polymorphism markers were obtained, which were divided into 3,780 bin markers. The seed-pod-related traits were analyzed by quantitative trait locus (QTL) mapping using CSSLs. A total of 170 QTLs were detected, and 32 QTLs were detected stably for more than 2 years. Through epistasis analysis, 955 pairs of epistasis QTLs related to seed-pod traits were obtained. Furthermore, the hundred-seed weight QTL was finely mapped to the region of 64.4 Kb on chromosome 12, and Glyma.12G088900 was identified as a candidate gene. Taken together, a set of wild soybean CSSLs was constructed and upgraded by a resequencing technique. The seed-pod-related traits were studied by bin markers, and a candidate gene for the hundred-seed weight was finely mapped. Our results have revealed the CSSLs can be an effective tool for QTL mapping, epistatic effect analysis, and gene cloning.

Genotype imputation for soybean nested association mapping population to improve precision of QTL detection

KEY MESSAGE

Software for high imputation accuracy in soybean was identified. Imputed dataset could significantly reduce the interval of genomic regions controlling traits, thus greatly improve the efficiency of candidate gene identification. Genotype imputation is a strategy to increase marker density of existing datasets without additional genotyping. We compared imputation performance of software BEAGLE 5.0, IMPUTE 5 and AlphaPlantImpute and tested software parameters that may help to improve imputation accuracy in soybean populations. Several factors including marker density, extent of linkage disequilibrium (LD), minor allele frequency (MAF), etc., were examined for their effects on imputation accuracy across different software. Our results showed that AlphaPlantImpute had a higher imputation accuracy than BEAGLE 5.0 or IMPUTE 5 tested in each soybean family, especially if the study progeny were genotyped with an extremely low number of markers. LD extent, MAF and reference panel size were positively correlated with imputation accuracy, a minimum number of 50 markers per chromosome and MAF of SNPs > 0.2 in soybean line were required to avoid a significant loss of imputation accuracy. Using the software, we imputed 5176 soybean lines in the soybean nested mapping population (NAM) with high-density markers of the 40 parents. The dataset containing 423,419 markers for 5176 lines and 40 parents was deposited at the Soybase. The imputed NAM dataset was further examined for the improvement of mapping quantitative trait loci (QTL) controlling soybean seed protein content. Most of the QTL identified were at identical or at similar position based on initial and imputed datasets; however, QTL intervals were greatly narrowed. The resulting genotypic dataset of NAM population will facilitate QTL mapping of traits and downstream applications. The information will also help to improve genotyping imputation accuracy in self-pollinated crops.

Nutritional Diversity in Native Germplasm of Maize Collected From Three Different Fragile Ecosystems of India

Native germplasm resources are adapted to specific ecological niches. They have sustained over generations owing to the preference of local communities for their unique taste, the utility to particular dishes, and the low cost of cultivation. They may help eradicate malnutrition and act as a source for trait-linked genes. The present dataset comprises thirty-three native germplasm of maize collected from Rajasthan, Himachal Pradesh, and Andhra Pradesh states of India with an altitudinal variation of 386–2,028 m. They were evaluated for proximate composition, minerals, nutritional attributes, and antioxidant activity and compared with the standard values reported in the Indian Food Composition Table 2017 (IFCT2017). The nutritional profile showed moisture content in the range of 7.16–10.9%, ash 0.73–1.93%, crude protein 8.68–12.0%, crude fat 3.72–8.03%, dietary fiber 5.21–11.2%, and available carbohydrates 60.6–69.8%. Three accessions, namely, Malan 11 (7.06%), Malan 24 (7.20%), and Yellow Chamba Local 02 (8.03%) exhibited almost double the crude fat content as compared with the values notified in IFCT2017 (3.77). Total sugar content obtained was in the range of 5.00–11.3%, whereas the starch content was found between 50.9 and 64.9%. All the germplasm except Yellow Chamba Local reflected a higher protein content than reported values in IFCT2017 (8.80). Sathi, Safed Chamba Local, and Ragal Makka had nearly 12% protein content. Mineral malnutrition, mainly due to iron (Fe) deficiency, is a worldwide issue to science, humanity, and society. The mineral profile revealed that most germplasm had a higher iron content. Accessions with the iron content of nearly three times of IFCT2017 reported value were identified in germplasm belonging to three states. A negative relationship was observed between the altitude of the sample collection site and available carbohydrate content. In contrast, available carbohydrate showed inverse correlations with dietary fiber, protein, and fat content. The information generated in this study can be utilized to promote these germplasm as nutrifood, nutritional surveillance, labeling, and crop improvement programs.

Development of a versatile resource for post-genomic research through consolidating and characterizing 1500 diverse wild and cultivated soybean genomes

BACKGROUND

With advances in next-generation sequencing technologies, an unprecedented amount of soybean accessions has been sequenced by many individual studies and made available as raw sequencing reads for post-genomic research.

RESULTS

To develop a consolidated and user-friendly genomic resource for post-genomic research, we consolidated the raw resequencing data of 1465 soybean genomes available in the public and 91 highly diverse wild soybean genomes newly sequenced. These altogether provided a collection of 1556 sequenced genomes of 1501 diverse accessions (1.5 K). The collection comprises of wild, landraces and elite cultivars of soybean that were grown in East Asia or major soybean cultivating areas around the world. Our extensive sequence analysis discovered 32 million single nucleotide polymorphisms (32mSNPs) and revealed a SNP density of 30 SNPs/kb and 12 non-synonymous SNPs/gene reflecting a high structural and functional genomic diversity of the new collection. Each SNP was annotated with 30 categories of structural and/or functional information. We further identified paired accessions between the 1.5 K and 20,087 (20 K) accessions in US collection as genomic "equivalent" accessions sharing the highest genomic identity for minimizing the barriers in soybean germplasm exchange between countries. We also exemplified the utility of 32mSNPs in enhancing post-genomics research through in-silico genotyping, high-resolution GWAS, discovering and/or characterizing genes and alleles/mutations, identifying germplasms containing beneficial alleles that are potentially experiencing artificial selection.

CONCLUSION

The comprehensive analysis of publicly available large-scale genome sequencing data of diverse cultivated accessions and the newly in-house sequenced wild accessions greatly increased the soybean genome-wide variation resolution. This could facilitate a variety of genetic and molecular-level analyses in soybean. The 32mSNPs and 1.5 K accessions with their comprehensive annotation have been made available at the SoyBase and Ag Data Commons. The dataset could further serve as a versatile and expandable core resource for exploring the exponentially increasing genome sequencing data for a variety of post-genomic research.

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

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

Novel target sites for soybean yield enhancement by photosynthesis

Photosynthesis plays an important role in plant growth and development. Increasing photosynthetic rate is a main objective of improving crop productivity. Chlorophyll fluorescence is an effective method for quickly evaluating photosynthesis. In this study, four representative chlorophyll fluorescence parameters, that is, maximum quantum efficiency of photosystem II, quantum efficiency of PSII, photochemical quenching, and non-photochemical quenching, of 219 diverse soybean accessions were measured across three environments. The underlying genetic architecture was analyzed by genome-wide association study. Forty-eight SNPs were detected to associate with the four traits and explained 10.43-20.41% of the phenotypic variation. Nine candidate genes in the stable QTLs were predicted. Great differences in the expression levels of the candidate genes existed between the high photosynthetic efficiency accessions and low photosynthetic efficiency accessions. In all, we uncover 17 QTLs associated with photosynthesis-related traits and nine genes that may participate in the regulation of photosynthesis, which can provide references for revealing the genetic mechanism of photosynthesis. These QTLs and candidate genes will provide new targets for crop yield improvement through increasing photosynthesis.

Novel QTL and Meta-QTL Mapping for Major Quality Traits in Soybean

Isoflavone, protein, and oil are the most important quality traits in soybean. Since these phenotypes are typically quantitative traits, quantitative trait locus (QTL) mapping has been an efficient way to clarify their complex and unclear genetic background. However, the low-density genetic map and the absence of QTL integration limited the accurate and efficient QTL mapping in previous researches. This paper adopted a recombinant inbred lines (RIL) population derived from 'Zhongdou27'and 'Hefeng25' and a high-density linkage map based on whole-genome resequencing to map novel QTL and used meta-analysis methods to integrate the stable and consentaneous QTL. The candidate genes were obtained from gene functional annotation and expression analysis based on the public database. A total of 41 QTL with a high logarithm of odd (LOD) scores were identified through composite interval mapping (CIM), including 38 novel QTL and 2 Stable QTL. A total of 660 candidate genes were predicted according to the results of the gene annotation and public transcriptome data. A total of 212 meta-QTL containing 122 stable and consentaneous QTL were mapped based on 1,034 QTL collected from previous studies. For the first time, 70 meta-QTL associated with isoflavones were mapped in this study. Meanwhile, 69 and 73 meta-QTL, respectively, related to oil and protein were obtained as well. The results promote the understanding of the biosynthesis and regulation of isoflavones, protein, and oil at molecular levels, and facilitate the construction of molecular modular for great quality traits in soybean.

Identification and validation of quantitative trait loci associated with seed yield in soybean

In soybean [Glycine max (L.) Merrill], the genetic analysis of seed yield is important to aid in the breeding of high-yielding cultivars. Seed yield is a complex trait, and the number of quantitative trait loci (QTL) involved in seed yield is high. The aims of this study were to identify QTL associated with seed yield and validate their effects on seed yield using near-isogenic lines. The QTL analysis was conducted using a recombinant inbred line population derived from a cross between Japanese cultivars 'Toyoharuka' and 'Toyomusume', and eight seed yield-associated QTL were identified. There were significant positive correlations between seed yield and the number of favorable alleles at QTL associated with seed yield in the recombinant inbred lines for three years. The effects of qSY8-1, a QTL promoting greater seed yield, was validated in the Toyoharuka background. In a two-year yield trial, the 100-seed weight and seed yield of Toyoharuka-NIL, the near-isogenic line having the Toyomusume allele at qSY8-1, were significantly greater than those of Toyoharuka (106% and 107%, respectively) without any change for days to flowering and maturity. Our results suggest that qSY8-1 was not associated with maturity genes, and contributed to the 100-seed weight.

Identification and Validation of Major QTLs, Epistatic Interactions, and Candidate Genes for Soybean Seed Shape and Weight Using Two Related RIL Populations

Understanding the genetic mechanism underlying seed size, shape, and weight is essential for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line (RIL) populations, LM6 and ZM6, were evaluated across multiple environments to identify and validate M-QTLs as well as identify candidate genes behind major and stable quantitative trait loci (QTLs). A total of 239 and 43 M-QTLs were mapped by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM) approaches, from which 180 and 18, respectively, are novel QTLs. Twenty-two QTLs including four novel major QTLs were validated in the two RIL populations across multiple environments. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Thirty-four QTLs associated with seed flatness index (FI) were identified and reported here for the first time. Seven QTL clusters comprising several QTLs for seed size, shape, and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17, and 19 were identified. Gene annotations, gene ontology (GO) enrichment, and RNA-seq analyses of the genomic regions of those seven QTL clusters identified 47 candidate genes for seed-related traits. These genes are highly expressed in seed-related tissues and nodules, which might be deemed as potential candidate genes regulating the seed size, weight, and shape traits in soybean. This study provides detailed information on the genetic basis of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning, and for marker-assisted selection (MAS) targeted at improving these traits individually or concurrently.

Identification of SNP markers associated with soybean fatty acids contents by genome-wide association analyses

Composition of fatty acids (FAs) in soybean seed is important for the quality and uses of soybean oil. Using gas chromatography, we have measured soybean FAs profiles of 621 soybean accessions (maturity groups I through IV) grown in five different environments; Columbus, OH (2015), Wooster, OH (2014 and 2015), Plymouth, NC (2015), and Urbana, IL (2015). Using publicly available SoySNP50K genotypic data and the FA profiles from this study, a genome-wide association analysis was completed with a compressed mixed linear model to identify 43 genomic regions significantly associated with a fatty acid at a genome wide significance threshold of 5%. Among these regions, one and three novel genomic regions associated with palmitic acid and stearic acid, respectively, were identified across all five environments. Additionally, nine novel environment-specific FA-related genomic regions were discovered providing new insights into the genetics of soybean FAs. Previously reported FA-related loci, such as FATB1a, SACPD-C, and KASIII, were also confirmed in this study. Our results will be useful for future functional studies and marker-assisted breeding for soybean FAs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01216-1.

GmNF-YC4-2 Increases Protein, Exhibits Broad Disease Resistance and Expedites Maturity in Soybean

The NF-Y gene family is a highly conserved set of transcription factors. The functional transcription factor complex is made up of a trimer between NF-YA, NF-YB, and NF-YC proteins. While mammals typically have one gene for each subunit, plants often have multigene families for each subunit which contributes to a wide variety of combinations and functions. Soybean plants with an overexpression of a particular NF-YC isoform GmNF-YC4-2 (Glyma.04g196200) in soybean cultivar Williams 82, had a lower amount of starch in its leaves, a higher amount of protein in its seeds, and increased broad disease resistance for bacterial, viral, and fungal infections in the field, similar to the effects of overexpression of its isoform GmNF-YC4-1 (Glyma.06g169600). Interestingly, GmNF-YC4-2-OE (overexpression) plants also filled pods and senesced earlier, a novel trait not found in GmNF-YC4-1-OE plants. No yield difference was observed in GmNF-YC4-2-OE compared with the wild-type control. Sequence alignment of GmNF-YC4-2, GmNF-YC4-1 and AtNF-YC1 indicated that faster maturation may be a result of minor sequence differences in the terminal ends of the protein compared to the closely related isoforms.

Comparative transcriptome analysis during seeds development between two soybean cultivars

Soybean is one of the important economic crops, which supplies a great deal of vegetable oil and proteins for human. The content of nutrients in different soybean seeds is different, which is related to the expression of multiple genes, but the mechanisms are complicated and still largely uncertain. In this study, to reveal the possible causes of the nutrients difference in soybeans A7 (containing low oil and high protein) and A35 (containing high oil and low protein), RNA-seq technology was performed to compare and identify the potential differential expressed genes (DEGs) at different seed developmental stages. The results showed that DEGs mainly presented at the early stages of seeds development and more DEGs were up-regulated at the early stage than the late stages. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis showed that the DEGs have diverged in A7 and A35. In A7, the DEGs were mainly involved in cell cycle and stresses, while in A35 were the fatty acids and sugar metabolism. Specifically, when the DEGs contributing to oil and protein metabolic pathways were analyzed, the differences between A7 and A35 mainly presented in fatty acids metabolism and seeds storage proteins (SSPs) synthesis. Furthermore, the enzymes, fatty acid dehydrogenase 2, 3-ketoacyl-CoA synthase and 9S-lipoxygenase, in the synthesis and elongation pathways of fatty acids, were revealed probably to be involved in the oil content difference between A7 and A35, the SSPs content might be due to the transcription factors: Leafy Cotyledon 2 and Abscisic acid-intensitive 3, while the sugar transporter, SWEET10a, might contribute to both oil and protein content differences. Finally, six DEGs were selected to analyze their expression using qRT-PCR, and the results were consistent with the RNA-seq results. Generally, the study provided a comprehensive and dynamic expression trends for the seed development processes, and uncovered the potential DEGs for the differences of oil in A7 and A35.

Pedigree-based genetic dissection of quantitative loci for seed quality and yield characters in improved soybean

As soybean plays an indispensable role in the supply of vegetable oil and protein, balancing the relationship between seed quality and yield traits according to human demand has become an important breeding goal for soybean improvement. Here, 256 intraspecific recombinant inbred lines (RILs), derived from a cross between Qi Huang No.34 (QH34) and Ji Dou No.17 (JD17), were used for quantitative trait loci (QTLs) mapping with remarkable four chemical and physical properties with a purpose for exploring the distribution of excellent alleles in germplasm resources in China. A total of 25 QTLs were detected, of which 10 QTLs inherited the alleles from the parent QH34. Pedigree research on favorable alleles on these QTLs showed the process of excellent alleles pyramided into QH34. Meta-analysis of the 25 QTLs by comparing with existed QTLs in previous study identified 17 novel QTLs. QTLs with pleiotropic effects have been detected. Furthermore, three representative elite recombinant inbred lines in different locations that have great potential in soybean breeding were selected, and finally, four seed weight-related candidate genes were identified. The discovery of these QTLs provides a new guidance for combining the diversity and rarity of germplasm resources, which can effectively increase population genetic diversity and broaden genetic basis of varieties.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01211-6.

Effects of type I Diacylglycerol O-acyltransferase (DGAT1) genes on soybean (Glycine max L.) seed composition

Type I Diacylglycerol acyltransferase (DGAT1) catalyzes the final step of the biosynthesis process of triacylglycerol (TAG), the major storage lipids in plant seeds, through the esterification of diacylglycerol (DAG). To characterize the function of DGAT1 genes on the accumulation of oil and other seed composition traits in soybean, transgenic lines were generated via trans-acting siRNA technology, in which three DGAT1 genes (Glyma.13G106100, Glyma.09G065300, and Glyma.17G053300) were downregulated. The simultaneous downregulation of the three isoforms in transgenic lines was found to be associated with the reduction of seed oil concentrations by up to 18 mg/g (8.3%), which was correlated with increases in seed protein concentration up to 42 mg/g (11%). Additionally, the downregulations also influenced the fatty acid compositions in the seeds of transgenic lines through increasing the level of oleic acid, up to 121 mg/g (47.3%). The results of this study illustrate the importance of DGAT1 genes in determining the seed compositions in soybean through the development of new potential technology for manipulating seed quality in soybean to meet the demands for its various food and industrial applications.

Genome-wide association mapping for protein, oil and water-soluble protein contents in soybean

As a globally important legume crop, soybean provides excellent sources of protein and oil for human and livestock nutrition. Improving seed protein and oil contents has always been an important objective in soybean breeding. Water-soluble protein plays a significant role in the processing and efficacy of soybean protein. Here, a genome-wide association study (GWAS) of seed compositions (protein, oil, and water-soluble protein contents) was conducted using 211 diverse soybean accessions genotyped with a 355 K SoySNP array. Three, four, and five QTLs were identified related to the protein, oil, and water-soluble protein contents, respectively. Furthermore, five QTLs (qPC-15-1, qOC-8-1, qOC-12-1, qOC-20-1 and qWSPC-8-1) were detected in multiple environments. Analysis of the favorable alleles for oil and water-soluble protein contents showed that qOC-8-1 (qWSPC-8-1) exerted inverse effects on oil and water-soluble protein synthesis. Relative expression analysis suggested that Glyma.15G049200 in qPC-15-1 affects protein synthesis and Glyma.08G107800 in qOC-8-1 and qWSPC-8-1 might be involved in oil and water-soluble protein synthesis, producing opposite effects. The candidate genes and significant SNPs detected in the present study will allow a deeper understanding of the genetic basis for the regulation of protein, oil and water-soluble protein contents and provide important information that could be utilized in marker-assisted selection for soybean quality improvement.

Genomic regions associated with important seed quality traits in food-grade soybeans

BACKGROUND

The production of soy-based food products requires specific physical and chemical characteristics of the soybean seed. Identification of quantitative trait loci (QTL) associated with value-added traits, such as seed weight, seed protein and sucrose concentration, could accelerate the development of competitive high-protein soybean cultivars for the food-grade market through marker-assisted selection (MAS). The objectives of this study were to identify and validate QTL associated with these value-added traits in two high-protein recombinant inbred line (RIL) populations.

RESULTS

The RIL populations were derived from the high-protein cultivar 'AC X790P' (49% protein, dry weight basis), and two high-yielding commercial cultivars, 'S18-R6' (41% protein) and 'S23-T5' (42% protein). Fourteen large-effect QTL (R2 > 10%) were identified associated with seed protein concentration. Of these QTL, seven QTL were detected in both populations, and eight of them were co-localized with QTL associated with either seed sucrose concentration or seed weight. None of the protein-related QTL was found to be associated with seed yield in either population. Sixteen candidate genes with putative roles in protein metabolism were identified within seven of these protein-related regions: qPro_Gm02-3, qPro_Gm04-4, qPro_Gm06-1, qPro_Gm06-3, qPro_Gm06-6, qPro_Gm13-4 and qPro-Gm15-3.

CONCLUSION

The use of RIL populations derived from high-protein parents created an opportunity to identify four novel QTL that may have been masked by large-effect QTL segregating in populations developed from diverse parental cultivars. In total, we have identified nine protein QTL that were detected either in both populations in the current study or reported in other studies. These QTL may be useful in the curated selection of new soybean cultivars for optimized soy-based food products.

QTL mapping for soybean (Glycine max L.) leaf chlorophyll-content traits in a genotyped RIL population by using RAD-seq based high-density linkage map

BACKGROUND

Different soybean (Glycine max L.) leaf chlorophyll-content traits are considered to be significantly linked to soybean yield. To map the quantitative trait loci (QTLs) of soybean leaf chlorophyll-content traits, an advanced recombinant inbred line (RIL, ZH, Zhonghuang 24 × Huaxia 3) population was adopted to phenotypic data acquisitions for the target traits across six distinct environments (seasons and soybean growth stages). Moreover, the restriction site-associated DNA sequencing (RAD-seq) based high-density genetic linkage map of the RIL population was utilized for QTL mapping by carrying out the composite interval mapping (CIM) approach.

RESULTS

Correlation analyses showed that most traits were correlated with each other under specific chlorophyll assessing method and were regulated both by hereditary and environmental factors. In this study, 78 QTLs for soybean leaf chlorophyll-content traits were identified. Furthermore, 13 major QTLs and five important QTL hotspots were classified and highlighted from the detected QTLs. Finally, Glyma01g15506, Glyma02g08910, Glyma02g11110, Glyma07g15960, Glyma15g19670 and Glyma15g19810 were predicted from the genetic intervals of the major QTLs and important QTL hotspots.

CONCLUSIONS

The detected QTLs and candidate genes may facilitate to gain a better understanding of the hereditary basis of soybean leaf chlorophyll-content traits and may be valuable to pave the way for the marker-assisted selection (MAS) breeding of the target traits.

Quantitative Trait Locus Analysis of Protein and Oil Content in Response to Planting Density in Soybean (Glycine max [L.] Merri.) Seeds Based on SNP Linkage Mapping

Soybean varieties suitable for high planting density allow greater yields. However, the seed protein and oil contents, which determine the value of this crop, can be influenced by planting density. Thus, it is important to understand the genetic basis of the responses of different soybean genotypes to planting density. In this study, we quantified the protein and oil contents in a four-way recombinant inbred line (FW-RIL) soybean population under two planting densities and the response to density. We performed quantitative trait locus (QTL) mapping using a single nucleotide polymorphism (SNP) linkage map generated by inclusive composite interval mapping. We identified 14 QTLs for protein content and 17 for oil content at a planting density of 2.15 × 10⁵ plant/ha (D1) and 14 QTLs for protein content and 20 for oil content at a planting density 3.0 × 10⁵ plant/ha (D2). Among the QTLs detected, two oil-content QTLs was detected at both plant densities. In addition, we identified 38 QTLs for the responses of protein and oil contents to planting density. Of the QTLs detected, 70 were identified in previous studies, while 33 were newly identified. Fourty-five QTLs accounted for over 10% of the phenotypic variation of the corresponding trait, based on 23 QTLs at a marker interval distance of ~600 kb detected under different densities and with the responses to density difference. Pathway analysis revealed four candidate genes involved in protein and oil biosynthesis/metabolism. These results improve our understanding of the genetic underpinnings of protein and oil biosynthesis in soybean, laying the foundation for enhancing protein and oil contents and increasing yields in soybean.

Identification of QTNs Controlling 100-Seed Weight in Soybean Using Multilocus Genome-Wide Association Studies

Hundred-seed weight (HSW) is an important measure of yield and a useful indicator to monitor the inheritance of quantitative traits affected by genotype and environmental conditions. To identify quantitative trait nucleotides (QTNs) and mine genes useful for breeding high-yielding and high-quality soybean (Glycine max) cultivars, we conducted a multilocus genome-wide association study (GWAS) on HSW of soybean based on phenotypic data from 20 different environments and genotypic data for 109,676 single-nucleotide polymorphisms (SNPs) in 144 four-way recombinant inbred lines. Using five multilocus GWAS methods, we identified 118 QTNs controlling HSW. Among these, 31 common QTNs were detected by various methods or across multiple environments. Pathway analysis identified three potential candidate genes associated with HSW in soybean. We used allele information to study the common QTNs in 20 large-seed and 20 small-seed lines and identified a higher percentage of superior alleles in the large-seed lines than in small-seed lines. These observations will contribute to construct the gene networks controlling HSW in soybean, which can improve the genetic understanding of HSW, and provide assistance for molecular breeding of soybean large-seed varieties.

Identification of QTNs and Their Candidate Genes for 100-Seed Weight in Soybean (Glycine max L.) Using Multi-Locus Genome-Wide Association Studies

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.

Identification and Mapping of Stable QTLs for Seed Oil and Protein Content in Soybean [Glycine max (L.) Merr.]

This research aimed to identify stable quantitative trait loci (QTL) associated with oil and protein content in soybean. A population of 196 recombinant inbred lines (RILs) derived from Huachun 2 × Wayao was used to evaluate these target traits. A high-density genetic linkage map was constructed by using high-throughput genome-wide sequencing technology, which contained 3413 recombination bin markers and spanned 5400.4 cM with an average distance of 1.58 cM between markers. Eighteen stable QTLs controlling oil and protein content were detected. Among them, qOil-11-1 was identified for the first time as a novel QTL, while qOil-5-1, qPro-10-1, and qPro-14-1 were strong and stable QTLs with high log-likelihood (LOD) values. Sixteen differentially expressed genes (DEGs) within these four QTLs were shown to be highly expressed during seed development based on RNA sequencing (RNA-seq) data analysis. Our results may contribute toward gene mining and marker-assisted selection (MAS) for breeding a high-quality soybean in the future.

Genome-wide association study of soybean seed germination under drought stress

Drought stress, which is increasing with climate change, is a serious threat to agricultural sustainability worldwide. Seed germination is an essential growth phase that ensures the successful establishment and productivity of soybean, which can lose substantial productivity in soils with water deficits. However, only limited genetic information is available about how germinating soybean seeds may exert drought tolerance. In this study, we examined the germinating seed drought-tolerance phenotypes and genotypes of a panel of 259 released Chinese soybean cultivars panel. Based on 4616 Single-Nucleotide Polymorphisms (SNPs), we conducted a mixed-linear model GWAS that identified a total of 15 SNPs associated with at least one drought-tolerance index. Notably, three of these SNPs were commonly associated with two drought-tolerance indices. Two of these SNPs are positioned upstream of genes, and 11 of them are located in or near regions where QTLs have been previously mapped by linkage analysis, five of which are drought-related. The SNPs detected in this study can both drive hypothesis-driven research to deepen our understanding of genetic basis of soybean drought tolerance at the germination stage and provide useful genetic resources that can facilitate the selection of drought stress traits via genomic-assisted selection.

Genetic dissection of yield-related traits via genome-wide association analysis across multiple environments in wild soybean (Glycine soja Sieb. and Zucc.)

MAIN CONCLUSION

A total of 41 SNPs were identified as significantly associated with five yield-related traits in wild soybean populations across multiple environments, and the candidate gene GsCID1 was found to be associated with seed weight. These results may facilitate improvements in cultivated soybean. Crop-related wild species contain new sources of genetic diversity for crop improvement. Wild soybean (Glycine soja Sieb. and Zucc.) is the progenitor of cultivated soybean [Glycine max (L.) Merr.] and can be used as an essential genetic resource for yield improvements. In this research, using genome-wide association study (GWAS) in 96 out of 113 wild soybean accessions with 114,090 single nucleotide polymorphisms (SNPs) (with minor allele frequencies ≤ 0.05), SNPs associated with five yield-related traits were identified across multiple environments. In total, 41 SNPs were significantly associated with the traits in two or more environments (significance threshold P ≤ 8.76 × 10-6), with 29, 7, 3, and 2 SNPs detected for 100-seed weight (SW), maturity time (MT), seed yield per plant (SY) and flowering time (FT), respectively. BLAST search against the Glycine soja W05 reference genome was performed, 20 candidate genes were identified based on these 41 significant SNPs. One candidate gene, GsCID1 (Glysoja.04g010563), harbored two significant SNPs-AX-93713187, with a non-synonymous mutation, and AX-93713188, with a synonymous mutation. GsCID1 was highly expressed during seed development based on public information resources. The polymorphisms in this gene were associated with SW. We developed a derived cleaved amplified polymorphic sequence (dCAPS) marker for GsCID1 that was highly associated with SW and was validated as a functional marker. In summary, the revealed SNPs/genes are useful for understanding the genetic architecture of yield-related traits in wild soybean, which could be used as a potential exotic resource to improve cultivated soybean yields.

Identifying a wild allele conferring small seed size, high protein content and low oil content using chromosome segment substitution lines in soybean

KEY MESSAGE

A wild soybean allele conferring 100-seed weight, protein content and oil content simultaneously was fine-mapped to a 329-kb region on Chromosome 15, in which Glyma.15g049200 was predicted a candidate gene. Annual wild soybean characterized with small 100-seed weight (100SW), high protein content (PRC), low oil content (OIC) may contain favourable alleles for broadening the genetic base of cultivated soybeans. To evaluate these alleles, a population composed of 195 chromosome segment substitution lines (SojaCSSLP4), with wild N24852 as donor and cultivated NN1138-2 as recurrent parent, was tested. In SojaCSSLP4, 10, 9 and 8 wild segments/QTL were detected for 100SW, PRC and OIC, respectively. Using a backcross-derived secondary population, one segment for the three traits (q100SW15, qPro15 and qOil15) and one for 100SW (q100SW18.2) were fine-mapped into a 329-kb region on chromosome 15 and a 286-kb region on chromosome 18, respectively. Integrated with the transcription data in SoyBase, 42 genes were predicted in the 329-kb region where Glyma.15g049200 showed significant expression differences at all seed development stages. Furthermore, the Glyma.15g049200 segments of the two parents were sequenced and compared, which showed two base insertions in CDS (coding sequence) in the wild N24852 comparing to the NN1138-2. Since only Glyma.15g049200 performed differential CDS between the two parents but related to the three traits, Glyma.15g049200 was predicted a pleiotropic candidate gene for 100SW, PRC and OIC. The functional annotation of Glyma.15g049200 indicated a bidirectional sucrose transporter belonging to MtN3/saliva family which might be the reason that this gene provides a same biochemical basis for 100SW, PRC and OIC, therefore, is responsible for the three traits. This result may facilitate isolation of the specific gene and provide prerequisite for understanding the other two pleiotropic QTL.

Deciphering the Genetic Architecture of Plant Height in Soybean Using Two RIL Populations Sharing a Common M8206 Parent

Plant height (PH) is an important agronomic trait that is closely related to soybean yield and quality. However, it is a complex quantitative trait governed by multiple genes and is influenced by environment. Unraveling the genetic mechanism involved in PH, and developing soybean cultivars with desirable PH is an imperative goal for soybean breeding. In this regard, the present study used high-density linkage maps of two related recombinant inbred line (RIL) populations viz., MT and ZM evaluated in three different environments to detect additive and epistatic effect quantitative trait loci (QTLs) as well as their interaction with environments for PH in Chinese summer planting soybean. A total of eight and 12 QTLs were detected by combining the composite interval mapping (CIM) and mixed-model based composite interval mapping (MCIM) methods in MT and ZM populations, respectively. Among these QTLs, nine QTLs viz., QPH-2, qPH-6-2_MT, QPH-6, qPH-9-1_ZM, qPH-10-1_ZM, qPH-13-1_ZM, qPH-16-1_MT, QPH-17 and QPH-19 were consistently identified in multiple environments or populations, hence were regarded as stable QTLs. Furthermore, Out of these QTLs, three QTLs viz., qPH-4-2_ZM, qPH-15-1_MT and QPH-17 were novel. In particular, QPH-17 could detect in both populations, which was also considered as a stable and major QTL in Chinese summer planting soybean. Moreover, eleven QTLs revealed significant additive effects in both populations, and out of them only six showed additive by environment interaction effects, and the environment-independent QTLs showed higher additive effects. Finally, six digenic epistatic QTLs pairs were identified and only four additive effect QTLs viz., qPH-6-2_MT, qPH-19-1_MT/QPH-19, qPH-5-1_ZM and qPH-17-1_ZM showed epistatic effects. These results indicate that environment and epistatic interaction effects have significant influence in determining genetic basis of PH in soybean. These results would not only increase our understanding of the genetic control of plant height in summer planting soybean but also provide support for implementing marker assisted selection (MAS) in developing cultivars with ideal plant height as well as gene cloning to elucidate the mechanisms of plant height.

Utilization of Interspecific High-Density Genetic Map of RIL Population for the QTL Detection and Candidate Gene Mining for 100-Seed Weight in Soybean

Seed-weight is one of the most important traits determining soybean yield. Hence, it is prerequisite to have detailed understanding of the genetic basis regulating seed-weight for the development of improved cultivars. In this regard, the present study used high-density interspecific linkage map of NJIR4P recombinant inbred population evaluated in four different environments to detect stable Quantitative trait loci (QTLs) as well as mine candidate genes for 100-seed weight. In total, 19 QTLs distributed on 12 chromosomes were identified in all individual environments plus combined environment, out of which seven were novel and eight are stable identified in more than one environment. However, all the novel QTLs were minor (R 2 < 10%). The remaining 12 QTLs detected in this study were co-localized with the earlier reported QTLs with narrow genomic regions, and out of these only 2 QTLs were major (R 2 > 10%) viz., qSW-17-1 and qSW-17-4. Beneficial alleles of all identified QTLs were derived from cultivated soybean parent (Nannong493-1). Based on Protein ANalysis THrough Evolutionary Relationships, gene annotation information, and literature search, 29 genes within 5 stable QTLs were predicted to be possible candidate genes that might regulate seed-weight/size in soybean. However, it needs further validation to confirm their role in seed development. In conclusion, the present study provides better understanding of trait genetics and candidate gene information through the use high-density inter-specific bin map, and also revealed considerable scope for genetic improvement of 100-seed weight in soybean using marker-assisted breeding.

Genome wide association mapping and candidate gene analysis for hundred seed weight in soybean [Glycine max (L.) Merrill]

BACKGROUND

The hundred seed weight (HSW) is one of the yield components of soybean [Glycine max (L.) Merrill] and is especially critical for various soybean food types. In this study, a representative sample consisting of 185 accessions was selected from Northeast China and analysed in three tested environments to determine the quantitative trait nucleotide (QTN) of HSW through a genome-wide association study (GWAS).

RESULT

A total of 24,180 single nucleotide polymorphisms (SNPs) with minor allele frequencies greater than 0.2 and missing data less than 3% were utilized to estimate linkage disequilibrium (LD) levels in the tested association panel. Thirty-four association signals were identified as associated with HSW via GWAS. Among them, nineteen QTNs were novel, and another fifteen QTNs were overlapped or located near the genomic regions of known HSW QTL. A total of 237 genes, derived from 31 QTNs and located near peak SNPs from the three tested environments in 2015 and 2016, were considered candidate genes, were related to plant growth regulation, hormone metabolism, cell, RNA, protein metabolism, development, starch accumulation, secondary metabolism, signalling, and the TCA cycle, some of which have been found to participate in the regulation of HSW. A total of 106 SNPs from 16 candidate genes were significantly associated with HSW in soybean.

CONCLUSIONS

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

Dissecting the Genetic Architecture of Seed Protein and Oil Content in Soybean from the Yangtze and Huaihe River Valleys Using Multi-Locus Genome-Wide Association Studies

Soybean is a globally important legume crop that provides a primary source of high-quality vegetable protein and oil. Seed protein and oil content are two valuable quality traits controlled by multiple genes in soybean. In this study, the restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS) procedure was performed to dissect the genetic architecture of seed protein and oil content in a diverse panel of 279 soybean accessions from the Yangtze and Huaihe River Valleys in China. We identified 26 quantitative trait loci (QTLs) for seed protein content and 23 for seed oil content, including five associated with both traits. Among these, 39 QTLs corresponded to previously reported QTLs, whereas 10 loci were novel. As reported previously, the QTL on chromosome 20 was associated with both seed protein and oil content. This QTL exhibited opposing effects on these traits and contributed the most to phenotype variation. From the detected QTLs, 55 and 51 candidate genes were identified for seed protein and oil content, respectively. Among these genes, eight may be promising candidate genes for improving soybean nutritional quality. These results will facilitate marker-assisted selective breeding for soybean protein and oil content traits.

Genome-wide association study of inflorescence length of cultivated soybean based on the high-throughout single-nucleotide markers

As an important and complex trait, inflorescence length (IL) of soybean [Glycine max (L.) Merr.] significantly affected seed yields. Therefore, elucidating molecular basis of inflorescence architecture, especially for IL, was important for improving soybean yield potentials. Longer IL meaned to have more pod and seed in soybean. Hence, increasing IL and improving yield are targets for soybean breeding. In this study, a association panel, comprising 283 diverse samples, was used to dissect the genetic basis of IL based on genome-wide association analysis (GWAS) and haplotype analysis. GWAS and haplotype analysis were conducted through high-throughout single-nucleotide polymorphisms (SNP) developed by SLAF-seq methodology. A total of 39, 057 SNPs (minor allele frequency ≥ 0.2 and missing data ≤ 10%) were utilized to evaluate linkage disequilibrium (LD) level in the tested association panel. A total of 30 association signals were identified to be associated with IL via GWAS. Among them, 13 SNPs were novel, and another 17 SNPs were overlapped or located near the linked regions of known quantitative trait nucleotide (QTN) with soybean seed yield or yield component. The functional genes, located in the 200-kb genomic region of each peak SNP, were considered as candidate genes, such as the cell division/ elongation, specific enzymes, and signaling or transport of specific proteins. These genes have been reported to participant in the regulation of IL. Ten typical long-IL lines and ten typical short-IL lines were re-sequencing, and then, six SNPs from five genes were obtained based on candidate gene-based association. In addition, 42 haplotypes were defined based on haplotype analysis. Of them, 11 haplotypes were found to regulate long IL (> 14 mm) in soybean. The identified 30 QTN with beneficial alleles and their candidate genes might be valuable for dissecting the molecular mechanisms of IL and further improving the yield potential of soybean.

Genome-Wide Detection of Major and Epistatic Effect QTLs for Seed Protein and Oil Content in Soybean Under Multiple Environments Using High-Density Bin Map

Seed protein and oil content are the two important traits determining the quality and value of soybean. Development of improved cultivars requires detailed understanding of the genetic basis underlying the trait of interest. However, it is prerequisite to have a high-density linkage map for precisely mapping genomic regions, and therefore the present study used high-density genetic map containing 2267 recombination bin markers distributed on 20 chromosomes and spanned 2453.79 cM with an average distance of 1.08 cM between markers using restriction-site-associated DNA sequencing (RAD-seq) approach. A recombinant inbred line (RIL) population of 104 lines derived from a cross between Linhefenqingdou and Meng 8206 cultivars was evaluated in six different environments to identify main- and epistatic-effect quantitative trait loci (QTLs)as well as their interaction with environments. A total of 44 main-effect QTLs for protein and oil content were found to be distributed on 17 chromosomes, and 15 novel QTL were identified for the first time. Out of these QTLs, four were major and stable QTLs, viz., qPro-7-1, qOil-8-3, qOil-10-2 and qOil-10-4, detected in at least two environments plus combined environment with R² values >10%. Within the physical intervals of these four QTLs, 111 candidate genes were screened for their direct or indirect involvement in seed protein and oil biosynthesis/metabolism processes based on gene ontology and annotation information. Based on RNA sequencing (RNA-seq) data analysis, 15 of the 111 genes were highly expressed during seed development stage and root nodules that might be considered as the potential candidate genes. Seven QTLs associated with protein and oil content exhibited significant additive and additive × environment interaction effects, and environment-independent QTLs revealed higher additive effects. Moreover, three digenic epistatic QTLs pairs were identified, and no main-effect QTLs showed epistasis. In conclusion, the use of a high-density map identified closely linked flanking markers, provided better understanding of genetic architecture and candidate gene information, and revealed the scope available for improvement of soybean quality through marker assisted selection (MAS).

Identification of Soybean Genes Related to Soybean Seed Protein Content Based on Quantitative Trait Loci Collinearity Analysis

Increasing the protein content of soybean seeds through a higher ratio of glycinin is important for soybean breeding and food processing; therefore, the integration of different quantitative trait loci (QTLs) is of great significance. In this study, we investigated the collinearity of seed protein QTLs. We identified 192 collinear protein QTLs that formed six hotspot regions. The two most important regions had seed protein 36-10 and seed protein 36-20 as hub nodes. We used a chromosome segment substitution line (CSSL) population for QTL validation and identified six CSSL materials with collinear QTLs. Five materials with higher protein and glycinin contents in comparison to the recurrent parent were analyzed. A total of 13 candidate genes related to seed protein from the QTL hotspot intervals were detected, 8 of which had high expression in mature soybean seeds. These results offer a new analysis method for molecular-assisted selection (MAS) and improvement of soybean product quality.

Genome-wide association mapping for seed protein and oil contents using a large panel of soybean accessions

Soybean is globally cultivated primarily for its protein and oil. The protein and oil contents of the seeds are quantitatively inherited traits determined by the interaction of numerous genes. In order to gain a better understanding of the molecular foundation of soybean protein and oil content for the marker-assisted selection (MAS) of high quality traits, a population of 185 soybean germplasms was evaluated to identify the quantitative trait loci (QTLs) associated with the seed protein and oil contents. Using specific length amplified fragment sequencing (SLAF-seq) technology, a total of 12,072 single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) ≥ 0.05 were detected across the 20 chromosomes (Chr), with a marker density of 78.7 kbp. A total of 31 SNPs located on 12 of the 20 soybean chromosomes were correlated with seed protein and oil content. Of the 31 SNPs that were associated with the two target traits, 31 beneficial alleles were identified. Two SNP markers, namely rs15774585 and rs15783346 on Chr 07, were determined to be related to seed oil content both in 2015 and 2016. Three SNP markers, rs53140888 on Chr 01, rs19485676 on Chr 13, and rs24787338 on Chr 20 were correlated with seed protein content both in 2015 and 2016. These beneficial alleles may potentially contribute towards the MAS of favorable soybean protein and oil characteristics.

Meta-analysis and transcriptome profiling reveal hub genes for soybean seed storage composition during seed development

Soybean is an important crop providing edible oil and protein source. Soybean oil and protein contents are quantitatively inherited and significantly affected by environmental factors. In this study, meta-analysis was conducted based on soybean physical maps to integrate quantitative trait loci (QTLs) from multiple experiments in different environments. Meta-QTLs for seed oil, fatty acid composition, and protein were identified. Of them, 11 meta-QTLs were located on hot regions for both seed oil and protein. Next, we selected 4 chromosome segment substitution lines with different seed oil and protein contents to characterize their 3 years of phenotype selection in the field. Using strand-specific RNA-sequencing analysis, we profile the time-course transcriptome patterns of soybean seeds at early maturity, middle maturity, and dry seed stages. Pairwise comparison and K-means clustering analysis revealed 7,482 differentially expressed genes and 45 expression patterns clusters. Weighted gene coexpression network analysis uncovered 46 modules of gene expression patterns. The 2 most significant coexpression networks were visualized, and 7 hub genes were identified that were involved in soybean oil and seed storage protein accumulation processes. Our results provided a transcriptome dataset for soybean seed development, and the candidate hub genes represent a foundation for further research.

Construction of a high-density genetic map and mapping of QTLs for soybean (Glycine max) agronomic and seed quality traits by specific length amplified fragment sequencing

BACKGROUND

Soybean is not only an important oil crop, but also an important source of edible protein and industrial raw material. Yield-traits and quality-traits are increasingly attracting the attention of breeders. Therefore, fine mapping the QTLs associated with yield-traits and quality-traits of soybean would be helpful for soybean breeders. In the present study, a high-density linkage map was constructed to identify the QTLs for the yield-traits and quality-traits, using specific length amplified fragment sequencing (SLAF-seq).

RESULTS

SLAF-seq was performed to screen SLAF markers with 149 F8:11 individuals from a cross between a semi wild soybean, 'Huapidou', and a cultivated soybean, 'Qihuang26', which generated 400.91 M paired-end reads. In total, 53,132 polymorphic SLAF markers were obtained. The genetic linkage map was constructed by 5111 SLAF markers with segregation type of aa×bb. The final map, containing 20 linkage groups (LGs), was 2909.46 cM in length with an average distance of 0.57 cM between adjacent markers. The average coverage for each SLAF marker on the map was 81.26-fold in the male parent, 45.79-fold in the female parent, and 19.84-fold average in each F8:11 individual. According to the high-density map, 35 QTLs for plant height (PH), 100-seeds weight (SW), oil content in seeds (Oil) and protein content in seeds (Protein) were found to be distributed on 17 chromosomes, and 14 novel QTLs were identified for the first time. The physical distance of 11 QTLs was shorter than 100 Kb, suggesting a direct opportunity to find candidate genes. Furthermore, three pairs of epistatic QTLs associated with Protein involving 6 loci on 5 chromosomes were identified. Moreover, 13, 14, 7 and 9 genes, which showed tissue-specific expression patterns, might be associated with PH, SW, Oil and Protein, respectively.

CONCLUSIONS

With SLAF-sequencing, some novel QTLs and important QTLs for both yield-related and quality traits were identified based on a new, high-density linkage map. Moreover, 43 genes with tissue-specific expression patterns were regarded as potential genes in further study. Our findings might be beneficial to molecular marker-assisted breeding, and could provide detailed information for accurate QTL localization.