Marker-assisted breeding for transgressive seed protein content in soybean [Glycine max (L.) Merr]

Genome-Wide Characterization and Haplotypic Variation Analysis of the YUC Gene Family in Foxtail Millet (Setaria italica)

Panicle development and grain production in crop species are essential breeding characteristics affected by the synthesis of auxin, which is influenced by flavin monooxygenase-encoding genes such as YUC (YUCCA) family members. In this trial, fourteen YUCs were identified and named uniformly in foxtail millet, an ancient crop species cultivated across the world. The phylogenetic analysis revealed that the SiYUCs were clustered into four subgroups; protein motif and gene structure analyses suggested that the closely clustered SiYUC genes were relatively conserved within each subgroup; while genome mapping analysis indicated that the SiYUC genes were unevenly distributed on foxtail millet chromosomes and colinear with other grass species. Transcription analysis revealed that the SiYUC genes differed greatly in expression pattern in different tissues and contained hormonal/light/stress-responding cis-elements. The haplotype characterization of SiYUC genes indicated many superior haplotypes of SiYUCs correlated with higher panicle and grain weight could be favorably selected by breeding. These results will be useful for the further study of the functional characteristics of SiYUC genes, particularly with regard to the marker-assisted pyramiding of beneficial haplotypes in foxtail millet breeding programs.

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.

Dysfunction of GmVPS8a causes compact plant architecture in soybean

Vacuolar Protein Sorting 8 (Vps8) protein is a specific subunit of the class C core vacuole/endosome tethering (CORVET) complex that plays a key role in endosomal trafficking in yeast (Saccharomyces cerevisiae). However, its functions remain largely unclear in plant vegetative growth. Here, we identified a soybean (Glycine max) T4219 mutant characterized with compact plant architecture. Map-based cloning targeted to a candidate gene GmVPS8a (Glyma.07g049700) and further found that two nucleotides deletion in the first exon of GmVPS8a causes a premature termination of the encoded protein in the T4219 mutant. Its functions were validated by CRISPR/Cas9-engineered mutation in the GmVPS8a gene that recapitulated the T4219 mutant phenotypes. Furthermore, NbVPS8a-silenced tobacco (Nicotiana benthamiana) plants exhibited similar phenotypes to the T4219 mutant, suggesting its conserved roles in plant growth. The GmVPS8a is widely expressed in multiple organs and its protein interacts with GmAra6a and GmRab5a. Combined analysis of transcriptomic and proteomic data revealed that dysfunction of GmVPS8a mainly affects pathways on auxin signal transduction, sugar transport and metabolism, and lipid metabolism. Collectively, our work reveals the function of GmVPS8a in plant architecture, which may extend a new way for genetic improvement of ideal plant-architecture breeding in soybean and other crops.

Comparison and Characterization of Phenotypic and Genomic Mutations Induced by a Carbon-Ion Beam and Gamma-ray Irradiation in Soybean (Glycine max (L.) Merr.)

Soybean (Glycine max (L.) Merr.) is a nutritious crop that can provide both oil and protein. A variety of mutagenesis methods have been proposed to obtain better soybean germplasm resources. Among the different types of physical mutagens, carbon-ion beams are considered to be highly efficient with high linear energy transfer (LET), and gamma rays have also been widely used for mutation breeding. However, systematic knowledge of the mutagenic effects of these two mutagens during development and on phenotypic and genomic mutations has not yet been elucidated in soybean. To this end, dry seeds of Williams 82 soybean were irradiated with a carbon-ion beam and gamma rays. The biological effects of the M₁ generation included changes in survival rate, yield and fertility. Compared with gamma rays, the relative biological effectiveness (RBE) of the carbon-ion beams was between 2.5 and 3.0. Furthermore, the optimal dose for soybean was determined to be 101 Gy to 115 Gy when using the carbon-ion beam, and it was 263 Gy to 343 Gy when using gamma rays. A total of 325 screened mutant families were detected from out of 2000 M₂ families using the carbon-ion beam, and 336 screened mutant families were found using gamma rays. Regarding the screened phenotypic M₂ mutations, the proportion of low-frequency phenotypic mutations was 23.4% when using a carbon ion beam, and the proportion was 9.8% when using gamma rays. Low-frequency phenotypic mutations were easily obtained with the carbon-ion beam. After screening the mutations from the M₂ generation, their stability was verified, and the genome mutation spectrum of M₃ was systemically profiled. A variety of mutations, including single-base substitutions (SBSs), insertion-deletion mutations (INDELs), multinucleotide variants (MNVs) and structural variants (SVs) were detected with both carbon-ion beam irradiation and gamma-ray irradiation. Overall, 1988 homozygous mutations and 9695 homozygous + heterozygous genotype mutations were detected when using the carbon-ion beam. Additionally, 5279 homozygous mutations and 14,243 homozygous + heterozygous genotype mutations were detected when using gamma rays. The carbon-ion beam, which resulted in low levels of background mutations, has the potential to alleviate the problems caused by linkage drag in soybean mutation breeding. Regarding the genomic mutations, when using the carbon-ion beam, the proportion of homozygous-genotype SVs was 0.45%, and that of homozygous + heterozygous-genotype SVs was 6.27%; meanwhile, the proportions were 0.04% and 4.04% when using gamma rays. A higher proportion of SVs were detected when using the carbon ion beam. The gene effects of missense mutations were greater under carbon-ion beam irradiation, and the gene effects of nonsense mutations were greater under gamma-ray irradiation, which meant that the changes in the amino acid sequences were different between the carbon-ion beam and gamma rays. Taken together, our results demonstrate that both carbon-ion beam and gamma rays are effective techniques for rapid mutation breeding in soybean. If one would like to obtain mutations with a low-frequency phenotype, low levels of background genomic mutations and mutations with a higher proportion of SVs, carbon-ion beams are the best choice.

Understandings and future challenges in soybean functional genomics and molecular breeding

Soybean (Glycine max) is a major source of plant protein and oil. Soybean breeding has benefited from advances in functional genomics. In particular, the release of soybean reference genomes has advanced our understanding of soybean adaptation to soil nutrient deficiencies, the molecular mechanism of symbiotic nitrogen (N) fixation, biotic and abiotic stress tolerance, and the roles of flowering time in regional adaptation, plant architecture, and seed yield and quality. Nevertheless, many challenges remain for soybean functional genomics and molecular breeding, mainly related to improving grain yield through high-density planting, maize-soybean intercropping, taking advantage of wild resources, utilization of heterosis, genomic prediction and selection breeding, and precise breeding through genome editing. This review summarizes the current progress in soybean functional genomics and directs future challenges for molecular breeding of soybean.

Unfolding molecular switches for salt stress resilience in soybean: recent advances and prospects for salt-tolerant smart plant production

The increasing sodium salts (NaCl, NaHCO3, NaSO4 etc.) in agricultural soil is a serious global concern for sustainable agricultural production and food security. Soybean is an important food crop, and their cultivation is severely challenged by high salt concentration in soils. Classical transgenic and innovative breeding technologies are immediately needed to engineer salt tolerant soybean plants. Additionally, unfolding the molecular switches and the key components of the soybean salt tolerance network are crucial for soybean salt tolerance improvement. Here we review our understandings of the core salt stress response mechanism in soybean. Recent findings described that salt stress sensing, signalling, ionic homeostasis (Na⁺/K⁺) and osmotic stress adjustment might be important in regulating the soybean salinity stress response. We also evaluated the importance of antiporters and transporters such as Arabidopsis K⁺ Transporter 1 (AKT1) potassium channel and the impact of epigenetic modification on soybean salt tolerance. We also review key phytohormones, and osmo-protectants and their role in salt tolerance in soybean. In addition, we discuss the progress of omics technologies for identifying salt stress responsive molecular switches and their targeted engineering for salt tolerance in soybean. This review summarizes recent progress in soybean salt stress functional genomics and way forward for molecular breeding for developing salt-tolerant soybean plant.

Transgressive Potential Prediction and Optimal Cross Design of Seed Protein Content in the Northeast China Soybean Population Based on Full Exploration of the QTL-Allele System

Northeast China is a major soybean production region in China. A representative sample of the Northeast China soybean germplasm population (NECSGP) composed of 361 accessions was evaluated for their seed protein content (SPC) in Tieling, Northeast China. This SPC varied greatly, with a mean SPC of 40.77%, ranging from 36.60 to 46.07%, but it was lower than that of the Chinese soybean landrace population (43.10%, ranging from 37.51 to 50.46%). The SPC increased slightly from 40.32-40.97% in the old maturity groups (MG, MGIII + II + I) to 40.93-41.58% in the new MGs (MG0 + 00 + 000). The restricted two-stage multi-locus genome-wide association study (RTM-GWAS) with 15,501 SNP linkage-disequilibrium block (SNPLDB) markers identified 73 SPC quantitative trait loci (QTLs) with 273 alleles, explaining 71.70% of the phenotypic variation, wherein 28 QTLs were new ones. The evolutionary changes of QTL-allele structures from old MGs to new MGs were analyzed, and 97.79% of the alleles in new MGs were inherited from the old MGs and 2.21% were new. The small amount of new positive allele emergence and possible recombination between alleles might explain the slight SPC increase in the new MGs. The prediction of recombination potentials in the SPC of all the possible crosses indicated that the mean of SPC overall crosses was 43.29% (+2.52%) and the maximum was 50.00% (+9.23%) in the SPC, and the maximum transgressive potential was 3.93%, suggesting that SPC breeding potentials do exist in the NECSGP. A total of 120 candidate genes were annotated and functionally classified into 13 categories, indicating that SPC is a complex trait conferred by a gene network.

Towards Developing Drought-smart Soybeans

Drought is one of the significant abiotic stresses threatening crop production worldwide. Soybean is a major legume crop with immense economic significance, but its production is highly dependent on optimum rainfall or abundant irrigation. Also, in dry periods, it may require supplemental irrigation for drought-susceptible soybean varieties. The effects of drought stress on soybean including osmotic adjustments, growth morphology and yield loss have been well studied. In addition, drought-resistant soybean cultivars have been investigated for revealing the mechanisms of tolerance and survival. Advanced high-throughput technologies have yielded remarkable phenotypic and genetic information for producing drought-tolerant soybean cultivars, either through molecular breeding or transgenic approaches. Further, transcriptomics and functional genomics have led to the characterisation of new genes or gene families controlling drought response. Interestingly, genetically modified drought-smart soybeans are just beginning to be released for field applications cultivation. In this review, we focus on breeding and genetic engineering approaches that have successfully led to the development of drought-tolerant soybeans for commercial use.

Seed protein content and its relationships with agronomic traits in pigeonpea is controlled by both main and epistatic effects QTLs

The genetic architecture of seed protein content (SPC) and its relationships to agronomic traits in pigeonpea is poorly understood. Accordingly, five F₂ populations segregating for SPC and four agronomic traits (seed weight (SW), seed yield (SY), growth habit (GH) and days to first flowering (DFF)) were phenotyped and genotyped using genotyping-by-sequencing approach. Five high-density population-specific genetic maps were constructed with an average inter-marker distance of 1.6 to 3.5 cM, and subsequently, integrated into a consensus map with average marker spacing of 1.6 cM. Based on analysis of phenotyping data and genotyping data, 192 main effect QTLs (M-QTLs) with phenotypic variation explained (PVE) of 0.7 to 91.3% were detected for the five traits across the five populations. Major effect (PVE ≥ 10%) M-QTLs included 14 M-QTLs for SPC, 16 M-QTLs for SW, 17 M-QTLs for SY, 19 M-QTLs for GH and 24 M-QTLs for DFF. Also, 573 epistatic QTLs (E-QTLs) were detected with PVE ranging from 6.3 to 99.4% across traits and populations. Colocalization of M-QTLs and E-QTLs explained the genetic basis of the significant (P < 0.05) correlations of SPC with SW, SY, DFF and GH. The nature of genetic architecture of SPC and its relationship with agronomic traits suggest that genomics-assisted breeding targeting genome-wide variations would be effective for the simultaneous improvement of SPC and other important traits.

A Combined Linkage and GWAS Analysis Identifies QTLs Linked to Soybean Seed Protein and Oil Content

Soybean is an excellent source of vegetable protein and edible oil. Understanding the genetic basis of protein and oil content will improve the breeding programs for soybean. Linkage analysis and genome-wide association study (GWAS) tools were combined to detect quantitative trait loci (QTL) that are associated with protein and oil content in soybean. Three hundred and eight recombinant inbred lines (RILs) containing 3454 single nucleotide polymorphism (SNP) markers and 200 soybean accessions, including 94,462 SNPs and indels, were applied to identify QTL intervals and significant SNP loci. Intervals on chromosomes 1, 15, and 20 were correlated with both traits, and QTL qPro15-1, qPro20-1, and qOil5-1 reproducibly correlated with large phenotypic variations. SNP loci on chromosome 20 that overlapped with qPro20-1 were reproducibly connected to both traits by GWAS (p < 10-4). Twenty-five candidate genes with putative roles in protein and/or oil metabolisms within two regions (qPro15-1, qPro20-1) were identified, and eight of these genes showed differential expressions in parent lines during late reproductive growth stages, consistent with a role in controlling protein and oil content. The new well-defined QTL should significantly improve molecular breeding programs, and the identified candidate genes may help elucidate the mechanisms of protein and oil biosynthesis.

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).

A high density SLAF-SNP genetic map and QTL detection for fibre quality traits in Gossypium hirsutum

Background

Upland Cotton (Gossypium hirsutum) is a very important cash crop known for its high quality natural fiber. Recent advances in sequencing technologies provide powerful tools with which to explore the cotton genome for single nucleotide polymorphism marker identification and high density genetic map construction toward more reliable quantitative trait locus mapping.

Results

In the present study, a RIL population was developed by crossing a Chinese high fiber quality cultivar (Yumian 1) and an American high fiber quality line (CA3084), with distinct genetic backgrounds. Specific locus amplified fragment sequencing (SLAF-seq) technology was used to discover SNPs, and a genetic map containing 6254 SNPs was constructed, covering 3141.72 cM with an average distance of 0.5 cM between markers. A total of 95 QTL were detected for fiber quality traits in three environments, explaining 5.5-24.6% of the phenotypic variance. Fifty-five QTL found in multiple environments were considered stable QTL. Nine of the stable QTL were found in all three environments. We identified 14 QTL clusters on 13 chromosomes, each containing one or more stable QTL.

Conclusion

A high-density genetic map of Gossypium hirsutum developed by using specific locus amplified fragment sequencing technology provides detailed mapping of fiber quality QTL, and identification of ‘stable QTL’ found in multiple environments. A marker-rich genetic map provides a foundation for fine mapping, candidate gene identification and marker-assisted selection of favorable alleles at stable QTL in breeding programs.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5294-5) contains supplementary material, which is available to authorized users.

Efficient QTL detection of flowering date in a soybean RIL population using the novel restricted two-stage multi-locus GWAS procedure

KEY MESSAGE

Eighty-six R1 QTLs accounting for 89.92% phenotypic variance in a soybean RIL population were identified using RTM-GWAS with SNPLDB marker which performed superior over CIM and MLM-GWAS with BIN/SNPLDB marker. A population (NJRIKY) composed of 427 recombinant inbred lines (RILs) derived from Kefeng-1 × NN1138-2 (MGII × MGV, MG maturity group) was applied for detecting flowering date (R1) quantitative trait locus (QTL) system in soybean. From a low-depth re-sequencing (~ 0.75 ×), 576,874 SNPs were detected and organized into 4737 BINs (recombination breakpoint determinations) and 3683 SNP linkage disequilibrium blocks (SNPLDBs), respectively. Using the association mapping procedures "Restricted Two-stage Multi-locus Genome-wide Association Study" (RTM-GWAS), "Mixed Linear Model Genome-wide Association Study" (MLM-GWAS) and the linkage mapping procedure "Composite Interval Mapping" (CIM), 67, 36 and 10 BIN-QTLs and 86, 14 and 23 SNPLDB-QTLs were detected with their phenotypic variance explained (PVE) 88.70-89.92% (within heritability 98.2%), 146.41-353.62% (overflowing) and 88.29-172.34% (overflowing), respectively. The RTM-GWAS with SNPLDBs which showed to be more efficient and reasonable than the others was used to identify the R1 QTL system in NJRIKY. The detected 86 SNPLDB-QTLs with their PVE from 0.02 to 30.66% in a total of 89.92% covered 51 out of 104 R1 QTLs in 18 crosses in SoyBase and 26 out of 139 QTLs in a nested association mapping population, while the rest 29 QTLs were novel ones. From the QTL system, 52 candidate genes were annotated, including the verified gene E1, E2, E9 and J, and grouped into 3 categories of biological processes, among which 24 genes were enriched into three protein-protein interaction networks, suggesting gene networks working together. Since NJRIKY involves only MGII and MGV, the QTL/gene system among MG000-MGX should be explored further.

Development of sequence-based markers for seed protein content in pigeonpea

Pigeonpea is an important source of dietary protein to over a billion people globally, but genetic enhancement of seed protein content (SPC) in the crop has received limited attention for a long time. Use of genomics-assisted breeding would facilitate accelerating genetic gain for SPC. However, neither genetic markers nor genes associated with this important trait have been identified in this crop. Therefore, the present study exploited whole genome re-sequencing (WGRS) data of four pigeonpea genotypes (~ 12X coverage) to identify sequence-based markers and associated candidate genes for SPC. By combining a common variant filtering strategy on available WGRS data with knowledge of gene functions in relation to SPC, 108 sequence variants from 57 genes were identified. These genes were assigned to 19 GO molecular function categories with 56% belonging to only two categories. Furthermore, Sanger sequencing confirmed presence of 75.4% of the variants in 37 genes. Out of 30 sequence variants converted into CAPS/dCAPS markers, 17 showed high level of polymorphism between low and high SPC genotypes. Assay of 16 of the polymorphic CAPS/dCAPS markers on an F₂ population of the cross ICP 5529 (high SPC) × ICP 11605 (low SPC), resulted in four of the CAPS/dCAPS markers significantly (P < 0.05) co-segregated with SPC. In summary, four markers derived from mutations in four genes will be useful for enhancing/regulating SPC in pigeonpea crop improvement programs.

An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding

KEY MESSAGE

The innovative RTM-GWAS procedure provides a relatively thorough detection of QTL and their multiple alleles for germplasm population characterization, gene network identification, and genomic selection strategy innovation in plant breeding. The previous genome-wide association studies (GWAS) have been concentrated on finding a handful of major quantitative trait loci (QTL), but plant breeders are interested in revealing the whole-genome QTL-allele constitution in breeding materials/germplasm (in which tremendous historical allelic variation has been accumulated) for genome-wide improvement. To match this requirement, two innovations were suggested for GWAS: first grouping tightly linked sequential SNPs into linkage disequilibrium blocks (SNPLDBs) to form markers with multi-allelic haplotypes, and second utilizing two-stage association analysis for QTL identification, where the markers were preselected by single-locus model followed by multi-locus multi-allele model stepwise regression. Our proposed GWAS procedure is characterized as a novel restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS, https://github.com/njau-sri/rtm-gwas ). The Chinese soybean germplasm population (CSGP) composed of 1024 accessions with 36,952 SNPLDBs (generated from 145,558 SNPs, with reduced linkage disequilibrium decay distance) was used to demonstrate the power and efficiency of RTM-GWAS. Using the CSGP marker information, simulation studies demonstrated that RTM-GWAS achieved the highest QTL detection power and efficiency compared with the previous procedures, especially under large sample size and high trait heritability conditions. A relatively thorough detection of QTL with their multiple alleles was achieved by RTM-GWAS compared with the linear mixed model method on 100-seed weight in CSGP. A QTL-allele matrix (402 alleles of 139 QTL × 1024 accessions) was established as a compact form of the population genetic constitution. The 100-seed weight QTL-allele matrix was used for genetic characterization, candidate gene prediction, and genomic selection for optimal crosses in the germplasm population.

Identification of quantitative trait loci underlying seed protein content of soybean including main, epistatic, and QTL × environment effects in different regions of Northeast China

The objective here was to identify QTL underlying soybean protein content (PC), and to evaluate the additive and epistatic effects of the QTLs. A mapping population, consisting of 129 recombinant inbred lines (RILs), was created by crossing 'Dongnong 46' and 'L-100'. Phenotypic data of the parents and RILs were collected for 4 years in three locations of Heilongjiang Province of China. A total of 213 SSR markers were used to construct a genetic linkage map. Eight QTLs, located on seven chromosomes (Chr), were identified to be associated with PC among the 10 tested environments. Of the seven QTLs, five QTLs, qPR-2 (Satt710, on Chr9), qPR-3 (Sat_122, on Chr12), qPR-5 (Satt543, on Chr17), qPR-7 (Satt163, on Chr18), and qPR-8 (Satt614, on Chr20), were detected in six, seven, seven, six, and seven environments, respectively, implying relatively stable QTLs. qPR-3 could explain 3.33%-11.26% of the phenotypic variation across eight tested environments. qPR-5 and qPR-8 explained 3.64%-10.1% and 11.86%-18.40% of the phenotypic variation, respectively, across seven tested environments. Eight QTLs associated with PC exhibited additive and (or) additive × environment interaction effects. The results showed that environment-independent QTLs often had higher additive effects. Moreover, five epistatic pairwise QTLs were identified in the 10 environments.

Meta-Analyses of QTLs Associated with Protein and Oil Contents and Compositions in Soybean [Glycine max (L.) Merr.] Seed

Soybean [Glycine max (L.) Merr.] is a valuable and nutritious crop in part due to the high protein meal and vegetable oil produced from its seed. Soybean producers desire cultivars with both elevated seed protein and oil concentrations as well as specific amino acid and fatty acid profiles. Numerous studies have identified quantitative trait loci (QTLs) associated with seed composition traits, but validation of these QTLs has rarely been carried out. In this study, we have collected information, including genetic location and additive effects, on each QTL for seed contents of protein and oil, as well as amino acid and fatty acid compositions from over 80 studies. Using BioMercator V. 4.2, a meta-QTL analysis was performed with genetic information comprised of 175 QTLs for protein, 205 QTLs for oil, 156 QTLs for amino acids, and 113 QTLs for fatty acids. A total of 55 meta-QTL for seed composition were detected on 6 out of 20 chromosomes. Meta-QTL possessed narrower confidence intervals than the original QTL and candidate genes were identified within each meta-QTL. These candidate genes elucidate potential natural genetic variation in genes contributing to protein and oil biosynthesis and accumulation, providing meaningful information to further soybean breeding programs.

Quantitative Trait Loci (QTL) Mapping for Glycinin and β-Conglycinin Contents in Soybean (Glycine max L. Merr.)

Compared to β-conglycinin, glycinin contains 3-4 times the methionine and cysteine (sulfur-containing amino acids), accounting for approximately 40 and 30%, respectively, of the total storage protein in soybean. Increasing the soybean storage protein content while improving the ratio of glycinin to β-conglycinin is of great significance for soybean breeding and soy food products. The objective of this study is to analyze the genetic mechanism regulating the glycinin and β-conglycinin contents of soybean by using a recombinant inbred line (RIL) population derived from a cross between Kefeng No. 1 and Nannong 1138-2. Two hundred and twenty-one markers were used to map quantitative trait loci (QTLs) for glycinin (11S) and β-conglycinin (7S) contents, the ratio of glycinin to β-conglycinin (RGC), and the sum of glycinin and β-conglycinin (SGC). A total of 35 QTLs, 3 pairs of epistatic QTLs, and 5 major regions encompassing multiple QTLs were detected. Genes encoding the subunits of β-conglycinin were localized to marker intervals sat_418-satt650 and sat_196-sat_303, which are linked to RGC and SGC; marker sat_318, associated with 11S, 7S, and SGC, was located near Glyma10g04280 (Gy4), which encodes a subunit of glycinin. These results, which take epistatic interactions into account, will improve our understanding of the genetic basis of 11S and 7S contents and will lay a foundation for marker-assisted selection (MAS) breeding of soybean and improving the quality of soybean products.