Identification of novel loci associated with maturity and yield traits in early maturity soybean plant introduction lines

Genome-wide association study revealed some new candidate genes associated with flowering and maturity time of soybean in Central and West Siberian regions of Russia

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

Assessment of the genetic parameters of soybean genotypes for precocity and productivity in the various cultivation conditions

Soybean (Glycine max [L.] Merr) plays a crucial role in the advancement of agriculture in Kazakhstan, serving as a promising food crop and feed source. The primary challenge in boosting soybean production in Northern Kazakhstan lies in the absence of soybean cultivars suited to the region's conditions. As such, the foremost focus of breeding initiatives should be on creating soybean varieties that possess both early maturity and satisfactory yield potential. The objective of this research was to assess the impact of maturity time (MT) on both the yield formation and the adaptive characteristics of soybean varieties from different origins. This evaluation was conducted by analyzing the outcomes of their testing under diverse cultivation conditions in the northern region of Kazakhstan. The soybean cultivars that were examined, originating from various sources, were classified into three primary groups. These groups varied in terms of their growing season duration as well as their yield levels. The way the alleles of the E1-E4 flowering genes were spread out in the identified clusters showed that for soybean varieties where recessive alleles of the E1-E4 genes build up, the growing season usually shorter. Cultivars of Chinese, Russian, and domestic selections isolated as a result of the research were good initial material for use in local breeding programs. Within the framework of the clusters, an environmental assessment of soybean accessions was carried out, which made it possible to determine their degree of plasticity and, in general, their adaptive potential in the conditions of Northern Kazakhstan. The best cultivars were the Chinese selection 'Dongnong 63' and the Russian selection 'SIBNIIK 315'. Hence, the present study successfully discovered soybean cultivars that possess exceptional adaptability and flexibility. These cultivars hold significant potential for cultivation and practical use in the specific environmental circumstances of northern Kazakhstan.

Genome-Wide Association Analysis of Yield-Related Traits and Candidate Genes in Vegetable Soybean

Owing to the rising demand for vegetable soybean products, there is an increasing need for high-yield soybean varieties. However, the complex correlation patterns among quantitative traits with genetic architecture pose a challenge for improving vegetable soybean through breeding. Herein, a genome-wide association study (GWAS) was applied to 6 yield-related traits in 188 vegetable soybean accessions. Using a BLINK model, a total of 116 single nucleotide polymorphisms (SNPs) were identified for plant height, pod length, pod number, pod thickness, pod width, and fresh pod weight. Furthermore, a total of 220 genes were found in the 200 kb upstream and downstream regions of significant SNPs, including 11 genes encoding functional proteins. Among them, four candidate genes, Glyma.13G109100, Glyma.03G183200, Glyma.09G102200, and Glyma.09G102300 were analyzed for significant haplotype variations and to be in LD block, which encode MYB-related transcription factor, auxin-responsive protein, F-box protein, and CYP450, respectively. The relative expression of candidate genes in V030 and V071 vegetable soybean (for the plant height, pod number, and fresh pod weight of V030 were lower than those of the V071 strains) was significantly different, and these genes could be involved in plant growth and development via various pathways. Altogether, we identified four candidate genes for pod yield and plant height from vegetable soybean germplasm. This study provides insights into the genomic basis for improving soybean and crucial genomic resources that can facilitate genome-assisted high-yielding vegetable soybean breeding.

Combining GWAS and comparative genomics to fine map candidate genes for days to flowering in mung bean

BACKGROUND

Mung bean (Vigna radiata (L.) Wilczek), is an important pulse crop in the global south. Early flowering and maturation are advantageous traits for adaptation to northern and southern latitudes. This study investigates the genetic basis of the Days-to-Flowering trait (DTF) in mung bean, combining genome-wide association studies (GWAS) in mung bean and comparisons with orthologous genes involved with control of DTF responses in soybean (Glycine max (L) Merr) and Arabidopsis (Arabidopsis thaliana).

RESULTS

The most significant associations for DTF were on mung bean chromosomes 1, 2, and 4. Only the SNPs on chromosomes 1 and 4 were heavily investigated using downstream analysis. The chromosome 1 DTF association is tightly linked with a cluster of locally duplicated FERONIA (FER) receptor-like protein kinase genes, and the SNP occurs within one of the FERONIA genes. In Arabidopsis, an orthologous FERONIA gene (AT3G51550), has been reported to regulate the expression of the FLOWERING LOCUS C (FLC). For the chromosome 4 DTF locus, the strongest candidates are Vradi04g00002773 and Vradi04g00002778, orthologous to the Arabidopsis PhyA and PIF3 genes, encoding phytochrome A (a photoreceptor protein sensitive to red to far-red light) and phytochrome-interacting factor 3, respectively. The soybean PhyA orthologs include the classical loci E3 and E4 (genes GmPhyA3, Glyma.19G224200, and GmPhyA2, Glyma.20G090000). The mung bean PhyA ortholog has been previously reported as a candidate for DTF in studies conducted in South Korea.

CONCLUSION

The top two identified SNPs accounted for a significant proportion (~ 65%) of the phenotypic variability in mung bean DTF by the six significant SNPs (39.61%), with a broad-sense heritability of 0.93. The strong associations of DTF with genes that have orthologs with analogous functions in soybean and Arabidopsis provide strong circumstantial evidence that these genes are causal for this trait. The three reported loci and candidate genes provide useful targets for marker-assisted breeding in mung beans.

Identification of the domestication gene GmCYP82C4 underlying the major quantitative trait locus for the seed weight in soybean

KEY MESSAGE

A major quantitative trait locus (QTL) for the hundred-seed weight (HSW) was identified and confirmed in the two distinct soybean populations, and the target gene GmCYP82C4 underlying this locus was identified that significantly associated with soybean seed weight, and it was selected during the soybean domestication and improvement process. Soybean is a major oil crop for human beings and the seed weight is a crucial goal of soybean breeding. However, only a limited number of target genes underlying the quantitative trait loci (QTLs) controlling seed weight in soybean are known so far. In the present study, six loci associated with hundred-seed weight (HSW) were detected in the first population of 573 soybean breeding lines by genome-wide association study (GWAS), and 64 gene models were predicted in these candidate QTL regions. The QTL qHSW_1 exhibits continuous association signals on chromosome four and was also validated by region association study (RAS) in the second soybean population (409 accessions) with wild, landrace, and cultivar soybean accessions. There were seven genes in qHSW_1 candidate region by linkage disequilibrium (LD) block analysis, and only Glyma.04G035500 (GmCYP82C4) showed specifically higher expression in flowers, pods, and seeds, indicating its crucial role in the soybean seed development. Significant differences in HSW trait were detected when the association panels are genotyped by single-nucleotide polymorphisms (SNPs) in putative GmCYP82C4 promoter region. Eight haplotypes were generated by six SNPs in GmCYP82C4 in the second soybean population, and two superior haplotypes (Hap2 and Hap4) of GmCYP82C4 were detected with average HSW of 18.27 g and 18.38 g, respectively. The genetic diversity of GmCYP82C4 was analyzed in the second soybean population, and GmCYP82C4 was most likely selected during the soybean domestication and improvement process, leading to the highest proportion of Hap2 of GmCYP82C4 both in landrace and cultivar subpopulations. The QTLs and GmCYP82C4 identified in this study provide novel genetic resources for soybean seed weight trait, and the GmCYP82C4 could be used for soybean molecular breeding to develop desirable seed weight in the future.

Genome-wide association study reveals GmFulb as candidate gene for maturity time and reproductive length in soybeans (Glycine max)

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

Molecular, genetic, and genomic basis of seed size and yield characteristics in soybean

Soybean (Glycine max L. Merr.) is a crucial oilseed cash crop grown worldwide and consumed as oil, protein, and food by humans and feed by animals. Comparatively, soybean seed yield is lower than cereal crops, such as maize, rice, and wheat, and the demand for soybean production does not keep up with the increasing consumption level. Therefore, increasing soybean yield per unit area is the most crucial breeding objective and is challenging for the scientific community. Moreover, yield and associated traits are extensively researched in cereal crops, but little is known about soybeans' genetics, genomics, and molecular regulation of yield traits. Soybean seed yield is a complex quantitative trait governed by multiple genes. Understanding the genetic and molecular processes governing closely related attributes to seed yield is crucial to increasing soybean yield. Advances in sequencing technologies have made it possible to conduct functional genomic research to understand yield traits' genetic and molecular underpinnings. Here, we provide an overview of recent progress in the genetic regulation of seed size in soybean, molecular, genetics, and genomic bases of yield, and related key seed yield traits. In addition, phytohormones, such as auxin, gibberellins, cytokinins, and abscisic acid, regulate seed size and yield. Hence, we also highlight the implications of these factors, challenges in soybean yield, and seed trait improvement. The information reviewed in this study will help expand the knowledge base and may provide the way forward for developing high-yielding soybean cultivars for future food demands.

Genome-wide association study of soybean (Glycine max [L.] Merr.) germplasm for dissecting the quantitative trait nucleotides and candidate genes underlying yield-related traits

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

Comparative genomics of flowering behavior in Cannabis sativa

Cannabis sativa L. is a phenotypically diverse and multi-use plant used in the production of fiber, seed, oils, and a class of specialized metabolites known as phytocannabinoids. The last decade has seen a rapid increase in the licit cultivation and processing of C. sativa for medical end-use. Medical morphotypes produce highly branched compact inflorescences which support a high density of glandular trichomes, specialized epidermal hair-like structures that are the site of phytocannabinoid biosynthesis and accumulation. While there is a focus on the regulation of phytocannabinoid pathways, the genetic determinants that govern flowering time and inflorescence structure in C. sativa are less well-defined but equally important. Understanding the molecular mechanisms that underly flowering behavior is key to maximizing phytocannabinoid production. The genetic basis of flowering regulation in C. sativa has been examined using genome-wide association studies, quantitative trait loci mapping and selection analysis, although the lack of a consistent reference genome has confounded attempts to directly compare candidate loci. Here we review the existing knowledge of flowering time control in C. sativa, and, using a common reference genome, we generate an integrated map. The co-location of known and putative flowering time loci within this resource will be essential to improve the understanding of C. sativa phenology.

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits

Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.

The Genotypic Variability among Short-Season Soybean Cultivars for Nitrogen Fixation under Drought Stress

Soybean fixes atmospheric nitrogen through the symbiotic rhizobia bacteria that inhabit root nodules. Drought stress negatively affect symbiotic nitrogen fixation (SNF) in soybean. The main objective of this study was to identify allelic variations associated with SNF in short-season Canadian soybean varieties under drought stress. A diversity panel of 103 early-maturity Canadian soybean varieties was evaluated under greenhouse conditions to determine SNF-related traits under drought stress. Drought was imposed after three weeks of plant growth, where plants were maintained at 30% field capacity (FC) (drought) and 80% FC (well-watered) until seed maturity. Under drought stress, soybean plants had lower seed yield, yield components, seed nitrogen content, % nitrogen derived from the atmosphere (%Ndfa), and total seed nitrogen fixed compared to those under well-watered conditions. Significant genotypic variability among soybean varieties was found for yield, yield parameters, and nitrogen fixation traits. A genome-wide association study (GWAS) was conducted using 2.16 M single nucleotide single nucleotide polymorphisms (SNPs) for different yield and nitrogen fixation related parameters for 30% FC and their relative performance (30% FC/80% FC). In total, five quantitative trait locus (QTL) regions, including candidate genes, were detected as significantly associated with %Ndfa under drought stress and relative performance. These genes can potentially aid in future breeding efforts to develop drought-resistant soybean varieties.

Genome wide association analysis for grain micronutrients and anti-nutritional traits in mungbean [Vigna radiata (L.) R. Wilczek] using SNP markers

Mungbean is an important food grain legume for human nutrition and nutritional food due to its nutrient-dense seed, liked palatability, and high digestibility. However, anti-nutritional factors pose a significant risk to improving nutritional quality for bio-fortification. In the present study, genetic architecture of grain micronutrients (grain iron and zinc concentration) and anti-nutritional factors (grain phytic acid and tannin content) in association mapping panel of 145 diverse mungbean were evaluated. Based on all four parameters genotypes PUSA 1333 and IPM 02-19 were observed as desired genotypes as they had high grain iron and zinc concentration but low grain phytic acid and tannin content. The next generation sequencing (NGS)-based genotyping by sequencing (GBS) identified 14,447 genome-wide SNPs in a diverse selected panel of 127 mungbean genotypes. Population admixture analysis revealed the presence of four different ancestries among the genotypes and LD decay of ∼57.6 kb kb physical distance was noted in mungbean chromosomes. Association mapping analysis revealed that a total of 20 significant SNPs were shared by both GLM and Blink models associated with grain micronutrient and anti-nutritional factor traits, with Blink model identifying 35 putative SNPs. Further, this study identified the 185 putative candidate genes. Including potential candidate genes Vradi07g30190, Vradi01g09630, and Vradi09g05450 were found to be associated with grain iron concentration, Vradi10g04830 with grain zinc concentration, Vradi08g09870 and Vradi01g11110 with grain phytic acid content and Vradi04g11580 and Vradi06g15090 with grain tannin content. Moreover, two genes Vradi07g15310 and Vradi09g05480 showed significant variation in protein structure between native and mutated versions. The identified SNPs and candidate genes are potential powerful tools to provide the essential information for genetic studies and marker-assisted breeding program for nutritional improvement in mungbean.

Identification of Loci Governing Agronomic Traits and Mutation Hotspots via a GBS-Based Genome-Wide Association Study in a Soybean Mutant Diversity Pool

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

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.

The SoyaGen Project: Putting Genomics to Work for Soybean Breeders

The SoyaGen project was a collaborative endeavor involving Canadian soybean researchers and breeders from academia and the private sector as well as international collaborators. Its aims were to develop genomics-derived solutions to real-world challenges faced by breeders. Based on the needs expressed by the stakeholders, the research efforts were focused on maximizing realized yield through optimization of maturity and improved disease resistance. The main deliverables related to molecular breeding in soybean will be reviewed here. These include: (1) SNP datasets capturing the genetic diversity within cultivated soybean (both within a worldwide collection of > 1,000 soybean accessions and a subset of 102 short-season accessions (MG0 and earlier) directly relevant to this group); (2) SNP markers for selecting favorable alleles at key maturity genes as well as loci associated with increased resistance to key pathogens and pests (Phytophthora sojae, Heterodera glycines, Sclerotinia sclerotiorum); (3) diagnostic tools to facilitate the identification and mapping of specific pathotypes of P. sojae; and (4) a genomic prediction approach to identify the most promising combinations of parents. As a result of this fruitful collaboration, breeders have gained new tools and approaches to implement molecular, genomics-informed breeding strategies. We believe these tools and approaches are broadly applicable to soybean breeding efforts around the world.

Genetic control and allele variation among soybean maturity groups 000 through IX

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

Genome-Wide Association Study to Map Genomic Regions Related to the Initiation Time of Four Growth Stage Traits in Soybean

The time to flowering (DF), pod beginning (DPB), seed formation (DSF), and maturity initiation (DMI) in soybean (Glycine max [L.] Merr) are important characteristics of growth stage traits (GSTs) in Chinese summer-sowing soybean, and are influenced by genetic as well as environmental factors. To better understand the molecular mechanism underlying the initiation times of GSTs, we investigated four GSTs of 309 diverse soybean accessions in six different environments and Best Linear Unbiased Prediction values. Furthermore, the genome-wide association study was conducted by a Fixed and random model Circulating Probability Unification method using over 60,000 single nucleotide polymorphism (SNP) markers to identify the significant quantitative trait nucleotide (QTN) regions with phenotypic data. As a result, 212 SNPs within 102 QTN regions were associated with four GSTs. Of which, eight stable regions were repeatedly detected in least three datasets for one GST. Interestingly, half of the QTN regions overlapped with previously reported quantitative trait loci or well-known soybean growth period genes. The hotspots associated with all GSTs were concentrated on chromosome 10. E2 (Glyma10g36600), a gene with a known function in regulating flowering and maturity in soybean, is also found on this chromosome. Thus, this genomic region may account for the strong correlation among the four GSTs. All the significant SNPs in the remaining 7 QTN regions could cause the significant phenotypic variation with both the major and minor alleles. Two hundred and seventy-five genes in soybean and their homologs in Arabidopsis were screened within ± 500 kb of 7 peak SNPs in the corresponding QTN regions. Most of the genes are involved in flowering, response to auxin stimulus, or regulation of seed germination, among others. The findings reported here provide an insight for genetic improvement which will aid in breeding of soybean cultivars that can be adapted to the various summer sowing areas in China and beyond.

Temporal Control of Seed Development in Dicots: Molecular Bases, Ecological Impact and Possible Evolutionary Ramifications

In flowering plants, seeds serve as organs of both propagation and dispersal. The developing seed passes through several consecutive stages, following a conserved general outline. The overall time needed for a seed to develop, however, may vary both within and between plant species, and these temporal developmental properties remain poorly understood. In the present paper, we summarize the existing data for seed development alterations in dicot plants. For genetic mutations, the reported cases were grouped in respect of the key processes distorted in the mutant specimens. Similar phenotypes arising from the environmental influence, either biotic or abiotic, were also considered. Based on these data, we suggest several general trends of timing alterations and how respective mechanisms might add to the ecological plasticity of the families considered. We also propose that the developmental timing alterations may be perceived as an evolutionary substrate for heterochronic events. Given the current lack of plausible models describing timing control in plant seeds, the presented suggestions might provide certain insights for future studies in this field.

Genome-wide association study and genomic selection for yield and related traits in soybean

Soybean [Glycine max (L.) Merr.] is a crop of great interest worldwide. Exploring molecular approaches to increase yield genetic gain has been one of the main challenges for soybean breeders and geneticists. Agronomic traits such as maturity, plant height, and seed weight have been found to contribute to yield. In this study, a total of 250 soybean accessions were genotyped with 10,259 high-quality SNPs postulated from genotyping by sequencing (GBS) and evaluated for grain yield, maturity, plant height, and seed weight over three years. A genome-wide association study (GWAS) was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model. Genomic selection (GS) was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that 20, 31, 37, and 23 SNPs were significantly associated with maturity, plant height, seed weight, and yield, respectively; Many SNPs were mapped to previously described maturity and plant height loci (E2, E4, and Dt1) and a new plant height locus was mapped to chromosome 20. Candidate genes were found in the vicinity of the two SNPs with the highest significant levels associated with yield, maturity, plant height, seed weight, respectively. A 11.5-Mb region of chromosome 10 was associated with both yield and seed weight. Overall, the accuracy of GS was dependent on the trait, year, and population structure, and high accuracy indicates that these agronomic traits can be selected in molecular breeding through GS. The SNP markers identified in this study can be used to improve yield and agronomic traits through the marker-assisted selection and GS in breeding programs.

Genome-wide association studies of plant architecture-related traits and 100-seed weight in soybean landraces

BACKGROUND

Plant architecture-related traits (e.g., plant height (PH), number of nodes on main stem (NN), branch number (BN) and stem diameter (DI)) and 100-seed weight (100-SW) are important agronomic traits and are closely related to soybean yield. However, the genetic basis and breeding potential of these important agronomic traits remain largely ambiguous in soybean (Glycine max (L.) Merr.).

RESULTS

In this study, we collected 133 soybean landraces from China, phenotyped them in two years at two locations for the above five traits and conducted a genome-wide association study (GWAS) using 82,187 single nucleotide polymorphisms (SNPs). As a result, we found that a total of 59 SNPs were repeatedly detected in at least two environments. There were 12, 12, 4, 4 and 27 SNPs associated with PH, NN, BN, DI and 100-SW, respectively. Among these markers, seven SNPs (AX-90380587, AX-90406013, AX-90387160, AX-90317160, AX-90449770, AX-90460927 and AX-90520043) were large-effect markers for PH, NN, BN, DI and 100-SW, and 15 potential candidate genes were predicted to be in linkage disequilibrium (LD) decay distance or LD block. In addition, real-time quantitative PCR (qRT-PCR) analysis was performed on four 100-SW potential candidate genes, three of them showed significantly different expression levels between the extreme materials at the seed development stage. Therefore, Glyma.05 g127900, Glyma.05 g128000 and Glyma.05 g129000 were considered as candidate genes with 100-SW in soybean.

CONCLUSIONS

These findings shed light on the genetic basis of plant architecture-related traits and 100-SW in soybean, and candidate genes could be used for further positional cloning.

Identification of quantitative trait nucleotides and candidate genes for soybean seed weight by multiple models of genome-wide association study

BACKGROUND

Seed weight is a complex yield-related trait with a lot of quantitative trait loci (QTL) reported through linkage mapping studies. Integration of QTL from linkage mapping into breeding program is challenging due to numerous limitations, therefore, Genome-wide association study (GWAS) provides more precise location of QTL due to higher resolution and diverse genetic diversity in un-related individuals.

RESULTS

The present study utilized 573 breeding lines population with 61,166 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and candidate genes for seed weight in Chinese summer-sowing soybean. GWAS was conducted with two single-locus models (SLMs) and six multi-locus models (MLMs). Thirty-nine SNPs were detected by the two SLMs while 209 SNPs were detected by the six MLMs. In all, two hundred and thirty-one QTNs were found to be associated with seed weight in YHSBLP with various effects. Out of these, seventy SNPs were concurrently detected by both SLMs and MLMs on 8 chromosomes. Ninety-four QTNs co-localized with previously reported QTL/QTN by linkage/association mapping studies. A total of 36 candidate genes were predicted. Out of these candidate genes, four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280 and Glyma19g28070) were identified by the integration of co-expression network. Among them, three were relatively expressed higher in the high HSW genotypes at R5 stage compared with low HSW genotypes except Glyma12g33280. Our results show that using more models especially MLMs are effective to find important QTNs, and the identified HSW QTNs/genes could be utilized in molecular breeding work for soybean seed weight and yield.

CONCLUSION

Application of two single-locus plus six multi-locus models of GWAS identified 231 QTNs. Four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280 & Glyma19g28070) detected via integration of co-expression network among the predicted candidate genes.

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.

Transcriptomic Insights into Mechanisms of Early Seed Maturation in the Garden Pea (Pisum sativum L.)

The garden pea (Pisum sativum L.) is a legume crop of immense economic value. Extensive breeding has led to the emergence of numerous pea varieties, of which some are distinguished by accelerated development in various stages of ontogenesis. One such trait is rapid seed maturation, which, despite novel insights into the genetic control of seed development in legumes, remains poorly studied. This article presents an attempt to dissect mechanisms of early maturation in the pea line Sprint-2 by means of whole transcriptome RNA sequencing in two developmental stages. By using a de novo assembly approach, we have obtained a reference transcriptome of 25,756 non-redundant entries expressed in pea seeds at either 10 or 20 days after pollination. Differential expression in Sprint-2 seeds has affected 13,056 transcripts. A comparison of the two pea lines with a common maturation rate demonstrates that while at 10 days after pollination, Sprint-2 seeds show development retardation linked to intensive photosynthesis, morphogenesis, and cell division, and those at 20 days show a rapid onset of desiccation marked by the cessation of translation and cell anabolism and accumulation of dehydration-protective and -storage moieties. Further inspection of certain transcript functional categories, including the chromatin constituent, transcription regulation, protein turnover, and hormonal regulation, has revealed transcriptomic trends unique to specific stages and cultivars. Among other remarkable features, Sprint-2 demonstrated an enhanced expression of transposable element-associated open reading frames and an altered expression of major maturation regulators and DNA methyltransferase genes. To the best of our knowledge, this is the first comparative transcriptomic study in which the issue of the seed maturation rate is addressed.

High-Resolution Mapping in Two RIL Populations Refines Major "QTL Hotspot" Regions for Seed Size and Shape in Soybean (Glycine max L.)

Seed size and shape are important traits determining yield and quality in soybean. However, the genetic mechanism and genes underlying these traits remain largely unexplored. In this regard, this study used two related recombinant inbred line (RIL) populations (ZY and K3N) evaluated in multiple environments to identify main and epistatic-effect quantitative trait loci (QTLs) for six seed size and shape traits in soybean. A total of 88 and 48 QTLs were detected through composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 15 QTLs were common among both methods; two of them were major (R2 > 10%) and novel QTLs (viz., qSW-1-1ZN and qSLT-20-1K3N). Additionally, 51 and 27 QTLs were identified for the first time through CIM and MCIM methods, respectively. Colocalization of QTLs occurred in four major QTL hotspots/clusters, viz., "QTL Hotspot A", "QTL Hotspot B", "QTL Hotspot C", and "QTL Hotspot D" located on Chr06, Chr10, Chr13, and Chr20, respectively. Based on gene annotation, gene ontology (GO) enrichment, and RNA-Seq analysis, 23 genes within four "QTL Hotspots" were predicted as possible candidates, regulating soybean seed size and shape. Network analyses demonstrated that 15 QTLs showed significant additive x environment (AE) effects, and 16 pairs of QTLs showing epistatic effects were also detected. However, except three epistatic QTLs, viz., qSL-13-3ZY, qSL-13-4ZY, and qSW-13-4ZY, all the remaining QTLs depicted no main effects. Hence, the present study is a detailed and comprehensive investigation uncovering the genetic basis of seed size and shape in soybeans. The use of a high-density map identified new genomic regions providing valuable information and could be the primary target for further fine mapping, candidate gene identification, and marker-assisted breeding (MAB).

Characterization and quantitative trait locus mapping of late-flowering from a Thai soybean cultivar introduced into a photoperiod-insensitive genetic background

The timing of both flowering and maturation determine crop adaptability and productivity. Soybean (Glycine max) is cultivated across a wide range of latitudes. The molecular-genetic mechanisms for flowering in soybean have been determined for photoperiodic responses to long days (LDs), but remain only partially determined for the delay of flowering under short-day conditions, an adaptive trait of cultivars grown in lower latitudes. Here, we characterized the late-flowering (LF) habit introduced from the Thai cultivar K3 into a photoperiod-insensitive genetic background under different photo-thermal conditions, and we analyzed the genetic basis using quantitative trait locus (QTL) mapping. The LF habit resulted from a basic difference in the floral induction activity and from the suppression of flowering, which was caused by red light-enriched LD lengths and higher temperatures, during which FLOWERING LOCUS T (FT) orthologs, FT2a and FT5a, were strongly down-regulated. QTL mapping using gene-specific markers for flowering genes E2, FT2a and FT5a and 829 single nucleotide polymorphisms obtained from restriction-site associated DNA sequencing detected three QTLs controlling the LF habit. Of these, a QTL harboring FT2a exhibited large and stable effects under all the conditions tested. A resequencing analysis detected a nonsynonymous substitution in exon 4 of FT2a from K3, which converted the glycine conserved in FT-like proteins to the aspartic acid conserved in TERMINAL FLOWER 1-like proteins (floral repressors), suggesting a functional depression in the FT2a protein from K3. The effects of the remaining two QTLs, likely corresponding to E2 and FT5a, were environment dependent. Thus, the LF habit from K3 may be caused by the functional depression of FT2a and the down-regulation of two FT genes by red light-enriched LD conditions and high temperatures.

Whole-genome mapping identified novel "QTL hotspots regions" for seed storability in soybean (Glycine max L.)

BACKGROUND

Seed aging in soybean is a serious challenge for agronomic production and germplasm preservation. However, its genetic basis remains largely unclear in soybean. Unraveling the genetic mechanism involved in seed aging, and enhancing seed storability is an imperative goal for soybean breeding. The aim of this study is to identify quantitative trait loci (QTLs) using high-density genetic linkage maps of soybean for seed storability. In this regard, two recombinant inbred line (RIL) populations derived from Zhengyanghuangdou × Meng 8206 (ZM6) and Linhefenqingdou × Meng 8206 (LM6) crosses were evaluated for three seed-germination related traits viz., germination rate (GR), normal seedling length (SL) and normal seedling fresh weight (FW) under natural and artificial aging conditions to map QTLs for seed storability.

RESULTS

A total of 34 QTLs, including 13 QTLs for GR, 11 QTLs for SL and 10 QTLs for FW, were identified on 11 chromosomes with the phenotypic variation ranged from 7.30 to 23.16% under both aging conditions. All these QTLs were novel, and 21 of these QTLs were clustered in five QTL-rich regions on four different chromosomes viz., Chr3, Chr5, Chr17 &Chr18, among them the highest concentration of seven and six QTLs were found in "QTL hotspot A" (Chr17) and "QTL hotspot B" (Chr5), respectively. Furthermore, QTLs within all the five QTL clusters are linked to at least two studied traits, which is also supported by highly significant correlation between the three germination-related traits. QTLs for seed-germination related traits in "QTL hotspot B" were found in both RIL populations and aging conditions, and also QTLs underlying "QTL hotspot A" are identified in both RIL populations under artificial aging condition. These are the stable genomic regions governing the inheritance of seed storability in soybean, and will be the main focus for soybean breeders.

CONCLUSION

This study uncovers the genetic basis of seed storability in soybean. The newly identified QTLs provides valuable information, and will be main targets for fine mapping, candidate gene identification and marker-assisted breeding. Hence, the present study is the first report for the comprehensive and detailed investigation of genetic architecture of seed storability in soybean.

The Complex Interactions Between Flowering Behavior and Fiber Quality in Hemp

Hemp, Cannabis sativa L., is a sustainable multipurpose fiber crop with high nutrient and water use efficiency and with biomass of excellent quality for textile fibers and construction materials. The yield and quality of hemp biomass are largely determined by the genetic background of the hemp cultivar but are also strongly affected by environmental factors, such as temperature and photoperiod. Hemp is a facultative short-day plant, characterized by a strong adaptation to photoperiod and a great influence of environmental factors on important agronomic traits such as "flowering-time" and "sex determination." This sensitivity of hemp can cause a considerable degree of heterogeneity, leading to unforeseen yield reductions. Fiber quality for instance is influenced by the developmental stage of hemp at harvest. Also, male and female plants differ in stature and produce fibers with different properties and quality. Next to these causes, there is evidence for specific genotypic variation in fiber quality among hemp accessions. Before improved hemp cultivars can be developed, with specific flowering-times and fiber qualities, and adapted to different geographical regions, a better understanding of the molecular mechanisms controlling important phenological traits such as "flowering-time" and "sex determination" in relation to fiber quality in hemp is required. It is well known that genetic factors play a major role in the outcome of both phenological traits, but the major molecular factors involved in this mechanism are not characterized in hemp. Genome sequences and transcriptome data are available but their analysis mainly focused on the cannabinoid pathway for medical purposes. Herein, we review the current knowledge of phenotypic and genetic data available for "flowering-time," "sex determination," and "fiber quality" in short-day and dioecious crops, respectively, and compare them with the situation in hemp. A picture emerges for several controlling key genes, for which natural genetic variation may lead to desired flowering behavior, including examples of pleiotropic effects on yield quality and on carbon partitioning. Finally, we discuss the prospects for using this knowledge for the molecular breeding of this sustainable crop via a candidate gene approach.

GWAS of a soybean breeding collection from South East and South Kazakhstan for resistance to fungal diseases

Soybean (Glycine max(L.) Merr) is an essential food, feed, and technical culture. In Kazakhstan the area under soybean is increasing every year, helping to solve the problem of protein deficiency in human nutrition and animal feeding. One of the main problems of soybean production is fungal diseases causing yields losses of up to 30 %. Modern genomic studies can be applied to facilitate efficient breeding research for improvement of soybean fungal disease tolerance. Therefore, the objective of this genome-wide association study (GWAS) was analysis of a soybean collection consisting of 182 accessions in relation to fungal diseases in the conditions of South East and South Kazakh­stan. Field evaluation of the soybean collection suggested thatFusariumspp. andCercospora sojinaaffected plants in the South region (RIBSP), andSeptoria glycines– in the South East region (KRIAPP). The major objective of the study was identification of QTL associated with resistance to fusarium root rot (FUS), frogeye leaf spot (FLS), and brown spot (BS). GWAS using 4 442 SNP (single nucleotide polymorphism) markers of Illumina iSelect array allowed for identification of fifteen marker trait associations (MTA) resistant to the three diseases at two different stages of growth. Two QTL both for FUS (chromosomes 13 and 17) and BS (chromosomes 14 and 17) were genetically mapped, including one presumably novel QTL for BS (chromo­some 17). Also, five presumably novel QTL for FLS were genetically mapped on chromosomes 2, 7, and 15. The results can be used for improvement of the local breeding projects based on marker-assisted selection approach.