Interval mapping of quantitative trait loci for reproductive, morphological, and seed traits of soybean (Glycine max L.)

Genome-Wide Association Study to Identify Soybean Lodging Resistance Loci and Candidate Genes

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

Network analysis combined with genome-wide association study helps identification of genes related to amino acid contents in soybean

BACKGROUND

Additional to total protein content, the amino acid (AA) profile is important to the nutritional value of soybean seed. The AA profile in soybean seed is a complex quantitative trait controlled by multiple interconnected genes and pathways controlling the accumulation of each AA. With a total of 621 soybean germplasm, we used three genome-wide association study (GWAS)-based approaches to investigate the genomic regions controlling the AA content and profile in soybean. Among those approaches, the GWAS network analysis we implemented takes advantage of the relationships between specific AAs to identify the genetic control of AA profile.

RESULTS

For Approach I, GWAS were performed for the content of 24 single AAs under all environments combined. Significant SNPs grouping into 16 linkage disequilibrium (LD) blocks from 18 traits were identified. For Approach II, the individual AAs were grouped by five families according to their metabolic pathways and were examined based on the sum, ratios, and interactions of AAs within the same biochemical family. Significant SNPs grouping into 35 LD blocks were identified, with SNPs associated with traits from the same biochemical family often positioned on the same LD blocks. Approach III, a correlation-based network analysis, was performed to assess the empirical relationships among AAs. Two groups were described by the network topology, Group 1: Ala, Gly, Lys, available Lys (Alys), and Thr and Group 2: Ile and Tyr. Significant SNPs associated with a ratio of connected AAs or a ratio of a single AA to its fully or partially connected metabolic groups were identified within 9 LD blocks for Group 1 and 2 LD blocks for Group 2. Among 40 identified QTL for AA or AA-derived traits, three genomic regions were novel in terms of seed composition traits (oil, protein, and AA content). An additional 24 regions had previously not been specifically associated with the AA content.

CONCLUSIONS

Our results confirmed loci identified from previous studies but also suggested that network approaches for studying AA contents in soybean seed are valuable. Three genomic regions (Chr 5: 41,754,397-41,893,109 bp, Chr 9: 1,537,829-1,806,586 bp, and Chr 20: 31,554,795-33,678,257 bp) were significantly identified by all three approaches. Yet, the majority of associations between a genomic region and an AA trait were approach- and/or environment-specific. Using a combination of approaches provides insights into the genetic control and pleiotropy among AA contents, which can be applied to mechanistic understanding of variation in AA content as well as tailored nutrition in cultivars developed from soybean breeding programs.

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.

Genetic Analysis of Soybean Flower Size Phenotypes Based on Computer Vision and Genome-Wide Association Studies

The dimensions of organs such as flowers, leaves, and seeds are governed by processes of cellular proliferation and expansion. In soybeans, the dimensions of these organs exhibit a strong correlation with crop yield, quality, and other phenotypic traits. Nevertheless, there exists a scarcity of research concerning the regulatory genes influencing flower size, particularly within the soybean species. In this study, 309 samples of 3 soybean types (123 cultivar, 90 landrace, and 96 wild) were re-sequenced. The microscopic phenotype of soybean flower organs was photographed using a three-eye microscope, and the phenotypic data were extracted by means of computer vision. Pearson correlation analysis was employed to assess the relationship between petal and seed phenotypes, revealing a strong correlation between the sizes of these two organs. Through GWASs, SNP loci significantly associated with flower organ size were identified. Subsequently, haplotype analysis was conducted to screen for upstream and downstream genes of these loci, thereby identifying potential candidate genes. In total, 77 significant SNPs associated with vexil petals, 562 significant SNPs associated with wing petals, and 34 significant SNPs associated with keel petals were found. Candidate genes were screened by candidate sites, and haplotype analysis was performed on the candidate genes. Finally, the present investigation yielded 25 and 10 genes of notable significance through haplotype analysis in the vexil and wing regions, respectively. Notably, Glyma.07G234200, previously documented for its high expression across various plant organs, including flowers, pods, leaves, roots, and seeds, was among these identified genes. The research contributes novel insights to soybean breeding endeavors, particularly in the exploration of genes governing organ development, the selection of field materials, and the enhancement of crop yield. It played a role in the process of material selection during the growth period and further accelerated the process of soybean breeding material selection.

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.

Comparative transcriptomic analysis reveals the molecular mechanism underlying seedling heterosis and its relationship with hybrid contemporary seeds DNA methylation in soybean

Heterosis is widely used in crop production, but phenotypic dominance and its underlying causes in soybeans, a significant grain and oil crop, remain a crucial yet unexplored issue. Here, the phenotypes and transcriptome profiles of three inbred lines and their resulting F1 seedlings were analyzed. The results suggest that F1 seedlings with superior heterosis in leaf size and biomass exhibited a more extensive recompilation in their transcriptional network and activated a greater number of genes compared to the parental lines. Furthermore, the transcriptional reprogramming observed in the four hybrid combinations was primarily non-additive, with dominant effects being more prevalent. Enrichment analysis of sets of differentially expressed genes, coupled with a weighted gene co-expression network analysis, has shown that the emergence of heterosis in seedlings can be attributed to genes related to circadian rhythms, photosynthesis, and starch synthesis. In addition, we combined DNA methylation data from previous immature seeds and observed similar recompilation patterns between DNA methylation and gene expression. We also found significant correlations between methylation levels of gene region and gene expression levels, as well as the discovery of 12 hub genes that shared or conflicted with their remodeling patterns. This suggests that DNA methylation in contemporary hybrid seeds have an impact on both the F1 seedling phenotype and gene expression to some extent. In conclusion, our study provides valuable insights into the molecular mechanisms of heterosis in soybean seedlings and its practical implications for selecting superior soybean varieties.

Genome-wide association study and high-quality gene mining related to soybean protein and fat

BACKGROUND

Soybean is one of the most important oil crops in the world, and its protein and fat are the primary sources of edible oil and vegetable protein. The effective components in soybean protein and fat have positive effects on improving human immunity, anti-tumor, and regulating blood lipids and metabolism. Therefore, increasing the contents of protein and fat in soybeans is essential for improving the quality of soybeans.

RESULTS

This study selected 292 soybean lines from different regions as experimental materials, based on SLAF-seq sequencing technology, and performed genome-wide association study (GWAS) on the phenotype data from 2019-2021 Planted at the experimental base of Jilin Agricultural University, such as the contents of protein and fat of soybeans. Through the GLM model and MLM model, four SNP sites (Gm09_39012959, Gm12_35492373, Gm16_9297124, and Gm20_24678362) that were significantly related to soybean fat content were associated for three consecutive years, and two SNP sites (Gm09_39012959 and Gm20_24678362) that were significantly related to soybean protein content were associated. By the annotation and enrichment of genes within the 100 Kb region of SNP loci flanking, two genes (Glyma.09G158100 and Glyma.09G158200) related to soybean protein synthesis and one gene (Glyma.12G180200) related to lipid metabolism were selected. By the preliminary verification of expression levels of genes with qPCR, it is found that during the periods of R6 and R7 of the accumulation of soybean protein and fat, Glyma.09G158100 and Glyma.09G158200 are positive regulatory genes that promote protein synthesis and accumulation, while Glyma.12G180200 is the negative regulatory gene that inhibits fat accumulation.

CONCLUSIONS

These results lay the basis for further verifying the gene function and studying the molecular mechanisms regulating the accumulation of protein and fat in soybean seeds.

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.

QNE1 is a key flowering regulator determining the length of the vegetative period in soybean cultivars

The soybean E1 gene is a major regulator that plays an important role in flowering time and maturity. However, it remains unclear how cultivars carrying the dominant E1 allele adapt to the higher latitudinal areas of northern China. We mapped the novel quantitative trait locus QNE1 (QTL near E1) for flowering time to the region proximal to E1 on chromosome 6 in two mapping populations. Positional cloning revealed Glyma.06G204300, encoding a TCP-type transcription factor, as a strong candidate gene for QNE1. Association analysis further confirmed that functional single nucleotide polymorphisms (SNPs) at nucleotides 686 and 1,063 in the coding region of Glyma.06G204300 were significantly associated with flowering time. The protein encoded by the candidate gene is localized primarily to the nucleus. Furthermore, soybean and Brassica napus plants overexpressing Glyma.06G204300 exhibited early flowering. We conclude that despite their similar effects on flowering time, QNE1 and E4 may control flowering time through different regulatory mechanisms, based on expression studies and weighted gene co-expression network analysis of flowering time-related genes. Deciphering the molecular basis of QNE1 control of flowering time enriches our knowledge of flowering gene networks in soybean and will facilitate breeding soybean cultivars with broader latitudinal adaptation.

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.

Genome-wide association analysis for yield-related traits at the R6 stage in a Chinese soybean mini core collection

BACKGROUND

Soybean (Glycine max (L.) Merr.) is an economically important crop for vegetable oil and protein production, and yield is a critical trait for grain/vegetable uses of soybean. However, our knowledge of the genes controlling the vegetable soybean yield remains limited.

OBJECTIVE

To better understand the genetic basis of the vegetable soybean yield.

METHODS

The 100-pod fresh weight (PFW), 100-seed fresh weight (SFW), kernel percent (KP) and moisture content of fresh seeds (MCFS) at the R6 stage are four yield-related traits for vegetable soybean. We investigated a soybean mini core collection composed of 224 germplasm accessions for four yield-related traits in two consecutive years. Based on 1514 single nucleotide polymorphisms (SNPs), genome-wide association studies (GWAS) were conducted using a mixed linear model (MLM).

RESULTS

Extensive phenotypic variation existed in the soybean mini core collection and significant positive correlations were shown among most of traits. A total of 16 SNP markers for PFW, SFW, KP and MCFS were detected in all environments via GWAS. Nine SNP markers were repeatedly identified in two environments. Among these markers, eight were located in or near regions where yield-related QTLs have been reported in previous studies, and one was a novel genetic locus identified in this study. In addition, we conducted candidate gene analysis to the large-effect SNP markers, a total of twelve genes were proposed as potential candidate genes of soybean yield at the R6 stage.

CONCLUSION

These results will be beneficial for understanding the genetic basis of soybean yield at the R6 stage and facilitating the pyramiding of favourable alleles for future high-yield breeding by marker-assisted selection in vegetable soybean.

Hybridization Outcomes Have Strong Genomic and Environmental Contingencies

AbstractExtreme F₂ phenotypes known as transgressive segregants can cause increased or decreased fitness in hybrids beyond the ranges seen in parental populations. Despite the usefulness of transgression for plant and animal breeding and its potential role in hybrid speciation, the genetic mechanisms and predictors of transgressive segregation remain largely untested. We generated seven hybrid crosses between five widely divergent Saccharomyces yeast species and measured the fitness of the parents and their viable F₁ and F₂ hybrids in seven stressful environments. We found that on average 16.6% of all replicate F₂ hybrids had higher fitness than both parents. Against our predictions, transgression frequency was not a function of parental genetic and phenotypic distances across test environments. Within environments, some relationships were significant, but not in the predicted direction; for example, genetic distance was negatively related to transgression in ethanol and hydrogen peroxide. Significant effects of hybrid cross, test environment, and cross × environment interactions suggest that the amount of transgression produced in a hybrid cross is highly context specific and that outcomes of hybridization differ even among crosses made from the same two parents. If the goal is to reliably predict hybrid fitness and forecast the evolutionary potential of admixed populations, we need more efforts to identify patterns beyond the idiosyncrasies caused by specific genomic or environmental contexts.

The Synchronized Efforts to Decipher the Molecular Basis for Soybean Maturity Loci E1, E2, and E3 That Regulate Flowering and Maturity

The general concept of photoperiodism, i.e., the photoperiodic induction of flowering, was established by Garner and Allard (1920). The genetic factor controlling flowering time, maturity, or photoperiodic responses was observed in soybean soon after the discovery of the photoperiodism. E1, E2, and E3 were named in 1971 and, thereafter, genetically characterized. At the centennial celebration of the discovery of photoperiodism in soybean, we recount our endeavors to successfully decipher the molecular bases for the major maturity loci E1, E2, and E3 in soybean. Through systematic efforts, we successfully cloned the E3 gene in 2009, the E2 gene in 2011, and the E1 gene in 2012. Recently, successful identification of several circadian-related genes such as PRR3a, LUX, and J has enriched the known major E1-FTs pathway. Further research progresses on the identification of new flowering and maturity-related genes as well as coordinated regulation between flowering genes will enable us to understand profoundly flowering gene network and determinants of latitudinal adaptation in soybean.

Mapping QTL/QTN and mining candidate genes for plant height and its response to planting densities in soybean [Glycine max (L.) Merr.] through a FW-RIL population

Plant height (PH) determines the morphology and seed yield of soybean, so it is an important breeding target, which is controlled by multiple genes and affected by plant density. In this research, it was used about a four-way recombinant inbred lines (FW-RIL) with 144 families constructed by double cross (Kenfeng 14 × Kenfeng 15) × (Heinong 48 × Kenfeng 19) as experimental materials, with the purpose to map QTL/QTN associated with PH under densities of 2.2×105 plant/ha (D1) and 3×105 plant/ha (D2) in five environments. The results showed that response of PH to densities varied in accordance to genotypes among environments. A total of 26 QTLs and 13 QTNs were identified specifically in D1; 20 QTLs and 21 QTNs were identified specifically in D2. Nine QTLs and one QTN were discovered commonly in two densities. Fifteen QTLs and 9 QTNs were repeatedly detected by multiple statistical methods, densities, or environments, which could be considered stable. Eighteen QTLs were detected, as well as 7 QTNs underlying responses of PH to density increment. Six QTNs, co-located in the interval of QTL, were detected in more than two environments or methods with a longer genome length over 3000 kb. Based on gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, five genes were predicted as candidates, which were likely to be involved in growth and development of PH. The results will help elucidate the genetic basis and improve molecular assistant selection of PH.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01209-0.

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.

Next-generation biological control: the need for integrating genetics and genomics

Biological control is widely successful at controlling pests, but effective biocontrol agents are now more difficult to import from countries of origin due to more restrictive international trade laws (the Nagoya Protocol). Coupled with increasing demand, the efficacy of existing and new biocontrol agents needs to be improved with genetic and genomic approaches. Although they have been underutilised in the past, application of genetic and genomic techniques is becoming more feasible from both technological and economic perspectives. We review current methods and provide a framework for using them. First, it is necessary to identify which biocontrol trait to select and in what direction. Next, the genes or markers linked to these traits need be determined, including how to implement this information into a selective breeding program. Choosing a trait can be assisted by modelling to account for the proper agro-ecological context, and by knowing which traits have sufficiently high heritability values. We provide guidelines for designing genomic strategies in biocontrol programs, which depend on the organism, budget, and desired objective. Genomic approaches start with genome sequencing and assembly. We provide a guide for deciding the most successful sequencing strategy for biocontrol agents. Gene discovery involves quantitative trait loci analyses, transcriptomic and proteomic studies, and gene editing. Improving biocontrol practices includes marker-assisted selection, genomic selection and microbiome manipulation of biocontrol agents, and monitoring for genetic variation during rearing and post-release. We conclude by identifying the most promising applications of genetic and genomic methods to improve biological control efficacy.

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 loci analysis of seed oil content and composition of wild and cultivated soybean

BACKGROUND

Soybean oil is a major source of edible oil, and the domestication of wild soybean has resulted in significant changes in oil content and composition. Extensive efforts have been made to identify genetic loci that are related to soybean oil traits. The objective of this study was to identify quantitative trait loci (QTLs) related to soybean seed oil and compare the fatty acid composition between wild and cultivated soybean.

RESULTS

Using the specific-locus amplified fragment sequencing (SLAF-seq) method, a total of 181 recombinant inbred lines (RILs) derived from a cross between wild soybean ZYD00463 (Glycine soja) and cultivated soybean WDD01514 (Glycine max) were genotyped. Finally, a high-density genetic linkage map comprising 11,398 single-nucleotide polymorphism (SNP) markers on 20 linkage groups (LGs) was constructed. Twenty-four stable QTLs for seed oil content and composition were identified by model-based composite interval mapping (CIM) across multiple environments. Among these QTLs, 23 overlapped with or were adjacent to previously reported QTLs. One QTL, qPA10_1 (5.94-9.98 Mb) on Chr. Ten is a novel locus for palmitic acid. In the intervals of stable QTLs, some interesting genes involved in lipid metabolism were detected.

CONCLUSIONS

We developed 181 RILs from a cross between wild soybean ZYD00463 and cultivated soybean WDD01514 and constructed a high-density genetic map using the SLAF-seq method. We identified 24 stable QTLs for seed oil content and compositions, which includes qPA10_1 on Chr. 10, a novel locus for palmitic acid. Some interesting genes in the QTL regions were also detected. Our study will provide useful information for scientists to learn about genetic variations in lipid metabolism between wild and cultivated soybean.

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.

Genetic Properties Responsible for the Transgressive Segregation of Days to Heading in Rice

Transgressive segregation produces hybrid progeny phenotypes that exceed the parental phenotypes. Unlike heterosis, extreme phenotypes caused by transgressive segregation are heritably stable. We examined transgressive phenotypes of flowering time in rice, and revealed transgressive segregation in F₂ populations derived from a cross between parents with similar (proximal) days to heading (DTH). The DTH phenotypes of the A58 × Kitaake F₂ progenies were frequently more extreme than those of either parent. These transgressive phenotypes were maintained in the F₃ and F₄ populations. Both A58 and Kitaake are japonica rice cultivars adapted to Hokkaido, Japan, which is a high-latitude region, and have a short DTH. Among the four known loci required for a short DTH, three loci had common alleles in A58 and Kitaake, implying there is a similar genetic basis for DTH between the two varieties. A genome-wide single nucleotide polymorphism (SNP) analysis based on the F₄ population identified five new quantitative trait loci (QTL) associated with transgressive DTH phenotypes. Each of these QTL had different degrees of additive effects on DTH, and two QTL had an epistatic effect on each other. Thus, a genome-wide SNP analysis facilitated the detection of genetic loci associated with extreme DTH phenotypes, and revealed that the transgressive phenotypes were produced by exchanging the complementary alleles of a few minor QTL in the similar parental phenotypes.

Integration of lodging resistance QTL in soybean

Poor lodging resistance could limit increases in soybean yield. Previously, a considerable number of observations of quantitative trait loci (QTL) for lodging resistance have been reported by independent studies. The integration of these QTL into a consensus map will provide further evidence of their usefulness in soybean improvement. To improve informative QTL in soybean, a mapping population from a cross between the Harosoy and Clark cultivars, which inherit major U.S. soybean genetic backgrounds, was used along with previous mapping populations to identify QTL for lodging resistance. Together with 78 QTL for lodging collected from eighteen independent studies, a total of 88 QTL were projected onto the soybean consensus map. A total of 16 significant QTL clusters were observed; fourteen of them were confirmed in either two or more mapping populations or a single population subjected to different environmental conditions. Four QTL (one on chromosome 7 and three on 10) were newly identified in the present study. Further, meta-analysis was used to integrate QTL across different studies, resulting in two significant meta-QTL each on chromosomes 6 and 19. Our results provide deeper knowledge of valuable lodging resistance QTL in soybean, and these QTL could be used to increase lodging resistance.

QTL fine-mapping of soybean (Glycine max L.) leaf type associated traits in two RILs populations

BACKGROUND

The different leaf type associated traits of soybean (Glycine max L.) including leaf area, leaf length, leaf width, leaf shape and petiole length are considered to be associated with seed yield. In order to identify quantitative trait loci (QTLs) affecting leaf type traits, two advanced recombinant inbred line (RIL, ZH, Zhonghuang 24 × Huaxia 3; GB, Guizao 1 × Brazil 13) populations were introduced to score phenotypic values in plants across nine different environments (years, seasons, locations and soybean growth stages). Two restriction site-associated DNA sequencing (RAD-seq) based high-density genetic linkage maps with an average distance of 1.00 centimorgan (cM) between adjacent bin markers were utilized for QTL fine mapping.

RESULTS

Correlation analysis showed that most of the traits were correlated with each other and regulated both by hereditary and environmental factors. A total of 190 QTLs were identified for leaf type associated traits in the two populations, of which 14 loci were found to be environmentally stable. Moreover, these detected QTLs were categorized into 34 QTL hotspots, and four important QTL hotspots with phenotypic variance ranging from 3.89-23.13% were highlighted. Furthermore, Glyma04g05840, Glyma19g37820, Glyma14g07140 and Glyma19g39340 were predicted in the intervals of the stable loci and important QTL hotspots for leaf type traits by adopting Gene Ontology (GO) enrichment analysis.

CONCLUSIONS

Our findings of the QTLs and the putative genes will be beneficial to gain new insights into the genetic basis for soybean leaf type traits and may further accelerate the breeding process for reasonable leaf type soybean.

Genetic map construction and QTL analysis of leaf-related traits in soybean under monoculture and relay intercropping

Soybean (Glycine max L.) is an important food and oil crop widely planted by intercropping in southwest China. The shade caused by intercropping changes plant growth traits, such as soybean leaf and dry mass, thereby reducing yields. To improve the yield and elucidate the genetic mechanism of the leaf-related traits in intercropped soybeans, we measured the F6:7-8 recombinant inbred lines (RILs) derived from the cross of 'Nandou 12' and 'Jiuyuehuang' for six leaf-related traits under monoculture and relay intercropping in 2015 and 2016. We found 6366 single-nucleotide polymorphisms (SNPs) markers that covered the whole genome of soybean distributed in 20 linkage groups, which spanned 2818.67 cM with an average interval of 0.44 cM between adjacent markers. Nineteen quantitative trait loci (QTLs) were detected in two environments in 2 years. Three candidate genes associated to leaf-related traits were found according to gene expression and GO enrichment analyses. These results revealed the susceptibility of leaf phenotype to shading and helped elucidate the mechanisms that control leaf-related traits.

Quantitative trait loci associated with short inter-node length in soybean

Manipulating the genetic control of plant height is essential in soybean breeding to increase yield through the enlargement of the plant size while preventing lodging. A Japanese soybean germplasm, Y2, has distinctively shorter inter-node lengths than those of recently developed Japanese cultivars and is expected to provide new variation to prevent lodging. A quantitative trait loci (QTL) analysis for plant height-related traits was conducted using F₂ individuals derived from a cross between the elite Japanese cultivar Fukuyutaka and Y2. A major QTL for average inter-node length (AIL) and plant height was identified on chromosome 13 and named qSI13-1 (QTL for short inter-node on chromosome 13). The Y2 allele of qSI13-1 was partially dominant for plant height. qSI13-1 exhibited no effect on either days to flowering or number of main stem nodes. The AILs and plant heights of the near-isogenic lines containing the Y2 allele of qSI13-1 in the genetic background of Fukuyutaka were significantly less than those of Fukuyutaka. No significant differences between the near-isogenic lines and Fukuyutaka were observed for seed yield and flowering date, indicating that qSI13-1 will be useful in developing cultivars with short plant heights without having negative effects on yield potential and days to flowering.

Identification of Major QTLs Associated With First Pod Height and Candidate Gene Mining in Soybean

First pod height (FPH) is a quantitative trait in soybean [Glycine max (L.) Merr.] that affects mechanized harvesting. A compatible combination of the FPH and the mechanized harvester is required to ensure that the soybean is efficiently harvested. In this study, 147 recombinant inbred lines, which were derived from a cross between 'Dongnong594' and 'Charleston' over 8 years, were used to identify the major quantitative trait loci (QTLs) associated with FPH. Using a composite interval mapping method with WinQTLCart (version 2.5), 11 major QTLs were identified. They were distributed on five soybean chromosomes, and 90 pairs of QTLs showed significant epistatic associates with FPH. Of these, 3 were main QTL × main QTL interactions, and 12 were main QTL × non-main QTL interactions. A KEGG gene annotation of the 11 major QTL intervals revealed 8 candidate genes related to plant growth, appearing in the pathways K14486 (auxin response factor 9), K14498 (serine/threonine-protein kinase), and K13946 (transmembrane amino acid transporter family protein), and 7 candidate genes had high expression levels in the soybean stems. These results will aid in building a foundation for the fine mapping of the QTLs related to FPH and marker-assisted selection for breeding in soybean.

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.

De novo assembly of a Chinese soybean genome

Soybean was domesticated in China and has become one of the most important oilseed crops. Due to bottlenecks in their introduction and dissemination, soybeans from different geographic areas exhibit extensive genetic diversity. Asia is the largest soybean market; therefore, a high-quality soybean reference genome from this area is critical for soybean research and breeding. Here, we report the de novo assembly and sequence analysis of a Chinese soybean genome for "Zhonghuang 13" by a combination of SMRT, Hi-C and optical mapping data. The assembled genome size is 1.025 Gb with a contig N50 of 3.46 Mb and a scaffold N50 of 51.87 Mb. Comparisons between this genome and the previously reported reference genome (cv. Williams 82) uncovered more than 250,000 structure variations. A total of 52,051 protein coding genes and 36,429 transposable elements were annotated for this genome, and a gene co-expression network including 39,967 genes was also established. This high quality Chinese soybean genome and its sequence analysis will provide valuable information for soybean improvement in the future.

Quantitative Trait Locus Mapping of Flowering Time and Maturity in Soybean Using Next-Generation Sequencing-Based Analysis

Soybean (Glycine max L.) is a major legume crop that is mainly distributed in temperate regions. The adaptability of soybean to grow at relatively high latitudes is attributed to natural variations in major genes and quantitative trait loci (QTLs) that control flowering time and maturity. Identification of new QTLs and map-based cloning of candidate genes are the fundamental approaches in elucidating the mechanism underlying soybean flowering and adaptation. To identify novel QTLs/genes, we developed two F8:10 recombinant inbred lines (RILs) and evaluated the traits of time to flowering (R1), maturity (R8), and reproductive period (RP) in the field. To rapidly and efficiently identify QTLs that control these traits, next-generation sequencing (NGS)-based QTL analysis was performed. This study demonstrates that only one major QTL on chromosome 4 simultaneously controls R1, R8, and RP traits in the Dongnong 50 × Williams 82 (DW) RIL population. Furthermore, three QTLs were mapped to chromosomes 6, 11, and 16 in the Suinong 14 × Enrei (SE) RIL population. Two major pleiotropic QTLs on chromosomes 4 and 6 were shown to affect flowering time, maturity, and RP. A QTL influencing RP was identified on chromosome 11, and QTL on chromosome 16 was associated with time to flowering responses. All these QTLs contributed to soybean maturation. The QTLs identified in this study may be utilized in fine mapping and map-based cloning of candidate genes to elucidate the mechanisms underlying flowering and soybean adaptation to different latitudes and to breed novel soybean cultivars with optimal yield-related traits.

Genotyping of Soybean Cultivars With Medium-Density Array Reveals the Population Structure and QTNs Underlying Maturity and Seed Traits

Soybean was domesticated about 5,000 to 6,000 years ago in China. Although genotyping technologies such as genotyping by sequencing (GBS) and high-density array are available, it is convenient and economical to genotype cultivars or populations using medium-density SNP array in genetic study as well as in molecular breeding. In this study, 235 cultivars, collected from China, Japan, USA, Canada and some other countries, were genotyped using SoySNP8k iSelect BeadChip with 7,189 single nucleotide polymorphisms (SNPs). In total, 4,471 polymorphic SNP markers were used to analyze population structure and perform genome-wide association study (GWAS). The most likely K value was 7, indicating this population can be divided into 7 subpopulations, which is well in accordance with the geographic origins of cultivars or accession studied. The LD decay rate was estimated at 184 kb, where r2 dropped to half of its maximum value (0.205). GWAS using FarmCPU detected a stable quantitative trait nucleotide (QTN) for hilum color and seed color, which is consistent with the known loci or genes. Although no universal QTNs for flowering time and maturity were identified across all environments, a total of 30 consistent QTNs were detected for flowering time (R1) or maturity (R7 and R8) on 16 chromosomes, most of them were corresponding to known E1 to E4 genes or QTL region reported in SoyBase (soybase.org). Of 16 consistent QTNs for protein and oil contents, 11 QTNs were detected having antagonistic effects on protein and oil content, while 4 QTNs soly for oil content, and one QTN soly for protein content. The information gained in this study demonstrated that the usefulness of the medium-density SNP array in genotyping for genetic study and molecular breeding.

Detecting the QTL-allele system conferring flowering date in a nested association mapping population of soybean using a novel procedure

KEY MESSAGE

The RTM-GWAS was chosen among five procedures to identify DTF QTL-allele constitution in a soybean NAM population; 139 QTLs with 496 alleles accounting for 81.7% of phenotypic variance were detected. Flowering date (days to flowering, DTF) is an ecological trait in soybean, closely related to its ability to adapt to areas. A nested association mapping (NAM) population consisting of four RIL populations (LM, ZM, MT and MW with M8206 as their common parent) was established and tested for their DTF under five environments. Using restriction-site-associated DNA sequencing the population was genotyped with SNP markers. The restricted two-stage multi-locus (RTM) genome-wide association study (GWAS) (RTM-GWAS) with SNP linkage disequilibrium block (SNPLDB) as multi-allele genomic markers performed the best among the five mapping procedures with software publicly available. It identified the greatest number of quantitative trait loci (QTLs) (139) and alleles (496) on 20 chromosomes covering almost all of the QTLs detected by four other mapping procedures. The RTM-GWAS provided the detected QTLs with highest genetic contribution but without overflowing and missing heritability problems (81.7% genetic contribution vs. heritability of 97.6%), while SNPLDB markers matched the NAM population property of multiple alleles per locus. The 139 QTLs with 496 alleles were organized into a QTL-allele matrix, showing the corresponding DTF genetic architecture of the five parents and the NAM population. All lines and parents comprised both positive and negative alleles, implying a great potential of recombination for early and late DTF improvement. From the detected QTL-allele system, 126 candidate genes were annotated and χ 2 tested as a DTF candidate gene system involving nine biological processes, indicating the trait a complex, involving several biological processes rather than only a handful of major genes.

Construction of high-density genetic map and QTL mapping of yield-related and two quality traits in soybean RILs population by RAD-sequencing

BACKGROUND

One of the overarching goals of soybean breeding is to develop lines that combine increased yield with improved quality characteristics. High-density-marker QTL mapping can serve as an effective strategy to identify novel genomic information to facilitate crop improvement. In this study, we genotyped a recombinant inbred line (RIL) population (Zhonghuang 24 × Huaxia 3) using a restriction-site associated DNA sequencing (RAD-seq) approach. A high-density soybean genetic map was constructed and used to identify several QTLs that were shown to influence six yield-related and two quality traits.

RESULTS

A total of 47,472 single-nucleotide polymorphisms (SNPs) were detected for the RILs that were integrated into 2639 recombination bin units, with an average distance of 1.00 cM between adjacent markers. Forty seven QTLs for yield-related traits and 13 QTLs for grain quality traits were found to be distributed on 16 chromosomes in the 2 year studies. Among them, 18 QTLs were stable, and were identified in both analyses. Twenty six QTLs were identified for the first time, with a single QTL (qNN19a) in a 56 kb region explaining 32.56% of phenotypic variation, and an additional 10 of these were novel, stable QTLs. Moreover, 8 QTL hotpots on four different chromosomes were identified for the correlated traits.

CONCLUSIONS

With RAD-sequencing, some novel QTLs and important QTL clusters for both yield-related and quality traits were identified based on a new, high-density bin linkage map. Three predicted genes were selected as candidates that likely have a direct or indirect influence on both yield and quality in soybean. Our findings will be helpful for understanding common genetic control mechanisms of co-localized traits and to select cultivars for further analysis to predictably modulate soybean yield and quality simultaneously.

A Genome-Wide Association Study for Agronomic Traits in Soybean Using SNP Markers and SNP-Based Haplotype Analysis

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

Genotyping-by-Sequencing-Based Investigation of the Genetic Architecture Responsible for a ∼Sevenfold Increase in Soybean Seed Stearic Acid

Soybean oil is highly unsaturated but oxidatively unstable, rendering it nonideal for food applications. Until recently, the majority of soybean oil underwent partial chemical hydrogenation, which produces trans fats as an unavoidable consequence. Dietary intake of trans fats and most saturated fats are conclusively linked to negative impacts on cholesterol levels and cardiovascular health. Two major soybean oil breeding targets are: (1) to reduce or eliminate the need for chemical hydrogenation, and (2) to replace the functional properties of partially hydrogenated soybean oil. One potential solution is the elevation of seed stearic acid, a saturated fat which has no negative impacts on cardiovascular health, from 3 to 4% in typical cultivars to > 20% of the seed oil. We performed QTL analysis of a population developed by crossing two mutant lines, one with a missense mutation affecting a stearoyl-acyl-carrier protein desaturase gene resulting in ∼11% seed stearic acid crossed to another mutant, A6, which has 24-28% seed stearic acid. Genotyping-by-sequencing (GBS)-based QTL mapping identified 21 minor and major effect QTL for six seed oil related traits and plant height. The inheritance of a large genomic deletion affecting chromosome 14 is the basis for largest effect QTL, resulting in ∼18% seed stearic acid. This deletion contains SACPD-C and another gene(s); loss of both genes boosts seed stearic acid levels to ≥ 18%. Unfortunately, this genomic deletion has been shown in previous studies to be inextricably correlated with reduced seed yield. Our results will help inform and guide ongoing breeding efforts to improve soybean oil oxidative stability.

A soybean quantitative trait locus that promotes flowering under long days is identified as FT5a, a FLOWERING LOCUS T ortholog

FLOWERING LOCUS T (FT) is an important floral integrator whose functions are conserved across plant species. In soybean, two orthologs, FT2a and FT5a, play a major role in initiating flowering. Their expression in response to different photoperiods is controlled by allelic combinations at the maturity loci E1 to E4, generating variation in flowering time among cultivars. We determined the molecular basis of a quantitative trait locus (QTL) for flowering time in linkage group J (Chromosome 16). Fine-mapping delimited the QTL to a genomic region of 107kb that harbors FT5a We detected 15 DNA polymorphisms between parents with the early-flowering (ef) and late-flowering (lf) alleles in the promoter region, an intron, and the 3' untranslated region of FT5a, although the FT5a coding regions were identical. Transcript abundance of FT5a was higher in near-isogenic lines for ef than in those for lf, suggesting that different transcriptional activities or mRNA stability caused the flowering time difference. Single-nucleotide polymorphism (SNP) calling from re-sequencing data for 439 cultivated and wild soybean accessions indicated that ef is a rare haplotype that is distinct from common haplotypes including lf The ef allele at FT5a may play an adaptive role at latitudes where early flowering is desirable.

Genome Regions Associated with Functional Performance of Soybean Stem Fibers in Polypropylene Thermoplastic Composites

Plant fibers can be used to produce composite materials for automobile parts, thus reducing plastic used in their manufacture, overall vehicle weight and fuel consumption when they replace mineral fillers and glass fibers. Soybean stem residues are, potentially, significant sources of inexpensive, renewable and biodegradable natural fibers, but are not curretly used for biocomposite production due to the functional properties of their fibers in composites being unknown. The current study was initiated to investigate the effects of plant genotype on the performance characteristics of soybean stem fibers when incorporated into a polypropylene (PP) matrix using a selective phenotyping approach. Fibers from 50 lines of a recombinant inbred line population (169 RILs) grown in different environments were incorporated into PP at 20% (wt/wt) by extrusion. Test samples were injection molded and characterized for their mechanical properties. The performance of stem fibers in the composites was significantly affected by genotype and environment. Fibers from different genotypes had significantly different chemical compositions, thus composites prepared with these fibers displayed different physical properties. This study demonstrates that thermoplastic composites with soybean stem-derived fibers have mechanical properties that are equivalent or better than wheat straw fiber composites currently being used for manufacturing interior automotive parts. The addition of soybean stem residues improved flexural, tensile and impact properties of the composites. Furthermore, by linkage and in silico mapping we identified genomic regions to which quantitative trait loci (QTL) for compositional and functional properties of soybean stem fibers in thermoplastic composites, as well as genes for cell wall synthesis, were co-localized. These results may lead to the development of high value uses for soybean stem residue.

QTL for seed protein and amino acids in the Benning × Danbaekkong soybean population

KEY MESSAGE

We identified QTL associated with protein and amino acids in a soybean mapping population that was grown in five environments. These QTL could be used in MAS to improve these traits. Soybean, rather than nitrogen-containing forages, is the primary source of quality protein in feed formulations for domestic swine, poultry, and dairy industries. As a sole dietary source of protein, soybean is deficient in the amino acids lysine (Lys), threonine (Thr), methionine (Met), and cysteine (Cys). Increasing these amino acids would benefit the feed industry. The objective of the present study was to identify quantitative trait loci (QTL) associated with crude protein (cp) and amino acids in the 'Benning' × 'Danbaekkong' population. The population was grown in five southern USA environments. Amino acid concentrations as a fraction of cp (Lys/cp, Thr/cp, Met/cp, Cys/cp, and Met + Cys/cp) were determined by near-infrared reflectance spectroscopy. Four QTL associated with the variation in crude protein were detected on chromosomes (Chr) 14, 15, 17, and 20, of which, a QTL on Chr 20 explained 55 % of the phenotypic variation. In the same chromosomal region, QTL for Lys/cp, Thr/cp, Met/cp, Cys/cp and Met + Cys/cp were detected. At these QTL, the Danbaekkong allele resulted in reduced levels of these amino acids and increased protein concentration. Two additional QTL for Lys/cp were detected on Chr 08 and 20, and three QTL for Thr/cp on Chr 01, 09, and 17. Three QTL were identified on Chr 06, 09 and 10 for Met/cp, and one QTL was found for Cys/cp on Chr 10. The study provides information concerning the relationship between crude protein and levels of essential amino acids and may allow for the improvement of these traits in soybean using marker-assisted selection.

Mapping quantitative trait loci for yield-related traits in soybean (Glycine max L.)

Development of soybean cultivars with high seed yield is a major focus in soybean breeding programs. This study was conducted to identify genetic loci associated with seed yield-related traits in soybean and also to clarify consistency of the detected QTLs with QTLs found by previous researchers. A population of 135 F2:3 lines was developed from a cross between a vegetable soybean line (MJ0004-6) and a landrace cultivar from Myanmar (R18500). They were evaluated in the experimental field of Kasetsart University, Kamphaeng Saen, Nakhon Pathom, Thailand in a randomized complete block design with two replications each in 2011 and 2012 growing seasons. The two parents exhibited contrasting characteristics for most of the traits that were mapped. Analysis of variance showed that the main effects of genotype and environment (year) were significant for all studied traits. Genotype by environment interaction was also highly significant for all the traits. The population was genotyped by 149 polymorphic SSR markers and the genetic map consisted of 129 SSR loci which converged into 38 linkage groups covering 1156 cM of soybean genome. There were 10 QTLs significantly associated with seed yield-related traits across two seasons with single QTLs explaining between 5.0% to 21.9% of the phenotypic variation. Three of these QTLs were detected in both years for days to flowering, days to maturity and 100 seed weight. Most of the detected QTLs in our research were consistent with earlier QTLs reported by previous researchers. However, four novel QTLs including SF1, SF2 and SF3 on linkage groups L and N for seed filling period and PN1 on linkage group D1b for pod number were identified in the present study.

Quantitative trait loci associated with lodging tolerance in soybean cultivar 'Toyoharuka'

Lodging tolerance (LT) is an important trait for high yield and combine-harvesting efficiency in soybean [Glycine max (L.) Merr.]. Many previous studies have investigated quantitative trait loci (QTLs) for lodging score (LS) in soybean. Most of the investigated QTLs were located in the proximal region of maturity or growth habit loci. The aim of this study was to identify genetic factors for LT not associated with maturity or growth habit. QTL analysis was performed using a recombinant inbred line (RIL) population derived from a cross between 'Toyoharuka' (TH), a lodging-tolerant cultivar, and 'Toyomusume' (TM). The genotypes of TH and TM were estimated as both e1e2E3E4 and dt1. The average LS over 4 years was used for QTL analysis, identifying a major and stable QTL, qLS19-1, on chromosome 19. The LS of the near-isogenic line (NIL) with the TH allele at Sat_099, the nearest marker to qLS19-1, was significantly lower than the NIL with the TM allele at that position. The TH allele at Sat_099 rarely had a negative influence on seed yield or other agronomic traits in both NILs and the TM-backcrossed lines. Our results suggest that marker-assisted selection for qLS19-1 is effective for improving LT in breeding programs.

Identification of QTLs for seed and pod traits in soybean and analysis for additive effects and epistatic effects of QTLs among multiple environments

Soybean seed and pod traits are important yield components. Selection for high yield style in seed and pod along with agronomic traits is a goal of many soybean breeders. The intention of this study was to identify quantitative trait loci (QTL) underlying seed and pod traits in soybean among eleven environments in China. 147 recombinant inbred lines were advanced through single-seed-descent method. The population was derived from a cross between Charleston (an American high yield soybean cultivar) and DongNong594 (a Chinese high yield soybean cultivar). A total of 157 polymorphic simple sequence repeat markers were used to construct a genetic linkage map. The phenotypic data of seed and pod traits [number of one-seed pod, number of two-seed pod, number of three-seed pod, number of four-seed pod, number of (two plus three)-seed pod, number of (three plus four)-seed pod, seed weight per plant, number of pod per plant] were recorded in eleven environments. In the analysis of single environment, fourteen main effect QTLs were identified. In the conjoint analysis of multiple environments, twenty-four additive QTLs were identified, and additive QTLs by environments interactions (AE) were evaluated and analyzed at the same time among eleven environments; twenty-three pairs of epistatic QTLs were identified, and epistasis (additive by additive) by environments interactions (AAE) were also analyzed and evaluated among eleven environments. Comparing the results of identification between single environment mapping and multiple environments conjoint mapping, three main effect QTLs with positive additive values and another three main effect QTLs with negative additive values, had no interactions with all environments, supported that these QTLs could be used in molecular assistant breeding in the future. These different effect QTLs could supply a good foundation to the gene clone and molecular asisstant breeding of soybean seed and pod traits.

Expression of anatomical leaf traits in homoploid hybrids between deciduous and evergreen species of Vaccinium

We investigated the anatomical expression of leaf traits in hybrids between evergreen Vaccinium vitis-idaea and deciduous V. myrtillus. We compared parents from four populations with their respective F1 hybrids and tested whether (i) transgression can be the source of novel anatomical traits in hybrids; (ii) expression of transgressive traits is more probable for traits with similar values in parents and intermediate for more distinct values, as predicted by theory; and (iii) independent origin of hybrids leads to identical trait expression profiles among populations. We found that anatomical leaf traits can be divided into four categories based on their similarity to parents: intermediate, parental-like, transgressive and non-significant. Contrary to the common view, parental-like trait values were equally important in shaping the hybrid profile, as were intermediate traits. Transgression was revealed in 17/144 cases and concerned mainly cell and tissue sizes. As predicted by theory, we observed transgressive segregation more often when there was little phenotypic divergence, but intermediate values when parental traits were differentiated. It is likely that cell and tissue sizes are phylogenetically more conserved due to stabilising selection, whereas traits such as leaf thickness and volume fraction of the intercellular spaces, showing a consistent intermediate pattern across populations, are more susceptible to directional selection. Hybrid populations showed little similarity in expression profile, with only three traits identically expressed across all populations. Thus local adaptation of parental species and specific genetic background may be of importance.

Multi-population QTL detection for aerial morphogenetic traits in the model legume Medicago truncatula

Medicago truncatula, as a model species, is useful to study the genetic control of traits of agronomic interest in legumes species. Aerial morphogenesis is a key component of forage and seed yield. It was measured in four mapping populations originating from five parental lines. Single and multi-population quantitative trait locus (QTL) detections were carried out. A large variation was observed within populations and transgressive segregation was noted. Most traits showed high heritabilities in all seasons. Length of primary branches (LPB, cm) was positively correlated to branch elongation rate (BER, cm day(-1)) and aerial dry matter (ADM, g). Flowering time (FT, °C day(-1)) showed negative correlations with length of main stem (LMS, cm) and BER. One hundred and forty-one QTLs for BER, LMS, FT, LPB, diameter of primary branches (DPB), number of primary branches (NPB), number of nodes (NI) and ADM were identified and localized over all eight chromosomes. Single and multi-population analyses showed that the most important regions for aerial morphogenetic traits were chromosomes 1, 2, 7 and 8. Multi-population analysis revealed three regions of major QTLs affecting aerial morphogenetic traits (LPB, LMS, NPB, BER and FT). A region involved in flowering time variation was revealed on chromosome 6 on a single population. These results were used to identify candidate genes that could control variation for aerial morphogenesis traits in this species and in related crop legume species.

Identification of single nucleotide polymorphisms and haplotypes associated with yield and yield components in soybean (Glycine max) landraces across multiple environments

Genome-wide association analysis is a powerful approach to identify the causal genetic polymorphisms underlying complex traits. In this study, we evaluated a population of 191 soybean landraces in five environments to detect molecular markers associated with soybean yield and its components using 1,536 single-nucleotide polymorphisms (SNPs) and 209 haplotypes. The analysis revealed that abundant phenotypic and genetic diversity existed in the studied population. This soybean population could be divided into two subpopulations and no or weak relatedness was detected between pair-wise landraces. The level of intra-chromosomal linkage disequilibrium was about 500 kb. Genome-wide association analysis based on the unified mixed model identified 19 SNPs and 5 haplotypes associated with soybean yield and yield components in three or more environments. Nine markers were found co-associated with two or more traits. Many markers were located in or close to previously reported quantitative trait loci mapped by linkage analysis. The SNPs and haplotypes identified in this study will help to further understand the genetic basis of soybean yield and its components, and may facilitate future high-yield breeding by marker-assisted selection in soybean.

Genetic and molecular bases of photoperiod responses of flowering in soybean

Flowering is one of the most important processes involved in crop adaptation and productivity. A number of major genes and quantitative trait loci (QTLs) for flowering have been reported in soybean (Glycine max). These genes and QTLs interact with one another and with the environment to greatly influence not only flowering and maturity but also plant morphology, final yield, and stress tolerance. The information available on the soybean genome sequence and on the molecular bases of flowering in Arabidopsis will undoubtedly facilitate the molecular dissection of flowering in soybean. Here, we review the present status of our understanding of the genetic and molecular mechanisms of flowering in soybean. We also discuss our identification of orthologs of Arabidopsis flowering genes from among the 46,367 genes annotated in the publicly available soybean genome database Phytozome Glyma 1.0. We emphasize the usefulness of a combined approach including QTL analysis, fine mapping, and use of candidate gene information from model plant species in genetic and molecular studies of soybean flowering.

Genetic analysis and QTL detection of reproductive period and post-flowering photoperiod responses in soybean

Reproductive period (RP) is an important trait of soybean [Glycine max (L.) Merr.] It is closely related to yield, quality and tolerances to environmental stresses. To investigate the inheritance and photoperiod response of RP in soybean, the F(1), F(2), and F(2:3) populations derived from nine crosses were developed. The inheritance of RP was analyzed through the joint segregation analysis. It was shown that the RP was controlled by one major gene plus polygenes. 181 recombinant inbred lines (RILs) generated from the cross of Xuyong Hongdou × Baohexuan 3 were further used for QTL mapping of RP under normal conditions across 3 environments, using 127 SSR markers. Four QTLs, designated qRP-c-1, qRP-g-1, qRP-m-1 and qRP-m-2, were mapped on C1, G and M linkage groups, respectively. The QTL qRP-c-1 on the linkage group C1 showed stable effect across environments and explained 25.6, 27.5 and 21.4% of the phenotypic variance in Nanjing 2002, Beijing 2003 and Beijing 2004, respectively. Under photoperiod-controlled conditions, qRP-c-1, and two different QTLs designated qRP-l-1 and qRP-o-1, respectively, were mapped on the linkage groups L and O. qRP-o-1 was detected under SD condition and can explained 10.70% of the phenotypic variance. qRP-c-1 and qRP-l-1 were detected under LD condition and for photoperiod sensitivity. The two major-effect QTLs can explain 19.03 and 19.00% of the phenotypic variance, respectively, under LD condition and 16.25 and 14.12%, respectively, for photoperiod sensitivity. Comparative mapping suggested that the two major-effect QTLs, qRP-c-1 and qRP-l-1, might associate with E8 or GmCRY1a and the maturity gene E3 or GmPhyA3, respectively. These results could facilitate our understanding of the inheritance of RP and provide information on marker-assisted breeding for high yield and wide adaptation in soybean.

QTL identification of flowering time at three different latitudes reveals homeologous genomic regions that control flowering in soybean

Since the genetic control of flowering time is very important in photoperiod-sensitive soybean (Glycine max (L.) Merr.), genes affecting flowering under different environment conditions have been identified and described. The objectives were to identify quantitative trait loci (QTLs) for flowering time in different latitudinal and climatic regions, and to understand how chromosomal rearrangement and genome organization contribute to flowering time in soybean. Recombinant inbred lines from a cross between late-flowering 'Jinpumkong 2' and early-flowering 'SS2-2' were used to evaluate the phenotypic data for days to flowering (DF) collected from Kamphaeng Saen, Thailand (14°01'N), Suwon, Korea (37°15'N), and Longjing, China (42°46'N). A weakly positive phenotypic correlation (r = 0.36) was found between DF in Korea and Thailand; however, a strong correlation (r = 0.74) was shown between Korea and China. After 178 simple sequence repeat (SSR) markers were placed on a genetic map spanning 2,551.7 cM, four independent DF QTLs were identified on different chromosomes (Chrs). Among them, three QTLs on Chrs 9, 13 and 16 were either Thailand- or Korea-specific. The DF QTL on Chr 6 was identified in both Korea and China, suggesting it is less environment-sensitive. Comparative analysis of four DF QTL regions revealed a syntenic relationship between two QTLs on Chrs 6 and 13. All five duplicated gene pairs clustered in the homeologous genomic regions were found to be involved in the flowering. Identification and comparative analysis of multiple DF QTLs from different environments will facilitate the significant improvement in soybean breeding programs with respect to control of flowering time.

Yield quantitative trait loci from wild tomato are predominately expressed by the shoot

Plant yield is the integrated outcome of processes taking place above and below ground. To explore genetic, environmental and developmental aspects of fruit yield in tomato, we phenotyped an introgression line (IL) population derived from a cross between the cultivated tomato (Solanum lycopersicum) and a wild species (Solanum pennellii). Both homozygous and heterozygous ILs were grown in irrigated and non-irrigated fields and evaluated for six yield components. Thirteen lines displayed transgressive segregation that increased agronomic yield consistently over 2 years and defined at least 11 independent yield-improving QTL. To determine if these QTL were expressed in the shoots or the roots of the plants, we conducted field trials of reciprocally grafted ILs; out of 13 lines with an effect on yield, 10 QTL were active in the shoot and only IL8-3 showed a consistent root effect. To further examine this unusual case, we evaluated the metabolic profiles of fruits from both the homo- and heterozygous lines for IL8-3 and compared these to those obtained from the fruit of their equivalent genotypes in the root effect population. We observed that several of these metabolic QTL, like the yield QTL, were root determined; however, further studies will be required to delineate the exact mechanism mediating this effect in this specific line. The results presented here suggest that genetic variation for root traits, in comparison to that present in the shoot, represents only a minor component in the determination of tomato fruit yield.