Identification and Validation of Candidate Genes Associated with Domesticated and Improved Traits in Soybean

Genome-Wide Association study for root system architecture traits in field soybean [Glycine max (L.) Merr.]

Roots play a crucial role in plant development, serving to absorb water and nutrients from the soil while also providing structural stability. However, the impacts of global warming can impede root growth by altering soil conditions that hinder overall plant growth. To address this challenge, there is a need to screen and identify plant genotypes with superior Root System Architecture traits (RSA), that can be used for future breeding efforts in enhancing their resilience to these environmental changes. In this project, 500 mid to late-maturity soybean accessions were grown on blue blotting papers hydroponically with six replicates and assessed seven RSA traits. Genome-Wide Association Studies (GWAS) were carried out with root phenotypic data and SNP data from the SoySNP50K iSelect SNP BeadChip, using both the TASSEL 5.0 and FarmCPU techniques. A total of 26 significant SNP-trait correlations were discovered, with 11 SNPs on chromosome 13. After SNP selection, we identified 14 candidate genes within 100-kb regions flanking the SNPs, which are related to root architecture. Notably, Glyma.17G258700, which exhibited substantial differential expression in root tips and its Arabidopsis homolog, AT4G24190 (GRP94) is involved in the regulation of meristem size and organization. Other candidate genes includes Glyma.03G023000 and Glyma.13G273500 that are also play a key role in lateral root initiation and root meristem growth, respectively. These findings significantly contribute to the discovery of key genes associated with root system architecture, facilitating the breeding of resilient cultivars adaptable to changing climates.

Identification of QTNs, QTN-by-environment interactions, and their candidate genes for salt tolerance related traits in soybean

BACKGROUND

Salt stress significantly reduces soybean yield. To improve salt tolerance in soybean, it is important to mine the genes associated with salt tolerance traits.

RESULTS

Salt tolerance traits of 286 soybean accessions were measured four times between 2009 and 2015. The results were associated with 740,754 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) using three-variance-component multi-locus random-SNP-effect mixed linear model (3VmrMLM). As a result, eight salt tolerance genes (GmCHX1, GsPRX9, Gm5PTase8, GmWRKY, GmCHX20a, GmNHX1, GmSK1, and GmLEA2-1) near 179 significant and 79 suggested QTNs and two salt tolerance genes (GmWRKY49 and GmSK1) near 45 significant and 14 suggested QEIs were associated with salt tolerance index traits in previous studies. Six candidate genes and three gene-by-environment interactions (GEIs) were predicted to be associated with these index traits. Analysis of four salt tolerance related traits under control and salt treatments revealed six genes associated with salt tolerance (GmHDA13, GmPHO1, GmERF5, GmNAC06, GmbZIP132, and GmHsp90s) around 166 QEIs were verified in previous studies. Five candidate GEIs were confirmed to be associated with salt stress by at least one haplotype analysis. The elite molecular modules of seven candidate genes with selection signs were extracted from wild soybean, and these genes could be applied to soybean molecular breeding. Two of these genes, Glyma06g04840 and Glyma07g18150, were confirmed by qRT-PCR and are expected to be key players in responding to salt stress.

CONCLUSIONS

Around the QTNs and QEIs identified in this study, 16 known genes, 6 candidate genes, and 8 candidate GEIs were found to be associated with soybean salt tolerance, of which Glyma07g18150 was further confirmed by qRT-PCR.

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.

The domestication-associated L1 gene encodes a eucomic acid synthase pleiotropically modulating pod pigmentation and shattering in soybean

Pod coloration is a domestication-related trait in soybean, with modern cultivars typically displaying brown or tan pods, while their wild relative, Glycine soja, possesses black pods. However, the factors regulating this color variation remain unknown. In this study, we cloned and characterized L1, the classical locus responsible for black pods in soybean. By using map-based cloning and genetic analyses, we identified the causal gene of L1 and revealed that it encodes a hydroxymethylglutaryl-coenzyme A (CoA) lyase-like (HMGL-like) domain protein. Biochemical assays showed that L1 functions as a eucomic acid synthase and facilitates the synthesis of eucomic acid and piscidic acid, both of which contribute to coloration of pods and seed coats in soybean. Interestingly, we found that L1 plants are more prone to pod shattering under light exposure than l1 null mutants because dark pigmentation increases photothermal efficiency. Hence, pleiotropic effects of L1 on pod color and shattering, as well as seed pigmentation, likely contributed to the preference for l1 alleles during soybean domestication and improvement. Collectively, our study provides new insights into the mechanism of pod coloration and identifies a new target for future de novo domestication of legume crops.

Genetic diversity and structure of the 4th cycle breeding population of Chinese fir (Cunninghamia lanceolata (lamb.) hook)

Studying population genetic structure and diversity is crucial for the marker-assisted selection and breeding of coniferous tree species. In this study, using RAD-seq technology, we developed 343,644 high-quality single nucleotide polymorphism (SNP) markers to resolve the genetic diversity and population genetic structure of 233 Chinese fir selected individuals from the 4^th cycle breeding program, representing different breeding generations and provenances. The genetic diversity of the 4^th cycle breeding population was high with nucleotide diversity (P_i ) of 0.003, and H_o and H_e of 0.215 and 0.233, respectively, indicating that the breeding population has a broad genetic base. The genetic differentiation level between the different breeding generations and different provenances was low (F_st < 0.05), with population structure analysis results dividing the 233 individuals into four subgroups. Each subgroup has a mixed branch with interpenetration and weak population structure, which might be related to breeding rather than provenance, with aggregation from the same source only being in the local branches. Our results provide a reference for further research on the marker-assisted selective breeding of Chinese fir and other coniferous trees.

Identification of QTN-by-environment interactions and their candidate genes for soybean seed oil-related traits using 3VmrMLM

Introduction

Although seed oil content and its fatty acid compositions in soybean were affected by environment, QTN-by-environment (QEIs) and gene-by-environment interactions (GEIs) were rarely reported in genome-wide association studies.

Methods

The 3VmrMLM method was used to associate the trait phenotypes, measured in five to seven environments, of 286 soybean accessions with 106,013 SNPs for detecting QTNs and QEIs.

Results

Seven oil metabolism genes (GmSACPD-A, GmSACPD-B, GmbZIP123, GmSWEET39, GmFATB1A, GmDGAT2D, and GmDGAT1B) around 598 QTNs and one oil metabolism gene GmFATB2B around 54 QEIs were verified in previous studies; 76 candidate genes and 66 candidate GEIs were predicted to be associated with these traits, in which 5 genes around QEIs were verified in other species to participate in oil metabolism, and had differential expression across environments. These genes were found to be related to soybean seed oil content in haplotype analysis. In addition, most candidate GEIs were co-expressed with drought response genes in co-expression network, and three KEGG pathways which respond to drought were enriched under drought stress rather than control condition; six candidate genes were hub genes in the co-expression networks under drought stress.

Discussion

The above results indicated that GEIs, together with drought response genes in co-expression network, may respond to drought, and play important roles in regulating seed oil-related traits together with oil metabolism genes. These results provide important information for genetic basis, molecular mechanisms, and soybean breeding for seed oil-related traits.

4D genetic networks reveal the genetic basis of metabolites and seed oil-related traits in 398 soybean RILs

Background

The yield and quality of soybean oil are determined by seed oil-related traits, and metabolites/lipids act as bridges between genes and traits. Although there are many studies on the mode of inheritance of metabolites or traits, studies on multi-dimensional genetic network (MDGN) are limited.

Results

In this study, six seed oil-related traits, 59 metabolites, and 107 lipids in 398 recombinant inbred lines, along with their candidate genes and miRNAs, were used to construct an MDGN in soybean. Around 175 quantitative trait loci (QTLs), 36 QTL-by-environment interactions, and 302 metabolic QTL clusters, 70 and 181 candidate genes, including 46 and 70 known homologs, were previously reported to be associated with the traits and metabolites, respectively. Gene regulatory networks were constructed using co-expression, protein–protein interaction, and transcription factor binding site and miRNA target predictions between candidate genes and 26 key miRNAs. Using modern statistical methods, 463 metabolite–lipid, 62 trait–metabolite, and 89 trait–lipid associations were found to be significant. Integrating these associations into the above networks, an MDGN was constructed, and 128 sub-networks were extracted. Among these sub-networks, the gene–trait or gene–metabolite relationships in 38 sub-networks were in agreement with previous studies, e.g., oleic acid (trait)–GmSEI–GmDGAT1a–triacylglycerol (16:0/18:2/18:3), gene and metabolite in each of 64 sub-networks were predicted to be in the same pathway, e.g., oleic acid (trait)–GmPHS–d-glucose, and others were new, e.g., triacylglycerol (16:0/18:1/18:2)–GmbZIP123–GmHD-ZIPIII-10–miR166s–oil content.

Conclusions

This study showed the advantages of MGDN in dissecting the genetic relationships between complex traits and metabolites. Using sub-networks in MGDN, 3D genetic sub-networks including pyruvate/threonine/citric acid revealed genetic relationships between carbohydrates, oil, and protein content, and 4D genetic sub-networks including PLDs revealed the relationships between oil-related traits and phospholipid metabolism likely influenced by the environment. This study will be helpful in soybean quality improvement and molecular biological research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-022-02191-1.

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

KEY MESSAGE

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

Identification and analysis of oil candidate genes reveals the molecular basis of cottonseed oil accumulation in Gossypium hirsutum L

Based on the integration of QTL-mapping and regulatory network analyses, five high-confidence stable QTL regions, six candidate genes and two microRNAs that potentially affect the cottonseed oil content were discovered. Cottonseed oil is increasingly becoming a promising target for edible oil with its high content of unsaturated fatty acids. In this study, a recombinant inbred line (RIL) cotton population was constructed to detect quantitative trait loci (QTLs) for the cottonseed oil content. A total of 39 QTLs were detected across eight different environments, of which five QTLs were stable. Forty-three candidate genes potentially involved in carbon metabolism, fatty acid synthesis and triacylglycerol biosynthesis processes were further obtained in the stable QTL regions. Transcriptome analysis showed that nineteen of these candidate genes expressed during  the developing cottonseed ovules and may affect the cottonseed oil content. Besides, transcription factor (TF) and microRNA (miRNA) co-regulatory network analyses based on the nineteen candidate genes suggested that six genes, two core miRNAs (ghr-miR2949b and ghr-miR2949c), and one TF GhHSL1 were considered to be closely associated with the cottonseed oil content. Moreover, four vital genes were validated by quantitative real-time PCR (qRT-PCR). These results provide insights into the oil accumulation mechanism in developing cottonseed ovules through the construction of a detailed oil accumulation model.

Domestication and improvement genes reveal the differences of seed size- and oil-related traits in soybean domestication and improvement

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Due to reduced diversity, it is essential to map domesticated and improved genes.

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13 known and 442 candidate genes were mined for seed size- and oil-related traits.

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All the genes were used to explain trait changes in domestication and improvement.

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56 domesticated and 15 improved genes may be valuable for future soybean breeding.

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This study provides useful gene resources for future breeding and biology research.

To address domestication and improvement studies of soybean seed size- and oil-related traits, a series of domesticated and improved regions, loci, and candidate genes were identified in 286 soybean accessions using domestication and improvement analyses, genome-wide association studies, quantitative trait locus (QTL) mapping and bulked segregant analyses in this study. As a result, 534 candidate domestication regions (CDRs) and 458 candidate improvement regions (CIRs) were identified in this study and integrated with those in five and three previous studies, respectively, to obtain 952 CDRs and 538 CIRs; 1469 loci for soybean seed size- and oil-related traits were identified in this study and integrated with those in Soybase to obtain 433 QTL clusters. The two results were intersected to obtain 245 domestication and 221 improvement loci for the above traits. Around these trait-related domestication and improvement loci, 7 domestication and 7 improvement genes were found to be truly associated with these traits, and 372 candidate domestication and 87 candidate improvement genes were identified using gene expression, SNP variants in genome, miRNA binding, KEGG pathway, DNA methylation, and haplotype analysis. These genes were used to explain the trait changes in domestication and improvement. As a result, the trait changes can be explained by their frequencies of elite haplotypes, base mutations in coding region, and three factors affecting their expression levels. In addition, 56 domestication and 15 improvement genes may be valuable for future soybean breeding. This study can provide useful gene resources for future soybean breeding and molecular biology research.

The CCCH zinc finger family of soybean (Glycine max L.): genome-wide identification, expression, domestication, GWAS and haplotype analysis

BACKGROUND

The CCCH zinc finger (zf_CCCH) is a unique subfamily featured one or more zinc finger motif(s) comprising of three Cys and one His residues. The zf_CCCH family have been reported involving in various processes of plant development and adaptation.

RESULTS

In this study, the zf_CCCH genes were identified via a genome-wide search and were systematically analyzed. 116 Gmzf_CCCHs were obtained and classified into seventeen subfamilies. Gene duplication and expansion analysis showed that tandem and segmental duplications contributed to the expansion of the Gmzf_CCCH gene family, and that segmental duplication play the main role. The expression patterns of Gmzf_CCCH genes were tissue-specific. Eleven domesticated genes were detected involved in the regulation of seed oil and protein synthesis as well as growth and development of soybean through GWAS and haplotype analysis for Gmzf_CCCH genes among the 164 of 302 soybeans resequencing data. Among which, 8 genes play an important role in the synthesis of seed oil or fatty acid, and the frequency of their elite haplotypes changes significantly among wild, landrace and improved cultivars, indicating that they have been strongly selected in the process of soybean domestication.

CONCLUSIONS

This study provides a scientific foundation for the comprehensive understanding, future cloning and functional studies of Gmzf_CCCH genes in soybean, meanwhile, it was also helpful for the improvement of soybean with high oil content.

Three-dimensional genetic networks among seed oil-related traits, metabolites and genes reveal the genetic foundations of oil synthesis in soybean

Although the biochemical and genetic basis of lipid metabolism is clear in Arabidopsis, there is limited information concerning the relevant genes in Glycine max (soybean). To address this issue, we constructed three-dimensional genetic networks using six seed oil-related traits, 52 lipid metabolism-related metabolites and 54 294 SNPs in 286 soybean accessions in total. As a result, 284 and 279 candidate genes were found to be significantly associated with seed oil-related traits and metabolites by phenotypic and metabolic genome-wide association studies and multi-omics analyses, respectively. Using minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) analyses, six seed oil-related traits were found to be significantly related to 31 metabolites. Among the above candidate genes, 36 genes were found to be associated with oil synthesis (27 genes), amino acid synthesis (four genes) and the tricarboxylic acid (TCA) cycle (five genes), and four genes (GmFATB1a, GmPDAT, GmPLDα1 and GmDAGAT1) are already known to be related to oil synthesis. Using this information, 133 three-dimensional genetic networks were constructed, 24 of which are known, e.g. pyruvate-GmPDAT-GmFATA2-oil content. Using these networks, GmPDAT, GmAGT and GmACP4 reveal the genetic relationships between pyruvate and the three major nutrients, and GmPDAT, GmZF351 and GmPgs1 reveal the genetic relationships between amino acids and seed oil content. In addition, GmCds1, along with average temperature in July and the rainfall from June to September, influence seed oil content across years. This study provides a new approach for the construction of three-dimensional genetic networks and reveals new information for soybean seed oil improvement and the identification of gene function.

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

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

Dissection of soybean populations according to selection signatures based on whole-genome sequences

BACKGROUND

Domestication and improvement processes, accompanied by selections and adaptations, have generated genome-wide divergence and stratification in soybean populations. Simultaneously, soybean populations, which comprise diverse subpopulations, have developed their own adaptive characteristics enhancing fitness, resistance, agronomic traits, and morphological features. The genetic traits underlying these characteristics play a fundamental role in improving other soybean populations.

RESULTS

This study focused on identifying the selection signatures and adaptive characteristics in soybean populations. A core set of 245 accessions (112 wild-type, 79 landrace, and 54 improvement soybeans) selected from 4,234 soybean accessions was re-sequenced. Their genomic architectures were examined according to the domestication and improvement, and accessions were then classified into 3 wild-type, 2 landrace, and 2 improvement subgroups based on various population analyses. Selection and gene set enrichment analyses revealed that the landrace subgroups have selection signals for soybean-cyst nematode HG type 0 and seed development with germination, and that the improvement subgroups have selection signals for plant development with viability and seed development with embryo development, respectively. The adaptive characteristic for soybean-cyst nematode was partially underpinned by multiple resistance accessions, and the characteristics related to seed development were supported by our phenotypic findings for seed weights. Furthermore, their adaptive characteristics were also confirmed as genome-based evidence, and unique genomic regions that exhibit distinct selection and selective sweep patterns were revealed for 13 candidate genes.

CONCLUSIONS

Although our findings require further biological validation, they provide valuable information about soybean breeding strategies and present new options for breeders seeking donor lines to improve soybean populations.

Genome-wide genetic diversity is maintained through decades of soybean breeding in Canada

KEY MESSAGE

Genetic diversity in Canadian soybean is maintained over decades of selection in two public breeding programs. Breeders have used a portion of the genetic diversity available in germplasm collections. Both public and private breeding efforts have been critical for the development of soybean cultivars grown around the world. Global genetic diversity of soybean has been well characterized; however, this diversity is not well studied at the breeding program scale. The objective of this study was to characterize genetic diversity over decades of breeding in two public soybean breeding programs at the University of Guelph, Canada. To address this objective, a pedigree-related panel combining 296 soybean accessions from the Ridgetown and Guelph Campus breeding programs was studied. The accessions were genotyped using genotyping-by-sequencing, imputed using the GmHapMap reference genotypes resulting in more than 3.8M SNPs, further filtered to 77k SNPs. Population structure analysis did not identify structure between the breeding programs and historical germplasm. The linkage disequilibrium decay ranged from 400 to 600 kb on average in euchromatic regions. Nucleotide diversity over decades of breeding shows that historical accessions had the highest nucleotide diversity, with significant decreases corresponding to the initial breeding activity in Canada; however, genetic diversity has increased in the last 20 years in both breeding programs. Maturity gene E2 was nearly fixed for e2 in Ridgetown accessions, while unfixed in Guelph accessions. Comparison of the breeding programs to the USDA germplasm collection reveals that breeders have only used a portion of the available genetic diversity, allowing future breeders to exploit this untapped resource. The approach used in this study may be of interest to other breeding programs for evaluating changes in genetic diversity resulting from breeding activities.

Effect of marker segregation distortion on high density linkage map construction and QTL mapping in Soybean (Glycine max L.)

Marker segregation distortion is a natural phenomenon. Severely distorted markers are usually excluded in the construction of linkage maps. We investigated the effect of marker segregation distortion on linkage map construction and quantitative trait locus (QTL) mapping. A total of 519 recombinant inbred lines of soybean from orthogonal and reciprocal crosses between LSZZH and NN493-1 were genotyped by specific length amplified fragment markers and seed linoleic acid content was measured in three environments. As a result, twenty linkage groups were constructed with 11,846 markers, including 1513 (12.77%) significantly distorted markers, on 20 chromosomes, and the map length was 2475.86 cM with an average marker-interval of 0.21 cM. The inclusion of distorted markers in the analysis was shown to not only improve the grouping of the markers from the same chromosomes, and the consistency of linkage maps with genome, but also increase genome coverage by markers. Combining genotypic data from both orthogonal and reciprocal crosses decreased the proportion of distorted markers and then improved the quality of linkage maps. Validation of the linkage maps was confirmed by the high collinearity between positions of markers in the soybean reference genome and in linkage maps and by the high consistency of 24 QTL regions in this study compared with the previously reported QTLs and lipid metabolism related genes. Additionally, linkage maps that include distorted markers could add more information to the outputs from QTL mapping. These results provide important information for linkage mapping, gene cloning and marker-assisted selection in soybean.

Association mapping of loci controlling genetic and environmental interaction of soybean flowering time under various photo-thermal conditions

BACKGROUND

Soybean (Glycine max (L.) Merr.) is a short day plant. Its flowering and maturity time are controlled by genetic and environmental factors, as well the interaction between the two factors. Previous studies have shown that both genetic and environmental factors, mainly photoperiod and temperature, control flowering time of soybean. Additionally, these studies have reported gene × gene and gene × environment interactions on flowering time. However, the effects of quantitative trait loci (QTL) in response to photoperiod and temperature have not been well evaluated. The objectives of the current study were to identify the effects of loci associated with flowering time under different photo-thermal conditions and to understand the effects of interaction between loci and environment on soybean flowering.

METHODS

Different photoperiod and temperature combinations were obtained by adjusting sowing dates (spring sowing and summer sowing) or day-length (12 h, 16 h). Association mapping was performed on 91 soybean cultivars from different maturity groups (MG000-VIII) using 172 SSR markers and 5107 SNPs from the Illumina SoySNP6K iSelectBeadChip. The effects of the interaction between QTL and environments on flowering time were also analysed using the QTXNetwork.

RESULTS

Large-effect loci were detected on Gm 11, Gm 16 and Gm 20 as in previous reports. Most loci associated with flowering time are sensitive to photo-thermal conditions. Number of loci associated with flowering time was more under the long day (LD) than under the short day (SD) condition. The variation of flowering time among the soybean cultivars mostly resulted from the epistasis × environment and additive × environment interactions. Among the three candidate loci, i.e. Gm04_4497001 (near GmCOL3a), Gm16_30766209 (near GmFT2a and GmFT2b) and Gm19_47514601 (E3 or GmPhyA3), the Gm04_4497001 may be the key locus interacting with other loci for controlling soybean flowering time.

CONCLUSION

The effects of loci associated with the flowering time of soybean were dependent upon the photo-thermal conditions. This study facilitates the understanding of the genetic mechanism of soybean flowering and molecular breeding for the improvement of soybean adaptability to specific and/or broad regions.

Comparison of Genetic Diversity between Chinese and American Soybean (Glycine max (L.)) Accessions Revealed by High-Density SNPs

Soybean is one of the most important economic crops for both China and the United States (US). The exchange of germplasm between these two countries has long been active. In order to investigate genetic relationships between Chinese and US soybean germplasm, 277 Chinese soybean accessions and 300 US soybean accessions from geographically diverse regions were analyzed using 5,361 SNP markers. The genetic diversity and the polymorphism information content (PIC) of the Chinese accessions was higher than that of the US accessions. Population structure analysis, principal component analysis, and cluster analysis all showed that the genetic basis of Chinese soybeans is distinct from that of the USA. The groupings observed in clustering analysis reflected the geographical origins of the accessions; this conclusion was validated with both genetic distance analysis and relative kinship analysis. F_ST-based and EigenGWAS statistical analysis revealed high genetic variation between the two subpopulations. Analysis of the 10 loci with the strongest selection signals showed that many loci were located in chromosome regions that have previously been identified as quantitative trait loci (QTL) associated with environmental-adaptation-related and yield-related traits. The pattern of diversity among the American and Chinese accessions should help breeders to select appropriate parental accessions to enhance the performance of future soybean cultivars.