Major soybean maturity gene haplotypes revealed by SNPViz analysis of 72 sequenced soybean genomes

Genome-Wide Association Studies Prioritize Genes Controlling Seed Size and Reproductive Period Length in Soybean

Hundred-seed weight (HSW) and reproductive period length (RPL) are two major agronomic traits critical for soybean production and adaptation. However, both traits are quantitatively controlled by multiple genes that have yet to be comprehensively elucidated due to the lack of major genes; thereby, the genetic basis is largely unknown. In the present study, we conducted comprehensive genome-wide association analyses (GWAS) of HSW and RPL with multiple sets of accessions that were phenotyped across different environments. The large-scale analysis led to the identification of sixty-one and seventy-four significant QTLs for HSW and RPL, respectively. An ortholog-based search analysis prioritized the most promising candidate genes for the QTLs, including nine genes (TTG2, BZR1, BRI1, ANT, KLU, EOD1/BB, GPA1, ABA2, and ABI5) for HSW QTLs and nine genes (such as AGL8, AGL9, TOC1, and COL4) and six known soybean flowering time genes (E2, E3, E4, Tof11, Tof12, and FT2b) for RPL QTLs. We also demonstrated that some QTLs were targeted during domestication to drive the artificial selection of both traits towards human-favored traits. Local adaptation likely contributes to the increased genomic diversity of the QTLs underlying RPL. The results provide additional insight into the genetic basis of HSW and RPL and prioritize a valuable resource of candidate genes that merits further investigation to reveal the complex molecular mechanism and facilitate soybean improvement.

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

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

The genetic architecture of soybean photothermal adaptation to high latitudes

Soybean is a major plant protein source for both human food and animal feed, but to meet global demands as well as a trend towards regional production, soybean cultivation needs to be expanded to higher latitudes. In this study, we developed a large diversity panel consisting of 1503 early-maturing soybean lines and used genome-wide association mapping to dissect the genetic architecture underlying two crucial adaptation traits, flowering time and maturity. This revealed several known maturity loci, E1, E2, E3, and E4, and the growth habit locus Dt2 as causal candidate loci, and also a novel putative causal locus, GmFRL1, encoding a homolog of the vernalization pathway gene FRIGIDA-like 1. In addition, the scan for quantitative trait locus (QTL)-by-environment interactions identified GmAPETALA1d as a candidate gene for a QTL with environment-dependent reversed allelic effects. The polymorphisms of these candidate genes were identified using whole-genome resequencing data of 338 soybeans, which also revealed a novel E4 variant, e4-par, carried by 11 lines, with nine of them originating from Central Europe. Collectively, our results illustrate how combinations of QTL and their interactions with the environment facilitate the photothermal adaptation of soybean to regions far beyond its center of origin.

Genetic architecture of protein and oil content in soybean seed and meal

Soybean is grown primarily for the protein and oil extracted from its seed and its value is influenced by these components. The objective of this study was to map marker-trait associations (MTAs) for the concentration of seed protein, oil, and meal protein using the soybean nested association mapping (SoyNAM) population. The composition traits were evaluated on seed harvested from over 5000 inbred lines of the SoyNAM population grown in 10 field locations across 3 years. Estimated heritabilities were at least 0.85 for all three traits. The genotyping of lines with single nucleotide polymorphism markers resulted in the identification of 107 MTAs for the three traits. When MTAs for the three traits that mapped within 5 cM intervals were binned together, the MTAs were mapped to 64 intervals on 19 of the 20 soybean chromosomes. The majority of the MTA effects were small and of the 107 MTAs, 37 were for protein content, 39 for meal protein, and 31 for oil content. For cases where a protein and oil MTAs mapped to the same interval, most (94%) significant effects were opposite for the two traits, consistent with the negative correlation between these traits. A coexpression analysis identified candidate genes linked to MTAs and 18 candidate genes were identified. The large number of small effect MTAs for the composition traits suggest that genomic prediction would be more effective in improving these traits than marker-assisted selection.

Characterization of the soybean KRP gene family reveals a key role for GmKRP2a in root development

Kip-related proteins (KRPs), as inhibitory proteins of cyclin-dependent kinases, are involved in the growth and development of plants by regulating the activity of the CYC-CDK complex to control cell cycle progression. The KRP gene family has been identified in several plants, and several KRP proteins from Arabidopsis thaliana have been functionally characterized. However, there is little research on KRP genes in soybean, which is an economically important crop. In this study, we identified nine GmKRP genes in the Glycine max genome using HMM modeling and BLASTP searches. Protein subcellular localization and conserved motif analysis showed soybean KRP proteins located in the nucleus, and the C-terminal protein sequence was highly conserved. By investigating the expression patterns in various tissues, we found that all GmKRPs exhibited transcript abundance, while several showed tissue-specific expression patterns. By analyzing the promoter region, we found that light, low temperature, an anaerobic environment, and hormones-related cis-elements were abundant. In addition, we performed a co-expression analysis of the GmKRP gene family, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) set enrichment analysis. The co-expressing genes were mainly involved in RNA synthesis and modification and energy metabolism. Furthermore, the GmKRP2a gene, a member of the soybean KRP family, was cloned for further functional analysis. GmKRP2a is located in the nucleus and participates in root development by regulating cell cycle progression. RNA-seq results indicated that GmKRP2a is involved in cell cycle regulation through ribosome regulation, cell expansion, hormone response, stress response, and plant pathogen response pathways. To our knowledge, this is the first study to identify and characterize the KRP gene family in soybean.

Altered regulation of flowering expands growth ranges and maximizes yields in major crops

Flowering time influences reproductive success in plants and has a significant impact on yield in grain crops. Flowering time is regulated by a variety of environmental factors, with daylength often playing an important role. Crops can be categorized into different types according to their photoperiod requirements for flowering. For instance, long-day crops include wheat (Triticum aestivum), barley (Hordeum vulgare), and pea (Pisum sativum), while short-day crops include rice (Oryza sativa), soybean (Glycine max), and maize (Zea mays). Understanding the molecular regulation of flowering and genotypic variation therein is important for molecular breeding and crop improvement. This paper reviews the regulation of flowering in different crop species with a particular focus on how photoperiod-related genes facilitate adaptation to local environments.

Pinpointing Genomic Regions and Candidate Genes Associated with Seed Oil and Protein Content in Soybean through an Integrative Transcriptomic and QTL Meta-Analysis

Soybean with enriched nutrients has emerged as a prominent source of edible oil and protein. In the present study, a meta-analysis was performed by integrating quantitative trait loci (QTLs) information, region-specific association and transcriptomic analysis. Analysis of about a thousand QTLs previously identified in soybean helped to pinpoint 14 meta-QTLs for oil and 16 meta-QTLs for protein content. Similarly, region-specific association analysis using whole genome re-sequenced data was performed for the most promising meta-QTL on chromosomes 6 and 20. Only 94 out of 468 genes related to fatty acid and protein metabolic pathways identified within the meta-QTL region were found to be expressed in seeds. Allele mining and haplotyping of these selected genes were performed using whole genome resequencing data. Interestingly, a significant haplotypic association of some genes with oil and protein content was observed, for instance, in the case of FAD2-1B gene, an average seed oil content of 20.22% for haplotype 1 compared to 15.52% for haplotype 5 was observed. In addition, the mutation S86F in the FAD2-1B gene produces a destabilizing effect of (ΔΔG Stability) -0.31 kcal/mol. Transcriptomic analysis revealed the tissue-specific expression of candidate genes. Based on their higher expression in seed developmental stages, genes such as sugar transporter, fatty acid desaturase (FAD), lipid transporter, major facilitator protein and amino acid transporter can be targeted for functional validation. The approach and information generated in the present study will be helpful in the map-based cloning of regulatory genes, as well as for marker-assisted breeding in soybean.

The Key to the Future Lies in the Past: Insights from Grain Legume Domestication and Improvement Should Inform Future Breeding Strategies

Crop domestication is a co-evolutionary process that has rendered plants and animals significantly dependent on human interventions for survival and propagation. Grain legumes have played an important role in the development of Neolithic agriculture some 12,000 years ago. Despite being early companions of cereals in the origin and evolution of agriculture, the understanding of grain legume domestication has lagged behind that of cereals. Adapting plants for human use has resulted in distinct morpho-physiological changes between the wild ancestors and domesticates, and this distinction has been the focus of several studies aimed at understanding the domestication process and the genetic diversity bottlenecks created. Growing evidence from research on archeological remains, combined with genetic analysis and the geographical distribution of wild forms, has improved the resolution of the process of domestication, diversification and crop improvement. In this review, we summarize the significance of legume wild relatives as reservoirs of novel genetic variation for crop breeding programs. We describe key legume features, which evolved in response to anthropogenic activities. Here, we highlight how whole genome sequencing and incorporation of omics-level data have expanded our capacity to monitor the genetic changes accompanying these processes. Finally, we present our perspective on alternative routes centered on de novo domestication and re-domestication to impart significant agronomic advances of novel crops over existing commodities. A finely resolved domestication history of grain legumes will uncover future breeding targets to develop modern cultivars enriched with alleles that improve yield, quality and stress tolerance.

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population

Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

A novel Synthetic phenotype association study approach reveals the landscape of association for genomic variants and phenotypes

INTRODUCTION

Genome-Wide Association Studies (GWAS) identify tagging variants in the genome that are statistically associated with the phenotype because of their linkage disequilibrium (LD) relationship with the causative mutation (CM). When both low-density genotyped accession panels with phenotypes and resequenced data accession panels are available, tagging variants can assist with post-GWAS challenges in CM discovery.

OBJECTIVES

Our objective was to identify additional GWAS evaluation criteria to assess correspondence between genomic variants and phenotypes, as well as enable deeper analysis of the localized landscape of association.

METHODS

We used genomic variant positions as Synthetic phenotypes in GWAS that we named "Synthetic phenotype association study" (SPAS). The extreme case of SPAS is what we call an "Inverse GWAS" where we used CM positions of cloned soybean genes. We developed and validated the Accuracy concept as a measure of the correspondence between variant positions and phenotypes.

RESULTS

The SPAS approach demonstrated that the genotype status of an associated variant used as a Synthetic phenotype enabled us to explore the relationships between tagging variants and CMs, and further, that utilizing CMs as Synthetic phenotypes in Inverse GWAS illuminated the landscape of association. We implemented the Accuracy calculation for a curated accession panel to an online Accuracy calculation tool (AccuTool) as a resource for gene identification in soybean. We demonstrated our concepts on three examples of soybean cloned genes. As a result of our findings, we devised an enhanced "GWAS to Genes" analysis (Synthetic phenotype to CM strategy, SP2CM). Using SP2CM, we identified a CM for a novel gene.

CONCLUSION

The SP2CM strategy utilizing Synthetic phenotypes and the Accuracy calculation of correspondence provides crucial information to assist researchers in CM discovery. The impact of this work is a more effective evaluation of landscapes of GWAS associations.

Dissection of the practical soybean breeding pipeline by developing ZDX1, a high-throughput functional array

KEY MESSAGE

We developed the ZDX1 high-throughput functional soybean array for high accuracy evaluation and selection of both parents and progeny, which can greatly accelerate soybean breeding. Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2214 representative soybean accessions, we developed the high-throughput functional array ZDX1, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to important traits. By application of the array, a total of 817 accessions were genotyped, including three subpopulations of candidate parental lines, parental lines and their progeny from practical breeding. The fixed SNPs were identified in progeny, indicating artificial selection during the breeding process. By identifying functional sites of target traits, novel soybean cyst nematode-resistant progeny and maturity-related novel sources were identified by allele combinations, demonstrating that functional sites provide an efficient method for the rapid screening of desirable traits or gene sources. Notably, we found that the breeding index (BI) was a good indicator for progeny selection. Superior progeny were derived from the combination of distantly related parents, with at least one parent having a higher BI. Furthermore, new combinations based on good performance were proposed for further breeding after excluding redundant and closely related parents. Genomic best linear unbiased prediction (GBLUP) analysis was the best analysis method and achieved the highest accuracy in predicting four traits when comparing SNPs in genic regions rather than whole genomic or intergenic SNPs. The prediction accuracy was improved by 32.1% by using progeny to expand the training population. Collectively, a versatile assay demonstrated that the functional ZDX1 array provided efficient information for the design and optimization of a breeding pipeline for accelerated soybean breeding.

CONSTANS Polymorphism Modulates Flowering Time and Maturity in Soybean

CONSTANS (CO) plays a critical role in the photoperiodic flowering pathway. However, the function of soybean CO orthologs and the molecular mechanisms in regulating flowering remain largely unknown. This study characterized the natural variations in CO family genes and their association with flowering time and maturity in soybeans. A total of 21 soybean CO family genes (GmCOLs) were cloned and sequenced in 128 varieties covering 14 known maturity groups (MG 0000-MG X from earliest to latest maturity). Regarding the whole genomic region involving these genes, GmCOL1, GmCOL3, GmCOL8, GmCOL9, GmCOL10, and GmCOL13 were conserved, and the remaining 15 genes showed genetic variation that was brought about by mutation, namely, all single-nucleotide polymorphisms (SNPs) and insertions-deletions (InDels). In addition, a few genes showed some strong linkage disequilibrium. Point mutations were found in 15 GmCOL genes, which can lead to changes in the potential protein structure. Early flowering and maturation were related to eight genes (GmCOL1/3/4/8/13/15/16/19). For flowering and maturation, 11 genes (GmCOL2/5/6/14/20/22/23/24/25/26/28) expressed divergent physiognomy. Haplotype analysis indicated that the haplotypes of GmCOL5-Hap2, GmCOL13-Hap2/3, and GmCOL28-Hap2 were associated with flowering dates and soybean maturity. This study helps address the role of GmCOL family genes in adapting to diverse environments, particularly when it is necessary to regulate soybean flowering dates and maturity.

Bioinformatics for plant and agricultural discoveries in the age of multiomics: A review and case study of maize nodal root growth under water deficit

Advances in next-generation sequencing and other high-throughput technologies have facilitated multiomics research, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics. The resultant emerging multiomics data have brought new challenges as well as opportunities, as seen in the plant and agriculture science domains. We reviewed several bioinformatic and computational methods, models, and platforms, and we have highlighted some of our in-house developed efforts aimed at multiomics data analysis, integration, and management issues faced by the research community. A case study using multiomics datasets generated from our studies of maize nodal root growth under water deficit stress demonstrates the power of these datasets and some other publicly available tools. This analysis also sheds light on the landscape of such applied bioinformatic tools currently available for plant and crop science studies and introduces emerging trends and how they may affect the future.

Geographic distribution of the E1 family of genes and their effects on reproductive timing in soybean

BACKGROUND

Soybean is an economically important crop which flowers predominantly in response to photoperiod. Several major loci controlling the quantitative trait for reproductive timing have been identified, of which allelic combinations at three of these loci, E1, E2, and E3, are the dominant factors driving time to flower and reproductive period. However, functional genomics studies have identified additional loci which affect reproductive timing, many of which are less understood. A better characterization of these genes will enable fine-tuning of adaptation to various production environments. Two such genes, E1La and E1Lb, have been implicated in flowering by previous studies, but their effects have yet to be assessed under natural photoperiod regimes.

RESULTS

Natural and induced variants of E1La and E1Lb were identified and introgressed into lines harboring either E1 or its early flowering variant, e1-as. Lines were evaluated for days to flower and maturity in a Maturity Group (MG) III production environment. These results revealed that variation in E1La and E1Lb promoted earlier flowering and maturity, with stronger effects in e1-as background than in an E1 background. The geographic distribution of E1La alleles among wild and cultivated soybean revealed that natural variation in E1La likely contributed to northern expansion of wild soybean, while breeding programs in North America exploited e1-as to develop cultivars adapted to northern latitudes.

CONCLUSION

This research identified novel alleles of the E1 paralogues, E1La and E1Lb, which promote flowering and maturity under natural photoperiods. These loci represent sources of genetic variation which have been under-utilized in North American breeding programs to control reproductive timing, and which can be valuable additions to a breeder's molecular toolbox.

Rapid excavating a FLOWERING LOCUS T-regulator NF-YA using genotyping-by-sequencing

Soybean (Glycine max (L.) Merrill) is one of the most important crop plants in the world as an important source of protein for both human consumption and livestock fodder. As flowering time contributes to yield, finding new QTLs and further identifying candidate genes associated with various flowering time are fundamental to enhancing soybean yield. In this study, a set of 120 recombinant inbred lines (RILs) which was developed from a cross of two soybean cultivars, Suinong4 (SN4) and ZK168, were genotyped by genotyping-by-sequencing (GBS) approach and phenotyped to expand the cognitive of flowering time by quantitative trait loci (QTL) analysis. Eventually, three stable QTLs related to flowering time which were detected separately located on chromosome 14, 18, and 19 under long-day (LD) conditions. We predicted candidate genes for each QTL and carried out association analyses between the putative causal alleles and flowering time. Moreover, a transient transfection assay was performed and showed that NUCLEAR FACTOR YA 1b (GmNF-YA1b) as a strong candidate for the QTL on chromosome 19 might affect flowering time by suppressing the expression of FLOWERING LOCUS T (GmFT) genes in soybean. QTLs detected in this study would provide fundamental resources for finding candidate genes and clarify the mechanisms of flowering which would be helpful for breeding novel high-yielding soybean cultivars.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01237-w.

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.

Genetic variation among 481 diverse soybean accessions, inferred from genomic re-sequencing

We report characteristics of soybean genetic diversity and structure from the resequencing of 481 diverse soybean accessions, comprising 52 wild (Glycine soja) selections and 429 cultivated (Glycine max) varieties (landraces and elites). This data was used to identify 7.8 million SNPs, to predict SNP effects relative to genic regions, and to identify the genetic structure, relationships, and linkage disequilibrium. We found evidence of distinct, mostly independent selection of lineages by particular geographic location. Among cultivated varieties, we identified numerous highly conserved regions, suggesting selection during domestication. Comparisons of these accessions against the whole U.S. germplasm genotyped with the SoySNP50K iSelect BeadChip revealed that over 95% of the re-sequenced accessions have a high similarity to their SoySNP50K counterparts. Probable errors in seed source or genotype tracking were also identified in approximately 5% of the accessions.

Soybean (Glycine max) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics

Here, we describe a worldwide haplotype map for soybean (GmHapMap) constructed using whole-genome sequence data for 1007 Glycine max accessions and yielding 14.9 million variants as well as 4.3 M tag single-nucleotide polymorphisms (SNPs). When sampling random subsets of these accessions, the number of variants and tag SNPs plateaued beyond approximately 800 and 600 accessions, respectively. This suggests extensive coverage of diversity within the cultivated soybean. GmHapMap variants were imputed onto 21 618 previously genotyped accessions with up to 96% success for common alleles. A local association analysis was performed with the imputed data using markers located in a 1-Mb region known to contribute to seed oil content and enabled us to identify a candidate causal SNP residing in the NPC1 gene. We determined gene-centric haplotypes (407 867 GCHs) for the 55 589 genes and showed that such haplotypes can help to identify alleles that differ in the resulting phenotype. Finally, we predicted 18 031 putative loss-of-function (LOF) mutations in 10 662 genes and illustrated how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for applied soybean genomics and breeding.

Soybean (Glycine max) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics

Here, we describe a worldwide haplotype map for soybean (GmHapMap) constructed using whole-genome sequence data for 1007 Glycine max accessions and yielding 14.9 million variants as well as 4.3 M tag single-nucleotide polymorphisms (SNPs). When sampling random subsets of these accessions, the number of variants and tag SNPs plateaued beyond approximately 800 and 600 accessions, respectively. This suggests extensive coverage of diversity within the cultivated soybean. GmHapMap variants were imputed onto 21 618 previously genotyped accessions with up to 96% success for common alleles. A local association analysis was performed with the imputed data using markers located in a 1-Mb region known to contribute to seed oil content and enabled us to identify a candidate causal SNP residing in the NPC1 gene. We determined gene-centric haplotypes (407 867 GCHs) for the 55 589 genes and showed that such haplotypes can help to identify alleles that differ in the resulting phenotype. Finally, we predicted 18 031 putative loss-of-function (LOF) mutations in 10 662 genes and illustrated how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for applied soybean genomics and breeding.

Molecular-assisted breeding for soybean with high oleic/low linolenic acid and elevated vitamin E in the seed oil

The uses of vegetable oils are determined by functional properties arising from their chemical composition. Soybean oil was previously used in margarines and baked foods after partial hydrogenation to achieve heat and oxidative stability. This process, however, generates trans fats that are now excluded from food use because of cardiovascular health risks. Also present in soybean oil are the anti-oxidant tocopherols, with α-tocopherol (vitamin E) typically present as a minor component compared to γ-tocopherol. Genetic improvement of the fatty acid profile and tocopherol profile is an attractive solution to increase the functional and health qualities of soybean oil. The objective of this research was to develop resources to directly select with molecular markers for the elevated vitamin E trait in soybean oil and to use a molecular breeding approach to combine elevated vitamin E with the high oleic/low linolenic acid seed oil trait that improves oil functionality and nutrition. New soybean germplasm was developed from the molecular breeding strategy that selected for alleles of six targeted genes. Seed oil from the novel soybean germplasm was confirmed to contain increased vitamin E α-tocopherol along with a high oleic acid/low linolenic acid profile.

Haplotype diversity underlying quantitative traits in Canadian soybean breeding germplasm

KEY MESSAGE

Identification of marker-trait associations and trait-associated haplotypes in breeding germplasm identifies regions under selection and highlights changes in haplotype diversity over decades of soybean improvement in Canada. Understanding marker-trait associations using genome-wide association in soybean is typically carried out in diverse germplasm groups where identified loci are often not applicable to soybean breeding efforts. To address this challenge, this study focuses on defining marker-trait associations in breeding germplasm and studying the underlying haplotypes in these regions to assess genetic change through decades of selection. Phenotype data were generated for 175 accessions across multiple environments in Ontario, Canada. A set of 76,549 SNPs were used in the association analysis. A total of 23 genomic regions were identified as significantly associated with yield (5), days to maturity (5), seed oil (3), seed protein (5) and 100-seed weight (5), of which 14 are novel. Each significant region was haplotyped to assess haplotype diversity of the underlying genomic region, identifying ten regions with trait-associated haplotypes in the breeding germplasm. The range of genomic length for these regions (7.2 kb to 6.8 Mb) indicates variation in regional LD for the trait-associated regions. Six of these regions showed changes between eras of breeding, from historical to modern and experimental soybean accessions. Continued selection on these regions may necessitate introgression of novel parental genetic diversity as some haplotypes were fixed within the breeding germplasm. This finding highlights the importance of studying associations and haplotype diversity at a breeding program scale to understand breeders' selections and trends in soybean improvement over time. The haplotypes may also be used as a tool for selection of parental germplasm to inform breeder's decisions on further soybean improvement.

Molecular-assisted breeding for improved carbohydrate profiles in soybean seed

KEY MESSAGE

Two independent variant raffinose synthase 3 (RS3) alleles produced an equivalent phenotype and implicated the gene as a key contributor to soybean seed carbohydrate phenotype. Soybean is an important crop because the processed seed is utilized as a vegetable oil and a high protein meal typically used in livestock feeds. Raffinose and stachyose, the raffinose family of oligosaccharides (RFO) carbohydrate components of the seed, are synthesized in developing soybean seeds from sucrose and galactinol. Sucrose is considered positive for metabolizable energy, while RFO are anti-nutritional factors in diets of monogastric animals such as humans, poultry, and swine. To increase metabolizable energy available in soybean seed meal, prior research has been successful in deploying variant alleles of key soybean raffinose synthase (RS) genes leading to reductions or near elimination of seed RFO, with significant increases in seed sucrose. The objective of this research was to investigate the specific role of variants of the RS3 gene in a genomic context and improve molecular marker-assisted selection for the ultra-low (UL) RFO phenotype in soybean seeds. The results revealed a new variant of the RS3 allele (rs3 snp5, rs3 snp6) contributed to the UL RFO phenotype when mutant alleles of RS2 were present. The variant RS3 allele identified was present in about 15% of a small set of soybean cultivars released in North America. A missense allele of the RS3 gene (rs3 G75E) also produced the UL RFO phenotype when combined with mutant alleles of RS2. The discoveries reported here enable direct marker-assisted selection for an improved soybean meal trait that has the potential to add value to soybean by improving the metabolizable energy of the meal.

The effects and interaction of soybean maturity gene alleles controlling flowering time, maturity, and adaptation in tropical environments

BACKGROUND

Soybean is native to the temperate zones of East Asia. Poor yields of soybean in West African countries may be partially attributed to inadequate adaptation of soybean to tropical environments. Adaptation will require knowledge of the effects of allelic combinations of major maturity genes (E1, E2, and E3) and stem architecture. The long juvenile trait (J) influences soybean flowering time in short, ~ 12 h days, which characterize tropical latitudes. Soybean plant architecture includes determinate or indeterminate stem phenotypes controlled by the Dt1 gene. Understanding the influence of these genetic components on plant development and adaptation is key to optimize phenology and improve soybean yield potential in tropical environments.

RESULTS

Soybean lines from five recombinant inbred populations were developed that varied in their combinations of targeted genes. The soybean lines were field tested in multiple environments and characterized for days to flowering (DTF), days to maturity (DTM), and plant height in locations throughout northern Ghana, and allelic combinations were determined for each line for associating genotype with phenotype. The results revealed significant differences based on genotype for DTF and DTM and allowed the comparison of different variant alleles of those genes. The mutant alleles of J and E1 had significant impact on DTF and DTM, and alleles of those genes interacted with each other for DTF but not DTM. The Dt1 gene significantly influenced plant height but not DTF or DTM.

CONCLUSIONS

This research identified major and minor effect alleles of soybean genes that can be combined to control DTF, DTM, and plant height in short day tropical environments in Ghana. These phenotypes contribute to adaptation to a low latitude environment that can be optimized in a soybean breeding program with targeted selection of desired allele combinations. The knowledge of the genetic control of these traits will enhance molecular breeding to produce optimally adapted soybean varieties targeted to tropical environments.

Quantitative Genomic Dissection of Soybean Yield Components

Soybean is a crop of major economic importance with low rates of genetic gains for grain yield compared to other field crops. A deeper understanding of the genetic architecture of yield components may enable better ways to tackle the breeding challenges. Key yield components include the total number of pods, nodes and the ratio pods per node. We evaluated the SoyNAM population, containing approximately 5600 lines from 40 biparental families that share a common parent, in 6 environments distributed across 3 years. The study indicates that the yield components under evaluation have low heritability, a reasonable amount of epistatic control, and partially oligogenic architecture: 18 quantitative trait loci were identified across the three yield components using multi-approach signal detection. Genetic correlation between yield and yield components was highly variable from family-to-family, ranging from -0.2 to 0.5. The genotype-by-environment correlation of yield components ranged from -0.1 to 0.4 within families. The number of pods can be utilized for indirect selection of yield. The selection of soybean for enhanced yield components can be successfully performed via genomic prediction, but the challenging data collections necessary to recalibrate models over time makes the introgression of QTL a potentially more feasible breeding strategy. The genomic prediction of yield components was relatively accurate across families, but less accurate predictions were obtained from within family predictions and predicting families not observed included in the calibration set.

Knowledge Base Commons (KBCommons) v1.1: a universal framework for multi-omics data integration and biological discoveries

BACKGROUND

Knowledge Base Commons (KBCommons) v1.1 is a universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating and visualizing multiple organisms' genomics and integrative omics data. KBCommons is designed and developed to integrate diverse multi-level omics data and to support biological discoveries for all species via a common platform.

METHODS

KBCommons has four modules including data storage, data processing, data accessing, and web interface for data management and retrieval. It provides a comprehensive framework for new plant-specific, animal-specific, virus-specific, bacteria-specific or human disease-specific knowledge base (KB) creation, for adding new genome versions and additional multi-omics data to existing KBs, and for exploring existing datasets within current KBs.

RESULTS

KBCommons has an array of tools for data visualization and data analytics such as multiple gene/metabolite search, gene family/Pfam/Panther function annotation search, miRNA/metabolite/trait/SNP search, differential gene expression analysis, and bulk data download capacity. It contains a highly reliable data privilege management system to make users' data publicly available easily and to share private or pre-publication data with members in their collaborative groups safely and securely. It allows users to conduct data analysis using our in-house developed workflow functionalities that are linked to XSEDE high performance computing resources. Using KBCommons' intuitive web interface, users can easily retrieve genomic data, multi-omics data and analysis results from workflow according to their requirements and interests.

CONCLUSIONS

KBCommons addresses the needs of many diverse research communities to have a comprehensive multi-level OMICS web resource for data retrieval, sharing, analysis and visualization. KBCommons can be publicly accessed through a dedicated link for all organisms at http://kbcommons.org/.

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

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

Revolutions in agriculture chart a course for targeted breeding of old and new crops

The dominance of the major crops that feed humans and their livestock arose from agricultural revolutions that increased productivity and adapted plants to large-scale farming practices. Two hormone systems that universally control flowering and plant architecture, florigen and gibberellin, were the source of multiple revolutions that modified reproductive transitions and proportional growth among plant parts. Although step changes based on serendipitous mutations in these hormone systems laid the foundation, genetic and agronomic tuning were required for broad agricultural benefits. We propose that generating targeted genetic variation in core components of both systems would elicit a wider range of phenotypic variation. Incorporating this enhanced diversity into breeding programs of conventional and underutilized crops could help to meet the future needs of the human diet and promote sustainable agriculture.

Genetic insights into the modification of the pre-fertilization mechanisms during plant domestication

Plant domestication is the process of adapting plants to human use by selecting specific traits. The selection process often involves the modification of some components of the plant reproductive mechanisms. Allelic variants of genes associated with flowering time, vernalization, and the circadian clock are responsible for the adaptation of crops, such as rice, maize, barley, wheat, and tomato, to non-native latitudes. Modifications in the plant architecture and branching have been selected for higher yields and easier harvests. These phenotypes are often produced by alterations in the regulation of the transition of shoot apical meristems to inflorescences, and then to floral meristems. Floral homeotic mutants are responsible for popular double-flower phenotypes in Japanese cherries, roses, camellias, and lilies. The rise of peloric flowers in ornamentals such as snapdragon and florists' gloxinia is associated with non-functional alleles that control the relative expansion of lateral and ventral petals. Mechanisms to force outcrossing such as self-incompatibility have been removed in some tree crops cultivars such as almonds and peaches. In this review, we revisit some of these important concepts from the plant domestication perspective, focusing on four topics related to the pre-fertilization mechanisms: flowering time, inflorescence architecture, flower development, and pre-fertilization self-incompatibility mechanisms.

Phenotypic Variation and Genetic Architecture for Photosynthesis and Water Use Efficiency in Soybean (Glycine max L. Merr)

Photosynthesis (A) and intrinsic water use efficiency (WUE) are physiological traits directly influencing biomass production, conversion efficiency, and grain yield. Though the influence of physiological process on yield is widely known, studies assessing improvement strategies are rare due to laborious phenotyping and specialized equipment needs. This is one of the first studies to assess the genetic architecture underlying A and intrinsic WUE, as well as to evaluate the feasibility of implementing genomic prediction. A panel of 383 soybean recombinant inbred lines were evaluated in a multi-environment yield trial that included measurements of A and intrinsic WUE, using an infrared gas analyzer during R4-R5 growth stages. Genetic variability was found to support the possibility of genetic improvement through breeding. High genetic correlation between grain yield (GY) and A (0.80) was observed, suggesting increases in GY can be achieved through the improvement of A. Genome-wide association analysis revealed quantitative trait loci (QTLs) for these physiological traits. Cross-validation studies indicated high predictive ability (>0.65) for the implementation of genomic prediction as a viable strategy to improve physiological efficiency while reducing field phenotyping. This work provides core knowledge to develop new soybean cultivars with enhanced photosynthesis and water use efficiency through conventional breeding and genomic techniques.

QTL architecture of vine growth habit and gibberellin oxidase gene diversity in wild soybean (Glycine soja)

Vine growth habit (VGH) is a beneficial phenotype in many wild plants, and is considered an important domesticated-related trait in soybean. However, its genetic basis remains largely unclear. Hence, in the present study we used an integrated strategy combining linkage mapping and population genome diversity analyses to reveal the genetics of VGH in soybean. In this regard, two recombinant inbred line (RIL) populations derived by crossing a common wild soybean genotype (PI342618B) with two cultivated lines viz., NN 86-4 and NN 493-1 were used to map quantitative trait loci (QTL) for VGH. Here, we identified seven and five QTLs at flowering stage (R1) and maturity stage (R8), respectively, and among them qVGH-18-1, qVGH-18-2, qVGH-19-3, qVGH-19-4 were identified as major loci (R2 > 10% and detection time ≥2). However, qVGH-18-2 was considered as a main QTL for VGH being consistently identified in both RIL populations as well as all growth stages and cropping years. Out of all the annotated genes within qVGH-18-2, Glyma18g06870 was identified as the candidate gene and named as VGH1, which was a gibberellin oxidase (GAox) belongs to 2-oxoglutarate-dependent dioxygenase (2- ODD). Interestingly, there was one member of 2-ODD/GAox in qVGH-18-1 and qVGH-19-4 named as VGH2 and VGH3, respectively. Moreover, from sequencing data analysis VGH1 and three other GAox genes were found significantly divergent between vine and erect soybean with FST value larger than 0.25. Hence, GAox was assumed to play a major role in governing inheritance of VGH in soybean. Therefore, elucidating the genetic mechanism of GAox is very useful for exploring VGH and other stem traits, as well as genetic improvement of plant type in soybean.

Genetic relationship, population structure analysis and allelic characterization of flowering and maturity genes E1, E2, E3 and E4 among 90 Indian soybean landraces

A set of 90 Indian soybean landraces were analysed for polymorphism at 43 SSRs and five allele specific markers of four major genes involved in regulating flowering and photoperiod response. A total of 42 polymorphic SSRs had amplified 126 alleles which served as raw data for estimation of genetic relationship and population structure among 90 accessions. Rare alleles of four and three SSRs were detected in accessions IC18768 and IC15089, respectively. Gene diversity in the population ranges from 0.065 to 0.717 with a mean value of 0.411. The polymorphism information content of 42 SSRs varied from 0.063 to 0.668. Hierarchical clustering based on neighbour-joining method identified three major clusters among 90 soybean accessions. Model based population structure analysis divided the 90 soybean accessions into four populations (K = 4). Mean value of Fst for different populations ranged between 0.4143 and 0.7239. Genotyping of 90 accessions with allele specific markers had identified accession IC15089 as triple recessive mutant of flowering genes E1, E2 and photoperiod sensitivity gene E3. The triple mutant IC15089 (e1, e3, e3) had been characterized phenotypically and identified as early maturing (88 days) and photoperiod insensitive genotype under extended photoperiod. The present study characterized genetic relationship among 90 Indian soybean landraces and had identified a few diverse and unique genotypes for utilization in soybean breeding programmes targeting development of short duration and photoperiod insensitive varieties through marker assisted selection.

Transcriptome-Enabled Network Inference Revealed the GmCOL1 Feed-Forward Loop and Its Roles in Photoperiodic Flowering of Soybean

Photoperiodic flowering, a plant response to seasonal photoperiod changes in the control of reproductive transition, is an important agronomic trait that has been a central target of crop domestication and modern breeding programs. However, our understanding about the molecular mechanisms of photoperiodic flowering regulation in crop species is lagging behind. To better understand the regulatory gene networks controlling photoperiodic flowering of soybeans, we elucidated global gene expression patterns under different photoperiod regimes using the near isogenic lines (NILs) of maturity loci (E loci). Transcriptome signatures identified the unique roles of the E loci in photoperiodic flowering and a set of genes controlled by these loci. To elucidate the regulatory gene networks underlying photoperiodic flowering regulation, we developed the network inference algorithmic package CausNet that integrates sparse linear regression and Granger causality heuristics, with Gaussian approximation of bootstrapping to provide reliability scores for predicted regulatory interactions. Using the transcriptome data, CausNet inferred regulatory interactions among soybean flowering genes. Published reports in the literature provided empirical verification for several of CausNet's inferred regulatory interactions. We further confirmed the inferred regulatory roles of the flowering suppressors GmCOL1a and GmCOL1b using GmCOL1 RNAi transgenic soybean plants. Combinations of the alleles of GmCOL1 and the major maturity locus E1 demonstrated positive interaction between these genes, leading to enhanced suppression of flowering transition. Our work provides novel insights and testable hypotheses in the complex molecular mechanisms of photoperiodic flowering control in soybean and lays a framework for de novo prediction of biological networks controlling important agronomic traits in crops.

Understanding genetic control of root system architecture in soybean: Insights into the genetic basis of lateral root number

Developing crops with better root systems is a promising strategy to ensure productivity in both optimum and stress environments. Root system architectural traits in 397 soybean accessions were characterized and a high-density single nucleotide polymorphisms (SNPs)-based genome-wide association study was performed to identify the underlying genes associated with root structure. SNPs associated with root architectural traits specific to landraces and elite germplasm pools were detected. Four loci were detected in landraces for lateral root number (LRN) and distribution of root thickness in diameter Class I with a major locus on chromosome 16. This major loci was detected in the coding region of unknown protein, and subsequent analyses demonstrated that root traits are affected with mutated haplotypes of the gene. In elite germplasm pool, 3 significant SNPs in alanine-glyoxalate aminotransferase, Leucine-Rich Repeat receptor/No apical meristem, and unknown functional genes were found to govern multiple traits including root surface area and volume. However, no major loci were detected for LRN in elite germplasm. Nucleotide diversity analysis found evidence of selective sweeps around the landraces LRN gene. Soybean accessions with minor and mutated allelic variants of LRN gene were found to perform better in both water-limited and optimal field conditions.

Genetic Architecture of Soybean Yield and Agronomic Traits

Soybean is the world's leading source of vegetable protein and demand for its seed continues to grow. Breeders have successfully increased soybean yield, but the genetic architecture of yield and key agronomic traits is poorly understood. We developed a 40-mating soybean nested association mapping (NAM) population of 5,600 inbred lines that were characterized by single nucleotide polymorphism (SNP) markers and six agronomic traits in field trials in 22 environments. Analysis of the yield, agronomic, and SNP data revealed 23 significant marker-trait associations for yield, 19 for maturity, 15 for plant height, 17 for plant lodging, and 29 for seed mass. A higher frequency of estimated positive yield alleles was evident from elite founder parents than from exotic founders, although unique desirable alleles from the exotic group were identified, demonstrating the value of expanding the genetic base of US soybean breeding.

Allelic Variation and Distribution of the Major Maturity Genes in Different Soybean Collections

Soybean time of flowering and maturity are genetically controlled by E genes. Different allelic combinations of these genes determine soybean adaptation to a specific latitude. The paper describes the first attempt to assess adaptation of soybean genotypes developed and realized at Institute of Field and Vegetable Crops, Novi Sad, Serbia [Novi Sad (NS) varieties and breeding lines] based on E gene variation, as well as to comparatively assess E gene variation in North-American (NA), Chinese, and European genotypes, as most of the studies published so far deal with North-American and Chinese cultivars and breeding material. Allelic variation and distribution of the major maturity genes (E1, E2, E3, and E4) has been determined in 445 genotypes from soybean collections of NA ancestral lines, Chinese germplasm, and European varieties, as well as NS varieties and breeding lines. The study showed that allelic combinations of E1-E4 genes significantly determined the adaptation of varieties to different geographical regions, although they have different impacts on maturity. In general, each collection had one major E genotype haplogroup, comprising over 50% of the lines. The exceptions were European varieties that had two predominant haplogroups and NA ancestral lines distributed almost evenly among several haplogroups. As e1-as/e2/E3/E4 was the most common genotype in NS population, present in the best-performing genotypes in terms of yield, this specific allele combination was proposed as the optimal combination for the environments of Central-Eastern Europe.

Developmental Pathways Are Blueprints for Designing Successful Crops

Genes controlling plant development have been studied in multiple plant systems. This has provided deep insights into conserved genetic pathways controlling core developmental processes including meristem identity, phase transitions, determinacy, stem elongation, and branching. These pathways control plant growth patterns and are fundamentally important to crop biology and agriculture. This review describes the conserved pathways that control plant development, using Arabidopsis as a model. Historical examples of how plant development has been altered through selection to improve crop performance are then presented. These examples, drawn from diverse crops, show how the genetic pathways controlling development have been modified to increase yield or tailor growth patterns to suit local growing environments or specialized crop management practices. Strategies to apply current progress in genomics and developmental biology to future crop improvement are then discussed within the broader context of emerging trends in plant breeding. The ways that knowledge of developmental processes and understanding of gene function can contribute to crop improvement, beyond what can be achieved by selection alone, are emphasized. These include using genome re-sequencing, mutagenesis, and gene editing to identify or generate novel variation in developmental genes. The expanding scope for comparative genomics, the possibility to engineer new developmental traits and new approaches to resolve gene-gene or gene-environment interactions are also discussed. Finally, opportunities to integrate fundamental research and crop breeding are highlighted.

Comprehensive description of genomewide nucleotide and structural variation in short-season soya bean

Next-generation sequencing (NGS) and bioinformatics tools have greatly facilitated the characterization of nucleotide variation; nonetheless, an exhaustive description of both SNP haplotype diversity and of structural variation remains elusive in most species. In this study, we sequenced a representative set of 102 short-season soya beans and achieved an extensive coverage of both nucleotide diversity and structural variation (SV). We called close to 5M sequence variants (SNPs, MNPs and indels) and noticed that the number of unique haplotypes had plateaued within this set of germplasm (1.7M tag SNPs). This data set proved highly accurate (98.6%) based on a comparison of called genotypes at loci shared with a SNP array. We used this catalogue of SNPs as a reference panel to impute missing genotypes at untyped loci in data sets derived from lower density genotyping tools (150 K GBS-derived SNPs/530 samples). After imputation, 96.4% of the missing genotypes imputed in this fashion proved to be accurate. Using a combination of three bioinformatics pipelines, we uncovered ~92 K SVs (deletions, insertions, inversions, duplications, CNVs and translocations) and estimated that over 90% of these were accurate. Finally, we noticed that the duplication of certain genomic regions explained much of the residual heterozygosity at SNP loci in otherwise highly inbred soya bean accessions. This is the first time that a comprehensive description of both SNP haplotype diversity and SV has been achieved within a regionally relevant subset of a major crop.

Loss of Function of the E1-Like-b Gene Associates With Early Flowering Under Long-Day Conditions in Soybean

Photoperiod response of flowering determines plant adaptation to different latitudes. Soybean, a short-day plant, has gained the ability to flower under long-day conditions during the growing season at higher latitudes, mainly through dysfunction of phytochrome A genes (E3 and E4) and the floral repressor E1. In this study, we identified a novel molecular genetic basis of photoperiod insensitivity in Far-Eastern Russian soybean cultivars. By testcrossing these cultivars with a Canadian cultivar Harosoy near-isogenic line for a recessive e3 allele, followed by association tests and fine mapping, we determined that the insensitivity was inherited as a single recessive gene located in an 842-kb interval in the pericentromeric region of chromosome 4, where E1-Like b (E1Lb), a homoeolog of E1, is located. Sequencing analysis detected a single-nucleotide deletion in the coding sequence of the gene in insensitive cultivars, which generated a premature stop codon. Near-isogenic lines (NILs) for the loss-of-function allele (designated e1lb) exhibited upregulated expression of soybean FLOWERING LOCUS T (FT) orthologs, FT2a and FT5a, and flowered earlier than those for E1Lb under long-day conditions in both the e3/E4 and E3/E4 genetic backgrounds. These NILs further lacked the inhibitory effect on flowering by far-red light-enriched long-day conditions, which is mediated by E4, but not that of red-light-enriched long-day conditions, which is mediated by E3. These findings suggest that E1Lb retards flowering under long-day conditions by repressing the expression of FT2a and FT5a independently of E1. This loss-of-function allele can be used as a new resource in breeding of photoperiod-insensitive cultivars, and may improve our understanding of the function of the E1 family genes in the photoperiod responses of flowering in soybean.

Evolutionary trajectories of duplicated FT homologues and their roles in soybean domestication

To clarify the molecular bases of flowering time evolution in crop domestication, here we investigate the evolutionary fates of a set of four recently duplicated genes in soybean: FT2a, FT2b, FT2c and FT2d that are homologues of the floral inducer FLOWERING LOCUS T (FT). While FT2a maintained the flowering inducer function, other genes went through contrasting evolutionary paths. FT2b evolved attenuated expression potentially associated with a transposon insertion in the upstream intergenic region, while FT2c and FT2d obtained a transposon insertion and structural rearrangement, respectively. In contrast to FT2b and FT2d whose mutational events occurred before the separation of G. max and G. soja, the evolution of FT2c is a G. max lineage specific event. The FT2c allele carrying a transposon insertion is nearly fixed in soybean landraces and differentiates domesticated soybean from wild soybean, indicating that this allele spread at the early stage of soybean domestication. The domesticated allele causes later flowering than the wild allele under short day and exhibits a signature of selection. These findings suggest that FT2c may have underpinned the evolution of photoperiodic flowering regulation in soybean domestication and highlight the evolutionary dynamics of this agronomically important gene family.

The development and use of a molecular model for soybean maturity groups

BACKGROUND

Achieving appropriate maturity in a target environment is essential to maximizing crop yield potential. In soybean [Glycine max (L.) Merr.], the time to maturity is largely dependent on developmental response to dark periods. Once the critical photoperiod is reached, flowering is initiated and reproductive development proceeds. Therefore, soybean adaptation has been attributed to genetic changes and natural or artificial selection to optimize plant development in specific, narrow latitudinal ranges. In North America, these regions have been classified into twelve maturity groups (MG), with lower MG being shorter season than higher MG. Growing soybean lines not adapted to a particular environment typically results in poor growth and significant yield reductions. The objective of this study was to develop a molecular model for soybean maturity based on the alleles underlying the major maturity loci: E1, E2, and E3.

RESULTS

We determined the allelic variation and diversity of the E maturity genes in a large collection of soybean landraces, North American ancestors, Chinese cultivars, North American cultivars or expired Plant Variety Protection lines, and private-company lines. The E gene status of accessions in the USDA Soybean Germplasm Collection with SoySNP50K Beadchip data was also predicted. We determined the E allelic combinations needed to adapt soybean to different MGs in the United States (US) and discovered a strong signal of selection for E genotypes released in North America, particularly the US and Canada.

CONCLUSIONS

The E gene maturity model proposed will enable plant breeders to more effectively transfer traits into different MGs and increase the overall efficiency of soybean breeding in the US and Canada. The powerful yet simple selection strategy for increasing soybean breeding efficiency can be used alone or to directly enhance genomic prediction/selection schemes. The results also revealed previously unrecognized aspects of artificial selection in soybean imposed by soybean breeders based on geography that highlights the need for plant breeding that is optimized for specific environments.

Identification of mega-environments in Europe and effect of allelic variation at maturity E loci on adaptation of European soybean

Soybean cultivation holds great potential for a sustainable agriculture in Europe, but adaptation remains a central issue. In this large mega-environment (MEV) study, 75 European cultivars from five early maturity groups (MGs 000-II) were evaluated for maturity-related traits at 22 locations in 10 countries across Europe. Clustering of the locations based on phenotypic similarity revealed six MEVs in latitudinal direction and suggested several more. Analysis of maturity identified several groups of cultivars with phenotypic similarity that are optimally adapted to the different growing regions in Europe. We identified several haplotypes for the allelic variants at the E1, E2, E3 and E4 genes, with each E haplotype comprising cultivars from different MGs. Cultivars with the same E haplotype can exhibit different flowering and maturity characteristics, suggesting that the genetic control of these traits is more complex and that adaptation involves additional genetic pathways, for example temperature requirement. Taken together, our study allowed the first unified assessment of soybean-growing regions in Europe and illustrates the strong effect of photoperiod on soybean adaptation and MEV classification, as well as the effects of the E maturity loci for soybean adaptation in Europe.

Molecular mechanisms of flowering under long days and stem growth habit in soybean

Precise timing of flowering is critical to crop adaptation and productivity in a given environment. A number of classical E genes controlling flowering time and maturity have been identified in soybean [Glycine max (L.) Merr.]. The public availability of the soybean genome sequence has accelerated the identification of orthologues of Arabidopsis flowering genes and their functional analysis, and has allowed notable progress towards understanding the molecular mechanisms of flowering in soybean. Great progress has been made particularly in identifying genes and modules that inhibit flowering in long-day conditions, because a reduced or absent inhibition of flowering by long daylengths is an essential trait for soybean, a short-day (SD) plant, to expand its adaptability toward higher latitude environments. In contrast, the molecular mechanism of floral induction by SDs remains elusive in soybean. Here we present an update on recent work on molecular mechanisms of flowering under long days and of stem growth habit, outlining the progress in the identification of genes responsible, the interplay between photoperiod and age-dependent miRNA-mediated modules, and the conservation and divergence in photoperiodic flowering and stem growth habit in soybean relative to other legumes, Arabidopsis, and rice (Oryza sativa L.).

Soybean domestication: the origin, genetic architecture and molecular bases

Domestication provides an important model for the study of evolution, and information learned from domestication research aids in the continued improvement of crop species. Recent progress in de novo assembly and whole-genome resequencing of wild and cultivated soybean genomes, in addition to new archeological discoveries, sheds light on the origin of this important crop and provides a clearer view on the modes of artificial selection that drove soybean domestication and diversification. This novel genomic information enables the search for polymorphisms that underlie variation in agronomic traits and highlights genes that exhibit a signature of selection, leading to the identification of a number of candidate genes that may have played important roles in soybean domestication, diversification and improvement. These discoveries provide a novel point of comparison on the evolutionary bases of important agronomic traits among different crop species.

Single Marker and Haplotype-Based Association Analysis of Semolina and Pasta Colour in Elite Durum Wheat Breeding Lines Using a High-Density Consensus Map

Association mapping is usually performed by testing the correlation between a single marker and phenotypes. However, because patterns of variation within genomes are inherited as blocks, clustering markers into haplotypes for genome-wide scans could be a worthwhile approach to improve statistical power to detect associations. The availability of high-density molecular data allows the possibility to assess the potential of both approaches to identify marker-trait associations in durum wheat. In the present study, we used single marker- and haplotype-based approaches to identify loci associated with semolina and pasta colour in durum wheat, the main objective being to evaluate the potential benefits of haplotype-based analysis for identifying quantitative trait loci. One hundred sixty-nine durum lines were genotyped using the Illumina 90K Infinium iSelect assay, and 12,234 polymorphic single nucleotide polymorphism (SNP) markers were generated and used to assess the population structure and the linkage disequilibrium (LD) patterns. A total of 8,581 SNPs previously localized to a high-density consensus map were clustered into 406 haplotype blocks based on the average LD distance of 5.3 cM. Combining multiple SNPs into haplotype blocks increased the average polymorphism information content (PIC) from 0.27 per SNP to 0.50 per haplotype. The haplotype-based analysis identified 12 loci associated with grain pigment colour traits, including the five loci identified by the single marker-based analysis. Furthermore, the haplotype-based analysis resulted in an increase of the phenotypic variance explained (50.4% on average) and the allelic effect (33.7% on average) when compared to single marker analysis. The presence of multiple allelic combinations within each haplotype locus offers potential for screening the most favorable haplotype series and may facilitate marker-assisted selection of grain pigment colour in durum wheat. These results suggest a benefit of haplotype-based analysis over single marker analysis to detect loci associated with colour traits in durum wheat.

The Evolution of Soybean Knowledge Base (SoyKB)

Soybean Knowledge Base (SoyKB) is a comprehensive all-inclusive web resource for bridging the gap between soybean translational genomics and molecular breeding. It provides information for six entities including genes/proteins, microRNAs (miRNAs)/small interfering RNAs (sRNA), metabolites, single nucleotide polymorphisms (SNPs), and plant introduction lines and traits. It has a user-friendly web interface publicly available at http://soykb.org , which integrates and presents data in an intuitive manner to the soybean researchers, breeders, and consumers. It incorporates several informatics and analytical tools for integrating and merging various multi-omics datasets.

Genetic separation of southern and northern soybean breeding programs in North America and their associated allelic variation at four maturity loci

North American soybean breeders have successfully developed a large number of elite cultivars with diverse maturity groups (MG) from a small number of ancestral landraces. To understand molecular and genetic basis underlying the large variation in their maturity and flowering times, we integrated pedigree and maturity data of 166 cultivars representing North American soybean breeding. Network analysis and visualization of their pedigree relationships revealed a clear separation of southern and northern soybean breeding programs, suggesting that little genetic exchange occurred between northern (MG 0-IV) and southern cultivars (MG V-VIII). We also analyzed the transcript sequence and expression levels of four major maturity genes (E1 to E4) and revealed their allelic variants in 75 major ancestral landraces and milestone cultivars. We observed that e1-as was the predominant e mutant allele in northern genotypes, followed by e2 and e3. There was no allelic variation at E4. Transcript accumulation of the e2 mutant allele was significantly reduced, which might be caused by its premature stop codon triggering the nonsense-mediated mRNA decay pathway. The large DNA deletion generating the e3 mutant allele also created a gene fusion transcript. The e alleles found in milestone cultivars were traced through pedigrees to their ancestral landraces and geographic origins. Our analysis revealed an approximate correlation between dysfunctional alleles and maturity groups for most of the 75 cultivars. However, single e mutant alleles and their combinations were not sufficient to fully explain their maturity diversity, suggesting that additional genes/alleles are likely involved in regulating maturity time.

QTLomics in Soybean: A Way Forward for Translational Genomics and Breeding

Food legumes play an important role in attaining both food and nutritional security along with sustainable agricultural production for the well-being of humans globally. The various traits of economic importance in legume crops are complex and quantitative in nature, which are governed by quantitative trait loci (QTLs). Mapping of quantitative traits is a tedious and costly process, however, a large number of QTLs has been mapped in soybean for various traits albeit their utilization in breeding programmes is poorly reported. For their effective use in breeding programme it is imperative to narrow down the confidence interval of QTLs, to identify the underlying genes, and most importantly allelic characterization of these genes for identifying superior variants. In the field of functional genomics, especially in the identification and characterization of gene responsible for quantitative traits, soybean is far ahead from other legume crops. The availability of genic information about quantitative traits is more significant because it is easy and effective to identify homologs than identifying shared syntenic regions in other crop species. In soybean, genes underlying QTLs have been identified and functionally characterized for phosphorous efficiency, flowering and maturity, pod dehiscence, hard-seededness, α-Tocopherol content, soybean cyst nematode, sudden death syndrome, and salt tolerance. Candidate genes have also been identified for many other quantitative traits for which functional validation is required. Using the sequence information of identified genes from soybean, comparative genomic analysis of homologs in other legume crops could discover novel structural variants and useful alleles for functional marker development. The functional markers may be very useful for molecular breeding in soybean and harnessing benefit of translational research from soybean to other leguminous crops. Thus, soybean crop can act as a model crop for translational genomics and breeding of quantitative traits in legume crops. In this review, we summarize current status of identification and characterization of genes underlying QTLs for various quantitative traits in soybean and their significance in translational genomics and breeding of other legume crops.

PGen: large-scale genomic variations analysis workflow and browser in SoyKB

BACKGROUND

With the advances in next-generation sequencing (NGS) technology and significant reductions in sequencing costs, it is now possible to sequence large collections of germplasm in crops for detecting genome-scale genetic variations and to apply the knowledge towards improvements in traits. To efficiently facilitate large-scale NGS resequencing data analysis of genomic variations, we have developed "PGen", an integrated and optimized workflow using the Extreme Science and Engineering Discovery Environment (XSEDE) high-performance computing (HPC) virtual system, iPlant cloud data storage resources and Pegasus workflow management system (Pegasus-WMS). The workflow allows users to identify single nucleotide polymorphisms (SNPs) and insertion-deletions (indels), perform SNP annotations and conduct copy number variation analyses on multiple resequencing datasets in a user-friendly and seamless way.

RESULTS

We have developed both a Linux version in GitHub ( https://github.com/pegasus-isi/PGen-GenomicVariations-Workflow ) and a web-based implementation of the PGen workflow integrated within the Soybean Knowledge Base (SoyKB), ( http://soykb.org/Pegasus/index.php ). Using PGen, we identified 10,218,140 single-nucleotide polymorphisms (SNPs) and 1,398,982 indels from analysis of 106 soybean lines sequenced at 15X coverage. 297,245 non-synonymous SNPs and 3330 copy number variation (CNV) regions were identified from this analysis. SNPs identified using PGen from additional soybean resequencing projects adding to 500+ soybean germplasm lines in total have been integrated. These SNPs are being utilized for trait improvement using genotype to phenotype prediction approaches developed in-house. In order to browse and access NGS data easily, we have also developed an NGS resequencing data browser ( http://soykb.org/NGS_Resequence/NGS_index.php ) within SoyKB to provide easy access to SNP and downstream analysis results for soybean researchers.

CONCLUSION

PGen workflow has been optimized for the most efficient analysis of soybean data using thorough testing and validation. This research serves as an example of best practices for development of genomics data analysis workflows by integrating remote HPC resources and efficient data management with ease of use for biological users. PGen workflow can also be easily customized for analysis of data in other species.

Landscape of genomic diversity and trait discovery in soybean

Cultivated soybean [Glycine max (L.) Merr.] is a primary source of vegetable oil and protein. We report a landscape analysis of genome-wide genetic variation and an association study of major domestication and agronomic traits in soybean. A total of 106 soybean genomes representing wild, landraces, and elite lines were re-sequenced at an average of 17x depth with a 97.5% coverage. Over 10 million high-quality SNPs were discovered, and 35.34% of these have not been previously reported. Additionally, 159 putative domestication sweeps were identified, which includes 54.34 Mbp (4.9%) and 4,414 genes; 146 regions were involved in artificial selection during domestication. A genome-wide association study of major traits including oil and protein content, salinity, and domestication traits resulted in the discovery of novel alleles. Genomic information from this study provides a valuable resource for understanding soybean genome structure and evolution, and can also facilitate trait dissection leading to sequencing-based molecular breeding.

Evaluation of genetic variation among Brazilian soybean cultivars through genome resequencing

BACKGROUND

Soybean [Glycine max (L.) Merrill] is one of the most important legumes cultivated worldwide, and Brazil is one of the main producers of this crop. Since the sequencing of its reference genome, interest in structural and allelic variations of cultivated and wild soybean germplasm has grown. To investigate the genetics of the Brazilian soybean germplasm, we selected soybean cultivars based on the year of commercialization, geographical region and maturity group and resequenced their genomes.

RESULTS

We resequenced the genomes of 28 Brazilian soybean cultivars with an average genome coverage of 14.8X. A total of 5,835,185 single nucleotide polymorphisms (SNPs) and 1,329,844 InDels were identified across the 20 soybean chromosomes, with 541,762 SNPs, 98,922 InDels and 1,093 CNVs that were exclusive to the 28 Brazilian cultivars. In addition, 668 allelic variations of 327 genes were shared among all of the Brazilian cultivars, including genes related to DNA-dependent transcription-elongation, photosynthesis, ATP synthesis-coupled electron transport, cellular respiration, and precursors of metabolite generation and energy. A very homogeneous structure was also observed for the Brazilian soybean germplasm, and we observed 41 regions putatively influenced by positive selection. Finally, we detected 3,880 regions with copy-number variations (CNVs) that could help to explain the divergence among the accessions evaluated.

CONCLUSIONS

The large number of allelic and structural variations identified in this study can be used in marker-assisted selection programs to detect unique SNPs for cultivar fingerprinting. The results presented here suggest that despite the diversification of modern Brazilian cultivars, the soybean germplasm remains very narrow because of the large number of genome regions that exhibit low diversity. These results emphasize the need to introduce new alleles to increase the genetic diversity of the Brazilian germplasm.