A new decade and new data at SoyBase, the USDA-ARS soybean genetics and genomics database.

SoyBase, a USDA genetic and genomics database, holds professionally curated soybean genetic and genomic data, which is integrated and made accessible to researchers and breeders. The site holds several reference genome assemblies, as well as genetic maps, thousands of mapped traits, expression and epigenetic data, pedigree information, and extensive variant and genotyping data sets. SoyBase displays include genetic, genomic, and epigenetic maps of the soybean genome. Gene expression data is presented in the genome viewer as heat maps and pictorial and tabular displays in gene report pages. Millions of sequence variants have been added, representing variations across various collections of cultivars. This variant data is explorable using new interactive tools to visualize the distribution of those variants across the genome, between selected accessions. SoyBase holds several reference-quality soybean genome assemblies, accessible via various query tools and browsers, including a new visualization system for exploring the soybean pan-genome. SoyBase also serves as a nexus of announcements pertinent to the greater soybean research community. The database also includes a soybean-specific anatomic and biochemical trait ontology. The database can be accessed at https://soybase.org.

Construction and comparison of three reference-quality genome assemblies for soybean.

We report reference-quality genome assemblies and annotations for two accessions of soybean (Glycine max) and for one accession of Glycine soja, the closest wild relative of G. max. The G. max assemblies provided are for widely used US cultivars: the northern line Williams 82 (Wm82) and the southern line Lee. The Wm82 assembly improves the prior published assembly, and the Lee and G. soja assemblies are new for these accessions. Comparisons among the three accessions show generally high structural conservation, but nucleotide difference of 1.7 single-nucleotide polymorphisms (snps) per kb between Wm82 and Lee, and 4.7 snps per kb between these lines and G. soja. snp distributions and comparisons with genotypes of the Lee and Wm82 parents highlight patterns of introgression and haplotype structure. Comparisons against the US germplasm collection show placement of the sequenced accessions relative to global soybean diversity. Analysis of a pan-gene collection shows generally high conservation, with variation occurring primarily in genomically clustered gene families. We found approximately 40-42 inversions per chromosome between either Lee or Wm82v4 and G. soja, and approximately 32 inversions per chromosome between Wm82 and Lee. We also investigated five domestication loci. For each locus, we found two different alleles with functional differences between G. soja and the two domesticated accessions. The genome assemblies for multiple cultivated accessions and for the closest wild ancestor of soybean provides a valuable set of resources for identifying causal variants that underlie traits for the domestication and improvement of soybean, serving as a basis for future research and crop improvement efforts for this important crop species.

GCViT: a method for interactive, genome-wide visualization of resequencing and SNP array data.

BACKGROUND: Large genotyping datasets have become commonplace due to efficient, cheap methods for SNP identification. Typical genotyping datasets may have thousands to millions of data points per accession, across tens to thousands of accessions. There is a need for tools to help rapidly explore such datasets, to assess characteristics such as overall differences between accessions and regional anomalies across the genome. RESULTS: We present GCViT (Genotype Comparison Visualization Tool), for visualizing and exploring large genotyping datasets. GCViT can be used to identify introgressions, conserved or divergent genomic regions, pedigrees, and other features for more detailed exploration. The program can be used online or as a local instance for whole genome visualization of resequencing or SNP array data. The program performs comparisons of variants among user-selected accessions to identify allele differences and similarities between accessions and a user-selected reference, providing visualizations through histogram, heatmap, or haplotype views. The resulting analyses and images can be exported in various formats. CONCLUSIONS: GCViT provides methods for interactively visualizing SNP data on a whole genome scale, and can produce publication-ready figures. It can be used in online or local installations. GCViT enables users to confirm or identify genomics regions of interest associated with particular traits. GCViT is freely available at https://github.com/LegumeFederation/gcvit . The 1.0 version described here is available at https://doi.org/10.5281/zenodo.4008713 .

Genome-wide associations and epistatic interactions for internode number, plant height, seed weight and seed yield in soybean.

BACKGROUND: Breeding programs benefit from information about marker-trait associations for many traits, whether the goal is to place those traits under active selection or to maintain them through background selection. Association studies are also important for identifying accessions bearing potentially useful alleles by characterizing marker-trait associations and allelic states across germplasm collections. This study reports the results of a genome-wide association study and evaluation of epistatic interactions for four agronomic and seed-related traits in soybean. RESULTS: Using 419 diverse soybean accessions, together with genotyping data from the SoySNP50K Illumina Infinium BeadChip, we identified marker-trait associations for internode number (IN), plant height (PH), seed weight (SW), and seed yield per plant (SYP). We conducted a genome-wide epistatic study (GWES), identifying candidate genes that show evidence of SNP-SNP interactions. Although these candidate genes will require further experimental validation, several appear to be involved in developmental processes related to the respective traits. For IN and PH, these include the Dt1 determinacy locus (a soybean meristematic transcription factor), as well as a pectinesterase gene and a squamosa promoter binding gene that in other plants are involved in cell elongation and the vegetative-to-reproductive transition, respectively. For SW, candidate genes include an ortholog of the AP2 gene, which in other species is involved in maintaining seed size, embryo size, seed weight and seed yield. Another SW candidate gene is a histidine phosphotransfer protein - orthologs of which are involved in cytokinin-mediated seed weight regulating pathways. The SYP association loci overlap with regions reported in previous QTL studies to be involved in seed yield. CONCLUSIONS: This study further confirms the utility of GWAS and GWES approaches for identifying marker-trait associations and interactions within a diverse germplasm collection.

Ten quick tips for sharing open genomic data.

As sequencing prices drop, genomic data accumulates-seemingly at a steadily increasing pace. Most genomic data potentially have value beyond the initial purpose-but only if shared with the scientific community. This, of course, is often easier said than done. Some of the challenges in sharing genomic data include data volume (raw file sizes and number of files), complexities, formats, nomenclatures, metadata descriptions, and the choice of a repository. In this paper, we describe 10 quick tips for sharing open genomic data.

Transcriptional profiling of mechanically and genetically sink-limited soybeans.

The absence of a reproductive sink causes physiological and morphological changes in soybean plants. These include increased accumulation of nitrogen and starch in the leaves and delayed leaf senescence. To identify transcriptional changes that occur in leaves of these sink-limited plants, we used RNAseq to compare gene expression levels in trifoliate leaves from depodded and ms6 male-sterile soybean plants and control plants. In both sink-limited tissues, we observed a deferral of the expression of senescence-associated genes and a continued expression of genes associated with leaf maturity. Gene Ontology-terms (GO-terms) associated with growth and development and storage proteins were over-represented in genes that were differentially expressed in sink-limited tissues. We also identified basic helix-loop-helix, auxin response factor, and squamosa binding protein transcription factors expressed in sink-limited tissues, and the senescing control leaves expressed WRKY and NAC transcription factors. We identified genes that were not expressed during normal leaf development but that were highly expressed in sink-limited plants, including the SGR3b "non-yellowing" gene. These differences highlighted several metabolic pathways that were involved in distinct modes of resource partitioning of leaves with the "stay green" phenotype.

Developmental profiling of gene expression in soybean trifoliate leaves and cotyledons.

BACKGROUND: Immediately following germination, the developing soybean seedling relies on the nutrient reserves stored in the cotyledons to sustain heterotrophic growth. During the seed filling period, developing seeds rely on the transport of nutrients from the trifoliate leaves. In soybean, both cotyledons and leaves develop the capacity for photosynthesis, and subsequently senesce and abscise once their function has ended. Before this occurs, the nutrients they contain are mobilized and transported to other parts of the plant. These processes are carefully orchestrated by genetic regulation throughout the development of the leaf or cotyledon. RESULTS: To identify genes involved in the processes of leaf or cotyledon development and senescence in soybean, we used RNA-seq to profile multiple stages of cotyledon and leaf tissues. Differentially expressed genes between stages of leaf or cotyledon development were determined, major patterns of gene expression were defined, and shared genes were identified. Over 38,000 transcripts were expressed during the course of leaf and cotyledon development. Of those transcripts, 5,000 were expressed in a tissue specific pattern. Of the genes that were differentially expressed between both later stage tissues, 90 % had the same direction of change, suggesting that the mechanisms of senescence are conserved between tissues. Analysis of the enrichment of biological functions within genes sharing common expression profiles highlights the main processes occurring within these defined temporal windows of leaf and cotyledon development. Over 1,000 genes were identified with predicted regulatory functions that may have a role in control of leaf or cotyledon senescence. CONCLUSIONS: The process of leaf and cotyledon development can be divided into distinct stages characterized by the expression of specific gene sets. The importance of the WRKY, NAC, and GRAS family transcription factors as major regulators of plant senescence is confirmed for both soybean leaf and cotyledon tissues. These results help validate functional annotation for soybean genes and promoters.

Doing Genetic and Genomic Biology Using the Legume Information System and Associated Resources.

In this chapter, we introduce the main components of the Legume Information System ( https://legumeinfo.org ) and several associated resources. Additionally, we provide an example of their use by exploring a biological question: is there a common molecular basis, across legume species, that underlies the photoperiod-mediated transition from vegetative to reproductive development, that is, days to flowering? The Legume Information System (LIS) holds genetic and genomic data for a large number of crop and model legumes and provides a set of online bioinformatic tools designed to help biologists address questions and tasks related to legume biology. Such tasks include identifying the molecular basis of agronomic traits; identifying orthologs/syntelogs for known genes; determining gene expression patterns; accessing genomic datasets; identifying markers for breeding work; and identifying genetic similarities and differences among selected accessions. LIS integrates with other legume-focused informatics resources such as SoyBase ( https://soybase.org ), PeanutBase ( https://peanutbase.org ), and projects of the Legume Federation ( https://legumefederation.org ).

Using Crop Databases to Explore Phenotypes: From QTL to Candidate Genes.

Seeds, especially those of certain grasses and legumes, provide the majority of the protein and carbohydrates for much of the world's population. Therefore, improvements in seed quality and yield are important drivers for the development of new crop varieties to feed a growing population. Quantitative Trait Loci (QTL) have been identified for many biologically interesting and agronomically important traits, including many seed quality traits. QTL can help explain the genetic architecture of the traits and can also be used to incorporate traits into new crop cultivars during breeding. Despite the important contributions that QTL have made to basic studies and plant breeding, knowing the exact gene(s) conditioning each QTL would greatly improve our ability to study the underlying genetics, biochemistry and regulatory networks. The data sets needed for identifying these genes are increasingly available and often housed in species- or clade-specific genetics and genomics databases. In this demonstration, we present a generalized walkthrough of how such databases can be used in these studies using SoyBase, the USDA soybean Genetics and Genomics Database, as an example.

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.