Location: Corn Insects and Crop Genetics Research
2021 Annual Report
Accomplishments
1. Incorporation of legume genomes into the Legume Information System. Genome sequences describe the order and content of the DNA in all of the chromosomes of an organism. A genome sequence provides a “road map” for the organism, and serves as a common framework or backbone for much of the work done by breeders and other researchers. This map or backbone provides the coordinates for identifying genes, genetic markers, and traits that can be mapped to chromosomal locations. Thus, the task of collecting and cataloging genome assemblies is part of the critical infrastructure for modern plant breeding and biology. ARS researchers in Ames, Iowa, have collected 15 new full genome assemblies, across eight legume species (not including soybean), and incorporated these into the Legume Information System (LIS) Data Store. This provides researchers with a single location to find genomic data that is often dispersed across the internet. These genome assemblies are for a broad collection of legumes, including cowpea, pea, common bean, alfalfa, and several wild relatives of peanut. These data will be of interest both to plant breeders, and to biologists working to understand the genes involved in adaptation to the various physical environments that these species occupy. Given a dense collection of genetic markers with known locations across a genome, researchers are able to identify corresponding traits. This information may be used, for example, to identify genetic markers for traits such as tolerance to increased heat, drought, or salinization. Breeding and research on legume crops impact people worldwide, as legumes provide protein and other nutrients for a large portion of the global population.
2. Incorporation of ten new soybean genome assemblies into SoyBase. Soybean varieties have been adapted to many environments, ranging from short-season varieties suited to Canada, to long-season southern varieties suited to Brazil; and also adapted to many uses, ranging from varieties used for oilseed applications, to varieties used for fresh vegetable (edamame) production. Researchers are gradually learning the genetic basis for the many trait differences among soybean varieties. Genome sequences, which represent the sequence of DNA letters in an organism, provide an important means for determining the genetic basis of traits. ARS researchers in Ames, Iowa, have incorporated 10 new soybean genomes, and the associated predicted genes, into the SoyBase and Legume Information System (LIS) Data Store. These data sets have been incorporated into SoyBase to allow detailed analysis and comparisons. The new assemblies and gene annotations include a wild soybean variety, as well as the widely used cultivar Fiskeby. The wild soybean genome will be useful for understanding soybean domestication and improvement. The cultivar Fiskeby is an important northern-adapted soybean variety, used in breeding programs for its superior tolerance to multiple environmental stresses. Functional annotation of Fiskeby gene models and display with other SoyBase data may facilitate the identification of disease resistance genes and molecular markers that will aid breeders in developing new early-flowering, stress-tolerant soybean lines.
3. Incorporation of the 2020 soybean variety trial data (Northern Uniform Soybean Tests) into SoyBase. For all major crops, variety trials are used to determine which new varieties are most suited to a particular region or to meeting particular grower and consumer objectives. Traits that are typically assessed in soybean variety trials include yield, tolerance against adverse field conditions such as nutrient deficiencies or pathogens, seed characteristics such as protein and oil concentration and quality, and growth harvest characteristics such as germination rate and plant architecture at harvest. ARS researchers in Ames, Iowa, have added soybean phenotypic data for 582 testing strains submitted to the Northern Uniform Soybean Tests (NUST). Additionally, parentage information for those strains were added to the SoyBase Soybean Parentage Database. Incorporating phenotypic data on these strains into SoyBase allows breeders access to performance data of testing strains from 1989 to the present. This will allow breeders to easily see the results of breeding activity across programs and to evaluate any increase in grain yield and other seed quality measurements and incorporate strains with superior genetics into their breeding programs.
4. Incorporation of two new datasets into the Expression Explorer Tool in SoyBase. Plant breeders and other researchers use information about gene expression to understand the functional roles of genes in plant development. Genes, which serve as the information source for a cell to make proteins (the building blocks of a cell), may be turned on or off during various life stages of a plant – or in response to particular environmental conditions or challenges. ARS researchers in Ames, Iowa, have incorporated two new gene expression atlases into a gene expression explorer tool at SoyBase. This tool provides researchers with a visual representation of each gene in soybean, showing when and at what levels each gene is “expressed” (turned on) in a plant. The tool was developed as part of a collaboration with the University of Toronto, Bio-analytic Resource for Plant Biology (UBAR) to graphically display gene atlas data along with tabular and graphical displays of other soybean gene expression studies. This allows soybean researchers to view gene expression levels in the various tissues and developmental time points. Including during several developmental timepoints in soybean seed development. These data can be used to identify candidate genes identified through other methods. This information can be used by breeders to produce improved soybean varieties.
Review Publications
Valliyodan, B., Brown, A.V., Wang, J., Patil, G., Liu, Y., Otyama, P.I., Nelson, R., Vuong, T., Song, Q., Musket, T.A., Wagner, R., Marri, P., Reddy, S., Sessions, A., Wu, X., Grant, D.M., Bayer, P., Roorkiwal, M., Varshney, R.K., Liu, X., Edwards, D., Xu, D., Joshi, T., Cannon, S.B., Nguyen, H.T. 2020. Genetic variation among 481 diverse soybean accessions, inferred from genomic re-sequencing. Scientific Data. 8. Article 50. https://doi.org/10.1038/s41597-021-00834-w.
Stai, J.S., Von Wettberg, E.B, Smykal, P., Cannon, S.B. 2020. Which came first: the tuber or the vine? A taxonomic overview of underground storage in the legumes. Legume Perspectives. (19):5-7.
Kalberer, S.R., Belamkar, V., Singh, J., Cannon, S.B. 2020. Apios americana: natural history and ethnobotany. Legume Perspectives. (19):29-32.
Wilkey, A., Brown, A.V., Cannon, S.B., Cannon, E.K. 2020. GCViT: a method for interactive, genome-wide visualization of resequencing and SNP array data. Biomed Central (BMC) Genomics. 21. Article 822. https://doi.org/10.1186/s12864-020-07217-2.
Brown, A.V., Connors, S., Huang, W., Wilkey, A., Grant, D.M., Weeks, N.T., Cannon, S.B., Graham, M.A., Nelson, R. 2020. A new decade and new data at SoyBase, the USDA-ARS soybean genetics and genomics database. Nucleic Acids Research. 49(D1):D1496-D1501. https://doi.org/10.1093/nar/gkaa1107.
Berendzen, J., Brown, A.V., Cameron, C.T., Campbell, J.D., Cleary, A.M., Dash, S., Hokin, S., Huang, W., Kalberer, S.R., Nelson, R., Redsun, S., Weeks, N.T., Wilkey, A., Farmer, A.D., Cannon, S.B. 2021. The legume information system and associated online genomic resources. Legume Science. Article e74. https://doi.org/10.1002/leg3.74.
Nelson, M.N., Jabbari, J.S., Turakulov, R., Pradhan, A., Pazos-Navarro, M., Stai, J.S., Cannon, S.B., Real, D. 2020. The first genetic map for a psoraleoid legume (Bituminaria bituminosa)reveals highly conserved synteny with phaseoloid legumes. Plants. 9(8). Article 973. https://doi.org/10.3390/plants9080973.
Yadav, A., Fernandez-Baca, D., Cannon, S.B. 2020. Family-specific gains and losses of protein domains in the legume and grass plant families. Evolutionary Bioinformatics. 16. https://doi.org/10.1177/1176934320939943.
Singh, J., Sun, M., Cannon, S.B., Wu, J., Khan, A. 2021. An accumulation of genetic variation and selection across the disease-related genes during apple domestication. Tree Genetics and Genomes. 17. Article 29. https://doi.org/10.1007/s11295-021-01510-1.
Cannon, S.B., Innes, R.W. 2021. A better mousetrap to guard against anthracnose disease in bean. Journal of Experimental Botany. 72(10):3487-3488. https://doi.org/10.1093/jxb/erab146.
