Location: Wheat Health, Genetics, and Quality Research
2023 Annual Report
Accomplishments
1. BRIDGE-cereal web app developed. A pan-genome with high quality gene assemblies from diverse crop varieties is shifting the biology research paradigm. Although the pan-genomes have been made publicly available after publication, leveraging these pan-genome remains challenging for most of scientists. ARS researchers in Pullman, Washington, developed two novo unsupervised machine learning algorithms, CHOICE and CLIPS, to overcome these challenges. Based on these two algorithms, ARS researchers further constructed a publicly accessible webapp, BRIDGEcereal (https://bridgecereal.scinet.usda.gov/) to provide a one-stop gateway to mine pan-genomes from five major cereal crops (approximately 120 assemblies). With this webapp, the only requirement for mining pan-genomes is the gene model ID to determine patterns of variants for a gene of interest. The BRIDGEcereal app is routinely accessed by worldwide users. Several major cereal genome databases hosted and maintained by USDA, ARS, including GrainGenes, MaizeGDB, and Sorghumbase, have added the link pointing to BRIDGEcereal. Scientists from MaizeGDB requested an in-depth collaboration to further incorporate CHOICE and CLIPS algorithms into MaizeGDB architecture.
2. Vivipary occurring in wheat grains contributes to low falling number. Farmers are assessed severe discounts for grain with low falling number (FN), an indicator that grain contains sufficiently elevated levels of the starch-digesting enzyme alpha-amylase which poses a risk to end-product quality and occurs due to preharvest sprouting and to late maturity alpha amylase. ARS researchers in Pullman, Washington, identified an additional cause, vivipary, which is visible sprouting occurring on immature grain. Experiments under controlled environmental conditions revealed that vivipary was more strongly induced under the cooler conditions (18°C day/7.5°C night) than warmer conditions (25°C day/18°C night). Investigation of the genetic causes of vivipary need to be considered when selecting wheat varieties for higher falling numbers. This knowledge is used by plant breeders and the grain industry to select for genetic resistance to low falling number, to schedule irrigation and manage grain for export customers.
3. Confirmation that TAMFT-3A and TAMFT-3B2 homeologs are associated with wheat preharvest sprouting. Farmers receive severe discounts for grain with low falling number caused by preharvest sprouting. ARS scientists in Pullman, Washington, collaborated with ARS researchers in Montana, to investigate the genetic control of preharvest sprouting in winter wheat doubled haploid population under controlled rain conditions. The researchers confirmed that the ‘mother of flowering time’ (MFT) genes on the group 3 chromosomes of wheat are major controllers of preharvest sprouting and that other loci that have been important in the literature were not detected. Molecular markers were developed that wheat breeders in the Great Plains use to select for this resistance in their breeding programs. These markers are critical to selection because the trait is difficult to phenotype.
4. Cameo club wheat is available for farmers to grow in Fall 2023. Club wheat is a major export commodity, grown commercially only in the Pacific Northwest of the United States, and growers need new competitive cultivars with improved disease resistance and end use quality. ARS scientists in Pullman, Washington, in collaboration with scientists at Washington State University, used classical and molecular breeding techniques to develop Cameo club wheat. As compared to other club wheat cultivars grown in the high rainfall region, Cameo has better agronomic performance than other club wheat cultivars, better stripe rust resistance, tolerance to soil borne mosaic virus and acid soils and similar excellent club wheat quality. Cameo is available as registered seed to seed companies and farmers in the region in the fall of 2023.
Review Publications
Shrestha, S.L., Garland-Campbell, K.A., Steber, C.M., Pan, W.L., Hulbert, S.H. 2022. Association of canopy temperature with agronomic traits in spring wheat inbred populations. Euphytica. 219. Article 7. https://doi.org/10.1007/s10681-022-03135-4.
Hauvermale, A.L., Parveen, R.S., Harris, T., Tuttle, K.M., Mikhaylenko, G.G., Nair, S., McCubbin, A.G., Pumphrey, M.O., Steber, C.M. 2023. Streamlined alpha-amylase assays for wheat preharvest sprouting and late maturity alpha-amylase detection. Agrosystems, Geosciences & Environment. 6(1). Article e20327. https://doi.org/10.1002/agg2.20327.
Tibbs-Cortes, L.E., Guo, T., Li, X., Tanaka, R., Vanous, A.E., Peters, D.W., Gardner, C.A., Magallanes-Lundback, M., Deason, N.T., DellaPenna, D., Gore, M.A., Yu, J. 2022. Genomic prediction of tocochromanols in exotic-derived maize. The Plant Genome. Article e20286. https://doi.org/10.1002/tpg2.20286.
Tanaka, R., Wu, D., Li, X., Tibbs-Cortes, L.E., Wood, J., Magallanes-Lundback, M., Bornowski, N., Hamilton, J.P., Vaillancourt, B., Li, X., Deason, N.T., Schoenbaum, G.R., Buell, C.R., DellaPenna, D., Yu, J., Gore, M.A. 2022. Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain. The Plant Genome. Article e20276. https://doi.org/10.1002/tpg2.20276.
Guo, T., Li, X. 2023. Machine learning for predicting phenotype from genotype and environment. Current Opinion in Biotechnology. 79. Article 102853. https://doi.org/10.1016/j.copbio.2022.102853.
Garland-Campbell, K.A., Bellinger, B.S., Carter, A.H., Chen, X., DeMacon, P., Engle, D., Hagerty, C., Kiszonas, A., Klarquist, E.F., Murray, T., Morris, C.F., Neely, C., Odubiyi, S., Rashad, A., See, D., Steber, C., Wen, N. 2022. Registration of 'Cameo' soft white club wheat. Journal of Plant Registrations. 16(3):585-596. https://doi.org/10.1002/plr2.20234.
Elshikha, D.M., Wang, G., Waller, P.M., Hunsaker, D.J., Dierig, D., Thorp, K.R., Thompson, A.L., Katterman, M.E., Herritt, M.T., Bautista, E., Ray, D.T., Wall, G.W. 2022. Guayule growth and yield responses to deficit irrigation strategies in the U.S. desert. Agricultural Water Management. 277. Article 108093. https://doi.org/10.1016/j.agwat.2022.108093.
Ayankojo, I.T., Thorp, K.R., Thompson, A.L. 2023. Advances in the application of small unoccupied aircraft systems (sUAS) for high-throughput plant phenotyping. Remote Sensing. 15(10). Article 2623. https://doi.org/10.3390/rs15102623.
Conley, M.M., Thompson, A.L., Hejl, R.W. 2023. Proximal active optical sensing operational improvement for research using the CropCircle ACS-470, implications for measurement of normalized difference vegetation index (NDVI). Sensors. 23(11). Article 5044. https://doi.org/10.3390/s23115044.
