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                    U.S. DEPARTMENT OF AGRICULTURE
 Plant Genetics Research: Columbia, MO
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ARS Home Midwest Area Columbia, Missouri Plant Genetics Research People & Locations







Jacob  Washburn
                        Plant Genetics Research

                        




                        
                        Research Geneticist
                        Phone: (573) 882-4305
                        
                        
                        Fax: (Employee information on this page comes from the REE Directory. Please contact your front office staff to update the REE Directory.)
                            ProjectsHeirlooms of the United States, Southeast Evaluation
                                                    Cooperative Agreement
                                                                    (A)Accession Number: 445723Heirlooms of the United States, Midwest Evaluation
                                                    Cooperative Agreement
                                                                    (A)Accession Number: 445724Adaptation of Grain Crops to Varying Environments Including Climates, Stressors, and Human Uses
                                                    In-House Appropriated
                                                                    (D)Accession Number: 443892Institutional Biological Safety Committee (IBC) Agreement - University of Missouri
                                                    Non-Funded Cooperative Agreement
                                                                    (N)Accession Number: 441686Missouri Maize Center - Joint Administration of the Genetics Farm
                                                    Non-Funded Cooperative Agreement
                                                                    (N)Accession Number: 442031**Publications**
                                                (Clicking on the reprint icon
                                                    Reprint Icon
                                                    will take you to the publication reprint.)Halophytes and heavy metals: a multi-omics approach to understand the role of gene and genome duplication in abiotic stress tolerance of Cakile maritima
                                                        

- (Peer Reviewed Journal)Thomas, S.K., Vanden Hoek, K., Ogoti, T., Duong, H., Angelovici, R., Pires, C.J., Mendoza-Cozatl, D., Washburn, J.D., Schenck, C.A. 2024. Halophytes and heavy metals: a multi-omics approach to understand the role of gene and genome duplication in abiotic stress tolerance of Cakile maritima. American Journal of Botany. 111. Article e16310. https://doi.org/10.1002/ajb2.16310.Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations
                                                        

- (Peer Reviewed Journal)Adak, A., Murray, S.C., Washburn, J.D. 2024. Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations. New Phytologist. 242:121-136. https://doi.org/10.1111/nph.19575.Leveraging data from the genomes-to-fields initiative to investigate genotype-by-environment interactions in maize in North America
                                                        

- (Peer Reviewed Journal)Lopez-Cruz, M., Aguate, F.M., Washburn, J.D., De Leon Gatti, N., Kaeppler, S.M., Lima, D., Tan, R., Thompson, A., De La Bretonne, L.W., De Los Campos, G. 2023. Leveraging data from the genomes-to-fields initiative to investigate genotype-by-environment interactions in maize in North America. Nature Communications. 14. Article 6904. https://doi.org/10.1038/s41467-023-42687-4.Ensemble of best linear unbiased predictor, machine learning and deep learning models predict maize yield better than each model alone
                                                        

- (Peer Reviewed Journal)Kick, D.R., Washburn, J.D. 2023. Ensemble of best linear unbiased predictor, machine learning and deep learning models predict maize yield better than each model alone. in silico Plants. 5(2). Article diad015. https://doi.org/10.1093/insilicoplants/diad015.GWAS analysis of maize host plant resistance to western corn rootworm (Coleoptera: Chrysomelidae) reveals candidate small effect loci for resistance breeding
                                                        

- (Peer Reviewed Journal)Washburn, J.D., LaFond, H.F., Lapadatescu, M.C., Pereira, A.E., Erb, M., Hibbard, B.E. 2023. GWAS analysis of maize host plant resistance to western corn rootworm (Coleoptera: Chrysomelidae) reveals candidate small effect loci for resistance breeding. Journal of Economic Entomology. 116(6):2184–2192. https://doi.org/10.1093/jee/toad181.Genomes to fields 2022 maize genotype by environment prediction competition
                                                        

- (Peer Reviewed Journal)Lima, D.C., Washburn, J.D., Varela, J.I., Chen, Q., Gage, J.L., Romay, M.C., Holland, J.B., Ertl, D., Lopez-Cruz, M., Aguate, F.M., De Los Campos, G., Kaeppler, S., Beissinger, T., Bohn, M., Buckler IV, E.S., Edwards, J.W., Flint Garcia, S.A., Gore, M.A., Hirsch, C.N., Knoll, J.E., Mckay, J., Minyo, R., Murray, S.C., Ortez, O.A., Schnable, J., Sekhon, R.S., Singh, M.P., Sparks, E.E., Thompson, A., Tuinstra, M., Wallace, J., Weldekidan, T., Xu, W., De Leon, N. 2023. Genomes to fields 2022 maize genotype by environment prediction competition. BMC Research Notes. 16: Article 148. https://doi.org/10.1186/s13104-023-06421-z.2018-2019 field seasons of the maize genomes to fields (G2F) G x E project
                                                        

- (Peer Reviewed Journal)Lima, D., Castro Aviles, A., Alpers, T., Mcfarlan, B., Kaeppler, S., Ertl, D., Romay, C., Gage, J., Holland, J.B., Beissinger, T., Bohn, M., Buckler, E., Edwards, J., Flint-Garcia, S., Hirsch, C., Hood, E., Hooker, D., Knoll, J., Kolkman, J., Liu, S., Mckay, J., Minyo, R., Moreta, D.E., Murray, S., Nelson, R., Schnable, J., Sekhon, R., Singh, M., Thomison, P., Thompson, A., Tuinstra, M., Wallace, J., Washburn, J.D., Weldekidan, T., Wisser, R., Xu, W. 2023. 2018-2019 field seasons of the maize genomes to fields (G2F) G x E project. BMC Genomic Data. 24:29. https://doi.org/10.1186/s12863-023-01129-2.RootBot: high-throughput root stress phenotyping robot
                                                        

- (Peer Reviewed Journal)Ruppel, M., Nelson, S., Sidberry, G., Mitchell, M., Kick, D.R., Thomas, S., Guill, K., Oliver, M., Washburn, J.D. 2023. RootBot: high-throughput root stress phenotyping robot. Applications in Plant Sciences. 11(6): Article e11541. https://doi.org/10.1002/aps3.11541.Ensemble of BLUP, machine learning, and deep learning models predict maize yield better than each model alone
                                                        

- (Pre-print Publication)Kick, D.R., Washburn, J.D. 2023. Ensemble of BLUP, machine learning, and deep learning models predict maize yield better than each model alone. bioRxiv. Article bioRxiv 2023.03.30.532932. https://doi.org/10.1101/2023.03.30.532932.Inclusive collaboration across plant physiology and genomics: now is the time!
                                                        

- (Other)Baxter, I., Ainsworth, E.A., Brooks, M.D., Castano-Duque, L.M., Londo, J.P., Washburn, J.D., McElrone, A.J., Coyne, C.J., et al. 2023. Inclusive collaboration across plant physiology and genomics: now is the time! Plant Direct. 7(5). Article e493. https://doi.org/10.1002/pld3.493.Enhancing the resilience of plant systems to climate change
                                                        

- (Other)Braun, D., Washburn, J.D., Wood, J.D. 2023. Enhancing the resilience of plant systems to climate change. Journal of Experimental Botany. 74(9): 2787-2789. https://doi.org/10.1093/jxb/erad090.Yield prediction through integration of genetic, environment, and management data through deep learning
                                                        

- (Peer Reviewed Journal)Kick, D.R., Wallace, J.G., Schnable, J.C., Kolkmann, J.M., Alaca, B., Beissinger, T.M., Edwards, J.W., Ertl, D., Flint-Garcia, S.A., Gage, J.L., Hirsch, C.N., Knoll, J.E., de Leon, N., Lima, D.C., Moreta, D., Singh, M.P., Thompson, A., Weldekidan, T., Washburn, J.D. 2023. Yield prediction through integration of genetic, environment, and management data through deep learning. G3, Genes/Genomes/Genetics. 13(4). Article jkad006. https://doi.org/10.1093/g3journal/jkad006.Yield prediction through integration of genetic, environment, and management data through deep learning
                                                        

- (Pre-print Publication)Kick, D.R., Wallace, J.G., Schnable, J.C., Kolkmann, J.M., Boris, A., Beissinger, T.M., Irtl, D., Flint Garcia, S.A., Gage, J.L., Hirsch, C.N., Knoll, J.E., De Leon, N., Lima, D.C., Moreta, D., Singh, M.P., Weldekidan, T., Washburn, J.D. 2022. Yield prediction through integration of genetic, environment, and management data through deep learning. bioRxiv. Article bioRxiv 2022.07.29.502051. https://doi.org/10.1101/2022.07.29.502051.Distinct C4 sub-types and C3 bundle sheath isolation in the Paniceae grasses
                                                        

- (Peer Reviewed Journal)Washburn, J.D., Strable, J., Dickinson, P., Kothapalli, S.S., Brose, J.M., Covshoff, S., Conant, G.C., Hibberd, J.M., Pires, C.J. 2021. Distinct C4 sub-types and C3 bundle sheath isolation in the Paniceae grasses. Plant Direct. 5(12): Article e373. https://doi.org/10.1002/pld3.373.Trait association and prediction through integrative K-mer analysis
                                                        

- (Pre-print Publication)He, C., Washburn, J.D., Hao, Y., Zhang, Z., Yang, J., Liu, S. 2021. Trait association and prediction through integrative K-mer analysis. bioRxiv. https://doi.org/10.1101/2021.11.17.468725.Predicting phenotypes from genetic, environment, management, and historical data using CNNs
                                                        

- (Peer Reviewed Journal)Washburn, J.D., Cimen, E., Ramstein, G., Reeves, T., O'Briant, P., McLean, G., Cooper, M., Hammer, G., Buckler IV, E.S. 2021. Predicting phenotypes from genetic, environment, management, and historical data using CNNs. Theoretical and Applied Genetics. 134:3997–4011. https://doi.org/10.1007/s00122-021-03943-7.The contributions from the progenitor genomes of the mesopolyploid brassiceae are evolutionarily distinct but functionally compatible
                                                        

- (Peer Reviewed Journal)Hao, Y., Mabry, M.E., Edger, P.P., Freeling, M., Zheng, C., Jin, L., VanBuren, R., Colle, M., An, H., Abrahams, R.S., Washburn, J.D., Qi, X., Barry, K., Daum, C., Shu, S., Schmutz, J., Sankoff, D., Barker, M.S., Lyons, E., Pires, C.J., Conant, G.C. 2021. The contributions from the progenitor genomes of the mesopolyploid brassiceae are evolutionarily distinct but functionally compatible. Genome Research. 31(5):799-810. https://doi.org/10.1101/gr.270033.120.Cytogenetics and fertility of an induced tetraploid Sorghum bicolor x S. propinquum hybrid
                                                        

- (Peer Reviewed Journal)Porter, N.T., Burson, B.L., Washburn, J.D., Klein, R.R., Jessup, R.W. 2021. Cytogenetics and fertility of an induced tetraploid Sorghum bicolor x S. propinquum hybrid. Crop Science. 61(3):1881-1889. https://doi.org/10.1002/csc2.20482.Phylogeny and multiple independent whole-genome duplication events in the brassicales
                                                        

- (Peer Reviewed Journal)Mabry, M.E., Brose, J.M., Blischak, P.D., Sutherland, B., Dismukes, W.T., Bottoms, C.A., Edger, P.P., Washburn, J.D., An, H., Hall, J.C., McKain, M.R., Al-Shehbaz, I., Barker, M.S., Schranz, E.M., Conant, G.C., Pires, C.J. 2020. Phylogeny and multiple independent whole-genome duplication events in the brassicales. American Journal of Botany. 107(8):1148-1164. https://doi.org/10.1002/ajb2.1514.


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