Location: Plant, Soil and Nutrition Research
2020 Annual Report
Accomplishments
1. The genomic toolbox for regulating genes is shared across flowering plants and crops. Flowering plants and crops have 20,000 to 60,000 genes, but those genes are controlled by a smaller set of two thousand regulator genes called transcription factors. Are the patterns for how these regulator genes bind DNA and turn on genes consistent across plants? In two large studies, ARS researchers in Ithaca, New York, along with collaborators, have shown that the interaction between regulator genes and DNA is evolutionarily consistent across flowering plants. The tremendous diversity of plants is the product of combining these regulator gene-DNA interactions into numerous new combinations. This suggests that plant scientists should work across species to develop a single model for the regulation of plant genes. Long term this will allow advanced genomic models to be applied to all crops.
2. Breeding Insight starts supporting ARS specialty crop and animal breeders. While specialty crops and animals are a large portion of gross US agricultural revenue, individually these small programs have not had access to innovations that benefited major crop and animal breeding programs and thus have lagged behind. ARS specialty breeders are often the sole source of publicly available new crop varieties for farmers and growers across the US and elsewhere. Breeding Insight is currently in a pilot phase focused on building support services for 6 ARS breeding programs (blueberry, table grape, sweet potato, alfalfa, rainbow trout, and North American Atlantic salmon), with the future goal of expansion to all ARS specialty crops, animal, and natural resource breeding programs. The project has identified the key workflows common to these diverse programs, and initiated the development of extensive software and genomics to support these efforts. A key early success was integration of the leading field data collection tool with the community’s leading database. Genomic support was delivered for all programs. Providing powerful information and genomic tools to ARS’s excellent specialty crop and animal breeders is helping to improve breeding decisions, meet public demands for more nutritious and flavorful foods, and improve food security for the US and its trade partners.
Review Publications
Falcon, C.M., Kaeppler, S.M., Spalding, E.P., Miller, N.D., Haase, N., Alkhalifah, N., Bohn, M., Buckler IV, E.S., Campbell, D.A., Ciampitti, I., Coffey, L., Edwards, J.W., Ertl, D., Flint Garcia, S.A., Gore, M.A., Graham, C., Hirsch, C.N., Holland, J.B., Jarquin, D., Knoll, J.E., Lauter, N.C., Lawrence-Dill, C.J., Lee, E.C., Lorenz, A., Lynch, J.P., Murray, S.C., Nelson, R., Romay, M., Rocheford, T., Schnable, P., Scully, B.T., Smith, M.C., Springer, N., Tuinstra, M., Walton, R., Weldekidan, T., Wisser, R.J., Xu, W., De Leon, N. Relative utility of agronomic, phenological, and morphological traits for assessing genotype-by-environment interaction in maize inbreds. Crop Science. 2020; 60:62-81. https://doi.org/10.1002/csc2.20035
Gage, J.L., Richards, E., Lepak, N.K., Kaczmar, N., Soman, C., Chowdhary, G., Gore, M.A., Buckler IV, E.S. 2019. In-field whole plant maize architecture characterized by Subcanopy Rovers and Latent Space Phenotyping. The Plant Phenome Journal. 2(1):1-11. https://doi.org/10.1101/763342.
Mcfarland, B.A., Alkhalifah, N., Bohn, M., Bubert, J., Buckler IV, E.S., Ciampitti, I., Edwards, J.W., Ertl, D., Gage, J.L., Falcon, C.M., Flint Garcia, S.A., Gore, M., Graham, C., Hirsch, C., Holland, J.B., Hood, E., Hooker, D., Jarquin, D., Kaeppler, S., Knoll, J.E., Kruger, G., Lauter, N.C., Lee, E.C., Lima, D.C., Lorenz, A., Lynch, J.P., Mckay, J., Miller, N.D., Moose, S.P., Murray, S.C., Nelson, R., Poudyal, C., Rocheford, T., Rodriguez, O., Romay, M., Schnable, J.C., Schnable, P.S., Scully, B.T., Sekhon, R., Silverstein, K., Singh, M., Smith, M., Spalding, E.P., Springer, N., Thelen, K., Thomison, P., Tuinstra, M., Wallace, J., Walls, R., Wills, D., Wisser, R.J., Xu, W., Yeh, C., De Leon, N. Maize genomes to fields (G2F): 2014 –2017 field seasons: genotype, phenotype, climatic, soil and inbred ear image datasets. BMC Research Notes. 13,71 (2020). https://doi.org/10.1186/s13104-020-4922-8.
Ricci, W.A., Lu, Z., Ji, L., Marand, A.P., Ethridge, C.L., Murphy, N.G., Noshay, J.M., Galli, M., Mejia-Guerra, M.K., Colome-Tatche, M., Johannes, F., Rowley, M., Corces, V.G., Zhai, J., Scanlon, M.J., Buckler IV, E.S., Gallavotti, A., Springer, N.M., Schmitz, R.J., Zhang, X. 2019. Widespread long-range cis-regulatory elements in the maize genome. Nature Plants. 5:1237-1249. https://doi.org/10.1038/s41477-019-0547-0.
Bukowski, R., Guo, X., Lu, Y., Zou, C., He, B., Rong, Z., Yang, B., Wang, B., Xu, D., Xie, C., Fan, L., Gao, S., Xy, X., Zhang, G., Li, Y., Jiao, Y., Doebley, J., Ross-Ibarra, J., Buffalo, V., Romay, C., Buckler IV, E.S., Wu, Y., Lai, J., Ware, D., Sun, Q. 2018. Construction of the third generation Zea mays haplotype map. Gigascience. 7(4):1-12.
Sun, S., Zhou, Y., Chen, J., Shi, J., Zhao, H., Zhao, H., Song, W., Zhang, M., Cui, Y., Dong, X., Liu, H., Ma, X., Yinping, J., Bo, W., Wei, X., Stein, J., Glaubitz, J., Lu, F., Yu, G., Liang, C., Fengler, K., Li, B., Rafalski, A., Schnable, P., Ware, D., Buckler IV, E.S., Lai, J. 2018. Extensive intraspecific gene order and gene structural variations between Mo17 and other maize genomes. Nature Genetics. https://doi.org/10.1038/s41588-018-0182-0.
Mejia-Guerra, M., Buckler IV, E.S. 2019. k-mer grammar uncovers maize regulatory architecture. Biomed Central (BMC) Plant Biology. 19:103. https://doi.org/10.1186/s12870-019-1693-2.
Gault, C., Kremling, K., Buckler IV, E.S. 2018. Tripsacum de novo transcriptome assemblies reveal parallel gene evolution with maize after ancient polyploidy. The Plant Genome. https://doi.org/10.1101/267682.
Ramstein, G.P., Jensen, S.E., Buckler IV, E.S. 2019. Breaking the curse of dimensionality to identify causal variants in Breeding 4. Theoretical and Applied Genetics. 132(3):559-567. https://doi.org/10.1007/s00122-018-3267-3.
Wang, H., Cimen, E., Singh, N., Buckler IV, E.S. 2020. Deep learning for plant genomics and crop improvement. Current Opinion in Plant Biology. 54:34-41. https://doi.org/10.1016/j.pbi.2019.12.010.
Gage, J., Monier, B., Giri, A., Buckler IV, E.S. 2020. Ten years of the maize Nested Association Mapping population: impact, limitations, and future directions. The Plant Cell. https://doi.org/10.1105/tpc.19.00951.
Wallace, J., Kremling, K., Buckler IV, E.S. 2019. Quantitative genetic analysis of the maize leaf microbiome. Phytobiomes Journal. 2(4):208-224. https://doi.org/10.1094/PBIOMES-02-18-0008-R.
Lozano, R., Booth, G.T., Omar, B.Y., Li, B., Buckler IV, E.S., Lis, J.T., Jannink, J., Pino Del Carpio, D. 2018. RNA polymerase mapping in plants identifies enhancers enriched in causal variants. bioRxiv. https://doi.org/10.1101/376640.
Washburn, J.D., Mejia Guerra, M., Ramstein, G., Kremling, K., Valluru, R., Buckler IV, E.S., Wang, H. 2019. Evolutionarily informed deep learning methods: Predicting transcript abundance from DNA sequence. Proceedings of the National Academy of Sciences. 116(12):5542-5549. https://doi.org/10.1073/pnas.1814551116.
Baseggio, M., Murray, M., Magallanes-Lundback, M., Kaczmar, N., Chamness, J., Buckler IV, E.S., Smith, M.E., Dellapenna, D., Tracy, W.F., Gore, M.A. 2018. Genome-wide association and genomic prediction models of tocochromanols in fresh sweet corn kernels. The Plant Genome. 12:180038. https://doi.org/10.3835/plantgenome2018.06.0038.
Oren, E., Tzuri, G., Vexler, L., Dafna, A., Meir, A., Faigenboim, A., Kenigswald, M., Portnoy, V., Schaffer, A.A., Levi, A., Buckler IV, E.S., Katzir, N., Burger, J., Tadmor, Y., Gur, A. 2019. The multi-allelic APRR2 Gene is associated with fruit pigment accumulation in melon and watermelon. Journal of Experimental Botany. https://doi.org/10.1093/jxb/erz182.
Valluru, R., Gazave, E., Fernandes, S., Ferguson, J., Lazano, R., Hirannaiah, P., Zuo, T., Brown, P., Leakey, A., Gore, M., Buckler IV, E.S., Bandillo, N. 2019. Deleterious mutation burden and its association with complex traits in sorghum (sorghum bicolor). Genetics. 211(3):1075-1087. https://doi.org/10.25386/genetics.7638122.
Zhou, S., Zhang, Y., Kremling, K., Ding, Y., Bennett, J., Bae, J., Kim, D., Kolomiets, M., Schmelz, E., Schroeder, F., Buckler Iv, E.S., Jander, G. 2018. Ethylene signaling regulates natural variation in the abundance of antifungal acetylated diferuloylsucroses and Fusarium graminearum resistance in maize seedling roots. New Phytologist. 221(4):2096-2111. https://doi.org/10.1111/nph.15520.
Valluru, R., Gazave, E.E., Fernandes, S.B., Ferguson, J.N., Lozano, R., Hirannaiah, P., Zuo, T., Brown, P.J., Leakey, A.D., Gore, M., Buckler IV, E.S., Bandillo, N. 2018. Leveraging mutational burden for complex trait prediction in sorghum. bioRxiv. https://doi.org/10.1101/357418.
Shaoqun, Z., Kremling, K.A., Bandillo, N., Richter, A., Zhang, Y.K., Ahern, K.R., Artyukhin, A.B., Hui, J.X., Younkin, G.C., Schroeder, F.C., Buckler IV, E.S., Jander, G. 2019. Metabolome-scale genome-wide association studies reveal chemical diversity and genetic control of maize specialized metabolites. The Plant Cell. 31:937-955. https://doi.org/10.1105/tpc.18.00772.
Alkhalifah, N., Campbell, D., Falcon, C., Gardiner, J.M., Miller, N., Romay, M., Walls, R., Walton, R., Yeh, C., Bohn, M., Bubert, J., Buckler Iv, E.S., Ciampitti, I., Flint Garcia, S.A., Gore, M., Graham, C., Hirsch, C., Holland, J.B., Hooker, D., Kaeppler, S., Knoll, J.E., Lauter, N.C., Lee, E., Lorenz, A., Lynch, J., Moose, S., Murray, S., Nelson, R., Rocheford, T., Rodriguez, O., Schnable, J., Scully, B.T., Smith, M., Springer, N., Thomison, P., Tuinstra, M., Wisser, R., Xu, W., Ertl, D., Schnable, P., De Leon, N., Spalding, E., Edwards, J.W., Lawrence-Dill, C. 2018. Maize genomes to fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets. BMC Research Notes. 11:452. https://doi.org/10.1186/s13104-018-3508-1.
Ding, Y., Murphy, K., Poretsky, E., Mafu, S., Yang, B., Char, S., Christensen, S.A., Saldivar, E., Wu, M., Wang, Q., Ji, L., Schmitz, R., Kremling, K., Buckler IV, E.S., Shen, Z., Briggs, S., Bohlmann, J., Sher, A., Castro-Falcon, G., Hughes, C., Huffaker, A., Zerbe, P., Schmelz, E. 2019. Multiple genes recruited from hormone pathways partition maize diterpenoid defences. Nature Plants. https://doi.org/10.1038/s41477-019-0509-6.
Yang, C., Samayoa, L., Bradbury, P., Olukolu, B.A., Xue, W., York, A.M., Tuholski, M.R., Wang, W., Daskalska, L.L., Neumeyer, M.A., Sanchez-Gonzales, J., Romay, M.C., Glaubitz, J.C., Sun, Q., Buckler IV, E.S., Holland, J.B., Doebley, J.F. 2019. The genetic architecture of teosinte catalyzed and constrained maize domestication. Proceedings of the National Academy of Sciences. 116:5643-5652.
Ramstein, G.P., Larsson, S.J., Cook, J.P., Edwards, J.W., Ersoz, E.S., Flint Garcia, S.A., Gardner, C.A., Holland, J.B., Lorenz, A.J., Mcmullen, M.D., Millard, M.J., Rocheford, T.R., Tuinstra, M.R., Bradbury, P., Buckler IV, E.S., Romay, M.C. 2020. Dominance effects and functional enrichments improve prediction of agronomic traits in hybrid maize. Genetics. 215:215-230. https://doi.org/10.1534/genetics.120.303025.
Baseggio, M., Murray, M., Magallanes-Lundback, M., Kaczmar, N., Chamness, J., Buckler IV, E.S., Smith, M.E., Dellapenna, D., Tracy, W.F., Gore, M.A. 2020. Natural variation for carotenoids in fresh kernels is controlled by uncommon variants in sweet corn. The Plant Genome. https://doi.org/10.1002/tpg2.20008.
Kremling, K., Diepenbrock, C., Gore, M., Buckler IV, E.S., Bandillo, N. 2019. Transcriptome-wide association supplements genome-wide association in Zea mays. Genes, Genomes, Genetics. 9(9):3023-3033. https://doi.org/10.1534/g3.119.400549.
Chen, Q., Samayoa, L., Yang, C.J., Bradbury, P., Olukolu, B., Neumeyer, M.A., Tomay, M., Sun, Q., Lorant, A., Buckler IV, E.S., Ross-Ibarra, J., Holland, J.B., Doebley, J.F. 2020. The genetic architecture of the maize progenitor, teosinte, and how it was altered during maize domestication. PLoS Genetics. 16(5):e1008791.
