Preprints
Bartlett, B., Cho, A., Laspisa, D., Gore, M.A., Kantar, M. 2023. Genomic resources for Macadamia tetraphylla and an examination of its historic use as a crop resource in Hawaii. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/2023.12.22.573098v1
Bullock, D., Mangeni, A., Wiesner-Hanks, T., DeChant, C., Stewart, E.L., Kaczmar, N., Kolkman, J.M., Nelson, R.J., Gore, M.A., and Lipson, H. 2019. Automated weed detection in aerial imagery with context. Preprint at arXiv:1910.00652v3. https://doi.org/10.48550/arXiv.1910.00652
Buckler, E.S., Ilut, D.C., Wang, X., Kretzschmar, T., Gore, M.A., and Mitchell, S.E. 2016. rAmpSeq: Using repetitive sequences for robust genotyping. Preprint at bioRxiv http://dx.doi.org/10.1101/096628
2024
Morales, N., Anche, M.T., Kaczmar, N.S., Lepak, N., Ni, P., Romay, M.C., Santantonio, N., Buckler, E.S., Gore, M.A., Mueller, L.A., and Robbins, K.R. 2022. Spatio-temporal modeling of high-throughput multi-spectral aerial images improves agronomic trait genomic prediction in hybrid maize. Genetics, iyae037 https://doi.org/10.1093/genetics/iyae037
2023
Lima, D.C., Aviles, A.C., Alpers, R.T., Perkins, A., Schoemaker, D.L., Costa, M., Michel, K.J., Kaeppler, S., Ertl, D., Romay, M.C., Gage, J.L., Holland, J., Beissinger, T., Bohn, M., Buckler, E., Edwards, J., Flint-Garcia, S., Gore, M.A., Hirsch, C.N., Knoll, J.E., McKay, J., Minyo, R., Murray, S.C., Schnable, J., Sekhon, R.S., Singh, M.P., Sparks, E.E., Thomison, P., Thompson, A., Tuinstra, M., Wallace, J., Washburn, J.D., Weldekidan, T., Xu, W. and de Leon, N. 2023. 2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative. BMC Research Notes 16:219. https://doi.org/10.1186/s13104-023-06430-y
Ferrão, L.F.V., Dhakal, R., Dias, R., Tieman, D., Whitaker, V., Gore, M.A., Messina, C., Resende Jr, M.F.R. 2023. Machine learning applications to improve flavor and nutritional content of horticultural crops through breeding and genetics. Current Opinion in Biotechnology 83:102968. https://doi.org/10.1016/j.copbio.2023.102968
Brzozowski, L.J., Campbell, M.T., Hu, H., Yao, L., Caffe, M., Gutiérrez, L., Smith, K.P., Sorrells, M.E., Gore, M.A., and Jannink, J.-L. 2023. Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa). The Plant Genome 16:e20370. https://acsess.onlinelibrary.wiley.com/doi/full/10.1002/tpg2.20370
Lima, D.C., Washburn, J.D., Varela, J. I., Chen, Q., Gage, J.L., Romay, M. C., Holland, J., Ertl, D., Lopez-Cruz, M., Aguate, F.M., de los Campos, G., Kaeppler, S., Beissinger, T., Bohn, M., Buckler, E., Edwards, J., Flint-Garcia, S., Gore, M.A., Hirsch, C.N., Knoll, J.E., McKay, J., Minyo, R., Murray, S.C., Ortez, O.A., Schnable, J.C., Sekhon, R.S., Singh, M.P., Sparks, E.E., Thompson, A., Tuinstra, M., Wallace, J., Weldekidan, T., Xu, W., and de Leon, N. 2023. Genomes to Fields 2022 maize genotype by environment prediction competition. BMC Research Notes 16:148. https://doi.org/10.1186/s13104-023-06421-z
Anche, M.T., Morales, N., Kaczmar, N.S., Santantonio, N., Gore, M.A., and Robbins, K.R. 2023. Scalable growth models for time-series multispectral images. The Plant Phenome Journal 6:e20064. https://doi.org/10.1002/ppj2.20064
Tibbs-Cortes, L.E., Guo, T., Li, X., Tanaka, R., Vanous, A.E., Peters, D., Gardner, C., Magallanes-Lundback, M., Deason, N.T., DellaPenna, D., Gore, M.A., and Yu, J. 2023. Genomic prediction of tocochromanols in exotic-derived maize. The Plant Genome 16:e20286. https://doi.org/10.1002/tpg2.20286
Tanaka, R., Wu, D., Li, X., Tibbs-Cortes, L.E., Wood, J.C., 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., and Gore, M.A. 2023. Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain. The Plant Genome 16:e20276. https://doi.org/10.1002/tpg2.20276
2022
Nkouaya Mbanjo, E.G., Hershberger, J., Peteti, P., Agbona, A., Ikpan A, Ogunpaimo, K., Kayondo, S.I., Abioye, R.S., Nafiu, K., Alamu, E.O., Adesokan, M., Maziya-Dixon, B., Parkes, E., Kulakow, P., Gore, M.A., Egesi, C., and Rabbi, I.Y. 2022. Predicting starch content in cassava fresh roots using near-infrared spectroscopy. Frontiers in Plant Science 13:990250. doi: 10.3389/fpls.2022.990250
The CROPPS Research Community. 2022. Digital biology to enable sustainable and resilient agriculture. The Bridge 52:30-34.
Vogel, G., Giles, G., Robbins, K.R., Gore, M.A., and Smart, C.D. 2022. Quantitative genetic analysis of interactions in the pepper-Phytophthora capsici pathosystem. Molecular Plant-Microbe Interactions 35:1018-1033. https://doi.org/10.1094/MPMI-12-21-0307-R
Wu, D., Li, X., Tanaka, R., Wood, J.C., Tibbs-Cortes, L.E., Magallanes-Lundback, M., Bornowski, N., Hamilton, J.P., Vaillancourt, B., Diepenbrock, C.H., Li, X., Deason, N.T., Schoenbaum, G.R., Yu, J., Buell, C.R., DellaPenna, D., and Gore, M.A. 2022. Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain. Genetics 221:iyac091. https://doi.org/10.1093/genetics/iyac091
Albert, E., Kim, S., Magallanes-Lundback, M., Bao, Y., Deason, N., Danilo, B., Wu, D., Li, X., Wood, J.C., Bornowski, N., Gore, M.A., Buell, C.R., and DellaPenna, D. 2022. Genome-wide association identifies a missing hydrolase for tocopherol synthesis in plants. Proceedings of the National Academy of Sciences 119:e2113488119. https://doi.org/10.1073/pnas.2113488119
Freiband, A., Dickin, K.L, Glass, M., Gore, M.A.. Hinestroza, J., Nelson, R., Platt, V., Rooks, N., Sachs, A., Stern, N., and Lehmann, J. 2022. Undisciplining the university through shared purpose, practice, and place. Humanities & Social Sciences Communications 9:172. https://doi.org/10.1057/s41599-022-01195-4
Lin, M., Qiao, P., Matschi, S., Vasquez, M., Ramstein, G.P., Bourgault, R., Mohammadi, M., Scanlon, M.J., Molina, I., Smith, L.G., and Gore, M.A. 2022. Integrating GWAS and TWAS to elucidate the genetic architecture of maize leaf cuticular conductance. Plant Physiology 189:2144-2158. https://doi.org/10.1093/plphys/kiac198
Brzozowski, L.J., Campbell, M.T., Hu, H., Caffe, M., Gutiérrez, L., Smith, K.P., Sorrells, M.E., Gore, M.A., and Jannink, J.-L. 2022. Generalizable approaches for genomic prediction of metabolites in plants. The Plant Genome 15:e20205. https://doi.org/10.1002/tpg2.20205
Morales, N…35 other authors…Hershberger, J.M., Gore, M.A.,…18 other authors…Mueller, L.A. 2022. Breedbase: a digital ecosystem for modern plant breeding. G3: Genes| Genomes| Genetics 12:jkac078. https://doi.org/10.1093/g3journal/jkac078
Hershberger, J., Mbanjo, E.G.N., Peteti, P., Ikpan, A.S., Ogunpaimo, K., Nafiu, K., Rabbi, I.Y., and Gore, M.A. 2022. Low-cost, handheld near-infrared spectroscopy for root dry matter content prediction in cassava. The Plant Phenome Journal 5:e20040. https://doi.org/10.1002/ppj2.20040
Hershberger, J., Tanaka, R., Wood, J.C., Kaczmar, N., Wu, D., Hamilton, J.P., DellaPenna, D., Buell, C.R., and Gore, M.A. 2022. Transcriptome-wide association and prediction for carotenoids and tocochromanols in fresh sweet corn kernels. The Plant Genome 15:e20197. https://doi.org/10.1002/tpg2.20197
Fumia, N., Rubinoff, D., Zenil-Ferguson, R., Khoury, C.K., Pironon, S., Gore, M.A., and Kantar, M.B. 2022. Interactions between breeding system and ploidy affect niche breadth in Solanum. Royal Society Open Science 9:211862. https://doi.org/10.1098/rsos.211862
Fumia, N., Pironon, S., Rubinoff, D., Khoury, C.K., Gore, M.A., and Kantar, M.B. 2022. Wild relatives of potato may bolster its adaptation to new niches under future climate scenarios. Food and Energy Security 11:e360. https://doi.org/10.1002/fes3.360
Brzozowski, L.J., Hu, H., Campbell, M.T., Broeckling, C.D., Caffe, M., Gutiérrez, L., Smith, K.P., Sorrells, M.E., Gore, M.A., and Jannink, J.-L. 2022. Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L). G3: Genes| Genomes| Genetics 12:jkab419. https://doi.org/10.1093/g3journal/jkab419
2021
Del Valle-Echevarria, A., Fumia, N., Gore, M. A., and Kantar, M. 2021. Accelerating crop domestication in the era of gene editing. In Plant Breeding Reviews I. Goldman (Ed.). https://doi.org/10.1002/9781119828235.ch4
Hislop, L., Stephanie, E., Flannery, P., Baseggio, M., Gore, M.A., and Tracy, W.F. 2021. Sugarcane mosaic virus resistance in the Wisconsin sweet corn diversity panel. Journal of the American Society for Horticultural Science 146:435-444. https://doi.org/10.21273/JASHS05097-21
Hu, H., Campbell, M.T., Yeats, T.H., Zheng, X., Runcie, D.E., Covarrubias-Pazaran, G., Broeckling, C., Yao, L., Caffe-Treml, M. Gutiérrez, L., Smith, K.P., Tanaka, J., Hoekenga, O.A., Sorrells, M.E., Gore, M.A., and Jannink, J.-L. 2021. Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations. Theoretical and Applied Genetics 134:4043-4054. https://doi.org/10.1007/s00122-021-03946-4
Feng, H., Acosta-Gamboa, L., Kruse, L.H., Tracy, J.D., Chung, S.H., Fereira, A.R.N., Shakir, S., Xu, H., Sunter, G., Gore, M.A., Casteel, C.L., Moghe, G.D., and Jander, G. 2021. Acylsugars protect Nicotiana benthamiana against insect herbivory and desiccation. Plant Molecular Biology https://doi.org/10.1007/s11103-021-01191-3
Rife, T.W., Courtney, C., Hershberger, J., Gore, M.A., Neilsen, M., and Poland, J. 2021. Prospector: A mobile application for portable, high-throughput near-infrared spectroscopy phenotyping. The Plant Phenome Journal 4:e20024. https://doi.org/10.1002/ppj2.20024
Pignon, C.P., Fernandes, S.B., Valluru, R., Bandillo, N., Lozano, R., Buckler, E., Gore, M.A., Long, S.P., Brown, P.J., and Leakey, A.D.B. 2021. Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes. Plant Physiology 187:2544-2562. https://doi.org/10.1093/plphys/kiab395
Ferguson, J.N., Fernandes, S.B., Monier, B., Miller, N.D., Allan, D., Dmitrieva, A., Schmuker, P., Lozano, R., Valluru, R., Buckler, E.S., Gore, M.A., Brown, P.J., Spalding, E.P., and Leakey, A.D.B. 2021. Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. Plant Physiology 187:1481-1500. https://doi.org/10.1093/plphys/kiab346
Mabry, M.E., Turner-Hissong, S.D., Gallagher, E.Y., McAlvay, A.C., An, H., Edger, P.P., Moore, J.D., Pink, D.A.C., Teakle, G.R., Stevens, C.J., Barker, G., Labate, J., Fuller, D.Q., Allaby, R.G., Beissinger, T., Decker, J.E., Gore, M.A., and Pires, J.C. 2021. The evolutionary history of wild, domesticated, and feral Brassica oleracea (Brassicaceae). Molecular Biology and Evolution 38:4419-4434. https://doi.org/10.1093/molbev/msab183
Jain, P., Liu, W., Zhu, S., Yao-Yun Chang, C., Melkonian, J., Rockwell, F.E., Pauli, D., Sun, Y., Zipfel, W.R., Holbrook, N. M., Riha, S.J., Gore, M.A., and Stroock, A.D. 2021. A minimally disruptive method for measuring water potential in-planta using hydrogel nanoreporters. Proceedings of the National Academy of Sciences 118: e2008276118. https://doi.org/10.1073/pnas.2008276118
Baseggio, M., Murray, M., Wu, D., Ziegler, G., Kaczmar, N., Chamness, J., Hamilton, J.P., Buell, C.R., Vatamaniuk, O.K., Buckler, E.S., Smith, M.E., Baxter, I., Tracy, W.F., and Gore, M.A. 2021. Genome-wide association study suggests an independent genetic basis of zinc and cadmium concentrations in fresh sweet corn kernels. G3: Genes| Genomes| Genetics 11:jkab186. https://doi.org/10.1093/g3journal/jkab186
Stajich, J.E., Vu, A.L., Judelson, H.S., Vogel, G.M., Gore, M.A., Carlson, M.O., Devitt, N., Jacobi, J., Mudge, J., Lamour, K.H., Smart, C.D. 2021. High-quality reference genome sequence for the oomycete vegetable pathogen Phytophthora capsici strain LT1534. Microbiology Resource Announcements 10:e00295-21. https://doi.org/10.1128/MRA.00295-21
Esuma, W., Ozimati, A., Kulakow, P., Gore, M.A., Wolfe, M.D., Nuwamanya, E., Egesi, C., and Kawuki, R.S. 2021. Effectiveness of genomic selection for improving provitamin A carotenoid content and associated traits in cassava. G3: Genes| Genomes| Genetics jkab160. https://doi.org/10.1093/g3journal/jkab160
Campbell, M.T., Hu, H., Yeats, T.H., Brzozowski, L.J., Caffe-Treml, M., Gutiérrez, L., Smith, K.P., Sorrells, M.E., Gore, M.A., and Jannink, J.-L. 2021. Improving genomic prediction for seed quality traits in oat (Avena sativa L.) using trait-specific relationship matrices. Frontiers in Genetics 12:643733. doi: 10.3389/fgene.2021.643733
Wu, D., Tanaka, R., Li, X., Ramstein, G.P., Cu, S., Hamilton, J.P., Buell, C.R., Stangoulis, J., Rocheford, T., and Gore, M.A. 2021. High-resolution genome-wide association study pinpoints metal transporter and chelator genes involved in the genetic control of element levels in maize grain. G3: Genes| Genomes| Genetics 11:jkab059. https://doi.org/10.1093/g3journal/jkab059
Hu, Y., Colantonio, V., Müller, B.S.F., Leach, K.A., Nanni, A., Finegan, C., Wang, B., Baseggio, M., Newton, C.J., Juhl, E.M., Hislop, L., Gonzalez, J.M., Rios, E.F., Hannah, L.C., Swarts, K., Gore, M.A., Hennen-Bierwagen, T.A., Myers, A.M., Settles, A.M., Tracy, W.F., and Resende Jr., M.F.R. 2021. Genome assembly and population genomic analysis provide insights into the evolution of modern sweet corn. Nature Communications 12:1227. https://doi.org/10.1038/s41467-021-21380-4
Hershberger, J., Morales, N., Simoes, C.C., Ellerbrock, B., Bauchet, G., Mueller, L.A., and Gore, M.A. 2021. Making waves in Breedbase: An integrated spectral data storage and analysis pipeline for plant breeding programs. The Plant Phenome Journal 4:e20012. https://doi.org/10.1002/ppj2.20012
Campbell, M.T., Hu, H., Yeats, T.H., Caffe-Treml, M., Gutiérrez, L., Smith, K.P., Sorrells, M.E., Gore, M.A., and Jannink, J.-L. 2021. Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.). Genetics 217:iyaa043. https://doi.org/10.1093/genetics/iyaa043
Rogers, A.R., Dunne, J.C., Romay, C., Bohn, M., Buckler, E.S., Ciampitti, I.A., Edwards, J., Ertl, D., Flint-Garcia, S., Gore, M.A., Graham, C., Hirsch, C.N., Hood, E., Hooker, D.C., Knoll, J., Lee, E.C., Lorenz, A., Lynch, J.P., McKay, J., Moose, S.P., Murray, S.C., Nelson, R., Rocheford, T., Schnable, J.C., Schnable, P.S., Sekhon, R., Singh, M., Smith, M., Springer, N., Thelen, K., Thomison, P., Thompson, A., Tuinstra, M., Wallace, J., Wisser, R.J., Xu, W., Gilmour, A.R., Kaeppler, S.M., de Leon, N., and Holland, J.B. 2021. The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment. G3: Genes| Genomes| Genetics 11:jkaa050. https://doi.org/10.1093/g3journal/jkaa050
Vogel, G., LaPlant, K.E., Mazourek, M., Gore, M.A., and Smart, C.D. 2021. A combined BSA-Seq and linkage mapping approach identifies genomic regions associated with Phytophthora root and crown rot resistance in squash. Theoretical and Applied Genetics 134:1015-1031. https://doi.org/10.1007/s00122-020-03747-1
Jarquin, D., de Leon, N., Romay, C., Bohn, M., Buckler, E.S., Ciampitti, I., Edwards, J., Ertl, D., Flint-Garcia, S., Gore, M.A., Graham, C., Hirsch, C.N., Holland, J.B., Hooker, D., Kaeppler, S.M., Knoll, J., Lee, E.C., Lawrence-Dill, C.J., Lynch, J.P., Moose, S.P., Murray, S.C., Nelson, R., Rocheford, T., Schnable, J.C., Schnable, P.S., Smith, M., Springer, N., Thomison, P., Tuinstra, M., Wisser, R.J., Xu, W., Yu, J., and Lorenz, A. 2021. Utility of climatic information via combining ability models to improve genomic prediction for yield within the genomes to fields maize project. Frontiers in Genetics 11:592769. doi: 10.3389/fgene.2020.592769
Kim, M.-S., Lozano, R., Kim, J.H., Bae, D.N., Kim, S.-T., Park, J.-H., Choi, M.S., Kim, J., Ok, H.C., Park, S.-K., Gore, M.A., Moon, J.-K., and Jeong, S.-C. 2021. The patterns of deleterious mutations during the domestication of soybean. Nature Communications 12:97. https://doi.org/10.1038/s41467-020-20337-3
Lozano, R., Gazave, E., dos Santos, J.P.R., Stetter, M., Valluru, R., Bandillo, N., Fernandes, S.B., Brown, P.J., Shakoor, N., Mockler, T.C., Cooper, E.A., Perkins, M.T., Buckler, E.S., Ross-Ibarra, J., and Gore, M.A. 2021. Comparative evolutionary genetics of deleterious load in sorghum and maize. Nature Plants 7:17-24. https://doi.org/10.1038/s41477-020-00834-5
Vogel, G., Gore, M.A., and Smart, C.D. 2021. Genome-wide association study in New York Phytophthora capsici isolates reveals loci involved in mating type and mefenoxam sensitivity. Phytopathology 111:204-216. https://doi.org/10.1094/PHYTO-04-20-0112-FI
Diepenbrock, C.H., Ilut, D.C., Magallanes-Lundback, M., Kandianis, C.B.,Lipka, A.E., Bradbury, P.J., Holland, J.B., Hamilton, J.P., Wooldridge, E., Vaillancourt, B., Góngora-Castillo, E., Wallace, J.G., Cepela, J., Mateos-Hernandez, M., Owens, B.F., Tiede, T., Buckler, E.S., Rocheford, T., Buell, C.R., Gore, M.A., and DellaPenna, D. 2021. Eleven biosynthetic genes explain the majority of natural variation for carotenoid levels in maize grain. The Plant Cell 33:882-900. https://doi.org/10.1093/plcell/koab032
2020
Matschi, S., Vasquez, M.F., Bourgault, R., Steinbach, P., Chamness, J., Kaczmar, N., Gore, M.A., Molina, I., and Smith, L.G. 2020. Structure-function analysis of the maize bulliform cell cuticle and its potential role in dehydration and leaf rolling. Plant Direct 4:e00282. https://doi.org/10.1002/pld3.282
Krause, M.R., Crossman, S., DuMond, T., Lott, R., Swede, J., Arliss, S., Robbins, R., Ochs, D., and Gore, M.A. 2020. Random forest regression for optimizing variable planting rates for corn and soybean using topographical and soil data. Agronomy Journal 112:5045-5066. https://doi.org/10.1002/agj2.20442
Krause, M.R., Mondal, S., Crossa, J., Singh, R.P., Pinto, F., Haghighattalab, A., Shrestha, S., Rutkoski, J., Gore, M.A., Sorrells, M.E., and Poland, J. 2020. Aerial high-throughput phenotyping enables indirect selection for grain yield at the early-generation, seed-limited stages in breeding programs. Crop Science 60:3096-3114. https://doi.org/10.1002/csc2.20259
Brzozowski, L.J., Gore, M.A., Agrawal, A.A., and Mazourek, M. 2020. Divergence of defensive cucurbitacins in independent Cucurbita pepo domestication events leads to differences in specialist herbivore preference. Plant, Cell & Environment 43:2812-2825. https://doi.org/10.1111/pce.13844
Qiao, P. Bourgault, R. Mohammadi, M., Gore, M.A., Molina, I., and Scanlon, M.J. 2020. A maize LIPID TRANSFER PROTEIN may bridge the gap between PHYTOCHROME-mediated light signaling and cuticle biosynthesis. Plant Signaling & Behavior 15:e1790824 https://doi.org/10.1080/15592324.2020.1790824
Anche, M.T., Kaczmar, N.S., Morales, N., Clohessy, J.W., Ilut, D.C., Gore, M.A., and Robbins, K.R. 2020. Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize. Theoretical and Applied Genetics 133:2853-2868. https://doi.org/10.1007/s00122-020-03637-6
Wang, D.R., Venturas, M.D., Mackay, D.S., Hunsaker, D.J., Thorp, K.R., Gore, M.A., and Pauli, D. 2020. Use of hydraulic traits for modeling genotype‐specific acclimation in cotton under drought. New Phytologist 228:898-909. https://doi.org/10.1111/nph.16751
Qiao, P. Bourgault, R. Mohammadi, M., Matschi, S., Philippe, G., Smith, L.G., Gore, M.A., Molina, I., and Scanlon, M.J. 2020. Transcriptomic network analyses shed light on the regulation of cuticle development in maize leaves. Proceedings of the National Academy of Sciences 117:12464-12471. https://doi.org/10.1073/pnas.2004945117
Morales, N., Kaczmar, N.S., Santantonio, N., Gore, M.A., Mueller, L.A., and Robbins, K.R. 2020. ImageBreed: Open-access plant breeding web–database for image-based phenotyping. The Plant Phenome Journal 3:e20004. https://doi.org/10.1002/ppj2.20004
Baseggio, M., Murray, M., Magallanes-Lundback, M., Kaczmar, N., Chamness, J., Buckler, E.S., Smith, M.E., DellaPenna, D., Tracy, W.F., and Gore, M.A. 2020. Natural variation for carotenoids in fresh kernels is controlled by uncommon variants in sweet corn. The Plant Genome e20008. https://doi.org/10.1002/tpg2.20008
Hu, H., Gutierrez-Gonzalez, J.J., Liu, X., Yeats, T.H., Garvin, D.F., Hoekenga, O.A., Sorrells, M.E., Gore, M.A., and Jannink, J.-L. 2020. Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed. Plant Biotechnology Journal 18:1211-1222. doi.org/10.1111/pbi.13286
Jensen, S., Charles, J.R., Muleta, K., Bradbury, P., Casstevens, T., Deshpande, S.P., Gore, M.A., Gupta, R., Ilut, D.C., Johnson, L., Lozano, R., Miller, Z., Ramu, P., Rathore, A., Romay, M.C., Upadhyaya, H.D., Varshney, R., Morris, G.P., Pressoir, G., Buckler, E., and Ramstein, G. 2020. A sorghum practical haplotype graph facilitates genome-wide imputation and cost-effective genomic prediction. The Plant Genome 13:e20009 https://doi.org/10.1002/tpg2.20009
Lin, M., Matschi, S., Vasquez, M., Chamness, J., Kaczmar, N., Baseggio, M., Miller, M., Stewart, E.L., Qiao, P., Scanlon, M.J., Molina, I., Smith, L.G. and Gore, M.A. 2020. Genome-wide association study for maize leaf cuticular conductance identifies candidate genes involved in the regulation of cuticle development. G3: Genes| Genomes| Genetics doi.org/10.1534/g3.119.400884
Iragaba,P., Kawuki, R. S., Bauchet, G., Ramu, P., Tufan, H.A., Earle, E.D, Gore, M.A., and Wolfe, M. 2020. Genomic characterization of Ugandan smallholder farmer‐preferred cassava (Manihot esculenta Crantz) varieties. Crop Science 60:1450-1461. https://doi.org/10.1002/csc2.20152
Falcon, C.M., Kaeppler, S.M., Spalding, E.P., Miller, N.D., Haase, N., AlKhalifah, N., Bohn, M., Buckler, E.S., Campbell, D.A. Ciampitti, I., Coffey, L., Edwards, J., Ert, D., Flint-Garcia, S., Gore, M.A., Graham, C., Hirsch, C.N., Holland, J.B., Jarquín, D., Knoll, J., Lauter, N., Lawrence-Dill, C.J., Lee, E.C., Lorenz, A., Lynch, J.P., Murray, S.C., Nelson, R., Romay, C.M., Rocheford, T., Schnable, P.S., Scully, B., Smith, M., Springer, N., Tuinstra, M., Walton, R., Weldekidan, T., Wisser, R.J., Xu, W., and de Leon, N. 2020. Relative utility of agronomic, phenological, and morphological traits for assessing genotype-by-environment interaction in maize inbreds. Crop Science 60:62-81. doi.org/10.1002/csc2.20035
Grover, C. E., Yoo, M.-J., Lin, M., Murphy, M.D., Harker, D.B., Byers, R.L., Lipka, A.E., Hu, G., Yuan, D., Conover, J.L., Udall, J.A., Paterson, A.H., Gore, M.A., and Wendel, J.F. 2020. Genetic analysis of the transition from wild to domesticated cotton (G. hirsutum L.). G3: Genes| Genomes| Genetics 10: 731-754.
dos Santos, J.P.R., Fernandes, S.B., McCoy, S., Lozano, R., Brown, P.J., Leakey, A.D.B., Buckler, E.S., Garcia, A.A.F., and Gore, M.A. 2020. Novel Bayesian networks for genomic prediction of developmental traits in biomass sorghum. G3: Genes| Genomes| Genetics 10:769-781.
Gazave, E., Tassone, E.E., Baseggio, M., Cryder, M., Byriel, K., Oblath, E., Lueschow, S., Poss, D., Hardy, C., Wingerson, M., Davis, J.B., Abdel-Haleem, H., Grant, D.M., Hatfield, J.L., Isbell, T.A., Vigil, M.F, Dyer, J.M., Jenks, M.A., Brown, J., Gore, M.A., and Pauli, D. 2020. Genome-wide association study identifies acyl-lipid metabolism candidate genes involved in the genetic control of natural variation for seed fatty acid traits in Brassica napus L. Industrial Crops and Products 145:112080. doi.org/10.1016/j.indcrop.2019.112080
McFarland, B.A., AlKhalifah, N., Bohn, M., Bubert, J., Buckler, E.S., Ciampitti, I., Edwards, J., Ertl, D., Gage, J.L., Falcon, C.M., Flint-Garcia, S., Gore, M.A., Graham, C., Hirsch, C.N., Holland, J.B., Hood, E., Hooker, D., Jarquin, D., Kaeppler, S.M., Knoll, J., Kruger, G., Lauter, N., 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.C., Schnable, J.C., Schnable, P.S., Scully, B., 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.-T., and de Leon, N. 2020. Maize genomes to fields (G2F): 2014–2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets. BMC Research Notes 13:71. doi.org/10.1186/s13104-020-4922-8
2019
Wiesner-Hanks, T., Wu, H., Stewart, E., DeChant, C., Kaczmar, N., Lipson, H., Gore, M.A., and Nelson, R.J. 2019. Millimeter-level plant disease detection from aerial photographs via deep learning and crowdsourced data. Frontiers in Plant Science 10:1550 doi: 10.3389/fpls.2019.01550
Gage, J.L., Richards, E., Lepak, N., Kaczmar, N., Soman, C., Chowdhary, G., Gore, M.A., and Buckler, E.S. 2019. In-field whole plant maize architecture characterized by subcanopy rovers and latent space phenotyping. The Plant Phenome Journal 2:190011 doi: 10.2135/tppj2019.07.0011
Qiao, P., Lin, M., Vasquez, M., Matschi, S., Chamness, J., Baseggio, M., Smith, L.G., Sabuncu, M.R., Gore, M.A., and Scanlon, M.J. 2019. Machine learning enables high-throughput phenotyping for analyses of the genetic architecture of bulliform cell patterning in maize. G3: Genes| Genomes| Genetics 9:4235-4243. doi.org/10.1534/g3.119.400757
Wu, H., Wiesner-Hanks, T., Stewart, E.L., DeChant, C., Kaczmar, N., Gore, M.A., Nelson, R.J., and Lipson, H. 2019. Autonomous detection of plant disease symptoms directly from aerial imagery. The Plant Phenome Journal 2:190006 doi: 10.2135/tppj2019.03.0006
Stewart, E.L., Wiesner-Hanks, T., Kaczmar, N., DeChant, C., Wu, H., Lipson, H., Nelson, R.J., and Gore, M.A. 2019. Quantitative phenotyping of northern blight in UAV images using deep learning. Remote Sensing 11:2209. doi.org/10.3390/rs11192209
Wu, D., Hought, J., Baseggio, M., Hart, J.P., Gore, M.A., and Ilut, D.C. 2019. Genomic characterization of the Native Seeds/SEARCH common bean collection and its seed coat patterns. Genetic Resources and Crop Evolution 7:1469-1482. doi.org/10.1007/s10722-019-00823-4
Khoury, C.K., Kisel, Y., Kantar, M., Barber, E., Ricciardi, V., Klirs, C., Kucera, L., Mehrabi, Z., Johnson, N., Klabin, S., Valiño, A., Nowakowski, K., Bartomeus, I., Ramankutty, N., Miller, A., Schipanski, M., Gore, M.A., and Novy, A. 2019. Science–graphic art partnerships to increase research impact. Nature Communications Biology 2:295. doi.org/10.1038/s42003-019-0516-1
Kremling, K.A.G., Diepenbrock, C.H., Gore, M.A., Buckler, E.S., and Bandillo, N.B. 2019. Transcriptome-wide association supplements genome-wide association in Zea mays. G3: Genes| Genomes| Genetics 9:3023-3033. doi.org/10.1534/g3.119.400549
Carlson, M.O., Montilla-Bascón, G., Hoekenga, O.A., Tinker, N.A., Poland, J., Baseggio, M., Sorrells, M., Jannink, J.-L., Gore, M.A., and Yeats, T.H. 2019. Multivariate genome-wide association analyses reveal the genetic basis of seed fatty acid composition in oat (Avena sativa L.). G3: Genes| Genomes| Genetics 9:2963-2975. doi.org/10.1534/g3.119.400228
Mbah, E. U., Nwankwo, B.C., Njoku, D.N., and Gore, M.A. 2019. Genotypic evaluation of twenty-eight high- and low-cyanide cassava in low-land tropics, southeast Nigeria. Heliyon 5: e01855. doi.org/10.1016/j.heliyon.2019.e01855
Owens, B.F., Mathew, D., Diepenbrock, C., Tiede, T., Wu, D., Mateos-Hernandez, M., Gore, M.A., and Rocheford, T. 2019. Genome-wide association study and pathway-level analysis of kernel color in maize. G3: Genes| Genomes| Genetics 9:1945-1955. doi.org/10.1534/g3.119.400040
Iragaba, P., Nuwamanya, E., Wembabazi, E., Baguma, Y., Dufour, D., Earle, E. D., Kerr, R.B., Tufan, H.A., Gore, M.A., and Kawuki, R.S. 2019. Estimates for heritability and consumer-validation of a penetrometer method for phenotyping softness of cooked cassava roots. African Crop Science Journal 27:147-163. doi.org/10.4314/acsj.v27i2.3
Krause, M.R., González-Pérez, L., Crossa, J., Pérez-Rodríguez, P., Montesinos-López, O., Singh, R.P., Dreisigacker, S., Poland, J., Rutkoski, J., Sorrells, M., Gore, M.A., and Mondal, S. 2019. Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat. G3: Genes| Genomes| Genetics 9:doi.org/10.1534/g3.118.200856
Glahn, R., Tako, E., and Gore, M.A. 2019. The germ fraction inhibits iron bioavailability of maize: Identification of an approach to enhance maize nutritional quality via processing and breeding. Nutrients 11:833 doi.org/10.3390/nu11040833
Gleason, S. M., Cooper, M., Wiggans, D.R., Bliss, C.A., Romay, M.C., Gore, M.A., Mickelbart, M.V., Topp, C.N., Zhang, H., DeJonge, K.C., and Comas, L.H. 2019. Stomatal conductance, xylem water transport, and root traits underpin improved performance under drought and well-watered conditions across a diverse panel of maize inbred lines. Field Crops Research 234:119-128. doi.org/10.1016/j.fcr.2019.02.001
Valluru, R., Gazave, E.E., Fernandes, S.B., Ferguson, J.N., Lozano, R., Hirannaiah, P., Zuo, T., Brown, P.J., Leakey, A.D.B., Gore, M.A., Buckler, E.S., and Bandillo, N. 2019. Deleterious mutation burden and its association with complex traits in sorghum (Sorghum bicolor). Genetics doi.org/10.1534/genetics.118.301742
Baseggio, M., Murray, M., Magallanes-Lundback, M., Kaczmar, N., Chamness, J., Buckler, E. S., Smith, M.E., DellaPenna, D., Tracy, W.F. and Gore, M.A. 2019. Genome-wide association and genomic prediction models of tocochromanols in fresh sweet corn kernels. The Plant Genome 12:1-17. doi:10.3835/plantgenome2018.06.0038
2018
Clohessy, J.W., Pauli, D., Kreher, K.M., Buckler V, E.S., Armstrong, P.R., Wu, T., Hoekenga, O.A., Jannink, J.-L., Sorrells, M.E., and Gore, M.A. 2018. A low-cost automated system for high-throughput phenotyping of single oat seeds. The Plant Phenome Journal 1:180005. doi: 10.2135/tppj2018.07.0005
Wiesner-Hanks, T., Stewart, E.L., Kaczmar, N., DeChant, C., Wu, H., Nelson, R.J., Lipson, H., and Gore, M.A. 2018. Image set for deep learning: field images of maize annotated with disease symptoms. BMC Research Notes 11:440. doi:10.1186/s13104-018-3548-6
AlKhalifah, N., Campbell, D.A., Falcon, C.M., Gardiner, J.M., Miller, N.D., Romay, M.C., Walls, R., Walton, R., Yeh, C.-T., Bohn, M., Bubert, J., Buckler, E.S., Ciampitti, I., Flint-Garcia, S., Gore, M.A., Graham, C., Hirsch, C., Holland, J.B., Hooker, D., Kaeppler, S., Knoll, J., Lauter, N., Lee, E.C., Lorenz, A., Lynch, J.P., Moose, S.P., Murray, S.C., Nelson, R., Rocheford, T., Rodriguez, O., Schnable, J.C., Scully, B., Smith, M., Springer, N., Thomison, P., Tuinstra, M., Wisser, R. J., Xu, W., Ertl, D., Schnable, P.S., De Leon, N., Spalding, E.P., Edwards, J., and Lawrence-Dill, C.J. 2018. Maize genomes to fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets. BMC Research Notes 11:452. doi:10.1186/s13104-018-3508-1
Pauli, D., Ziegler, G. Ren, M., Jenks, M.A., Hunsaker, D.J., Zhang, M., Baxter, I. and Gore, M.A. 2018. Multivariate analysis of the cotton seed ionome reveals a shared genetic architecture. G3: Genes| Genomes| Genetics 8:1147-1160. doi:
Zhu, X., Sun, L., Kuppu, S., Hu, R., Mishra, N., Smith, J., Esmaeili, N., Herath, M., Gore, M.A., Payton, P., Shen, G., and Zhang, H. 2018. The yield difference between wild-type cotton and IPT-transgenic cotton depends on when water-deficit stress is applied. Scientific Reports 8:2538. doi:10.1038/s41598-018-20944-7
Byrne, P.F., Volk, G.M., Gardner, C., Gore, M.A., Simon, P.W., and Smith, S. 2018. Sustaining the future of plant breeding: The Critical role of the USDA-ARS National Plant Germplasm System. Crop Science 58:451-468. doi:10.2135/cropsci2017.05.0303
2017
Diepenbrock, C.H., Kandianis, C.B., Lipka, A.E., Magallanes-Lundback, M., Vaillancourt, B., Góngora-Castillo, E., Wallace, J.G., Cepela, J., Mesberg, A., Bradbury, P.J., Ilut, D.C., Mateos-Hernandez, M., Hamilton, J., Owens, B. F., Tiede, T., Buckler, E.S., Rocheford, T., Buell, C.R., Gore, M.A., and DellaPenna, D. 2017. Novel loci underlie natural variation of vitamin E levels in maize grain. The Plant Cell 29:2374-2392.
Pauli, D., White, J.W., Andrade-Sanchez, P., Conley, M., Heun, J., Thorp, K.R., French, A.N., Hunsaker, D.J., Carmo-Silva, E.A., Wang, G., and Gore, M.A. 2017. Investigation of the influence of leaf thickness on canopy reflectance and physiological traits in upland and Pima cotton populations. Frontiers in Plant Science 8:1405. doi:10.3389/fpls.2017.01405
DeChant, C., Wiesner-Hanks, T., Chen, S., Stewart, E., Yosinski, J., Gore, M.A., Nelson, R., Lipson, H. 2017. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology doi:10.1094/PHYTO-11-16-0417-R
Ilut, D.C., Sanchez, P.L., Coffelt, T. A., Dyer, J.M., Jenks, M.A., and Gore, M.A. 2017. A century of guayule: Comprehensive genetic characterization of the US national guayule (Parthenium argentatum A. Gray) germplasm collection. Industrial Crops and Products 109:300-309. doi:10.1016/j.indcrop.2017.08.029
Ariga, H., Katori, T., Tsuchimatsu, T., Hirase, T., Tajima, Y., Parker, J.E., Alcázar, R., Koornneef, M., Hoekenga, O., Lipka, A.E., Gore, M.A., Sakakibara, H., Kojima, M., Kobayashi, Y., Iuchi, S., Kobayashi, M., Shinozaki, K., Sakata, Y., Hayashi, T., Saijo, Y., and Taji, T. 2017. NLR locus-mediated trade-off between abiotic and biotic stress adaptation in Arabidopsis. Nature Plants 3:17072. doi:10.1038/nplants.2017.72
Holdsworth, W.L., Gazave, E., Cheng, P., Myers, J.R., Gore, M.A., Coyne, C.J., McGee, R.J., and Mazourek, M. 2017. A community resource for exploring and utilizing genetic diversity in the USDA pea single plant plus collection. Horticulture Research 4:17017. doi:10.1038/hortres.2017.17
Grover, C.E., Gallagher, J.P., Szadkowski, E.P., Page, J.T., Gore, M.A., Udall, J.A., and Wendel, J.F. 2017. Nucleotide diversity in the two co-resident genomes of allopolyploid cotton. Plant Systematics and Evolution 303:1–22. doi:10.1007/s00606-017-1411-1
Dabbert, T.A., Pauli, D., and Gore, M.A. 2017. Influences of the combination of high temperature and water deficit on the heritabilities and correlations of agronomic and fiber quality traits in upland cotton. Euphytica 213:6. doi:10.1007/s10681-016-1798-8
Angelovici, R., Batushansky, A., Deason, N., Gonzalez-Jorge, S., Gore, M.A., Fait, A., and DellaPenna, D. 2017. Network-guided GWAS improves identification of genes affecting free amino acids. Plant Physiology 173:872-886. doi:10.1104/pp.16.01287
Carlson, M.O., Gazave, E., Gore, M.A., and Smart, C.D. 2017. Temporal genetic dynamics of an experimental, biparental field population of Phytophthora capsici. Frontiers in Genetics 8:26. doi:10.3389/fgene.2017.00026.
Bird, K.A., An, H., Gazave, E., Gore, M.A., Pires, J.C., Robertson, L.D., and Labate, J.A. 2017. Population structure and phylogenetic relationships in a diverse panel of Brassica rapa L. Frontiers in Plant Science 8:321. doi:10.3389/fpls.2017.00321
Thompson, A.L., Pauli, D., Tomasi, P., Yurchenko, O., Jenks, M.A., Dyer, J.M., and Gore, M.A. 2017. Chemical variation for fiber cuticular wax levels in upland cotton (Gossypium hirsutum L.) evaluated under contrasting irrigation regimes. Industrial Crops and Products 100:153-162. doi:10.1016/j.indcrop.2017.02.030
2016
Pauli, D., Chapman, S.C., Bart, R., Topp, C.N., Lawrence-Dill, C.J., Poland, J., and Gore, M.A. 2016. The quest for understanding phenotypic variation via integrated approaches in the field environment. Plant Physiology 172:622-634. doi:10.1104/pp.16.00592
Gonzalez-Jorge, S., Mehrshahi, P., Magallanes-Lundback, M., Lipka, A.E., Angelovici, R., Gore, M.A., and DellaPenna, D. 2016. ZEAXANTHIN EPOXIDASE activity potentiates carotenoid degradation in maturing Arabidopsis seed. Plant Physiology 171:1837-1851. doi:10.1104/pp.16.00604
French, A.N., Gore, M.A., and Thompson, A. 2016. Cotton phenotyping with lidar from a track-mounted platform. Proceedings of SPIE 9866, Autonomous air and ground sensing systems for agricultural optimization and phenotyping, SPIE Proceedings 9866. doi:10.1117/12.2224423
Gazave, E., Tassone E.E., Ilut, D.C., Wingerson, M., Datema, E., Witsenboer, H., Davis, J.B., Grant, D., Dyer, J.M., Jenks, M.A., Brown, J., and Gore, M.A. 2016. Population genomic analysis reveals differential evolutionary histories and patterns of diversity across subgenomes and subpopulations of Brassica napus L. Frontiers in Plant Science 7:525. doi:10.3389/fpls.2016.00525
Pauli, D., Andrade-Sanchez, P., Carmo-Silva, A.E., Gazave, E., French, A.N., Heun, J., Hunsaker, D.J., Lipka, A.E., Setter, T.L., Strand, R.J., Thorp, K.R., Wang, S., White, J.W., and Gore, M.A. 2016. Field-based high-throughput plant phenotyping reveals the temporal patterns of quantitative trait loci associated with stress-responsive traits in cotton. G3: Genes| Genomes| Genetics 6:865-879. doi:10.1534/g3.115.023515
Hinze, L.L., Gazave, E., Gore, M.A., Fang, D.D., Scheffler, B.E., Yu, J.Z., Jones, D.C., Frelichowski, J., and Percy, R.G. 2016. Genetic diversity of the two commercial tetraploid cotton species in the Gossypium Diversity Reference Set. Journal of Heredity 107:274-286. doi:10.1093/jhered/esw004
Ilut, D.C., Lipka, A.E., Jeong, N., Bae, D.N., Kim, D.H., Kim, J.H., Redekar, N., Yang, K., Park, W., Kang, S.-T., Kim, N., Moon, J.-K., Saghai Maroof, M.A., Gore, M.A., and Jeong, S.-C. 2016. Identification of haplotypes at the Rsv4 genomic region in soybean associated with durable resistance to soybean mosaic virus. Theoretical and Applied Genetics 129:453-468. doi:10.1007/s00122-015-2640-8
Tassone, E.E., Lipka, A.E., Tomasi, P., Lohrey, G.T., Dyer, J.M., Gore, M.A., and Jenks, M.A. 2016. Chemical variation for leaf cuticular wax levels revealed in a diverse panel of Brassica napus L. Industrial Crops and Products 79:77-83. doi:10.1016/j.indcrop.2015.10.047
2015
Thorp, K.R., Gore, M.A., Andrade-Sanchez, P., Carmo-Silva, E., Welch, S.M., White, J.W., and French, A.N. 2015. Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics. Computers and Electronics in Agriculture 118:225-236. doi:10.1016/j.compag.2015.09.005
Ilut, D.C., Sanchez, P.L., Costich, D.E., Friebe, B., Coffelt, T.A., Dyer, J.M., Jenks, M.A., and Gore, M.A. 2015. Genomic diversity and phylogenetic relationships in the genus Parthenium (Asteraceae). Industrial Crops and Products 76:920-929. doi:10.1016/j.indcrop.2015.07.035
Grover, C.E., Gallagher, J.P., Jareczek, J.J., Page, J.T., Udall, J.A., Gore, M.A., and Wendel, J.F. 2015. Re-evaluating the phylogeny of allopolyploid Gossypium L. Molecular Phylogenetics and Evolution 92:45-52. doi:10.1016/j.ympev.2015.05.023
Hulse-Kemp, A.M., Lemm, J., Plieske, J., Ashrafi, H., Buyyarapu, R., Fang, D.D., Frelichowski, J., Giband, M., Hague, S., Hinze, L.L., Kochan, K., Riggs, P., Scheffler, J.A., Udall, J.A., Ulloa, M., Wang, S.S., Zhu, Q.-H., Bag, S.K., Bhardwaj, A., Burke, J.J., Byers, R.L., Claverie, M., Gore, M.A., et al. and Stelly, D.M. 2015. Development of a 63K SNP array for Gossypium and high-density mapping of intra- and inter-specific populations of cotton (G. hirsutum L.). G3: Genes| Genomes| Genetics 5:1187-1209. doi:10.1534/g3.115.018416
Diepenbrock, C.H. and Gore, M.A. 2015. Closing the divide between human nutrition and plant breeding. Crop Science 55:1437-1448. doi:10.2135/cropsci2014.08.0555
Lipka, A.E., Kandianis, C.B., Hudson, M.E., Yu, J., Drnevich, J., Bradbury, P.J., and Gore, M.A. 2015. From association to prediction: statistical methods for the dissection and selection of complex traits in plants. Current Opinion in Plant Biology 24:110-118. doi:10.1016/j.pbi.2015.02.010
Hinze, L.L., Fang, D.D., Gore, M.A., Scheffler, B.E., Yu, J.Z., Frelichowski, J., and Percy, R.G. 2015. Molecular characterization of the Gossypium diversity reference set of the US national cotton germplasm collection. Theoretical and Applied Genetics 128:313-327. doi:10.1007/s00122-014-2431-7
2014
Dabbert, T.A. and Gore, M.A. 2014. Challenges and perspectives on improving heat and drought stress resilience in cotton. The Journal of Cotton Science 18:393-409.
Owens, B.F., Lipka, A.E., Magallanes-Lundback, M., Tiede, T., Diepenbrock, C.H., Kandianis, C.B., Kim, E., Cepela, J., Mateos Hernandez, M., Buell, C.R., Buckler, E.S., DellaPenna, D., Gore, M.A., and Rocheford, T. 2014. A foundation for provitamin A biofortification of maize: Genome-wide association and genomic prediction models of carotenoid levels. Genetics 198:1699–1716. doi:10.1534/genetics.114.169979
Yurchenko, O.P., Park, S., Ilut, D.C., Inmon, J.J., Millhollon, J.C., Liechty, Z., Page, J.T., Jenks, M.A., Chapman, K. D., Udall, J. A., Gore, M. A., and Dyer, J. M. 2014. Genome-wide analysis of the omega-3 fatty acid desaturase gene family in Gossypium. BMC Plant Biology 14:312.
Peiffer, J.A., Romay, M.C., Gore, M.A., Flint-Garcia, S.A., Zhang, Z., Millard, M.J., Gardner, C.A.C., McMullen, M.D., Holland, J.B., Bradbury, P.J., and Buckler, E.S. 2014. The genetic architecture of maize height. Genetics 196:1337-1356.
Gore, M.A., Fang, D.D., Poland, J.A., Zhang, J., Percy, R.G., Cantrell, R.G., Thyssen, G., and Lipka, A.E. 2014. Linkage map construction and quantitative trait locus analysis of agronomic and fiber quality traits in cotton. The Plant Genome 7:1-10.
Sanchez, P.L., Chen, M.-K., Pessarakli, M., Hill, H.J., Gore, M.A., and Jenks, M.A. 2014. Effects of temperature and salinity on germination of non-pelleted and pelleted guayule (Parthenium argentatum A. Gray) seeds. Industrial Crops and Products 55:90-96.
Sanchez, P.L., Costich, D.E., Friebe, B., Coffelt, T.A., Jenks, M.A., and Gore, M.A. 2014. Genome size variation in guayule and mariola: Fundamental descriptors for polyploid plant taxa. Industrial Crops and Products 54:1-5.
Fang, H., Zhou, H., Sanogo, S., Lipka, A.E., Fang, D.D., Percy, R.G., Hughs, S.E., Jones, D.C., Gore, M.A., and Zhang, J. 2014. Quantitative trait locus analysis of Verticillium wilt resistance in an introgressed recombinant inbred population of Upland cotton. Molecular Breeding 33:709-720.
Tyagi, P., Gore, M.A., Bowman, D.T., Campbell, B.T., Udall, J.A., and Kuraparthy, V. 2014. Genetic diversity and population structure in the US Upland cotton (Gossypium hirsutum L.). Theoretical and Applied Genetics 127:283-295.
Andrade-Sanchez, P., Gore, M.A., Heun, J.T., Thorp, K.R., Carmo-Silva, A.E., French, A.N., Salvucci, M.E., and White, J.W. 2014. Development and evaluation of a field-based high-throughput phenotyping platform. Functional Plant Biology 41:68-79.
2013
Angelovici, R., Lipka, A.E., Deason, N., Gonzalez–Jorge, S., Lin, H., Cepela, J., Buell, R., Gore, M.A., and DellaPenna, D. 2013. Genome-wide analysis of branched-chain amino acid levels in Arabidopsis seed. The Plant Cell 25:4827-4843.
Gonzalez-Jorge, S., Ha, S.-H., Gilliland, L.U., Little, H., Zhou, A., Magallanes-Lundback, M., Lipka, A.E., Cepela, J., Buell, R., Gore, M.A., and DellaPenna, D. 2013. CAROTENOID CLEAVAGE DIOXYGENASE4 is a negative regulator of β-carotene content in Arabidopsis seed. The Plant Cell 25:4812-4826.
Lipka, A.E., Gore, M.A., Magallanes-Lundback, M., Mesberg, A., Lin, H., Tiede, T., Chen, C., Buell, C.R., Buckler, E.S., Rocheford, T., and DellaPenna, D. 2013. Genome-wide association study and pathway level analysis of tocochromanol levels in maize grain. G3: Genes| Genomes| Genetics 3:1287-1299.
Chandler, K., Lipka, A.E., Owens, B.F., Li, H., Buckler, E.S., Rocheford, T., and Gore, M.A. 2013. Genetic analysis of visually scored orange kernel color in maize. Crop Science 53:189-200.
Yu, J., Yu, S., Gore, M., Wu, M., Zhai, H., Li, X., Shuli, F., Song, M., and Zhang, J. 2013. Identification of quantitative trait loci across interspecific F2, F2:3 and testcross populations for agronomic and fiber traits in tetraploid cotton. Euphytica 191:375-389.
2012
Lipka, A.E., Tian, F., Wang Q., Peiffer, J., Li, M., Bradbury, P.J., Gore, M.A., Buckler, E.S., and Zhang, Z. 2012. GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397-2399.
Thorp, K., Andrade-Sanchez, P., Gore, M., White, J., and French, A. 2012. Information technologies for field-based high-throughput phenotyping. Resource-Engineering and Technology for a Sustainable World 19:8-9.
Chia, J., Song, C., Bradbury, P., Costich, D., De Leon, N., Doebley, J., Elshire, R., Gaut, B., Geller, L., Glaubitz, J., Gore, M., Guill, K., Holland, J., Hufford, M., Lai, J., Li, M., Liu, X., Lu, Y., McCombie, R., Nelson, R., Poland, J.A., Prasanna, B.M., Phyäjärvi, T., Rong, T., Sekhon, R., Sun, Q., Tenaillon, M., Tian, F., Wang, J., Xu, X., Zhang, Z., Kaeppler, S.M., Ross-Ibarra, J., McMullen, M.D., Buckler, E.S., Zhang, G., Xu, Y., and Ware, D. 2012. Maize HapMap2 identifies extant variation from a genome in flux. Nature Genetics 44:803-807.
Carmo-Silva, E.A., Gore, M.A., Andrade-Sanchez, P., French, A.N., Hunsaker, D.J., and Salvucci, M.E. 2012. Decreased CO2 availability and inactivation of Rubisco limit photosynthesis in cotton plants under heat and drought stress in the field. Environmental and Experimental Botany 83:1-11.
White, J.W., Andrade-Sanchez, P., Gore, M.A., Bronson, K.F., Coffelt, T.A., Conley, M.M., Feldmann, K.A., French, A.N., Heun, J.T., Hunsaker, D.J., Jenks, M.A, Kimball, B.A., Roth, R.L., Strand, R.J., Thorp, K.R., Wall, G.W., and Wang, G. 2012. Field-based phenomics for plant genetics research. Field Crops Research 133:101-112.
Gore, M.A., Percy, R.G., Zhang, J., Fang, D.D., and Cantrell, R.G. 2012. Registration of the TM-1/NM24016 cotton recombinant inbred mapping population. Journal of Plant Registrations 6:124-127.
2011
Gore, M.A., Coyle, G., Friebe, B., Coffelt, T.A., and Salvucci, M.E. 2011. Complex ploidy level variation in guayule breeding programs. Crop Science 51:210-216.
Setter, T.L., Yan, J., Warburton, M., Ribaut, J.-M., Xu, Y., Sawkins, M., Buckler, E.S., Zhang, Z., and Gore, M.A. 2011. Genetic association mapping identifies single nucleotide polymorphisms in genes that affect abscisic acid levels in maize floral tissues during drought. Journal of Experimental Botany 62:701-716.
Gore, M. 2011. Molecular plant breeding. Field Crops Research 123:183-184.
2010
Zhang, Z., Ersoz, E., Lai, C.-Q., Todhunter, R.J., Tiwari, H.K., Gore, M.A., Bradbury, P.J., Yu, J., Arnett, D.K., Ordovas, J.M., and Buckler, E.S. 2010. Mixed linear model approach adapted for genome-wide association studies. Nature Genetics 42:355-360.
Li, Q., Yang, X., Bai, G., Warburton, M., Mahuku, G., Gore, M., Dai, J., Li, J., and Yan, J. 2010. Cloning and characterization of a putative GS3 ortholog involved in maize kernel development. Theoretical and Applied Genetics 120:753-763.
2009
Gore, M.A., Chia, J.-M., Elshire, R.J., Sun, Q., Ersoz, E.S., Hurwitz, B.L., Peiffer, J.A., McMullen, M.D., Grills, G.S., Ross-Ibarra, J., Ware, D.H., and Buckler, E.S. 2009. A first generation haplotype map of maize. Science 326:1115-1117.
Gore, M.A., Wright, M.H., Ersoz, E.S., Bouffard, P., Szekeres, E.S., Jarvie, T.P., Hurwitz, B.L., Narechania, A., Harkins, T.T., Grills, G.S., Ware, D.H., and Buckler, E.S. 2009. Large-scale discovery of gene-enriched SNPs. The Plant Genome 2:121-133.
2008
Zhu, C., Gore, M., Buckler, E.S., and Yu, J. 2008. Status and prospects of association mapping in plants. The Plant Genome 1:5-20.
2007
Buckler, E., and Gore, M. 2007. An Arabidopsis haplotype map takes root. Nature Genetics 39: 1056-1057.
Gore, M., Bradbury, P., Hogers, R., Kirst, M., Verstege, E., van Oeveren, J., Peleman, J., Buckler, E., and van Eijk, M. 2007. Evaluation of target preparation methods for single-feature polymorphism detection in large complex plant genomes. Crop Science 47: S135–S148.
2004
Hayes, A.J., Jeong, S.C., Gore, M.A., Yu, Y.G., Buss, G.R., Tolin, S.A., and Saghai Maroof, M.A. 2004. Recombination within a nucleotide-binding-site/leucine-rich-repeat gene cluster produces new variants conditioning resistance to soybean mosaic virus in soybeans. Genetics 166: 493-503.
2003
Padidam, M., Gore, M., Lu, D. L., and Smirnova, O. 2003. Chemical-inducible, ecdysone receptor-based gene expression system for plants. Transgenic Research 12: 101-109.
2002
Gore, M.A., Hayes, A.J., Jeong, S.C., Yue, Y.G., Buss, G.R., and Saghai Maroof, M.A. 2002. Mapping tightly linked genes controlling potyvirus infection at the Rsv1 and Rpv1 region in soybean. Genome 45: 592-599.