Skip to main content
Skip to main content
Give      University of Florida

Horticultural Sciences

Horticultural Sciences

Dr. Marcio Resende

Associate Professor

Mailing Address:
Fifield Hall
2550 Hull Road
PO Box 110690
Gainesville, Florida 32611

Office: 2135 Fifield Hall
Phone: (352) 273-4772
E-mail: mresende@ufl.edu

  • Areas of Research

    Breeding, genomics, quantitative genetics, bioinformatics, maize.

Selected Publications

AuthorsYearTitle / DescriptionPublication
Sousa T.V., Caixeta E.T., Alkimim E.R., Oliveira A.C.B., Pereira A.A., Sakiyama N.S., RESENDE M.F.R., Zambolim L. 2017 Population structure and genetic diversity of coffee progenies derived from Catuaí and Híbrido de Timor revealed by genome-wide SNP marker. Tree Genetics and Genomics 13:124.
Fahrenkrog A.M., Neves L.G., RESENDE M.F.R, Dervinis C., Davenport R., Barbazuk W.B., Kirst, M. 2017 Population genomics of the eastern cottonwood (Populus deltoides). Ecology and Evolution. 7:9426–9440.
Muller, B., Neves, L.G., Filho, J.A., RESENDE M.F.R., Munoz, P., Santos, P.E.T., Filho, E.P., Kirst, M., Grattapaglia, D. 2017 Genomic prediction of growth traits in breeding populations of Eucalyptus benthamii and E. pellita and a contrast to genome-wide association in explaining heritable variation. BMC genomics 18 (1), 524.
Tieman, D.M., Zhu, G., RESENDE M.F.R., Nguyen, C., Bies, D., Rambla, J.L., Beltran, K.S.O, Taylor, M., Zhang, B., Ikeda, H., Liu, Z., Fisher, J., Monforte, A., Zamir, D., Granell, A., Kirst, M., Huang, S., Klee, H.J. 2017 A genetic roadmap to improved tomato flavor. Science 355 (6323), 391-394.
Fahrenkrog, A.M., Neves, L.G., RESENDE M.F.R., Vazquez, A.I., de los Campos, G., Dervinis, C., Sykes, R., Davis, M., Davenport, R., Barbazuk, W., Kirst, M. 2017 Genome‐wide association study reveals putative regulators of bioenergy traits in Populus deltoides. DOI: 10.1111/nph.14154. New Phytologist.
Azevedo, C.F., Resende, M.D.V., Silva, F.F., Viana, J.M.S., Valente, M.S.F., RESENDE M.F.R. 2016 New estimators for the experimental accuracy of genomic selection. DOI: 10.4238/gmr.15048838, Genetics and Molecular Research.
Filho J.A., Rodrigues, J.F.G., Silva, F.F., Resende, M.D.V., Munoz, P., Kirst, M., RESENDE M.F.R. 2016 The contribution of dominance to phenotype prediction in pine breeding and simulated population. DOI: 10.1038/hdy.2016.23, Heredity.
Amadeu, R., Cellon, C., Olmstead J., Garcia, A., RESENDE M.F.R., Munoz, P. 2016 AGHmatrix: R package to construct relationship matrices for autotetraploid and diploid species, a Blueberry Example. DOI: 103835/plantgenome2016.01.0009. The Plant Genome.
Song, J., Yang, X., RESENDE M.F.R., Neves, L.G., Todd, J., Zhang, J., Comstock, J., Wang, J. 2016 Natural allelic variation in the germplasm of highly polyploid sugarcane. DOI: 10.3389/fpls.2016.00804. Frontiers in Plant Science.
Vazquez, A., Veturi, Y., Behring, M., Shrestha, S., Kirst, M., RESENDE M.F.R., de los Campos, G. 2016 Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with use of Whole-Genome Multi-Omic Profiles. DOI: 10.1534/genetics.115.185181, Genetics.
Munoz P., RESENDE M.F.R.; Gezan S. A., Resende M.D.V., de los Campos G., Kirst M., Huber D, Peter G.F. 2014 Unraveling Additive from Non-additive Effects using Genomic Relationship Matrices. Genetics, 198: 1759-1768.
Munoz P.R., RESENDE M.F.R., Huber D.A., Quesada T., Resende M.D.V., Neale D.B., Wegrzyn J.L., Kirst M., Peter G.F. 2014 Genomic relationship matrix for correcting pedigree errors in breeding populations: impact on genetic parameters and genomic selection accuracy. Crop Science, 54:1115–1123.
RESENDE M.F.R., Munoz P., Resende M.D.V., Garrick D.J., Fernando R.L., Davis J.M., Jokela E.J., Martin T.A., Peter G.F., Kirst M. 2012 Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics, 190: 1503–1510.
RESENDE M.F.R., Munoz P., Acosta J.J., Peter G.F., Davis J.M., Grattapaglia D., Resende M.D.V., Kirst M. 2012 Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytologist, 193: 617–624.