1675 Observatory Dr
440 Animal Sciences
Madison, WI 53706
Guilherme Rosa obtained an M.S. in Animal Sciences from Sao Paulo State University (UNESP) – Brazil in 1994, and a Ph.D. in Statistics and Agricultural Experimentation from the University of Sao Paulo (USP) – Brazil in 1998. Guilherme Rosa started his professional career as a faculty member of the Department of Biostatistics at UNESP (1994-2001), then moved to the USA as a faculty member at Michigan State University (2002-2006), and is currently a Professor at the Department of Animal and Dairy Sciences at the University of Wisconsin-Madison (since 2006), with an affiliate appointment at the Department of Biostatistics & Medical Informatics.
Guilherme Rosa teaches courses and develops research on statistical and computational tools for the analysis of livestock data, including beef and dairy cattle, swine, poultry among others. Examples of applications include the analysis of farm-level operational data for optimization of management practices, high-throughput phenotyping techniques for real-time monitoring of individual animals and disease surveillance, as well as quantitative genetics/genomics and breeding. Guilherme has published 10 book chapters and over 200 refereed papers in scientific journals and has funded his program with outside grants valued at over $10 million.
Selected Peer-Reviewed Articles
Fernandes, A. F. A., Dorea, J. R. R., Valente, B. D., Fitzgerald, R., Herring, W. and Rosa, G. J. M. Comparison of data analytics strategies in computer vision systems to predict pig body composition traits from 3D images. Journal of Animal Science 98(8): skaa250, 2020.
Passafaro, T. L., Lopes, F. B., Dorea, J. R. R., Craven, M., Breen, V., Hawken, R. J. and Rosa, G. J. M. Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers. BMC Genomics 21: 771, 2020.
Aiken, V. C. F., Fernandes, A. F. A., Passafaro, T. L., Acedo, J. S., Dias, F. G., Dórea, J. R. R. and Rosa, G. J. M. Forecasting beef production and quality using large-scale integrated data from Brazil. Journal of Animal Science 98(4): skaa089, 2020.
Fernandes, A. F. A., Turra, E. M., Alvarenga, E. R., Passafaro, T. L., Lopes, F. B., Alves, G. F. O., Singh, V. and Rosa, G. J. M. Deep Learning image segmentation for extraction of fish body measurements and prediction of body weight and carcass traits in Nile tilapia. Computers and Electronics in Agriculture 170: 105274, 2020.
Passafaro, T.L., Fernandes, A. F. A., Valente, B. D., Williams, N. H. and Rosa, G. J. M. Network analysis of swine movements in a multi-site pig production system in Iowa, USA. Preventive Veterinary Medicine 174: 104856, 2020.
Cominotte, A., Fernandes, A. F. A., Dorea, J. R. R., Rosa, G. J. M., Ladeira, M. M., van Cleeff, E. H. C. B., Pereira, G. L., Baldassinic, W. A. and Machado Neto, O. Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases. Livestock Science 232: 103904, 2020.
Mota, L. F. M., Lopes, F. B., Junior, G. A. F., Rosa, G. J. M., Magalhães, A. F. B., Carvalheiro, R. and Albuquerque, L. G. Genome-wide scan highlights the role of candidate genes on phenotypic plasticity for age at first calving in Nellore heifers. Scientific Reports 10: 6481, 2020.
Roudbar, M. A., Mohammadabadi, M. R., Mehrgardi, A. A., Abdollahi-Arpanahi, R., Momen, M., Morota, G., Lopes, F. B., Gianola, D. and Rosa, G. J. M. Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls. Heredity 124: 658–674,
Fernandes, A. F. A., Dorea, J. R. R., Fitzgerald, R., Herring, W. and Rosa, G. J. M. A novel automated system to acquire biometric and morphological measurements, and predict body weight of pigs via 3D computer vision. Journal of Animal Science 97: 496-508, 2019.
Passafaro, T.L., Van de Stroet, D., Bello, N. M., Williams, N. H. and Rosa, G. J. M. Generalized additive mixed model on the analysis of total transport losses of market-weight pigs. Journal of Animal Science 97: 2025-2034, 2019.
Aiken, V. C. F., Dórea, J. R. R., Acedo, J. S., Sousa, F. G., Dias, F. G. and Rosa, G. J. M. Record linkage for farm-level data analytics: Comparison of deterministic, stochastic and machine learning methods. Computers and Electronics in Agriculture 163: 104857, 2019.
Koltes, J. E., Cole, J. B., Clemmens, R., Dilger, R. N., Kramer, L. M., Lunney, J. K., McCue, M. E., McKay, S. D., Mateescu, R. G., Murdoch, B. M., Reuter, R., Rexroad, C.E., Rosa, G. J. M., Serão, N. V. L., White, S. N., Woodward-Greene, M. J., Worku, M., Zhang, H. and Reecy, J. M. A Vision for development and utilization of high-throughput phenotyping and big data analytics in livestock. Frontiers in Genetics 10: 1197, 2019.
Bello, N. M., Ferreira, V. C., Gianola, D. and Rosa, G. J. M. Conceptual framework for investigating causal effects from observational data in livestock. Journal of Animal Science 96(10): 4045-4062, 2018.
Momen, M., Mehrgardi, A. A., Sheikhi, A., Kranis, A., Tusell, L., Morota, G., Rosa, G. J. M. and Gianola, D. Predictive ability of genome-assisted statistical models under various forms of gene action. Scientific Reports 8:12309, 2018.
Huang, X., Elston, R. C., Rosa, G. J. M., Mayer, J., Ye, Z., Kitchner, T., Brilliant, M. H., Page, D. and Hebbring, S. J. Applying family analyses to electronic health records to facilitate genetic research. Bioinformatics 34(4): 635-642, 2018.
Selected Book Chapters
Hutchins, J., Hueth, B. and Rosa, G. J. M. Quantifying Heterogeneous Returns to Genetic Selection: Evidence from Wisconsin Dairies. In: Economics of Research and Innovation in Agriculture, National Bureau of Economic Research, Inc., 2020.
Rosa, G. J. M., Felipe, V. P. S. and Peñagaricano, F. Applications of Graphical Models in Quantitative Genetics and Genomics. In: Systems Biology in Animal Production and Health, Volume 1. Kadarmideen, H. (Ed.) Springer, 2016.
Rosa, G. J. M. and Valente, B. D. Structural Equation Models for Studying Causal Phenotype Networks in Quantitative Genetics. In: Probabilistic Graphical Models for Genetics, Genomics and Postgenomics. Sinoquet, C. and Mourad, R. (Eds.) Oxford University Press, 2014.
Rosa, G. J. M. Basic Genetic Model for Quantitative Traits. In: Molecular and Quantitative Animal Genetics. Khatib, H. (Ed.) Wiley-Blackwell, Oxford, UK, 2014.
Rosa, G. J. M. Heritability and Repeatability. In: Molecular and Quantitative Animal Genetics. Khatib, H. (Ed.) Wiley-Blackwell, Oxford, UK, 2014.
Valente, B. D. and Rosa, G. J. M. Mixed effects structural equation models and phenotypic causal networks. In: Genome-Wide Association Studies. Gondro, C., van der Werf, J. and Hayes, B. (Eds.) Springer, 2013.
Rosa, G. J. M. Quantitative Trait. In: Brenner’s Encyclopedia of Genetics, 2nd ed. Maloy, S. and Hughes, K. (Editors). San Diego: Academic Press – Elsevier, 2013.
Rosa, G. J. M. Progeny Test. In: Brenner’s Encyclopedia of Genetics, 2nd ed. Maloy, S. and Hughes, K. (Editors). San Diego: Academic Press – Elsevier, 2013.
Rosa, G. J. M. Foundations of Animal Breeding. In: Sustainable Food Production. Christou, P., Savin, R., Costa-Pierce, B., Misztal, I. and Whitelaw, B. (Editors). New York: Springer, 2013.
Rosa, G. J. M. and Tempelman, R. J. Bayesian Mapping Methodology. In: Genetic Analysis of Complex Traits with SAS. Saxton, A. (Editor). Cary, NC: SAS Institute Inc., 2004.
Tempelman, R. J. and Rosa, G. J. M. Empirical Bayes Approaches to Mixed Model Inference in Quantitative Genetics. In: Genetic Analysis of Complex Traits with SAS. Saxton, A. (Editor). Cary, NC: SAS Institute Inc., 2004.
An Sci/Dy Sci 361 – Introduction to Veterinary Genetics (2 credits, Spring)
Description: The molecular basis for inheritance of monogenic and polygenic traits related to animal disease and production. An introduction to the principles of improving animal health and performance by selection and mating systems in companion animals, horses, livestock, and poultry.
Requirements: Genetics 160 or 466 or con reg course in statistics
An Sci/Dy Sci 363 – Principle of Animal Breeding (2 credits, Spring)
Description: Application of the principles of quantitative genetics to the improvement of livestock and poultry; breeding value estimation and selection techniques; effects of inbreeding and hybrid vigor; crossbreeding systems.
Requirements: Dy Sci/AN SCI/DY SCI 361
An Sci/Dy Sci 610 – Quantitative Genetics (3 credits, Fall)
Description: An advanced approach with emphasis on statistical foundations. Classical theory with extensions to maternal and paternal effects. Selection theory is considered in depth.
Requirements: GENETICS 466and Statistics 572 or cons inst
Co-organizer, 1st International Symposium on Animal Functional Genomics, 2003.
Chair, NCR-204: The Interface of Molecular and Quantitative Genetics in Plant and Animal Breeding, 2005.
Co-organizer, 2nd International Symposium on Animal Functional Genomics, 2006.
Co-organizer, Symposium on Statistical Genetics of Livestock for the Post-Genomics Era, 2009.
Chair, Session on Genomics, XXV International Biometric Conference, 2010.
Member, Research Advisory Committee (RAC) – UW, 2011-2012
Member, Capital Equipment Committee, Department of Animal and Dairy Sciences, UW-Madison
Member, Prospective Students and Scholarships and Loans Committee, CALS, UW-Madison
Member, Steering Committee of the Brazil Initiative, University of Wisconsin-Madison
Co-organizer, 5th International Conference on Quantitative Genetics, Madison-WI, 2016.
Co-chair, Gordon Research Conference and Seminar on Quantitative Genetics and Genomics, 2019.
Ad-hoc Reviewer and Panel Member of various USDA-NIFA Research Grant Programs.
Ad-hoc Reviewer for various UW-Madison Hatch grants
Panel Member, NIH-NIDDK
Referee for various scientific journals including: Animal Genetics, Bioinformatics, Biometrical Journal, Biometrics, BMC Bioinformatics, BMC Genetics, Computational Statistics & Data Analysis, Frontiers in Genetics, Genetical Research, Genetics, G3, Genetics Selection Evolution, Heredity, Human Heredity, Journal of Agricultural, Biological and Environmental Statistics, Journal of Animal Science, Journal of Animal Breeding and Genetics, Journal of Clinical Epidemiology, Journal of Dairy Science, 2009 Journal of Heredity, Journal of the Royal Statistical Society, Livestock Science, Nuclei Acids, Physiological Genomics, PLoS ONE, Poultry Science, Shankia, Statistical Applications in Genetics and Molecular Biology, Veterinary Immunology and Immunopathology