University of Guelph  

Animal Biosciences



Media Guide

Flavio S. Schenkel

BASc, M.Sc. UFRGS-Brazil, Ph.D. Guelph


Director of the Centre for Genetic Improvement of Livestock (CGIL)
President of the Canadian Society of Animal Science

Faculty member in the Bioinformatics Program
Faculty affiliated to the One Health Program



Flavio's Animal Breeder's Pedigree
Google Scholar      ResearchGate

Academic Interests

  • Teaching
    • Instructor in:
      - Graduate course ANSC*6390 - QTL and Markers (W)
      This graduate course is intended to teach statistical models and methods used in the detection of Quantitative Trait Loci (QTL) and in marker assisted/genomic selection of livestock.
      - Graduate course ANSC*6370 - Quantitative Genetics and Animal Models (F)
      The course covers quantitative genetics theory associated with animal models; linear models applied to genetic evaluation of animals; estimation of genetic parameters for animal models; and computing algorithms for large datasets. 
    • Past instructor in:
    - Graduate course ANSC*6050 - Biometry for Animal Sciences (F)
    This graduate course is intended for students involved in animal research. The course will provide outlines of appropriate presentation and analysis of experimental data with emphasis on different analytical techniques.
    - Undergraduate course UNIV*1200 - First Year Seminar Course (F)
    The goal of this course is to provide opportunities for students to participate in small, discussion-oriented classes in their first year in a topic of general interest. The goal of the Fall 2008 session was to discuss social, economic, political, and ecological issues on Brazilian Amazon deforestation and how it can affect and be linked to people living in Canada.

  • Research
    • Main research interests:
        Genetic improvement of livestock, including but not limited to:
      - Estimation of genetic and environmental parameters required for genetic evaluations;
      - Genetic evaluation and improvement of livestock through statistical modeling;
      - Combining molecular and quantitative genetic information into genetic evaluations;
      - Discovery of DNA polymorphisms in candidate genes related to economically important traits;
      - Detection of Quantitative Trait Loci for economically relevant traits;
      - Genomic selection;
      - Conservation of genetic variability and diversity.


  • Ph.D. in Animal Breeding (1994-1998)
    Department of Animal and Poultry Science, University of Guelph
    Advisor: L. R. Schaeffer
  • M.Sc. in Animal Breeding (1989-1991)
    Department of Animal Science, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
    Advisor: L. A. Fries
  • B.A.Sc. in Agronomy (1982-87)
    Department of Animal Science, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
  • B.A. in Business Administration (1985-1990)
    Faculty of Business Administration, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil

Main Research Projects (Current and Recent)

1. Increasing feed efficiency and reducing methane emissions through genomics: a new promising goal for the Canadian dairy industry (Large scale applied research project competition- Genome Canada, Schenkel (Co-applicant/Principle Investigator since January 2019), 2015 to 2020)
The overall goal of the project is to produce genomic predictions for Feed Efficiency (FE) and Methane Emissions (ME) that are ready for breeding application in Canada’s dairy cattle industry. These tools will enable producers to select cattle for improved FE and reduced ME, while still maintaining the high productivity, health and fertility of dairy cows.

2. Designing a reference population to accelerate genetic gains for novel traits in Canadian Holstein project (AAFC- Dairy cluster III Grant, Schenkel (Principal Investigator/Principle Investigator since January 2019), 2018 to 2022)
The main objective of this project is to generate tools to maximize the rate of genetic progress for novel traits by designing an enlarged female reference population for genomic prediction of novel traits with ssGBLUP and to investigate the incorporation of additional “-omics” data in Canadian dairy cattle breeding programs.

3. Understanding the impact of cutting-edge genomic technologies and novel phenotypes on breeding strategies for optimum sustainable genetic progress in Canadian dairy cattle project (AAFC- Dairy cluster III Grant, Schenkel (Co-applicant), 2018 to 2022)
The development of novel traits (e.g. feed efficiency, methane emission, etc.), new genotyping technologies (e.g. genotyping by sequencing), and novel tools (e.g. gene editing) applied in the dairy industry is advancing at an unprecedented rate. Wide-spread application of these new technologies will fundamentally change the accuracy of breeding values and the selection strategies used for genetic evaluation of dairy cattle. While these novel traits, technologies and tools are expected to further increase accuracy of genetic evaluations, the medium and long-term effects of their implementation into routine breeding programs at a population level are largely unknown. There is a clear need to assess current and prospective breeding strategies, and to compare the benefits of various strategies and tools for genetic improvement and selection. Ideally, the use of these new technologies will help ensure sustainability, genetic diversity, and will help to further improve production efficiency. The objective of this proposal is to analyze and compare the benefits of various strategies and novel tools for breed improvement.

4. Breeding livestock for climate resilience: the capacity to maintain production and fitness in a changing climate (Canada First Research Excellence Fund, Schenkel (Principle investigator), 2017 to 2019)
This project is part of the CFREF Food from Thought - Agricultural Systems for a Healthy Planet project led by the University of Guelph.  The main goal of this project is to identify genes, as well as structural and regulatory regions of the genome of livestock species (with a focus on ruminant species such as beef and dairy cattle, sheep and goats), that are involved in adapting to different stressors triggered through climate change for allowing efficient selection for robust livestock tolerant to extreme temperatures and more productively efficient.       

5. Implementation of genomic selection to improve productivity and health traits in Ontario dairy goats (Gov-OMAFRA Agreement Research Programs, Schenkel (Principal Investigator), 2017 to 2019)
The overarching objective of this project is to implement genomic selection in the dairy goat industry to promote faster genetic progress in production, conformation, reproduction and health traits in collaboration with Canadian Centre for Swine Improvement, leveraging from a previous genomic project. Specific objectives include increase the size of the reference population for the two major dairy goat breeds in Canada, named Alpine and Saanen; evaluate and validate prediction methods and corresponding genomic evaluation tools; achieve a better understanding of the genetic background of the traits of interest by estimating genetic parameters using genomic information and also performing GWAS studies; Increase the accuracies of genomic breeding values for various economically important traits by an increased reference population size and an optimized genomic evaluation model; transfer the genomic tools to Canadian Centre for Swine Improvement for the use by the dairy goat producers.

6. Genetic Improvement of Canadian Lamb Carcass Yield, Quality and Growth Traits (NSERC-CRD, Schenkel (Principal Investigator), 2016 to 2019)
In the Canadian lamb industry, carcass yield and quality traits are of considerable importance because these relate directly supply chain production efficiency, economic profitability and consumer choice of domestic products. This study seeks to examine genetic bases of carcass yield, fat depth and conformation in commercial lambs, and consider genetic relationships with early growth, ultrasound measurements and other economically important production and reproduction traits. The goal is to find optimal selection methods to improve carcass yield, quality and growth in commercial lamb breeding programs. This will be accomplished by analysing carcass processing data that were recorded over at least 2.5 years from commercial lambs that are part of the Canadian Sheep Genetic Evaluation System (CSGES) hosted at CGIL.

Recent Publications

1. Vargas*, G., Schenkel, F. S., Brito, L. F., Neves, H. H.d., Munari, D. P., Lucia Galvão de Albuquerque; Roberto Carvalheiro (2020).  Genomic regions associated with principal components for growth, visual score and reproductive traits in Nellore cattle.   Livestock Science, 233, 103936, doi: 10.1016/j.livsci.2020.103936.

2. Piccoli*, M., Braccini, J., Rojas de Oliveira, H., Cardoso, F., Roso, V., Mehdi Sargolzaei, Flavio Schenkel (2020).  Genomic prediction of adaptation and productive efficiency traits in Braford and Hereford cattle.   Journal of Animal Breeding and Genetics, 231, 103864, doi: 10.1016/j.livsci.2019.103864.

3. Alves*, K., Brito, L. F., Baes, C. F., Sargolzaei, M., Robinson, J. B., Flavio S. Schenkel (2020).  Estimation of additive and non-additive genetic effects for fertility and reproduction traits in north American Holstein cattle using genomic information.   Journal of Animal Breeding and Genetics., doi: 10.1111/jbg.12466.

4. Do, D. N., Schenkel, F., Miglior, F., Zhao, X., & Ibeagha‐Awemu, E. M. (2020).  Targeted genotyping to identify potential functional variants associated with cholesterol content in bovine milk.   Animal Genetics., doi: 10.1111/age.12901.

5. Cruz, V. A., Oliveira, H. R., Brito, L. F., Fleming, A., Larmer, S., Filippo Miglior, and Flavio S. Schenkel (2019).  Genome-wide association study for milk fatty acids in Holstein cattle accounting for the DGAT1 gene effect.   Animals, 9 (11), 997, doi: 10.3390/ani9110997.

6. Ventura, R. V., Brito, L. F., Oliveira Junior, G. A., Daetwyler, H. D., Schenkel, F., Mehdi Sargolzaei, Gordon Vandervoort, Fabyano Fonseca e Silva, Stephen Miller, Minos E. Carvalho, Miguel H. A. Santana, Elisangela C. Mattos, Pablo Fonseca, Joanir P. Eler, and Jose Bento Sterman Ferraz (2019).  An in-depth comparison of two HD SNP panels and the development of an ultra-HD panel for Nellore cattle reveal differences on genomic analyses.   Animal Production Science, 60 (3), 333-346, doi: 10.1071/AN18305.

7. Jaton, C., Schenkel, F. S., Chud, T. C., Malchiodi, F., Sargolzaei, M., C. A. Price, A. Canovàs, C. Baes and F. Miglior (2019).  Genetic and genomic analyses of embryo production in dairy cattle.   Reproduction, Fertility and Development, 32 (2), 50-55, doi: 10.1071/RD19275.

8. Munro*, J. C., Physick-Sheard, P. W., Pyle, W. G., Schenkel, F. S., Miller, S. P., Yuri Regis Montanholi (2019).  Cardiac function and feed efficiency: increased right-heart workload in feed inefficient beef cattle.   Livestock Science, 229, 159-169, doi: 10.1016/j.livsci.2019.09.029.

9. Seymour, D., Cánovas, A., Chud, T., Cant, J., Osborne, V., Christine Baes, Flavio Schenkel, Filippo Miglior (2019).  The dynamic behaviour of feed efficiency in primiparous dairy cattle.   Journal of Dairy Science, 103 (2), 1528-1540, doi: 10.3168/jds.2019-17414.

10. Begli, H. E., Wood, B., Abdalla, E., Balzani, A., Willems, O., Schenkel, Flavio; Harlander-Matauschek, Alexandra; Baes, Christine (2019).  Genetic Parameters for Clutch and Broodiness Traits in the Turkey (Meleagris Gallopavo) and their Relationship with Bodyweight and Egg Production.   Poultry Science., doi: 10.3382/ps/pez446.

11. Abdalla, E. E.A., Schenkel, F. S., Begli, H. E., Willems, O. W., Peeters, K., Pieter Van As, Marco Bink, Ryley Vanderhout, Ben J. Wood, Christine Francoise Baes (2019).  Single-step methodology for genomic evaluation in turkeys (Meleagris gallopavo).   Frontiers in Genetics., doi: 10.3389/fgene.2019.01248.

12. Fleming, A., Schenkel, F. S., Ali, R. A., Corredig, M., Carta, S., C. M. Gregu, F. Malchiodi, N. P. P. Macciotta, and F. Miglior (2019).  Phenotypic investigation of fine milk components in bovine milk and their prediction using mid-infrared spectroscopy.   Canadian Journal of Animal Science, 99 (2), 218-227, doi: 10.1139/CJAS-2018-0058.

13. da Cruz, V. A., Brito, L. F., Schenkel, F., de Oliveira, H. R., Jafarikia, M., Zeny Feng (2019).  Strategies for within-litter selection of piglets using ultra-low density SNP panels.   Livestock Science, 220, 173-179, doi: 10.1016/j.livsci.2018.12.027.

14. Nayeri, S., Schenkel, F., Fleming, A., Kroezen, V., Sargolzaei, M., Christine Baes, Angela Cánovas; James Squires, and Filippo Miglior (2019).  Genome-wide Association Analysis for β-hydroxybutyrate Concentration in Milk in Holstein Dairy Cattle.   BMC Genetics, 20 (58)., doi: 10.1186/s12863-019-0761-9.

15. Stephenson*, M., Darlington, G., Schenkel, F., Squires, J., & Ali, A. (2019).  DSRIG: Incorporating Graphical Structure in the Regularized Modeling of SNP Data.   Journal of Bioinformatics and Computational Biology, 17 (3), 1950017, doi: 10.1142/S0219720019500173.

16. Vargas*, G., Neves, H. H., Brito, L. F., Schenkel, F. S., Albuquerque, L. G., Roberto Carvalheiro (2019).  Genetic and genomic analyses of testicular hypoplasia in Nellore cattle.   PLoS ONE, 14 (1), e0211159, doi: 10.1371/journal.pone.0211159.

17. Oliveira*, H., Brito, L., Lourenco, D., Fonseca e Silva, F., Jamrozik, J., Lawrence Schaeffer and Flavio Schenkel (2019).  Advances and applications of random regression models: from quantitative genetics to genomics.   Journal of Dairy Science, 102 (9), 7664–7683, doi: 10.3168/jds.2019-16265.

18. Oliveira*, H., Cant, J., Brito, L., Feitosa, F., Chud, T., Janusz Jamrozik, Fabyano Fonseca e Silva, Daniela Lourenco, and Flavio Schenkel (2019).  Genome-wide association for milk yield and milk composition traits in different lactation stages of Ayrshire, Holstein and Jersey dairy cattle.   Journal of Dairy Science, 102 (9), 8159–8174, doi: 10.3168/jds.2019-16451.

19. Guarini*, A., Sargolzaei, M., Brito, L., Kroezen, V., Lourenco, D., ; Baes, Christine; Miglior, Filippo; Cole, John; Schenkel, Flavio (2019).  Estimating the impact of the deleterious recessive haplotypes AH1 and AH2 on reproduction performance of Ayrshire cattle.   Journal of Dairy Science, 102 (6), 5315-5322, doi: 10.3168/jds.2018-15366.

20. Oliveira*, H., Lourenco, D., Masuda, Y., Misztal, I., Tsuruta, S., Janusz Jamrozik; Luiz Brito; Fabyano Fonseca e Silva; Flavio Schenkel (2019).  Application of single-step genomic evaluation using multiple-trait random regression test-day models in dairy cattle.   Journal of Dairy Science, 102 (3), 2365–2377, doi: 10.3168/jds.2018-15466.

Graduate Students and Post-doctoral Fellows

As an advisor and teacher, Dr. Schenkel believes a professor should be a role model and a mentor, and enable each student to reach their individual potential. A good professor should teach students to be independent thinkers and responsible for their own learning and intellectual growth. If students learn something well, regardless of the discipline, they will be prepared to adapt to any circumstances and be a productive member of the society.

Currently Dr. Schenkel supervise or co-supervise the following graduate students and PDF:

Lucas Alcantara

Ph.D. Student

Baes, Schenkel

Mohammed Boareki

Ph.D. Student

Canovas, Schenkel

Ivan Campos

Ph.D. Student


Dr. Tatiane Chud

Post-Doctoral Fellow

Google Scholar


Jinhuan Dou

Visiting Ph.D. Student

Google Scholar


Saranya Gunasegaram Narayana

Ph.D. Student

Barkema, Schenkel

Kerry Houlahan

Ph.D. Student

Google Scholar

Baes, Schenkel

Colin Lynch

Ph.D. Student

Baes, Schenkel

Samla Marques Freire Cunha

Ph.D. Student

Schenkel, Canovas

Audrey Martin

Ph.D. Student

Baes, Schenkel

Erin Massender

Ph.D. Student

Google Scholar


Paige Rockett

M.Sc. Student


Dr. Hinayah Rojas de Oliveira

Post-Doctoral Fellow

Google Scholar

Google Scholar


Saeed Shadpour

Post-Doctoral Fellow

Baes, Tulpan, Schenkel

Research Group                                                                                

        CGIL CGIL People

Flavio's education and academic timeline