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Flavio Schenkel

Position/Title: CGIL Director, Professor
Phone: (519) 824-4120 ext. 58650
Office: ANNU 121

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Dr. Schenkel is a Full Professor with research interests ranging from theoretical to applied genetics and genomics in livestock breeding. Current research focuses on the use of genomic information to enhance genetic evaluation of livestock species with emphasis on genomic selection. His research program is supported by industry and governmental funds, including various funding agencies. Since 2006, he is a member of influential industry boards in Canada, including the DairyGen Council of Canadian Dairy Network and the Dairy Cattle Genetic Evaluation Board. Dr. Schenkel was a professor at a Federal University in Brazil from 1993 to 2000, and a Research Associate at University of Guelph from 2000 until he became an Assistant Professor in 2005. In 2009 Dr. Schenkel changed his status to an Associate Professor and in 2014 he became a Full Professor. Since 2013, Dr. Schenkel is the Director of the Centre for Genetic Improvement of Livestock at University of Guelph. In his scientific career, Dr. Schenkel published over 145 peer-reviewed scientific papers and has contributed to formation of several high qualified personnel, including 21 graduate students and 17 post-doctoral fellows.. Dr. Schenkel also serves on several international journal editorial boards and maintains strong research collaboration with researchers in Brazil and other countries.



Academic History

  • Ph.D.   University of Guelph,  Guelph, Ontario,  Animal Breeding (Statistics and Quantitative Genetics minors),  1998
  • M.Sc.   Federal University of Rio Grande do Sul,  Porto Alegre, Brazil,  Animal Breeding,  1991
  • B.B.A.   Pontifical Catholic University of Rio Grande do Sul,  Porto Alegre, Brazil,  Business Administration,  1990
  • B.Sc.   Federal University of Rio Grande do Sul,  Porto Alegre, Brazil,  Agronomy,  1987
  • Other   Federal University of Rio Grande do Sul,  Porto Alegre, Brazil,  Specialization in Irrigation and Drainage,  1987


Affiliations and Partnerships

  • American Dairy Science Association
  • American Society of Animal Science
  • Canadian Society of Animal Science
  • American Association for the Advancement of Science


Awards and Honours       

2018:  The ADSA J.L. Lush Award in Animal Breeding for outstanding research in the field of animal breeding.
2014:  The CSAS Award in Technical Innovation in the Production of Safe and Affordable Food, Canadian Society of Animal Science.
1997:  Brian W. Kennedy Memorial Scholarship, Brian W. Kennedy Memorial.    
1997:  Ontario Graduate Scholarship, Ontario Ministry of Education.
1996:  Mary Edmunds Williams Scholarship, Mary Edmunds Williams.    
1995-1998:  Ph.D. Graduate Fellowship, Brazilian Federal Agency for Higher Studies (CAPES).   
1995:  Ontario Animal Breeders Scholarship, Ontario Animal Breeders.    
1994:  University of Guelph Graduate Scholarship, University of Guelph Graduate.    
1991:  Brossard award for achievements during undergraduate studies in Agronomy, Provincial Board of Education.    
1988-1991:  M.Sc. Graduate Scholarship, Brazilian National Council of Research (CNPq).


Research Impact

Dr. Schenkel’s research interests range from theoretical to applied genetics and genomics in livestock breeding. Current research focuses on the use of genomic information to enhance genetic evaluation of livestock species with emphasis on genomic selection. His research program has been supported by industry and governmental funds, including various funding agencies and expressive industry support funds. Dr. Schenkel has published 145 peer-reviewed scientific papers. In Google Scholar, his h-index is currently 36 and the i10-index is 93 overall. In ResearchGate, his RG Score is 41.37.

Dr. Schenkel’s most significant research contributions while at the University of Guelph include:
- Pioneering research on genome-wide selection led to the implementation of the first official genomic evaluation in Holstein cattle in Canada by the Canadian Dairy Network (CND) in 2009. Along with USA, Canada was the first country in the world to officially implement genomic selection in dairy cattle.
- The genomic projects led to the creation of reference datasets of genotyped animals in all major Canadian dairy breeds, which facilitated the implementation of genomic selection in other smaller population-sized dairy breeds (Jersey, Brown Swiss and Ayrshire) and opened the opportunity for other genomic related research, such as fine mapping of QTL, genome-wide imputation, etc.
- Innovative research and development on genome-wide imputation from low to high density SNP panels with substantial impact on number of genotyped animals (especially cows) in the genomic evaluation in Canada. Imputation research contributed to implementation of genomic evaluation of dairy cattle with imputed genotypes in 2011 by the CDN.
- Several software applications were developed in recent years, which allowed for state of art research and development in genomics, such as QMSim (genome simulator software), Gebv (genomic breeding value prediction software), and FImpute (genome-wide imputation software), which have been used world-wide. Both Gebv and Fimpute are currently used in the routine genomic evaluations of dairy cattle in Canada by CDN.
- Investigation into the possible genetic background underlying the liability of Standardbred racehorses to atrial fibrillation (AF) strongly indicated a genetic predisposition to AF in the Standardbreds, with the arrhythmia particularly prevalent in one popular sire line. These findings will have substantial impact on the Canadian Standardbred racehorse industry and on future research efforts towards reducing the incidence of this arrhythmia.
- Development of national genetic evaluation for disease resistance, including mastitis and other 7 diseases (lameness, cystic ovarian disease, displaced abomasum, ketosis, metritis/uterine disease, milk fever and retained placenta). Routine genetic evaluation for mastitis resistance was implemented and for metabolic disorders is currently being implemented by CDN.


Current and Recent Research Projects

1. Breeding livestock for climate resilience: the capacity to maintain production and fitness in a changing climate (Canada First Research Excellence Fund, Schenkel (Principle investigator))

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.        

2. Implementation of genomic selection to improve productivity and health traits in Ontario dairy goats (Gov-OMAFRA Agreement Research Programs, Schenkel (Principal Investigator))

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.

3. Canada's ten thousand cow genomes project (AAFC- Dairy cluster II Grant, Schenkel (Principal Investigator))

The general objective of the project is to increase the accuracy of genomic predictions by using additional knowledge from analyses conducted on a large genotyped cow population (Illumina 50k SNP panel) with high quality phenotypes, including some new traits of great interest (immune response, hoof health, feed efficiency and related traits, and milk spectral data), and imputed 777k genotypes and sequence SNP genotypes.

4. Development and testing of new methods for genomic evaluation in dairy cattle (AAFC- Dairy cluster II Grant, Schenkel (Principal Investigator))

The main objective of this project is to improve the accuracy of genomic estimated breeding values (GEBV) for young bulls, heifers and cows by developing new genomic evaluation methods or testing promising ones. Over the next five years to achieve this will require prioritizing emerging methods based on their potential for increased predictive ability and their applicability to the Canadian context, and transfer the knowledge and results to CDN for national implementation.

5. 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))

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.

6. Improving cow health and the nutraceutical value of milk with Infra-red technology (AAFC- Dairy cluster II Grant, Schenkel (Co-applicant))

Milk laboratories quantify major milk components such as fat or protein using mid-infrared (MIR) spectrometry. These predictions are used for milk payment as well as for animal performance recording. Collecting MIR spectra is very efficient, and the data extracted from the spectra today is just a small portion of the whole information. The MIR spectrum is indeed a fingerprint of the whole milk composition; however, very little has been carried out so far to extract further information. The overall objective is to study the phenotypic and genetic variability of milk spectral data in order to improve cow robustness, nutritional quality of milk for human consumption and to develop a series of calibration equations for several milk components.

7. Genetic Improvement of Canadian Lamb Carcass Yield, Quality and Growth Traits (NSERC-CRD, Schenkel (Principal Investigator))

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.

8. Computing hardware for big data editing, storage, and analysis . (NSERC- RESEARCH TOOLS AND INSTRUMENTS (RTI), Schenkel (Principal Investigator))

The big data era of research has caused an avalanche of data that has buried the current computer storage and processing technology in the Department of Animal Biosciences at University of Guelph. This project will support the purchase of three high performance computer nodes, a storage server and a fabric network to integrate the new nodes and storage server to the computer nodes and storage currently available in the Department for interdisciplinary and collaborative research involving big data, a common feature of novel research in precision agriculture.


Graduate Student Information

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 adivses or co-advises the following graduate students:


Featured Publications

  1. Bouwman, A. C., Daetwyler, H., Chamberlain, A., Ponce, C. H., Sargolzaei, M., Flavio S. Schenkel, Goutam Sahana, Armelle Govignon-Gion, Simon Boitard, Marlies Dolezal, Hubert Pausch, Rasmus F. Brøndum, Phil J. Bowman, Bo Thomsen, Bernt Guldbrandtsen, Mogens S. Lund, Bertrand Servin, Dorian J. Garrick, James Reecy, Johanna Vilkki, Alessandro Bagnato, Min Wang, Jesse L. Hoff, Robert D. Schnabel, Jeremy F. Taylor, Anna A. E. Vinkhuyzen, Frank Panitz, Christian Bendixen, Lars-Erik Holm, Birgit Gredler, Chris Hozé, Mekki Boussaha, Marie-Pierre Sanchez, Dominique Rocha, Aurelien Capitan, Thierry Tribout, Anne Barbat, Pascal Croiseau, Cord Drögemüller, Vidhya Jagannathan, Christy Vander Jagt, John J. Crowley, Anna Bieber, Deirdre C. Purfield, Donagh P. Berry, Reiner Emmerling, Kay-Uwe Götz, Mirjam Frischknecht, Ingolf Russ, Johann Sölkner, Curtis P. Van Tassell, Ruedi Fries, Paul Stothard, Roel F. Veerkamp, Didier Boichard, Mike E. Goddard, and Ben J. Hayes (2018).  Meta-analysis of genome wide association studies for the stature of cattle reveals numerous common genes that regulate size in mammals.   Nature Genetics., doi:
  2. Fleming, A., Schenkel, F., Malchiodi, F., Ali, A., Mallard, B., Mehdi Sargolzaei, Janusz Jamrozik, Jarmila B. Johnston, Filippo Miglior (in press, 2018).  Genetic correlations of mid-infrared predicted milk fatty acid groups with milk production traits.  Journal of Dairy Science.
  3. Piccoli*, M. L., Brito, L. F., Braccini, J., Brito, F. V., Cardoso, F. F., Jaime A. Cobuci, Mehdi Sargolzaei, Flávio S. Schenkel (in press, 2018).  A comprehensive comparison between single- and two-step GBLUP methods in a simulated beef cattle population.   Canadian Journal of Animal Science.
  4. de oliveira, H. R., Silva, F. F., Brito, L. F., Guarini, A. R., Jamrozik, J., Flávio Schramm Schenkel (in press, 2018).  Comparing de-regression methods for genomic prediction of test day traits in dairy cattle.   Journal of Animal Breeding and Genetics.
  5. Alves, K., Schenkel, F., Brito, L. F., & Robinson, J. A.B. (in press, 2018).  Estimation of direct and maternal genetic parameters for individual birth weight and probe weight using cross-fostering information.   Canadian Journal of Animal Science.
  6. Dehnavi, E., Mahyari, S. A., Schenkel, F., & Sargolzaei, M. (in press, 2018).  The impact of using cow genomic information on accuracy and bias of genomic breeding values in a simulated Holstein dairy cattle population. Journal of Dairy Science.
  7. Forutan, M., Mahyari, S. A., Baes, C., Melzer, N., Schenkel, F., and Mehdi Sargolzaei (2018).  Inbreeding and runs of homozygosity before and after genomic selection in North American Holstein cattle.   BMC Genomics, 19 (98)., doi: 10.1186/s12864-018-4453-z.
  8. Bore, R., Brito, L. F., Jafarikia, M., Bouquet, A., Maignel, L., Brian Sullivan, Flavio S. Schenkel (in press, 2018).  Genomic data reveals large similarities among Canadian and French maternal pig lines.   Canadian Journal of Animal Science.
  9. Buzanskas, M. E., Grossi, D. d.A., Ventura, R. V., Schenkel, F., Chud, T. C.S., Nedenia Bonvino Stafuzza, Luciana Diniz Rola, Sarah Laguna Conceição Meirelles, Fabiana Barichello Mokry, Maurício de Alvarenga Mudadu, Roberto Hiroshi Higa, Marcos Vinícius Gualberto Barbosa da Silva, Mauricio Mello Alencar, Luciana Correia de Almeida Regitano, Danisio Prado Munari (2017).  Candidate genes for male and female reproductive traits in Canchim beef cattle.   Journal of Animal Science and Biotechnology, 8 (67)., doi: 10.1186/s40104-017-0199-8.
  10. Abo-Ismail, M. K., Brito, L. F., Miller, S. P., Sargolzaei, M., Grossi, D. A., S.S. Moore, G. Plastow, P. Stothard, S. Nayeri, F. Schenkel (2017).  Genome-wide association studies and genomic prediction of breeding values for calving performance and body conformation traits in Holstein cattle.   Genetics Selection Evolution, 49 (82), 1-29, doi: 10.1186/s12711-017-0356-8.
  11. Larmer, S. G., Sargolzaei, M., Brito, L., Ventura, R., & Schenkel, F. (2017).  Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy.   BMC Genetics, 18 (120)., doi: 10.1186/s12863-017-0588-1.
  12. Brito, L. F., McEwan, J. C., Miller, S. P., Bain, W. E., Lee, M. A., Ken G Dodds, Sheryl-Anne N Newman, Natalie K Pickering, Flávio S Schenkel, and Shannon M Clarke (2017).  Genetic parameters for various growth, carcass and meat quality traits in a New Zealand sheep population.   Small Ruminant Research, 154, 81-91, doi:
  13. Lam, S., Munro, J. C., Zhou, M., Guan, L. L., Schenkel, F. S., M. A. Steele, S. P. Miller, and Y. R. Montanholi (2017).  Associations of rumen parameters with feed efficiency and sampling routine in beef cattle.   Animal, 1-9, doi: 10.1017/S1751731117002750.
  14. Brito, L. F., Clarke, S. M., McEwan, J. C., Miller, S. P., Pickering, N. K., W E Bain, K G Dodds, M Sargolzaei, F S Schenkel (2017).  Prediction of genomic breeding values for growth, carcass and meat quality traits in a multi-breed sheep population using a HD SNP chip.   BMC Genetics, 18 (7)., doi: 10.1186/s12863-017-0476-8.
  15. Grossi, D. A., Jafarikia, M., Brito, L. F., Buzanskas, M. E., Sargolzaei, M., F S Schenkel (2017).  Genetic diversity, extent of linkage disequilibrium and persistence of gametic phase in Canadian pigs.   BMC Genetics, 18 (6)., doi: 10.1186/s12863-017-0473-y.
  16. Piccoli, M., Brito, L. F., Braccini, J., Cardoso, F. F., Sargolzaei, M., F S Schenkel (2017).  Genomic predictions for economically important traits in Brazilian Braford and Hereford beef cattle using true and imputed genotypes.   BMC Genetics, 18 (2)., doi: 10.1186/s12863-017-0475-9.
  17. Jaton, C., Schenkel, F., Malchiodi, F., Sargolzaei, M., Price, C., Christine Baes, and Filippo Miglior (2017).  Genetic analysis for quality of frozen embryos produced by Holstein cattle donors in Canada.   Journal of Dairy Science, 100 (9), 7320-7329, doi:
  18. Narayana, S. G., Schenkel, F. S., Fleming, A., Koeck, A., Malchiodi, F., J. Jamrozik, J. Johnston, M. Sargolzaei, and F. Miglior (2017).  Genetic analysis of groups of mid - infrared predicted fatty acids in milk.   Journal of Dairy Science, 100 (6), 4731-4744, doi: 10.3168/jds.2016-12244.
  19. Fleming, A., Schenkel, F. S., Chen, J., Malchiodi, F., Bonfatti, V., R.A. Ali, B. Mallard, M. Corredig, and F. Miglior (2017).  Prediction of milk fatty acid content with mid-infrared spectroscopy in Canadian dairy cattle using differently distributed model development sets.   Journal of Dairy Science, 100 (6), 5073-5081, doi: 10.3168/jds.2016-12102.
  20. Fleming, A., Schenkel, F., Koeck, A., Malchiodi, F., Ali, A., Corredig, Milena; Mallard, Bonnie; Sargolzaei, Mehdi; Miglior, Filippo (2017).  Heritabilities of measured and mid-infrared predicted milk fat globule size, milk fat and protein percentages, and their genetic correlations.   Journal of Dairy Science, 100 (5), 3735–3741, doi: 10.3168/jds.2016-12243.
  21. Miar, Y., Sargolzaei, M., & Schenkel, F. (2017).  A Comparison of Different Algorithms for Phasing Haplotypes from Large Population Genotype and Pedigree Data.   Journal of Dairy Science, 100 (4), 2837–2849, doi: 10.3168/jds.2016-11590.
  22. Malchiodi, F., Koeck, A., Mason, S., Christen, A., Kelton, D., Flavio S. Schenkel, Filippo Miglior (2017).  Genetic parameters of hoof health traits estimated with linear and threshold models using alternative cohorts.   Journal of Dairy Science, 100 (4), 1-9, doi: 10.3168/jds.2016-11558.
  23. Fleming, A., Schenkel, F., Chen, J., Malchiodi, F., Ali, R. A., B. Mallard, M. Sargolzaei, M. Corredig, and F. Miglior (2017).  Variation in fat globule size in bovine milk and its prediction using mid-infrared spectroscopy.   Journal of Dairy Science, 100 (3), 1640–1649, doi: 10.3168/jds.2016-11427.
  24. Nayeri, S., Sargolzaei, M., Abo-Ismail, M. K., Miller, S., Schenkel, F., Moore S.S, and Stothard P. (2017).  Genome-wide association study for lactation persistency, female fertility, longevity and lifetime performance traits in Holstein dairy cattle.   Journal of Dairy Science, 100 (2), 1246–1258, doi: 10.3168/jds.2016-11770.
  25. Brito, L. F., Kijas, J. W., Ventura, R. V., Sargolzaei, M., Porto-Neto, L. R., A Cánovas, Z Feng, M Jafarikia, F S Schenkel (2017).  Genetic diversity and signatures of selection in various goat breeds revealed by genome-wide SNP markersGenetic diversity and signatures of selection in various goat breeds revealed by genome-wide SNP markers.   BMC Genomics (18), 229, doi: 10.1186/s12864-017-3610-0.
  26. Brito, L. F., McEwan, J. C., Miller, S. P., Pickering, N. K., Bain, W. E., K G Dodds, F S Schenkel, S M Clarke (2017).  Genetic diversity of a new zealand multi-breed sheep population and composite breeds' history revealed by a high-density SNP chip.   BMC Genetics (18), 25, doi: 10.1186/s12863-017-0492-8.
  27. Kraus, M., Physick-Sheard, P. W., Brito, L. F., & Schenkel, F. (2017).  Estimates of heritability of atrial fibrillation in the Standardbred racehorses.   Equine Veterinary Journal., doi: 10.1111/evj.12687.
  28. Buzanskas, M. E., Ventura, R. V., Chud, T. C.S., Bernardes, P. A., de Abreu Santos, D. J., L. C. A. Regitano, M. M. de Alencar, M. A. Mudadu, R. Zanella, M. V. G. B. da Silva, C. Li, F. S. Schenkel, D. P. Munari (2017).  Study on the introgression of beef breeds in Canchim cattle using single nucleotide polymorphism markers.   PLoS ONE., doi: 10.1371/journal.pone.0171660. 

  For a full list of publications, please visit Dr. Schenkel's Google Scholar page.