Professor, CGIL Director
(519) 824-4120 ext. 58650
Personal Web Site: Flavio Schenkel's Site
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. In his scientific career, Dr. Schenkel published over 109 peer-reviewed scientific papers and has contributed to formation of several high qualified personnel. Dr. Schenkel also serves on several international journal editorial boards and maintains strong research collaboration with researchers in Brazil and other countries.
- 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
Awards and Honours
2013-2014: The CSAS Award in Technical Innovation in the Production of Safe and Affordable Food, Canadian Society of Animal Science.
1997-1998: Ph.D. Graduate Fellowship, Brazilian Federal Agency for Higher Studies (CAPES).
1996-1997: Ph.D. Graduate Fellowship, Brazilian Federal Agency for Higher Studies (CAPES).
1996-1997: Brian W. Kennedy Memorial Scholarship, Brian W. Kennedy Memorial.
1996-1997: Ontario Graduate Scholarship, Ontario Ministry of Education.
1995-1996: Ph.D. Graduate Fellowship, Brazilian Federal Agency for Higher Studies (CAPES).
1995-1996: Mary Edmunds Williams Scholarship, Mary Edmunds Williams.
1994-1995: Ontario Animal Breeders Scholarship, Ontario Animal Breeders.
1993-1994: University of Guelph Graduate Scholarship, University of Guelph Graduate.
1990-1991: Brossard award for achievements during undergraduate studies in Agronomy, Provincial Board of Education.
1988-1989 – 1990-1991: M.Sc. Graduate Scholarship, Brazilian National Council of Research (CNPq).
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 109 peer-reviewed scientific papers. In Google Scholar, his h-index is currently 32 and the i10-index is 68 overall. In ResearchGate, his RG Score is 37.8.
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 Research Projects
1. Canada's ten thousand cows genome project (AAFC- Dairy cluster II Grant, awarded Jan 2014- Dec 2017, 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.
2. Development and testing of new methods for genomic evaluation in dairy cattle (AAFC- Dairy cluster II Grant, awarded Jan 2014- Dec 2017, 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.
3. 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, awarded September 2015- September 2019, 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.
4. Improving cow health and the nutraceutical value of milk with Infra-red technology (AAFC- Dairy cluster II Grant, awarded Jan 2014- Dec 2017, 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.
5. Genetic Improvement of Canadian Lamb Carcass Yield, Quality and Growth Traits (NSERC-CRD awarded in October 2015 - September 2017, 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.
6. Computing hardware for big data editing, storage, and analysis . (NSERC- RESEARCH TOOLS AND INSTRUMENTS (RTI) awarded June 2016 - June 2017, 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.
- Jaton, C., Koeck, A., Sargolzaei, M., Malchiodi, F., Price, C. A., F. S. Schenkel, and F. Miglior (2016). Genetic analysis of superovulatory response of Holstein cows in Canada. Journal of Dairy Science, 99 (5), 3612–3623, doi: 10.3168/jds.2015-10349.
- Ventura, R., Larmer, S., Schenkel, F. S., Miller, S., & Sullivan, P. (2016). Genomic Clustering helps to improve prediction in a multi-breed population. Journal of Animal Science, 94 (5), 1844-1856, doi: 10.2527/jas.2016-0322.
- De Melo, T. P., Takada, L., Baldi, F., De Oliveira, H. N., Dias, M. M., Haroldo H. R. Neves, Flavio S. Schenkel, Lucia G. Albuquerque, Roberto Carvalheiro (2016). Assessing the value of phenotypic information of non-genotyped animals in QTL mapping of complex traits in real and simulated populations. BMC Genetics, 17, 89, doi: 10.1186/s12863-016-0394-1.
- Nayeri, S., Sargolzaei, M., Abo-Ismail, M. K., May, N., Miller, S., F. Schenkel, S.S. Moore, and P. Stothard (2016). Genome-wide association for milk production and female fertility in Canadian dairy Holstein cattle. BMC Genetics, 17, 75, doi: 10.1186/s12863-016-0386-1.
- Stothard, P., Liao, X., Arantes, A. S., De Pauw, M., Coros, C., Graham S. Plastow, Mehdi Sargolzaei, John J. Crowley, John A. Basarab, Flavio Schenkel, Stephen Moore, Stephen P. Miller (2015). A large and diverse collection of bovine genome sequences from the Canadian Cattle Genome Project. GigaScience, 4, 49, doi: 10.1186/s13742-015-0090-5.
- Brito, L. F., Jafarikia, M., Grossi, D. A., Kijas, J. W., Porto-Neto, L. R., Ricardo V Ventura, Mehdi Salgorzaei, Flavio S Schenkel (2015). Characterization of linkage disequilibrium, consistency of gametic phase and admixture in Australian and Canadian goats. BMC Genetics, 16, 67, doi: 10.1186/s12863-015-0220-1.
- Koeck, A., Jamrozik, J., Schenkel, F., Moore, R. K., Lefebvre, D. M., D.F. Kelton, and F. Miglior (2014). Genetic analysis of milk ß-hydroxybutyrate and its association with fat to protein ratio, body condition score, clinical ketosis and displaced abomasum in early first lactation Canadian Holsteins. Journal of Dairy Science, 97 (11), 7286-7292, doi: 10.3168/jds.2014-8405.
- Chen, L., Li, C., & Schenkel, F. (2014). An alternative computing strategy for genomic prediction using a Bayesian mixture model. Canadian Journal of Animal Science, 95 (1), 1-11, doi: 10.4141/cjas-2014-091.
- Piccoli, M. L., Braccini, J., Cardoso, F. F., Sargolzaei, M., & Schenkel, F. S. (2014). Accuracy of genome-wide imputation in Braford and Hereford beef cattle. BMC Genetics, 15, 157, doi: 10.1186/s12863-014-0157-9.
- Larmer, S.G., Sargolzaei, M., & Schenkel, F. S. (2014). Extent of Linkage Disequilibrium, consistency of gametic phase and imputation accuracy within and across Canadian Dairy breeds. Journal of Dairy Science, 97 (5), 3128-3141, doi: 10.3168/jds.2013-6826.
- Koeck, A., Loker, S., Miglior, F., Kelton, D. F., & Schenkel, F. (2014). Genetic relationships of mastitis, cystic ovaries and lameness with milk yield and somatic cell score in first lactation Canadian Holsteins. Journal of Dairy Science, 97, 1-8, doi: 10.3168/jds.2013-7785.
- Karrow, N. A., Goliboski, K., Stonos, N., & Schenkel, F. (2014). Genetics of Helminth Resistance in Sheep. Canadian Journal of Animal Science, 94 (1), 1-9, doi: 10.4141/CJAS2013-036.
- Ventura, R.V., Lu, D., Schenkel, F.S., Wang, Z., Li, C., S.P. Miller (2014). Impact of reference population on accuracy of imputation from 6k to 50K SNP chips in multi-breed beef cattle populations. Journal of Animal Science, 92 (4), 1433-1444, doi: 10.2527/jas.2013-6638.
- Chen, L., Li, C., Sargolzaei, M., & Schenkel, F. (2014). Impact of Genotype Imputation on the Performance of GBLUP and Bayesian Methods for Genomic Prediction. PLoS ONE, 9 (7), e101544, doi: 10.1371/journal.pone.0101544.
- Thompson, K. A., Sargolzaei, M., Ventura, R., Abo-Ismail, M., Miglior, F., F.S. Schenkel, and B.A. Mallard (2014). A Genome-Wide Association Study for Immune Response Traits in Canadian Holstein Cattle. BMC Genomics, 15 (1), 559, doi: 10.1186/1471-2164-15-559.
- Sargolzaei, M., Chesnais, J. P., & Schenkel, F. (2014). A new approach for efficient genotype imputation using information from relatives. BMC Genomics, 15, 478, doi: 10.1186/1471-2164-15-478.
- Chen, L., Li, C., Miller, S., & Schenkel, F. (2014). Multi-population genomic prediction using a multi-task Bayesian learning model. BMC Genetics, 15, 53, doi: 10.1186/1471-2156-15-53.