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Flavio Schenkel


Position/Title: CGIL Director, Professor
email: schenkel@uoguelph.ca
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 190 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.

Sedona

 

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.
2018:  The Ontario Agriculture College Alumni Distinguished Researcher Award, University of Guelph.
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 over 160 peer-reviewed scientific papers. In Google Scholar, his h-index is currently 37 and the i10-index is 106 overall. In ResearchGate, his RG Score is 42.19.

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.

 

Main Research Projects (Current and Recent)

1. Integrating genomic approaches to improve dairy cattle resilience: A comprehensive goal to enhance Canadian dairy industry sustainability (Large scale applied research project competition- Genome Canada, Schenkel (Co-investigator), 2020 to 2024)

The overall aim of this project is to develop genomic tools to enable implementation of selection to increase dairy cow resilience, defined as the capacity of the animal to adapt rapidly to changing environmental conditions, without compromising its productivity, health or fertility while becoming more resource-efficient and reducing its environmental burden.

2. 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.

3. 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.

4. 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.

5. 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.        

6. 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.

7. 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.

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

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.

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

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.

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

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.

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

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 advises or co-advises the following graduate students:

 

Post-doctoral Information

Dr. Schenkel also supervises the following post-doctoral fellows:

 

Most 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.

 

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

 

►  Dr. Schenkel's Animal Breeders Pedigree

 

►  Dr. Schenkel's Education and Academic Timeline