University of Guelph  
 
 CGIL

Animal Biosciences

              Faculty

     
GBI


Media Guide




Flavio S. Schenkel
Professor

BASc, M.Sc. UFRGS-Brazil, Ph.D. Guelph
email: schenkel@uoguelph.ca

Sedona

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

Schenkel

             
                         DCGG





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.

Education

  • 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. 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.


Recent Publications


1. Feitosa, F. L.B., Pereira, A. S.C., Mueller, L. F., Fonseca, P. A.d., Braza, C. U., Sabrina Amorim, Rafael Espigolan, Marcos Antunes de Lemos, Lucia Galvão de Albuquerque, Flavio Schramm Schenkel, Luiz Fernando Brito, Nedenia Bonvino Stafuzza, and Fernando Baldi (2021).  Genome-wide association study for beef fatty acid profile using haplotypes in Nellore cattle.   Livestock Science, 245, 104396, doi: 10.1016/j.livsci.2021.104396.

2. Freitas, P. H., Wang, Y., Yan, P., Oliveira, H. R., Schenkel, F. S., Yi Zhang, Qing Xu, Luiz F. Brito (2021).  Genetic diversity and signatures of selection for thermal stress in cattle and other two Bos species adapted to divergent climatic conditions.   Frontiers in Genetics, 12, 102, doi: 10.3389/fgene.2021.604823.

3. Oliveira, H. R., Miller, S. P., Brito, L. F., & Schenkel, F. S. (2021).  Impact of censored or penalized data in the genetic evaluation of longevity using random regression models in North American Angus cattle.   Animals, 11 (3), 800, doi: 10.3390/ani11030800.

4. Lam, S., Miglior, F., Fonseca, P. A., Gomez-Redondo, I., Zeidan, J., Suarez-Vega, Aroa; Schenkel, Flavio; Guan, Leluo; Waters, Sinead; Stothard, Paul; Cánovas, Angela (2021).  Identification of functional candidate variants and genes for feed efficiency in Holstein and Jersey cattle breeds using RNA-Sequencing.   Journal of Dairy Science, 104 (2), 1928-1950, doi: 10.3168/jds.2020-18241.

5. Soares*, R. A., Vargas, G., Muniz, M. M., Soares, M. A., Cánovas, A., F. Schenkel, and E. J. Squires (2021).  Differential gene expression in dairy cows under negative energy balance and ketosis: A systematic review and meta-analysis.   Journal of Dairy Science, 104 (1), 602-615, doi: 10.3168/jds.2020-18883.

F. Schenkel and E. J. Squires share the senior authorship.

6. Richardson, C., Nguyen, T., Abdelsayed, M., Moate, P., Williams, R., Chud, Tatiane; Schenkel, Flavio; Goddard, Mike; van den Berg, Irene; Cocks, Ben; Marett, Leah; Wales, William; Pryce, Jennie (2021).  Genetic parameters for methane emission traits in Australian dairy cows.   Journal of Dairy Science, 104 (1), 539-549, doi: 10.3168/jds.2020-18565.

7. Abdalla, E., Id-Lahoucine, S., Cánovas, A., Casellas, J., Schenkel, F. S., Ben J. Wood, Chirstine F. Baes (2020).  Discovering lethal alleles across the turkey genome using transmission ratio distortion approach.   Animal Genetics, 51 (6), 876-889, doi: 10.1111/age.13003.  [Citations: Open access.]

8. Makanjuola, B. O., Maltecca, C., Miglior, F., Schenkel, F., & Baes, C. F. (2020).  Effect of recent and ancient inbreeding on production and fertility traits in Canadian Holsteins.   BMC Genomics, 21, 605, doi: 10.1186/s12864-020-07031-w.

9. Feitosa, F., Pereira, A., Amorim, S., Peripolli, E., Silva, R. M.d., Adrielle Ferrinho, Flavio Schenkel, Luiz Brito, Rafael Espigolan, Lucia Albuquerque, Fernando Baldi (2020).  Comparison between haplotype-based and individual snp-based genomic predictions for beef fatty acid profile in Nellore cattle.   Journal of Animal Breeding and Genetics, 137 (5), 468-476, doi: 10.1111/jbg.12463.

10. Teissier, M., Larroque, H., Brito, L., Rupp, R., Schenkel, F., Christèle Robert-Granié (2020).  Genomic predictions based on haplotypes fitted as pseudo-SNPs for milk production and udder type traits and somatic cell score in French dairy goats.   Journal of Dairy Science, 103 (12), 11559-11573, doi: 10.3168/jds.2020-18662.

11. Chen, S., Oliveira, H. R., Schenkel, F. S., Pedrosa, V. B., Melka, M., Luiz F. Brito (2020).  Using imputed whole-genome sequence variants to uncover candidate mutations and genes affecting milking speed and temperament in Holstein cattle.   Journal of Dairy Science, 103., doi: 10.3168/jds.2020-18897.

12. Oliveira, H. R., Brito, L. F., Miller, S. P., & Schenkel, F. S. (2020).  Using random regression models to genetically evaluate functional longevity traits in North American Angus cattle.   Animals, 10 (12), 2410, doi: 10.3390/ani10122410.

13. Narayana*, S. G., Schenkel, F., Miglior, F., Chud, T., Abdalla, E., Naqvi, Syed Ali; Malchiodi, Francesca; Barkema, Herman (2020).  Genetic Analysis of Pathogen-Specific Intramammary Infections in Dairy Cows.   Journal of Dairy Science., doi: 10.3168/jds.2020-19062.

14. Martins, R., Machado, P. C., Pinto, L. F., Silva, M. R., Schenkel, F. S., Luiz F. Brito, Victor B. Pedrosa (2020).  Genome-wide association study and pathway analysis for fat deposition traits in Nellore cattle raised in pasture-based systems.   Journal of Animal Breeding and Genetics, 1-19, doi: 10.1111/jbg.12525.

15. Devos*, J. J., Spence, K. M., Warren, C. T., Ferriman, N. N., Schenkel, F., K.M. Wood, C. P. Campbell and I. B. Mandell (2020).  Effects of frequency of supplementation of low-quality gestation diets on beef cow performance from mid-gestation through lactation and preweaning calf performance.   Applied Animal Science, 36 (2), 237-248, doi: 10.15232/aas.2019-01933.

16. 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.

17. 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.

18. Mallikarjunappa, S., Schenkel, F., Brito, L., Bissonnette, N., Miglior, F., Chesnais, Jacques; Lohuis, Michael; Meade, (CA) Kieran; Karrow, Niel (2020).  Association of genetic polymorphisms related to Johne's disease with estimated breeding values of Holstein sires for milk ELISA test scores.   BMC Veterinary Research, 16 (165)., doi: 10.1186/s12917-020-02381-9.

19. 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, 137 (3), 316-330, doi: 10.1111/jbg.12466.

20. Freitas, P. F., Oliveira, H., Fonseca e Silva, F., Fleming, A., Miglior, F., Flavio Schenkel, Luiz Brito (2020).  Genomic analyses for predicted milk fatty acid composition along the lactation in North American Holstein cattle.   Journal of Dairy Science, 103 (7), 6318-6331, doi: 10.3168/jds.2019-17628.

21. Makanjuola*, B., Miglior, F., Abdalla, E., Maltecca, C., Schenkel, F., Christine Baes (2020).  Effect of genomic selection on rate of inbreeding and coancestry and effective population size of Holstein and Jersey cattle populations.   Journal of Dairy Science, 103 (6), 5183-5199, doi: 10.3168/jds.2019-18013.

22. Freitas, P. F., Oliveira, H., Fonseca e Silva, F., Flaming, A., Schenkel, F., Filippo Miglior, Luiz Brito (2020).  Short-communication: Time-dependent genetic parameters and single-step genome-wide association analyses for predicted milk fatty acid composition in Ayrshire and Jersey dairy cattle.   Journal of Dairy Science, 103 (6), 5263-5265, doi: 10.3168/jds.2019-17820.

23. Malchiodi, F., Jamrozik, J., Christen, A., Fleming, A., Kistemaker, G., Caeli Richardson, Vic Daniel, David Kelton, Flavio Schenkel, Filippo Miglior (2020).  Symposium review: Multiple-trait single-step genomic evaluation for hoof health.   Journal of Dairy Science, 103 (6), 5346-5353, doi: 10.3168/jds.2019-17755.

24. Nayeri, S., Schenkel, F., Martin, P., Flaming, A., Jamrozik, J., Francesca Malchiodi, Luiz F. Brito, Christine F. Baes, Mehdi Sargolzaei, and Filippo Miglior (2020).  Estimation of Genetic Parameters for Mid-infrared Predicted Lactoferrin and Milk Fat Globule Size in Holsteins.   Journal of Dairy Science, 103 (3), 2487-2497, doi: 10.3168/jds.2019-16850.

25. Wang, M., Do, D. N., Peignier, C., Dudemaine, P., Schenkel, F. S., Yongjiang Mao, Filippo Miglior, Xin Zhao, Eveline M. Ibeagha-Awemu (2020).  Cholesterol Deficiency Haplotypes Frequency and Their Impact on Milk Production and Milk Cholesterol Content in Canadian Holstein Cows.   Canadian Journal of Animal Science, 100 (4)., doi: 10.1139/cjas-2019-0068.

26. Butty, A., Chud, T., Miglior, F., Schenkel, F., Kommadath, A., Arun Kommadath , Kirill Krivushin , Jason Grant , Irene Häfliger , Cord Drögemüller , Angela Canovas , Paul Stothard, Christine Baes (2020).  High confidence copy number variants identified in Holstein dairy cattle from whole genome sequence and genotype array data.   Scientific Reports, 10 (8044)., doi: 10.1038/s41598-020-64680-3.

27. Boareki*, M. N., Brito, L. F., Cánovas, A., Osborne, V., & Schenkel, F. (2020).  Estimation of Genetic Parameters and Selection Response for Reproductive and Growth Traits in Rideau-Arcott sheep.   Canadian Journal of Animal Science., doi: 10.1139/CJAS-2019-0152.

28. Brito, L. F., Oliveira, H. R., Houlahan, K., Fonseca, P. A., Lam, S., Adrien M. Butty, Dave J. Seymour, Giovana Vargas, Tatiane C. S. Chud, Fabyano F. Silva, Christine F. Baes, Ángela Cánovas, Filippo Miglior, Flavio S. Schenkel (2020).  INVITED REVIEW: Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle.   Canadian Journal of Animal Science., doi: 10.1139/CJAS-2019-0193.

29. 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.

30. Massender*, E., Brito, L. F., Cánovas, A., Baes, C., Kennedy, D., Flavio S. Schenkel (2020).  The value of incorporating carcass trait phenotypes in terminal sire selection indexes to improve carcass yield and quality of heavy lambs.   Journal of Animal Breeding and Genetics., doi: abs/10.1111/jbg.12484.


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:


Student



Advisor
Lucas Alcantara Ph.D. Student

Baes, Schenkel
Mohammed Boareki Ph.D. Student

Schenkel, Canovas
Ivan Campos Ph.D. Student

Schenkel
Jinhuan Dou Visiting Ph.D. Student
Google Scholar Schenkel
Saranya Gunasegaram Narayana Ph.D. Student Google Scholar Google Scholar Schenkel, Barkema
Kerry Houlahan Ph.D. Student
Google Scholar Baes, Schenkel
Colin Lynch Ph.D. Student

Baes, Schenkel
Audrey Martin Ph.D. Student
Google Scholar Baes, Schenkel
Erin Massender Ph.D. Student
Google Scholar Schenkel
Dr. Gerson Oliveira Jr. Post-Doctoral Fellow
Google Scholar Baes, Schenkel
Paige Rockett M.Sc. Student

Schenkel
Dr. Hinayah Rojas de Oliveira Post-Doctoral Fellow Google Scholar Google Scholar Schenkel
Dr. Saeed Shadpour Post-Doctoral Fellow
Google Scholar Baes, Tulpan, Schenkel
Research Group                                                                                
   

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Publications

Flavio's education and academic timeline