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. Chen, Shi-Yi, Schenkel, Flavio S., Melo, Ana L. P., Oliveira, Hinayah R., Pedrosa, Victor B., Araujo, Andre C., . . . Brito, Luiz F. (2022, April). Identifying pleiotropic variants and candidate genes for fertility and reproduction traits in Holstein cattle via association studies based on imputed whole genome sequence genotypes. BMC Genomics. (Accepted)

2. *Seymour, Dave, Cant, John P., Osborne, Vern R., Chud, Tatiane C. S., Schenkel, Flavio S. & Miglior, Filippo. (2022, April). A novel method of estimating 24-h corrected milk yields in automated milking systems. Animal - Open Space. (Accepted)

3. *Massender, E, Brito, L, Maignel, L, Oliveira, H, Jafarikia, M, Baes, Christine, . . . Schenkel, Flavio. (2022, March). Single-Step Genomic Evaluation for Milk Production Traits in Canadian Alpine and Saanen Dairy Goats. Journal of Dairy Science, 105(3), 2393-2407. doi:10.3168/jds.2021-20558 (Published)

4. *Massender, Erin, Brito, Luiz, Maignel, Laurence, Oliveira, Hinayah, Jafarikia, Mohsen, Baes, Christine, . . . Schenkel, Flavio. (2022, March). Single- and Multiple-Breed Genomic Evaluations for Conformation Traits in Canadian Alpine and Saanen Dairy Goats. Journal of Dairy Science. (Accepted)

5. Liu, Rui, Hailemariam, Dagnachew, Yang, Tianfu, Miglior, Filippo, Schenkel, Flavio, Wang, Zhiquan, . . . Plastow, Graham. (2022, March). Predicting enteric methane emission in lactating Holsteins based on reference methane data collected by the GreenFeed system. Animals, 16(3), 100469. doi:10.1016/j.animal.2022.100469 (Published)

6. *Campos, I. L., Chud, T. C. S, Oliveira, H. R., Baes, C. F., Cánovas, A. & Schenkel, F. S. (2022, March). Using publicly available weather station data to investigate the effects of heat stress on milk production traits in Canadian Holstein cattle. Canadian Journal of Animal Science. https://doi.org/10.1139/cjas-2021-0088 (Published)

7. *Gunasegaram Narayana, Saranya, de Jong, Ellen, Schenkel, Flavio, Fonseca, Pablo, Chud, Tatiane, Powell, Diana, . . . Barkema, Herman. (2022, February). Underlying genetic architecture of mastitis in dairy cattle: A systematic review and gene prioritization analysis of genome-wide association studies. Journal of Dairy Science. (Submitted)

8. Araujo, A C, Carneiro, P L, Oliveira, H R, Schenkel, F, Veroneze, R & Daniela A L Lourenco, Luiz F Brito. (2022). A comprehensive comparison of haplotype-based single-step genomic predictions in livestock populations with different genetic diversity levels: a simulation study. Frontiers in Genetics, 12. doi:10.3389/fgene.2021.729867 (Published)

9. Fonseca, P, Schenkel, F & Canovas, A. (2022, January). Genome-wide association study using haplotype libraries and repeated measures model to identify candidate genomic regions for stillbirth in Holstein cattle. Journal of Dairy Science. doi:10.3168/jds.2021-20936 (Published)

10. Bolormaa, S, MacLeod, I, Khansefid, M, Marett, L, Wales, W, Nieuwhof, Gert, . . . Pryce, Jennie. (2022). Evaluation of updated Feed Saved breeding values developed in Australian Holstein dairy cattle. JDS Communications. doi:10.3168/jdsc.2021-0150 (In Press)

11. Teissier, Marc, Brito, Luiz, Schenkel, Flavio, Bruni, Guido, Fresi, Pancrazio, Robert-Granie, Christèle & Larroque, Hélène. (2022, January). Genetic Characterization and Population Connectedness of North American and European Dairy Goats. Frontiers in Genetics. (Submitted)

12. *Alcantara, Lucas M., Baes, Christine F., de Oliveira Junior, Gerson A. & Schenkel, Flavio S. (2022, January). Conformation traits of Holstein cows and their association with a Canadian economic selection index. Canadian Journal of Animal Science. (Accepted)

13. *Dou, Jinhuan, Luo, Hanpeng, Lou, Wenqi, Wang, Di, Schenkel, Flavio, Yu, Ying, . . . Wand, Yachun. (2021, December). Integrative analysis of transcriptome and epigenome reveals key regulators of heat stress in rats. Journal of Animal Science and Biotechnology. (Submitted)

14. *Asselstine, V., Medrano, J. F., Stothard, S., Miglior, F., Karrow, N. A., Baes, C. F., . . . Cánovas, A. (2021, December). Identification of structural variants within functional regulatory elements associated with mastitis in Holstein dairy cows using whole genome sequencing and RNA-Sequencing. (Submitted)

15. Shadpour, Saeed, Chud, Tatiane C. S., Hailemariam, Dagnachew, Oliveira, Hinayah R., Plastow, Graham, Stothard, Paul, . . . Schenkel, Flavio S. (2021, December). Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks. Journal of Dairy Science. (Submitted)

16. *Alcantara, L.M., Schenkel, F.S., Lynch, C., Oliveira Junior, G.A., Baes, C.F. & Tulpan, D. (2021, December). Machine learning classification of hormonal synchronization protocols for Canadian Holsteins cows. Journal do Dairy Science. (Submitted)

17. Martin, Audrey A.A., de Oliveira Jr, Gerson, Madureira, Augusto M.L., Miglior, Filippo, LeBlanc, Stephen J., Cerri, Ronaldo L. A., . . . Schenkel, Flavio S. (2021, November). Reproductive tract size and position score: Estimation of genetic parameters for a novel fertility trait in dairy cows. Journal of Dairy Science. (Submitted)

18. Pedrosa, Victor B., Schenkel, Flavio S., Chen, Shi-Yi, Oliveira, Hinayah R., Casey, Theresa M., Melka, Melkaye G. & Brito, Luiz F. (2021, November). Genome-wide association analyses of lactation persistency and milk production traits in Holstein cattle based on imputed whole-genome sequence data. Genes, 12(11), 1830. doi:10.3390/genes12111830 (Published)

19. Boareki*, M, Schenkel, F, Kennedy, D, C&aacute & novas, A. (2021, September). Prediction of Genetic Resistance for Scrapie in Ungenotyped Sheep Using a Linear Animal Model. Genes, 12(9), 1432. doi:10.3390/genes12091432 (Published)

20. Oliveira Jr., G, Schenkel, F, Alcantara, L, Houlahan, K, Lynch, C & Baes, Christine. (2021, August). Estimated genetic parameters for all genetically evaluated traits in Canadian Holsteins. Journal of Dairy Science, 104(8), P9002-9015. doi:10.3168/jds.2021-20227 (Published)

21. Manzanilla-Pech, C, L&oslash, vendahl, P, Gordo, D M, Difford, G, Pryce, J, . . . Lassen, Jan. (2021, August). Breeding for reduced methane emission and feed efficient Holstein cows: an international response. Journal of Dairy Science, 104(8), P8983-9001. doi:10.3168/jds.2020-19889 (Published)

22. Martins, R, Machado, P C, Pinto, L F, Silva, M R, Schenkel, F S & Luiz F. Brito, and Victor B. Pedrosa. (2021, August). Genome-wide association study and pathway analysis for carcass fatness in Nellore cattle measured by ultrasound. Animal Genetics, 52(5), 730-733. doi:10.1111/age.13129 (Published)

23. Mallikarjunappa, S, Brito, L, Pant, S, Schenkel, F, Meade, K G & and Niel Karrow. (2021, August). Johne's disease in Dairy Cattle: An immunogenetic perspective. Frontiers in Veterinary Science, 8, 910. doi:10.3389/fvets.2021.718987 (Published)

24. Lynch*, C, Oliveira Jr, G, Schenkel, F & Baes, C. (2021, August). Effect of synchronized breeding on genetic evaluations of fertility traits in dairy cattle. Journal of Dairy Science. doi:10.3168/jds.2021-20495 (Published)

25. Makanjuola, B O, Maltecca, C, Miglior, F, Marras, G, Emhimad, A, Flavio S. Schenkel & Christine F. Baes. (2021, August). Identification of unique ROH regions with unfavorable effects on production and fertility traits in Canadian Holsteins. Genetics Selection Evolution, 53(68). doi:10.1186/s12711-021-00660-z (Published)

26. Martins, R, Machado, P C, Pinto, L F, Silva, M R, Schenkel, F S & Luiz F. Brito, Victor B. Pedrosa. (2021, May). 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, 138(3), 360-378. doi:10.1111/jbg.12525 (Published)

27. Oliveira Junior, G A, Schaffer, L R, Schenkel, F, Tiezzi, F & Baes, C F. (2021, April). Potential effects of hormonal synchronized breeding on genetic evaluations of fertility traits in dairy cattle: A simulation study. Journal of Dairy Science, 104(4), 4404-4412. doi:10.3168/jds.2020-18944 (Published)

28. Freitas, A d, Santana Junior, M L, Schenkel, F S, Zerlotti, M, Cyrillo, J d & Claudia Cristina Paro de Paz. (2021, April). Different selection practices affect the environmental sensitivity of beef cattle. PLoS ONE, 16(4), e0248186. doi:10.1371/journal.pone.0248186 (Published)

29. Dou, J, C&aacute, novas, A, Brito, L F, Yu, Y, Schenkel, F S & Yachun Wang. (2021, April). Comprehensive RNA-Seq Profiling Reveals Spatial and Temporal Changes in Gene Expression in Sprague–Dawley Rats as Response to Heat Stress Challenges. Frontiers in Genetics, 12, 651979. doi:10.3389/fgene.2021.651979 (Published)

30. Seymour, D, C&aacute, novas, A, Chud, T, Cant, J, Osborne, V & Christine Baes, Flavio Schenkel, Filippo Miglior. (2021, April). Associations between feed efficiency and aspects of lactation curves in primiparous Holstein dairy cattle. Journal of Dairy Science. doi:10.3168/jds.2020-20010 (Published)

31. Houlahan*, K, Schenkel, F S, Hailemariam, D, Lassen, J, Kargo, M & John B Cole, Erin E Connor, Silvia Wegmann, Gerson A Oliveira, Filippo Miglior, Allison Fleming, Tatiane C.S. Chud, Christine F Baes. (2021, April). Effects of incorporating dry matter intake and residual feed intake into a selection index for dairy cattle using deterministic modeling. Animals, 11, 1157. doi:10.3390/ani11041157 (Published)

32. Butty, A, Chud, T, Cardoso, D, Lopes, L, Miglior, F, Flavio Schenkel, Angela C&aacute, . . . ller, Paul Stothard, Francesca Malchiodi, Christine Baes. (2021, April). Genome-wide association study between copy number variants and hoof health traits in Holstein dairy cattle. Journal of Dairy Science, 104. doi:10.3168/jds.2020-19879 (Published)

33. Boareki*, M N, Brito, L F, C&aacute, novas, A, Osborne, V & Schenkel, F. (2021, March). Estimation of Genetic Parameters and Selection Response for Reproductive and Growth Traits in Rideau-Arcott sheep. Canadian Journal of Animal Science, 101(1), 134-142. doi:10.1139/CJAS-2019-0152 (Published)

34. Oliveira, H R, Miller, S P, Brito, L F & Schenkel, F S. (2021, March). 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 (Published)

35. Lam, S, Miglior, F, Fonseca, P A, Gomez-Redondo, I, Zeidan, J, Suarez-Vega, Aroa, . . . novas, Angela. (2021, February). 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 (Published)

36. Narayana*, S G, Schenkel, F, Miglior, F, Chud, T, Abdalla, E, Naqvi, Syed Ali, . . . Barkema, Herman. (2021, February). Genetic Analysis of Pathogen-Specific Intramammary Infections in Dairy Cows. Journal of Dairy Science, 104(2), 1982-1992. doi:10.3168/jds.2020-19062 (Published)

37. Freitas, P H, Wang, Y, Yan, P, Oliveira, H R, Schenkel, F S & Yi Zhang, Qing Xu, Luiz F. Brito. (2021, February). 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 (Published)

38. Dou, J, Hu, L, Khan, A, Kahn, M Z, Yu, Y & Yajing Wang, Flavio Schenkel and Yachun Wang. (2021, February). Genome-Wide Identification and Functional Prediction of Long Non-coding RNAs in Sprague-Dawley Rats during Heat Stress. BMC Genomics, 22, 122. doi:10.1186/s12864-021-07421-8 (Published)

39. Massender*, E, Brito, L F, C&aacute, novas, A, Baes, C, Kennedy, D & Flavio S. Schenkel. (2021, January). 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, 138, 91–107. doi:abs/10.1111/jbg.12484 (Published)

40. Richardson, C, Nguyen, T, Abdelsayed, M, Moate, P, Williams, R, Chud, Tatiane, . . . Pryce, Jennie. (2021, January). Genetic parameters for methane emission traits in Australian dairy cows. Journal of Dairy Science, 104(1), 539-549. doi:10.3168/jds.2020-18565 (Published)

41. Soares*, R A, Vargas, G, Muniz, M M, Soares, M A, C&aacute, novas, A & F. Schenkel, and E. J. Squires. (2021, January). 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 (Published)

42. Feitosa, F L, Pereira, A S, Mueller, L F, Fonseca, P A, Braza, C U, Sabrina Amorim, Rafael Espigolan, Marcos Antunes de Lemos, Lucia Galv&atilde & o de Albuquerque, Flavio Schramm Schenkel, Luiz Fernando Brito, Nedenia Bonvino Stafuzza, and Fernando Baldi. (2021, January). 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 (Published)

43. Soares*, R A, Vargas, G, Duffield, T, Schenkel, F & Squires, J E. (2021, January). Genome-wide association study and functional analyses of clinical and subclinical ketosis in Holstein cattle. Journal of Dairy Science. doi:10.3168/jds.2020-20101 (Published)

44. Shadpour, Saeed, Chud, Tatiane C. S, Hailemariam, Dagnachew, Plastow, Graham, Oliveira, Hinayah R., Stothard, Paul, . . . Schenkel, Flavio S. (2021). Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks. Journal of Dairy Science. (Submitted)

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: