Main 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. 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.
4. Computing hardware for big data editing, storage, and analysis, (NSERC- Research Tools and Instruments (RTI), Schenkel (Principal Investigator)).
This project supported the purchase of 3 high performance computer nodes, 1 storage server and a fabric network to integrate these new nodes and storage server to the existent computer nodes and storage in the Department of Animal Biosciences for interdisciplinary and collaborative research involving big data. Over the last ten years the direction of research in the department and around the world has moved towards precision agriculture related technologies, such as the different omics (e.g. genomics, proteomics, metabolomics), real time recording and monitoring, etc., which yield to big amounts of data to be processed, stored and analyzed.
5. Canada's ten thousand cow genomes project (AAFC- Dairy cluster II Grant, Schenkel (Principal Investigator))
The general objective of this 4-year 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.
6. 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 4-year 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.
7. 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 this 5-year 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.
8. 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.
9. Genetic analysis of milk spectral data to develop management and selection tools to improve the nutraceutical properties of milk (NSERC-CRD, Schenkel (Principal Investigator))
The overall objective of this project is to study directly the phenotypic and genotypic variability of the spectral data in order to improve cow robustness and nutritional quality of milk for human consumption. Specific objectives include reduction of the dimensionality of spectral data; study of spectral variability in relation with changes of animal health and reproduction status; and development of calibration equations for milk fatty acids for the Canadian system.
Flavio's academic timeline
Please visit our Centre for Genetic Improvement of Livestock (CGIL) webpage: http://cgil.uoguelph.ca/