Main 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 analysis of milk spectral data to develop management and selection tools to improve the nutraceutical properties of milk (NSERC-CRD, awarded September 2013- March 2016, 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.
6. Genetic Improvement of Canadian Lamb Carcass Yield, Quality and Growth Traits (NSERC-CRD awarded in October 2015, 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.
Flavio's academic timeline
Please visit our Centre for Genetic Improvement of Livestock (CGIL) webpage: http://cgil.uoguelph.ca/