P524
The Canadian Cattle Genome Project

Date: Monday, January 13, 2014
Room: Grand Exhibit Hall
Mary A. De Pauw , University of Alberta, Edmonton, AB, Canada
Paul Stothard , University of Alberta, Edmonton, AB, Canada
Stephen P Miller , CGIL, University of Guelph, Guelph, ON, Canada
Stephen Moore , University of Queensland, St. Lucia, Australia
Graham S Plastow , University of Alberta, Edmonton, AB, Canada
Flavio Schenkel , Centre for the Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
John Basarab , Lacombe Research Centre, Lacombe, AB, Canada
Ellen Goddard , University of Alberta, Edmonton, AB, Canada
Xiaoping Liao , University of Alberta, Edmonton, AB, Canada
John Crowley , University of Alberta, Edmonton, AB, Canada
Katarzyna Stachowicz , AbacusBio Limited, Dunedin, New Zealand
Honghao Li , CGIL, University of Guelph, Guelph, ON, Canada
Mehdi Sargolzaei , Semex Alliance, Saint-Hyacinthe, QC, Canada
Duc Lu , University of Alberta, Edmonton, AB, Canada
Preliminary data and early findings will be presented from research conducted through The Canadian Cattle Genome Project (CCGP).  The CCGP is a large ($8.2M) international research project. It involves scientists and data from Canada and across the world.  The goal of this project is to develop low cost Genome Wide Selection (GWS) methodologies for the Canadian cattle industry for efficient production of high quality beef.  To date, we have sequenced 289 animals at an average coverage of 8-10X with a final goal of sequencing 325 animals.  We have genotyped over 10,000 animals with a combination of 770K and 50K platforms.  This data has been used to develop accurate methodologies for imputing to high density and sequence.  The imputation team has developed an efficient imputation methodology coded in the FImpute program. Imputation from HD and 50K to sequence genotypes was tested using FImpute and Beagle.  FImpute was more accurate than BEAGLE and required less memory and CPU time.  The project has also collated a large dataset of animals with feed efficiency and carcass quality data, all with 50K genotypes.  This dataset will be used to develop Genomic Estimated Breeding Values (GEBVs) for Residual Feed Intake (RFI), to validate RFI GEBVs using observed RFI data and to quantify the impact of RFI GEBVs on carcass/meat quality. Finally, the project is conducting research to define the social and economic benefits and costs of using genomic technology in livestock improvement and to better understand the barriers to adoption.
Handouts
  • Genome Canada poster PAG 2014 FINAL.pdf (56.1 MB)