Developing a Customizable Whole Beef Cattle Herd Genetic Selection Tool
Project Title
Development and Demonstration of a Genomics-Enhanced Whole Herd Genetic Management Platform to improve Beef Production Efficiency and Quality
Researchers
Changxi Li, AAFC [email protected]
John Basarab (project co-lead), University of Alberta Graham Plastow, University of Alberta Paul Stothard, University of Alberta Carolyn Fitzsimmons, AAFC Arun Kommadath, AAFC Mohammad Khakbazan, AAFC Ghader Manafiazar, Dalhousie University Matthew Spangler, University of Nebraska-Lincoln, USA
| Status | Project Code |
|---|---|
| Completed August, 2024 | FDE.05.20 |
Background
Constant improvement on beef production efficiency and quality is essential to reducing production costs, increasing profitability, and thus enhancing competitiveness of the beef industry. One way to do this is through genetic selection. However, the commercial beef industry in Canada is varied by location and management type, meaning there is no one size fits all selection goal to maximize on-farm success. Additionally, there are also relatively few tools for commercial beef producers to use to enhance genetic selection meaning there is a need for tools that allow specific customizations to reflect the regional realities and goals of individual operations.
Objectives
- To further develop a web-based genomics-enhanced whole herd genetic management platform for producers to access customizable genetic data and selection tools
What they Did
Genetic tools were developed and refined to predict genetic merit (EPDs) on growth, feed intake, feed efficiency, carcass merit, and female fertility traits using genetic samples from 11, 292 animals collected from previous research. These predictions were based solely on DNA marker genotypes, achieving moderate to moderately high accuracy on commercial beef cattle. They were also able to use the tools to reliably predict the breed composition, hybrid vigor, multiple trait selection indexes, and genetic potential of candidate bulls producing hybrid vigor in the herd. Finally, the team built a web-based genomics-enhanced whole herd genetic management platform has been created to integrate producer-submitted animal information with the genomic analysis pipeline.
What They Learned
- To ensure the genomic predications of any tool are reliable and can have industry application, the size of the reference populations and quality of both physical expression and genetics data are essential. Although the prediction platform developed through this project provides a useful tool for Canadian beef industry to improve herd genetics, there is still a need for researchers and genetic evaluation organizations to continuously collect and update the data to improve accuracy and reliability
- Advanced genomic prediction methods including multi-trait Genomic Best Linear Unbiased Prediction (GBLUP) and machine learning have the potential to improve genomic prediction for some traits. However, in consideration of feasibility and consistency across traits, along with computational requirements, the single trait GBLUP remains an appropriate genomic prediction method in beef cattle at this time
- Having intuitive tools that can guide producer decisions specifically regarding genetics management also has the potential to introduce and show value of other genomic tools to producers
- The team was also able to validate the economic benefits of the platform
What This Means
The platform created through this project allows beef producers to access the genomic tools easily and has been utilized by 88 producers over the course of this project accounting for over 10,000 cattle. The economic benefits of implementing these genomic tools have been validated by collaborative projects using independent industry beef cattle data, demonstrating that the platform enables commercial beef producers to select and breed beef cattle with their available genetic information to improve production efficiency and quality.