Source: Beef Farmers of Ontario
This project seeks to improve the competitiveness of Canadian beef producers by significantly increasing selection efficiency combining the estimation of genomic breeding values with functional studies. A more complete understanding of the genes and regulatory pathways and networks involved in economically important traits such as feed efficiency and methane emissions (among others) in beef cattle will provide knowledge to help improve genetic selection and contribute to the suitability of cattle production.
Bioinformatics analysis of high throughput – OMICS data (i.e., transcriptomics, metagenomics, metabolomics, epigenetics (methylation sequencing) and gene networks amongst several others), will advance identification of functional genes and provide the tools to enable a systems biology approach to genetic improvement. These enhanced genomic tools will enable producers to select and manage cattle for improved feed efficiency and reduced methane emissions, while still maintaining the high productivity, health and fertility of these animals.
The data and information collected in this project will generate added value by leveraging existing data, collected in earlier studies, to generate more accurate GEPDs. In addition, the project links directly to the international initiative on the Functional Annotation of Animal Genomes (FAANG); a coordinated international action to accelerate Genome to Phenome (see web site for more detail: http://www.faang.org/index). All the RNA-Sequencing data generated in this project to study the transcriptome associated with feed efficiency in beef cattle will be made available to the international FAANG project.
The project will seek to develop tools to increase selection efficiency by adding functional genomic information to EBV (estimate breeding values) with functional genomics studies. Specifically, the project will:
- Examine the transcriptome, metagenome, metabolome and methylated genomic regions using high throughput technologies by collecting samples from extreme animals experimentally assessed for feed efficiency (LOW and HIGH).
- Combine the resulting experimental – OMICS data (i.e., transcriptomics (gene expression), metagenomics (sequencing of microbial populations), metabolomics (metabolic biomarkers), methylation (epigenetics, sequencing by bisufite) and gene networks) to focus on systems biology and bioinformatics approaches to identify metabolic pathways and genes (key regulator genes) affecting feed efficiency and methane emissions.
- Combine the new information with data (genotypes and phenotypes) from other ongoing projects estimating gEBVs (Genomic Estimated Breeding Values) in order to develop more robust approaches for genomic selection in industry breeding plans to improve feed efficiency and reduce methane emission in beef cattle.
Completion date: 2019