Developing Ovine Immune 'Omics for sheep genomic improvement
Lead Research Organisation:
University of Edinburgh
Department Name: The Roslin Institute
Abstract
The aim of the Project is to use an 'omics approach to identify genetic variants that can potentially be used in the development of a next generation of productive sheep breeds or crossbreeds resilient to the on-going effects of changing disease pressures.
A major constraint on sheep production worldwide is the level of biological inefficiency associated with highly prevalent infectious diseases. This Project will focus on understanding how genetic variation is driving immunity to infectious disease and exploiting the derived information in a breeding programme to mitigate future disease risk.
We will prioritise variants, underlying immunity, for genomic selection in sheep using gene expression information. A key component of this approach is to build a model to impute gene expression levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue or cell type (Hu et al., 2019).
As such, the Project has four (4) main objectives: i) collection of blood samples from sixty (60) sheep, Ficoll separation and MACS cell sorting of immune cell types (e.g. monocytes, B-Cells and T-Cells (if these samples cannot be collected we will use white blood cell populations isolated from whole blood); ii) RNA-extraction from one cell type (the remainder of the cell types will be banked as a valuable resource), qPCR and mRNA-sequencing; iii) identification of functional variants based on analysis of the RNA-Seq data with genotype data from the same animals; and iv) ranking and prioritisation of the functional variants in genomic selection algorithms.
Extrapolating from human studies with sixty (60) animals we expect over one thousand eight hundred (1800) genes to have a heritability p value < 0.01 and a suitable prediction model for imputation. By co-localising expression information with loci identified in GWAS for immune-mediated traits in sheep (e.g. Zhang et al. 2018), we will identify functional immune variants that can be prioritised in genomic selection algorithms.
The strategic goal of this Project is to help the sheep industry become ready for a global trend towards using genomic selection to design efficient breeds/breeding programmes. Our Project will contribute to the competitiveness of the sheep sector in response to the on-going and rapid effects of disease pressures. This information will effectively help to 'future-proof' the sheep sector against potential challenges from pathogens and provide a 'proof of concept' for other potential pressures the industry may face going forwards.
References
Hu, Y et al. (2019) A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics 51(3), 568-576.
Zhang, Z et al. (2018) Exploring the Genetic Correlation Between Growth and Immunity Based on Summary Statistics of Genome-Wide Association Studies. Frontiers in Genetics - Livestock Genomics, 9, 393.
Clark, EL et al. (2017) A high-resolution atlas of gene expression in the domestic sheep (Ovis aries). PLoS Genetics, 13(9):e1006997.
A major constraint on sheep production worldwide is the level of biological inefficiency associated with highly prevalent infectious diseases. This Project will focus on understanding how genetic variation is driving immunity to infectious disease and exploiting the derived information in a breeding programme to mitigate future disease risk.
We will prioritise variants, underlying immunity, for genomic selection in sheep using gene expression information. A key component of this approach is to build a model to impute gene expression levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue or cell type (Hu et al., 2019).
As such, the Project has four (4) main objectives: i) collection of blood samples from sixty (60) sheep, Ficoll separation and MACS cell sorting of immune cell types (e.g. monocytes, B-Cells and T-Cells (if these samples cannot be collected we will use white blood cell populations isolated from whole blood); ii) RNA-extraction from one cell type (the remainder of the cell types will be banked as a valuable resource), qPCR and mRNA-sequencing; iii) identification of functional variants based on analysis of the RNA-Seq data with genotype data from the same animals; and iv) ranking and prioritisation of the functional variants in genomic selection algorithms.
Extrapolating from human studies with sixty (60) animals we expect over one thousand eight hundred (1800) genes to have a heritability p value < 0.01 and a suitable prediction model for imputation. By co-localising expression information with loci identified in GWAS for immune-mediated traits in sheep (e.g. Zhang et al. 2018), we will identify functional immune variants that can be prioritised in genomic selection algorithms.
The strategic goal of this Project is to help the sheep industry become ready for a global trend towards using genomic selection to design efficient breeds/breeding programmes. Our Project will contribute to the competitiveness of the sheep sector in response to the on-going and rapid effects of disease pressures. This information will effectively help to 'future-proof' the sheep sector against potential challenges from pathogens and provide a 'proof of concept' for other potential pressures the industry may face going forwards.
References
Hu, Y et al. (2019) A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics 51(3), 568-576.
Zhang, Z et al. (2018) Exploring the Genetic Correlation Between Growth and Immunity Based on Summary Statistics of Genome-Wide Association Studies. Frontiers in Genetics - Livestock Genomics, 9, 393.
Clark, EL et al. (2017) A high-resolution atlas of gene expression in the domestic sheep (Ovis aries). PLoS Genetics, 13(9):e1006997.
People |
ORCID iD |
| Emily Clark (Primary Supervisor) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| BB/T00875X/1 | 01/10/2020 | 30/09/2028 | |||
| 2441480 | Studentship | BB/T00875X/1 | 01/10/2020 | 30/09/2024 |