Award details

14-ATC2. Using genomic technologies to reduce mastitis in meat sheep

Reference	BB/M018377/1
Principal Investigator / Supervisor	Dr Joanne Conington
Co-Investigators / Co-Supervisors
Institution	SRUC
Department	Research
Funding type	Research
Value (£)	132,171
Status	Completed
Type	Research Grant
Start date	01/04/2015
End date	30/09/2016
Duration	18 months

Abstract

This project aims to address the sustainable intensification of sheep meat through the exploration of genomic selection for disease resistance. With pure- and crossbred Texel sheep, new high (700K ) and existing (50K and 1K) density genome screening ('SNP') technology and bioinformatic procedures will be used to, i) identify SNPS that are significantly related to mastitis, and ii) for new imputation analyses that will underpin future R&D and exploitation of use with lower density (6-9K), more affordable SNP technology in the future. The project will build on two previous 'Spark Award' projects by the Texel Society on mastitis, to establish which different phenotyping strategies are the best predictors of clinical and subclinical mastitis and importantly, which are most likely to be routinely undertaken by farmers to include as part of their wider breeding goals. These include a combination of farmer-driven phenotyping protocols for recording, conformation scoring and lab-based procedures including the California Mastitis Testing (CMT) procedure that can be undertaken without the need for laboratory testing. As part of this exploratory research, the first genomic predictions of breeding value (GEBVs) for the different definitions of mastitis will be undertaken and delivered to the participating breeders with a view to them being routinely undertaken in tandem with other breeding goal traits that are collected as part of Signet's Sheepbreeder breed improvement plan.

Summary

Impact Summary

The future sustainability of the UK livestock industry relies on farmers being able to produce a marketable product while meeting societal requirements for animal welfare and environmental sustainability. As it is clear that failure to address known genetic antagonisms between some aspects of livestock health and productivity result in deterioration in animal welfare, this project offers a unique and innovative approach to prepare the UK sheep industry to address the challenges it will face. The partners in this project have previous experience in defining animal health and welfare traits and incorporating them into breeding tools for the industry. For example SRUC developed lambing ease, lamb vigour scoring, footrot scoring and recently lamb survival breeding goals designed for industry breeding programmes. A key to this success has been an understanding of the basic biology of the animal, including behaviour and health traits, to produce a balanced approach to animal breeding. Sheep breed improvement in the UK is worth over £17m and if dissemination of genetic material is extended to the entire ram breeding industry these benefits would extend to over £110.8M. Now with 1.9M pure- and cross-bred Texel ewes in the UK (2012 census), the annual economic cost of mastitis is in the region of £16M. We anticipate that after 5 years of selection and implementing the data collection and analysis procedures developed as part of this project, average prevalence levels will be reduced by 10%, (i.e from 12% to 10.8%), and the annual economic benefit is estimated to be around £1.6M. A formal dissemination strategy will be developed as part of the research process and deliverables that are accessible to specialists and non-specialists will be an integral part of the project milestones. This will include the development of display tools and information sheets for participation in key industry dissemination events, such as Sheep2016, NorthSheep, ScotSheep, the Royal Highland Show and for public science events, including Knowledge Scotland, Edinburgh Science Festival and school events. Information will also be available to policy makers from Defra, The Scottish Government and the Animal Health and Welfare Committees in Wales and England, amongst others. Given the inter-disciplinary nature of this project, the impacts of the proposed work will go beyond the direct academic beneficiaries, having positive economic and societal impacts across a range of stakeholders. The topic is likely to generate interest both within and outside of the farming community. The work will be publicised through presentations to the media and through the use of social media to communicate key project outcomes. The project will put in place key logistical knowledge for the implementation of large-scale livestock health screening processes. Having methods to rapidly capture mastitis indicators is critical to the implementation of SNP technology in large-scale breeding programmes, which some sectors of the UK sheep breeding industry are currently working towards. Working together with developers of new SNP technology, the useful SNP information from the data analyses will be made available for inclusion onto lower density (5-9K) arrays that are already becoming the unit of currency in SNP array terms, for use in small ruminant breeding schemes. We will work closely with key players in the international sheep genome consortium, to ensure that our findings will be included in the design of the low density SNP arrays in the future, and we will publish the significant SNPs as part of this process. The UK overall will benefit through the linkages between the research innovation represented in the partners and those delivering knowledge and tools to the industry (e.g. Veterinary surveillance services). The outcomes will result in a shorter timelag from primary research through to on-farm implementation helping UK agriculture reap the benefits of this project.

Committee	Not funded via Committee
Research Topics	Animal Health, Animal Welfare
Research Priority	X – Research Priority information not available
Research Initiative	Agri-Tech Catalyst (ATC) [2013-2015]
Funding Scheme	X – not Funded via a specific Funding Scheme

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