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

14-ATC2。

基本信息

批准号：
BB/M018377/1
负责人：
Joanne Conington
金额：
$ 16.84万
依托单位：
Scotland's Rural College
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FM018377%2F1
关键词：
14 ATC2.Using genomic technologies

项目摘要

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.

该项目旨在通过探索抗病基因组选择来解决羊肉的可持续集约化问题。对于纯种和杂交的Texel绵羊，新的高（700 K）和现有（50 K和1 K）密度基因组筛选（SNP）技术和生物信息学程序将用于：i）鉴定与乳腺炎显著相关的SNP，和ii）用于新的插补分析，其将支持未来的研发和开发使用较低密度（6- 9 K）、未来更可负担的SNP技术。该项目将建立在特塞尔协会之前关于乳腺炎的两个“火花奖”项目的基础上，以确定哪些不同的表型策略是临床和亚临床乳腺炎的最佳预测因子，重要的是，哪些策略最有可能被农民常规采用，包括作为他们更广泛的育种目标的一部分。这些包括农民驱动的表型分析方案的组合，用于记录、构象评分和基于实验室的程序，包括加州乳腺炎检测（CMT）程序，该程序可以在不需要实验室检测的情况下进行。作为这项探索性研究的一部分，将对乳腺炎的不同定义进行育种价值（GEBV）的第一次基因组预测，并将其交付给参与的育种者，以便与作为Signet的绵羊育种改良计划的一部分收集的其他育种目标性状一起进行常规预测。