Suppr超能文献

研讨会综述:打造更优质奶牛——澳大利亚的经验与未来展望。

Symposium review: Building a better cow-The Australian experience and future perspectives.

机构信息

Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia.

Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia.

出版信息

J Dairy Sci. 2018 Apr;101(4):3702-3713. doi: 10.3168/jds.2017-13377. Epub 2018 Feb 14.

Abstract

Genomic selection has led to opportunities for developing new breeding values that rely on phenotypes in dedicated reference populations of genotyped cows. In Australia, it has been applied to 2 novel traits: feed efficiency, which was released in 2015 as feed saved breeding values, and heat tolerance genomic breeding values, released for the first time in 2017. Feed saved is already included in the national breeding objective, which is focused on profitability and designed to be in line with farmer preferences. Our future focus is on traits associated with animal health, either directly or in combination with predictor traits, such as mid-infrared spectral data and, into the future, automated data capture. Although it is common for many evaluated traits to have genomic reliabilities ranging between 60 and 75%, many new, genomic information-only traits are likely to have reliabilities of less than 50%. Pooling of phenotype data internationally and investing in maintenance of reference populations is one option to increase the reliability of these traits; the other is to apply improved genomic prediction methods. For example, advances in the use of sequence data, in addition to gene expression studies, can lead to improved persistence of genomic breeding values across breeds and generations and potentially lead to greater reliabilities. Lower genomic reliabilities of novel traits could reduce the overall index reliability. However, provided these traits contribute to the overall breeding objective (e.g., profit), they are worth including. Bull selection tools and personalized genetic trends are already available, but increased access to economic and automatic capture farm data may see even better use of data to improve farm management and selection decisions.

摘要

基因组选择为开发新的育种值提供了机会,这些新的育种值依赖于专门的基因分型奶牛参考群体中的表型。在澳大利亚,已经将其应用于两个新的性状:饲料效率,这是在 2015 年作为节省饲料的育种值发布的;耐热性基因组育种值,于 2017 年首次发布。节省饲料已包含在国家育种目标中,该目标专注于盈利能力,并旨在与农民的偏好保持一致。我们未来的重点是与动物健康直接相关的性状,或者与预测性状(如中红外光谱数据)结合使用,未来则是自动数据捕获。尽管许多评估性状的基因组可靠性通常在 60%至 75%之间,但许多新的、仅基于基因组信息的性状的可靠性可能低于 50%。国际上对表型数据进行汇集并投资于参考群体的维护是增加这些性状可靠性的一种选择;另一种选择是应用改进的基因组预测方法。例如,除了基因表达研究之外,序列数据的使用进展可以提高基因组育种值在不同品种和世代中的持久性,并有可能提高可靠性。新性状的基因组可靠性较低可能会降低总体指数可靠性。然而,只要这些性状有助于总体育种目标(例如利润),就值得包括在内。公牛选择工具和个性化遗传趋势已经可用,但增加对经济和自动捕获农场数据的访问可能会更好地利用数据来改善农场管理和选择决策。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验