Temperature x humidity independent genomic and phenotypic predictions of heat tolerance in dairy cows using innovative and integrative strategies including milk infrared spectral data

使用包括牛奶红外光谱数据在内的创新和综合策略，对奶牛的耐热性进行独立于温度 x 湿度的基因组和表型预测

• 批准号: 511669534
• 负责人: Professor Dr. Sven König
• 金额: --
• 依托单位: Lehrstuhl für Tierzucht und Haustiergenetik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/511669534?language=en
• 关键词: Temperature; humidity; independent; genomic; phenotypic

Heat stress (HS) is a major challenge for the dairy industry. Therefore, many studies evaluated the impact of HS, i.e., often authored by UGI and ULiège. Nevertheless, there is a serious bottleneck in the current approaches. They all rely on the knowledge of the reaction of a cow to the temperature humidity index (THI). This implies two things. First, that the challenge of a specific cow is really exposed to has to be precisely known. Second, that the observed reaction of the cow is really reflecting HS. There is one tool that is widely available and has proven its usefulness in the study of dozens of traits by the ULiège, UGI and CRA-W, i.e., the use of milk mid-infrared (MIR) spectral data in milk. The objectives are therefore i) to use innovative and integrative strategies to validate the HS status of preselected reference animals using THI based tools, ii) to calibrate their validated HS status against milk composition allowing its inference based on milk composition, iii) to extend the use of MIR data, only available monthly, to near infrared (NIR) spectra, a technology more transferable to farms, iv) to develop based on these advances THI independent phenotypic and genomic predictions of heat tolerance. In this regard, we created six connected work packages. WP 1 utilizes the comprehensive existing datasets from both partners UGI and ULiège including novel traits, cow genotypes and climate data in multi-trait approaches to identify heterogeneous herds with resilient and susceptible cows in response to THI. The selected herds and cows will be used in WP 2 for deeper phenotyping strategies including additionally MIR and NIR data. In the genomic WP 3, we consider an extreme sub-sample of cows from WP 2 for whole-genome sequencing. The sequenced cows will be used for GWAS and ongoing gene annotations, suggesting chromosome segments for studying gene expression profiles. WP 4 is an enhancement of multi-trait modeling approaches, considering the most relevant traits from WP1, the novel traits from WP 2 and the differentially expressed genes from WP 3, aiming on the identification of a clear THI-independent HS status of the cows allowing its inference based on milk composition. In WP 5, we focus on ongoing genetic and genomic association analyses for the overall THI-independent HS status, including the estimation of additive and non-additive (e.g., dominance) effects, as well as validations for the THI-independent HS status with THI-dependent criteria. All relevant elements generated in previous WPs will be integrated into genomic and phenotypic prediction equations in WP 6. The expected outcomes are both scientific and practical including novel phenotypes, annotated candidate genes, validated reference data, genetic parameters, early selection and early warning systems for HS. The across-country approach based on the merged datasets plus the expertise of UGI and ULiège regarding HS studies, is imperative to conduct such comprehensive analyses.

热应激（HS）是乳品行业面临的主要挑战。因此，许多研究评估了HS的影响，即，通常由UGI和ULiège撰写。然而，目前的办法存在严重的瓶颈。它们都依赖于奶牛对温度湿度指数（THI）的反应的知识。这意味着两件事。首先，必须准确了解特定奶牛真正面临的挑战。第二，观察到的奶牛反应确实反映了HS。有一个工具是广泛可用的，并已证明其有用的研究几十个性状的ULiège，UGI和CRA-W，即，利用牛奶中红外（MIR）光谱数据分析牛奶。因此，目标是i）使用创新和综合策略，使用基于THI的工具验证预选参考动物的HS状态，ii）根据乳汁成分校准其经验证的HS状态，允许其基于乳汁成分的推断，iii）将MIR数据（仅每月可用）的使用扩展到近红外（NIR）光谱，这是一种更适用于农场的技术，iv）基于这些进展开发THI独立的耐热性表型和基因组预测。为此，我们创建了六个相互关联的工作包。WP 1利用来自合作伙伴UGI和ULiège的全面现有数据集，包括多性状方法中的新性状，奶牛基因型和气候数据，以识别具有弹性和易感奶牛的异质牛群。选定的牛群和奶牛将用于WP 2，以进行更深入的表型分析策略，包括额外的MIR和NIR数据。在基因组WP 3中，我们考虑了来自WP 2的奶牛的极端子样本用于全基因组测序。测序的奶牛将用于GWAS和正在进行的基因注释，为研究基因表达谱提供染色体片段。WP 4是多性状建模方法的增强，考虑了WP 1中最相关的性状、WP 2中的新性状和WP 3中的差异表达基因，旨在鉴定奶牛的明确的THI独立的HS状态，从而允许基于牛奶成分进行推断。在WP 5中，我们专注于正在进行的遗传和基因组关联分析，用于整体THI独立的HS状态，包括估计加性和非加性（例如，优势）的影响，以及验证THI独立的HS状态与THI依赖的标准。在以前的WP中产生的所有相关元素将被整合到WP 6的基因组和表型预测方程中。预期的结果是科学和实用的，包括新的表型，注释的候选基因，验证的参考数据，遗传参数，早期选择和早期预警系统的HS。基于合并数据集的跨国方法加上UGI和ULiège关于HS研究的专业知识，对于进行这种全面分析至关重要。