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

热应激(HS)是乳制品行业面临的一大挑战。因此，许多研究评估了HS的影响，即通常由UGI和Uliège撰写。然而，目前的方法存在严重的瓶颈。它们都依赖于奶牛对温湿度指数(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面临的挑战。其次，观察到的奶牛的反应确实反映了HS。有一种广泛可用的工具，已经证明它在研究Uliège、UGI和CRA-W的数十个特征方面是有用的，即使用牛奶的中红外(MIR)光谱数据。因此，其目标是：i)采用创新的综合战略，利用基于THI的工具验证预先选择的参考动物的HS状态；ii)对照牛奶成分校正其验证的HS状态，从而使其能够根据牛奶成分进行推断；iii)将仅每月提供的MIR数据的使用扩展到近红外(NIR)光谱，近红外光谱是一种更可向牧场转让的技术；iv)基于这些进展，开发关于耐热性的独立表型和基因组预测。在这方面，我们创建了六个相互关联的工作包。WP 1利用来自UGI和Uliège两个合作伙伴的全面现有数据集，包括多性状方法中的新性状、奶牛基因和气候数据，以识别具有弹性和易感奶牛的不同牛群，以应对THI。选定的牛群和奶牛将在WP 2中用于更深层次的表型策略，包括额外的MIR和NIR数据。在基因组WP3中，我们考虑了来自WP2的一个极端的奶牛亚样本进行全基因组测序。测序的奶牛将用于GWAs和正在进行的基因注释，建议染色体片段用于研究基因表达谱。WP4是对多性状建模方法的改进，考虑了WP1中最相关的性状、WP2中的新性状和WP3中差异表达的基因，目的是识别奶牛明确的非THI依赖HS状态，以便根据牛奶成分进行推断。在WP 5中，我们集中于正在进行的对Thi非依赖HS状态的遗传和基因组关联分析，包括对加性和非加性(例如显性)效应的估计，以及使用Thi依赖标准对Thi非依赖HS状态的验证。以前的WPS中产生的所有相关元素都将被整合到WP6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HS的早期预警系统。以合并后的数据集为基础的全国性方法，加上UGI和Uliège在HS研究方面的专门知识，是进行这种全面分析的当务之急。

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.