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

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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.