Determination of phenotypic inter-relationships between parameters in predominantly milk and blood with physiological status, health, welfare, fertility, environmental footprint and production in dairy cows
Leaders: Frédéric Dehareng (Walloon Agricultural Research Centre) and Klaus Lønne Ingvartsen (Aarhus University)
Task 3.1. Experiments to generate and develop predictor phenotypes in research herds
(i) Data on putative milk biomarkers will be obtained from detailed experiments that enrol ~200 Holstein-Friesian cows from 6 partners and used to evaluate new phenotypes. Cows will be fed and managed to provoke physiological imbalance that is linked to increased risk of subclinical diseases and suboptimal production and reproduction.
(ii) Data on verified and well documented milk biomarkers will be obtained in the experiments above. Methane emissions will be measured in a subset of n=100 of these cows. Study design, feeding and management will be harmonized to allow cross experiment comparisons and allow conclusions on low frequency phenotypes.
Detailed data on feed composition and intake, diseases, disease treatments, milk production and reproduction will be obtained in both studies and traditional phenotypes for metabolic status, health status, fertility, production and gaseous emissions will be recorded.
Task 3.2. Relationship of milk biomarkers to specific phenotypes
The milk biomarkers will be related to production, reproduction, health, metabolic status and methane emissions. This task includes detailed statistical analyses to generate indices of physiological imbalances that can be used as predictors of subclinical and clinical diseases. All data will be entered into the database developed in WP2 to facilitate data storage and analyses. The relationships will be used in a general evaluation of the biomarkers for selection of appropriate phenotypes to be determined in the large GWAS study (WP4).
Task 3.3. Relationship of milk MIR spectra and glycan profiles to traditional phenotypes
After standardization of the MIR spectra and glycan profiles, they will related to production, reproduction, health, metabolic status and methane production records to explore potential relationships. We will develop and validate equations of prediction using statistical methodologies.
Task 3.4. Input to other WPs
This WP will provide key milk ‘predictor’ phenotypes (metabolites, enzymes, hormones, MIR spectra and glycan profiles) that can be used in WP4 for the proposed large scale GWAS study and in WP6 for development of management protocols. Tissue biopsies and blood samples will be provided for WP5.