Multi-omics analysis

Multi-omic analysis to differentiate between negative and positive energy balance in lactating Holstein cows.

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Abstract

Negative energy balance (NEB) in the transition period, a consequence of inadequate feed intake and increased energy demands for lactation, results in an elevated risk of metabolic diseases (eg ketosis) and reduced reproductive performance in dairy cows. However, it is not feasible to generate a large training population where NEB is measured directly. Therefore, this study examines multi-omic measurements as traits correlated with NEB and as means to identify genomic variants associated with NEB. We contrasted multi-omic measurements taken in early lactation (0-14 days in milk) when cows were in NEB with measurements taken in mid lactation (70-80 days in milk) when energy balance was neutral or positive (PEB). Blood and milk was collected from 377 cows of mixed parity at both time points. Serum samples from all animals, at both time points, were assayed for 13 biomarkers previously associated with animal health, as well as, metabolites. Serum and milk samples were assayed for lipids. Whole blood was used to measure gene expression in 327 samples from time point 1 (NEB) and 296 samples from time point 2 (PEB). All animals had imputed whole genome sequence genotypes. Data analysis focused on differential analysis and genome wide association studies (GWAS). Differential analyses were undertaken for biomarker, gene, metabolite and lipid counts contrasting NEB and PEB, identifying phenotypes significantly differentially abundant (DA). The model fitted parity, age, cohort, haemolysis and formation of fibrin clot in blood samples as fixed effects. GWAS were undertaken for each molecular phenotype in three ways; using phenotypes from all animals at 1) time point 1 (NEB) and 2) time point 2 (PEB), 3) using the difference between time point 1 and 2 for each animal (change, CH). Linear mixed models were used to perform GWAS fitting the same fixed effects. Health biomarker results indicate that the selected time points corresponded to NEB and PEB. All omics datasets were able to differentiate samples in NEB and PEB. Over 22,000 molecular phenotypes were analysed with more than 11,000 showing significant differences between NEB and PEB. Among all NEB, PEB and CH phenotypes 32,675 had heritability estimates >0.1 and 5,237 associated with genetic variants. Serum and milk lipids showed moderate predictability for subclincal ketosis. Of particular interest were phenotypes that were both DA but also associated with variation in the genome. Cholesterol, 1,012 genes, 591 metabolites, 188 serum and 83 milk lipids were DA and had a significant QTL. Future work will assess if genetic variants identified here improve genomic predictions for health and fertility.

Keywords: 2026

How to Cite:

Chamberlain, A., Tahir, M., Liu, Z., Reich, C., Hemsworth, J., Thakur, P., Mason, B., Nguyen, T., MacLeod, I., Marett, L., Berkhart, M., Rochfort, S. & Goddard, M., (2026) “Multi-omic analysis to differentiate between negative and positive energy balance in lactating Holstein cows.”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2286151. doi: https://doi.org/10.31274/wcgalp.23862

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