Structural equation models to infer relationships between energy-related blood metabolites and milk daily energy output in Holstein cows

During lactation, high-yielding cows experience metabolic disturbances due to milk production. Metabolic monitoring offers valuable insights into how cows manage these challenges throughout the lactation period, making it a topic of considerable interest to breeders. In this study, we used Bayesian networks to uncover potential dependencies among various energy-related blood metabolites, i.e., glucose, urea, beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), cholesterol (CHOL), and daily milk energy output (dMEO) in 1,254 Holstein cows. The inferred causal structure was then incorporated into structural equation models (SEM) to estimate heritabilities and additive genetic correlations among these phenotypes using both pedigree and genotypes from a 100k chip. Dependencies among traits were determined using the Hill-Climbing algorithm, implemented with the posterior distribution of the residuals obtained from the standard multiple-trait model. These identified relationships were then used to construct the SEM, considering both direct and indirect relationships. The relevant dependencies and path coefficients obtained, expressed in units of measurement variation of 1σ, were as follows: dMEO → CHOL (0.181), dMEO → BHB (−0.149), dMEO → urea (0.038), glucose → BHB (−0.55), glucose → urea (−0.194), CHOL → urea (0.175), BHB → urea (−0.049), and NEFA → urea (−0.097). Heritabilities for traits of concern obtained with SEM ranged from 0.09 to 0.2. Genetic correlations with a minimum 95% probability (P) of the posterior mean being >0 for positive means or <0 for negative means include those between dMEO and glucose (−0.583, P = 100), dMEO and BHB (0.349, P = 99), glucose and CHOL (0.325, P = 100), glucose and NEFA (−0.388, P = 100), and NEFA and BHB (0.759, P = 100). The results of this analysis revealed the existence of recursive relationships among the energy-related blood metabolites and dMEO. Understanding these connections is paramount for establishing effective genetic selection strategies, enhancing production and animal welfare.