Investigating functional relationships among health and fertility traits in dairy cows

The association between health and pregnancy per artificial insemination, maintenance of pregnancy, and interval to pregnancy in dairy cattle is well documented. However, the understanding of the causal relationships among health and fertility traits is limited. Although information on heritabilities and genetic correlations between traits is key for the development of multiple-trait selection strategies, only knowledge about functional and causal relationships among them allow the prediction of the consequences of external interventions such as management decisions. Thus, this research was carried out to investigate potential causal relationships among multiple health events (retained fetal membranes [RFM], metritis [MET], clinical endometritis [CE], lameness [LS]) and fertility indicators including resumption of ovarian cyclicity (CY) by 50 days in milk and pregnancy on day 60 (P60) after the first artificial insemination in Holstein cows. Mixed-effects structural equation models (SEM) were fitted conditionally on a causal structure among traits, represented by a directed acyclic graph, inferred from the data using the inductive causation (IC) algorithm. The posterior mean and standard deviation of heritability for RFM, MET, CE, LS, CY and P60 were 0.20 ± 0.07, 0.09 ± 0.02, 0.10 ± 0.04, 0.35 ± 0.04, 0.15 ± 0.03, and 0.10 ± 0.03, respectively. The moderate estimates of heritability found for CY and P60 suggest that selective breeding could be effective for the genetic improvement of these traits. The estimates of genetic correlation among traits ranged from -0.82 ± 0.07 (MET and CY) to 0.70 ± 0.13 (RFM and MET). Biological considerations and results from the inductive causation algorithm analysis retrieved six potential causal connections between traits: RFM→MET; RFM→CE; LS→CE; CE→P60; CY→P60; and CY→CE. In addition to the data-driven network recovered, some additional connections or variations on their directions were also considered on the SEM analyses based on biological and chronological knowledge. Overall, the SEM results were very similar to those obtained from the traditional multi-trait mixed model analysis. The advantage of the SEM analysis is that it sheds some light onto the potential causal relationships between traits, which can aid better decisions to manage events occurring during the postpartum of dairy cattle.