Investigating potential causal relationships among carcass and meat quality traits using structural equation model in Nellore cattle.

The objective of this study was to investigate potential causal relationships among hot carcass weight (HCW), longissimus muscle area (LMA), backfat thickness (BF), Warner-Bratzler shear force (WBSF), and marbling score (MB) traits in Nellore cattle using structural equation models (SEM). The SEM fitted comprises the following links between traits: WBSF → LMA, WBSF → HCW, HCW → LMA, BF → HCW, and BF → MB, where the arrows indicate the causal direction between traits, with structural coefficients posterior means (posterior standard deviation) equal to -0.29 cm2/kg (0.09), 0.43 kg/kg (0.29), 0.10 cm2/kg (0.006), 1.92 kg/mm (0.28), and 0.03 score-grade/mm (0.006), respectively. The final SEM revealed some important putative causal relationships among the traits studied here. The implied causal effects suggest that interventions on meat tenderness and fat content would affect overall growth and muscle deposition. Knowledge regarding potential causal relationships inferred among the traits studied here can have important implications for the genetic selection and management of Nellore cattle for improvement of carcass and meat quality.

Mixed effects structural equation models and phenotypic causal networks.

Complex networks with causal relationships among variables are pervasive in biology. Their study, however, requires special modeling approaches. Structural equation models (SEM) allow the representation of causal mechanisms among phenotypic traits and inferring the magnitude of causal relationships. This information is important not only in understanding how variables relate to each other in a biological system, but also to predict how this system reacts under external interventions which are common in fields related to health and food production. Nevertheless, fitting a SEM requires defining a priori the causal structure among traits, which is the qualitative information that describes how traits are causally related to each other. Here, we present directions for the applications of SEM to investigate a system of phenotypic traits after searching for causal structures among them. The search may be performed under confounding effects exerted by genetic correlations.