title: Mixed effects structural equation models and phenotypic causal networks.
abstract: 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.
authors.affiliation: Department of Animal Science, University of Wisconsin-Madison, Madison, WI, USA.
authors.forename: Bruno Dourado
authors.initials: BD
authors.lastname: Valente
authors.forename: Guilherme Jordão
authors.initials: GJ
authors.lastname: de Magalhães Rosa
mesh_terms: D000465:Algorithms; D000818:Animals; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D040641:Quantitative Trait Loci