Inferring Causal Gene-Phenotype Networks Underlying Complex Traits using Multi-Omics Data

Association studies have been successful in identifying genomic regions associated with phenotypic traits in livestock species. However, the identification of the individual genes responsible for the phenotypic variation remains challenging. Additionally, these analyses do not provide in general any information about the molecular pathways underlying the phenotype under study. One way to unravel these molecular mechanisms is to expand the traits under analysis; one of such traits is gene expression. Indeed, the integration of phenotypic data with genotypic information and transcriptional profiling has the potential to uncover the genetic control of gene activity and phenotypic variation, as well as shed light on the manner and extent of connectedness among these variables. These connections have been explored mainly in terms of associations, i.e. connections among variables without causal interpretation. Knowledge regarding causal relationships among genes and phenotypes can be used to predict the behavior of complex systems, as well as to optimize management practices and selection strategies. Here, we describe a multistep procedure for inferring causal molecular networks underlying complex traits using multi-omics data. We initially explore marginal associations between genotypes and phenotypic and expression traits through the use of whole-genome scans, and then, in those regions where multiple significant hits co-localize, we attempt network reconstruction using causal structural learning algorithms. As a proof of principle of the significance of this integrative approach, we show the construction of causal molecular networks underlying carcass fat deposition and loin muscle weight integrating multi-omics data obtained from an F2 Duroc x Pietrain resource population.