Joint modeling and analysis of phenotypic, genotypic and transcriptomic data have 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. Current studies mainly report associations; that is, undirected 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 performed a multistep procedure for inferring causal networks underlying carcass fat deposition and muscularity in pigs using multi-omics data obtained from an F₂ Duroc × Pietrain resource pig population. The data set included several carcass and meat quality phenotypes, genotypic information spanning the whole swine genome, and gene expression data from loin muscle for a total of 171 F₂ individuals. We initially explored marginal associations between genotypes and phenotypic and expression traits through whole-genome scans, and then, in genomic regions with multiple significant hits, we assessed gene-phenotype network reconstruction using causal structural learning algorithms. One genomic region in SSC6 showed significant associations with 3 relevant phenotypes, midline 10th-rib backfat thickness, loin muscle weight, and average intramuscular fat percentage, and also with the expression of 7 genes, including ZNF24, SSX2IP, and AKR7A2. The inferred network indicated that the genotype affects the 3 phenotypes mainly through the expression of several genes. Among the phenotypes, fat deposition traits negatively affected loin muscle weight. Overall, our findings shed light on the antagonist relationship between carcass fat deposition and meat lean content in pigs. In addition, the procedure described in this study has the potential to unravel gene-phenotype networks underlying complex phenotypes.