Integrating expression profiling and whole-genome association for dissection of fat traits in a porcine model

Traits related to fatness, important as economic factors in pork production, are associated with serious diseases in humans. Genetical genomics is a useful approach for studying the effects of genetic variation at the molecular level in biological systems. Here we applied a whole-genome association analysis to hepatic gene expression traits, focusing on transcripts with expression levels that correlated with fatness traits in a porcine model. A total of 150 crossbred pigs [Pietrain × (German Large White × German Landrace)] were studied for transcript levels in the liver. The 24K Affymetrix expression microarrays and 60K Illumina single nucleotide polymorphism (SNP) chips were used for genotyping. A total of 663 genes, whose expression significantly correlated with the trait "fat area," were analyzed for enrichment of functional annotation groups as defined in the Ingenuity Pathways Knowledge Base (IPKB). Genes involved in metabolism of various macromolecules and nutrients as well as functions related to dynamic cellular processes correlated with fatness traits. Regions affecting the transcription levels of these genes were mapped and revealed 4,727 expression quantitative trait loci (eQTL) at P < 10⁻⁵, including 448 cis-eQTL. In this study, genome-wide association analysis of trait-correlated expression was successfully used in a porcine model to display molecular networks and list genes relevant to fatness traits.