Integrating genomic information in metabolomic networks to dissect molecular phenotypes in pigs

The complexity of an organism arises from the interplay of different molecular layers (e.g. the genome, transcriptome, proteome, and metabolome) that generate biological processes and pathways that, in turn, define the animal phenome. In this study, we combined metabolomics and genomics to explore and gain knowledge on the interplay between these two layers using systems biology approaches. For these aims, we used both genome (~60,000 SNPs) and plasma metabolome information (~1,000 metabolites obtained from targeted and untargeted platforms) from ~1,000 Italian Large White and Italian Duroc pigs. Metabolomics data were used to reconstruct pathways through two network generation approaches, i.e. correlation networks and Gaussian graphical models. Then, genome-wide association studies were carried out to identify genetic loci influencing metabolites (mQTL). A first comprehensive catalog of mQTL was obtained, including a few hundreds of putative causative genes. These loci were subsequentially studied and used to inform metabolite networks. The inclusion of mQTL in the metabolic pathways improved the estimation and generation of simple correlation networks and revealed relationships between known and unknown metabolic features. These results provided a first picture of genetic factors and metabolic interactions affecting the pig metabolism. Acknowledgements: This study has received funding from the European Union’s Horizon Europe research and innovation programme under the grant agreement No. 01059609 (Re-Livestock project).