High-throughput GWAS for more than 250,000 metabolomic features provides novel insights on the genetic mechanisms influencing pig metabolism

Over the past few years, metabolomics has established itself as one of the most promising approaches for high-throughput phenotyping. By measuring all molecules contributing to the metabolism of an organism (i.e., the metabolome), its molecular phenome can be integrated with a large number of molecular phenotypes, many of which serve as proxies for complex end phenotypes. The metabolome is a highly interconnected entity, with metabolites influenced by the genetic background, their interaction with other metabolites as substrates and products of enzymatic reactions, and environmental factors. Through the analysis of the relationship between metabolites, i.e., metabolite ratios, we can determine novel phenotypes that extend the molecular phenome and allow for the emergence of genetic associations that are not evident when considering single metabolites. In this study we analyzed the genomic and blood metabolomic profile of approximately 700 Italian Large White pigs. We obtained 722 plasma metabolite levels using an untargeted metabolomic platform from Metabolon. All pigs were genotyped with a high-density SNP chip panel. We used GEMMA for metabolite genome-wide association studies (mGWAS) on both individual metabolite levels and over 250,000 ratios reflecting the relationship between metabolites. Single metabolite mGWAS revealed several metabolite QTL (mQTL) regions linking 236 metabolites. These regions included genes encoding enzymes, transporters and regulators directly involved with the corresponding metabolites. Using ratios between metabolites in the mGWAS, we identified mQTL for other 120 metabolites. These results demonstrate the potential of this approach in providing a more comprehensive view of the molecular phenome by considering the relationship between its components, resulting in a ~350 fold increase in screened phenotypes, many of which can serve as proxy markers for complex traits. Acknowledgments: This study has received funding from the European Union’s Horizon Europe research and innovation program under grant agreement No. 01059609 (Re-Livestock project).