Exploiting the mediating role of the metabolome to unravel transcript-to-phenotype associations

Despite the success of genome-wide association studies (GWASs) in identifying genetic variants associated with complex traits, understanding the mechanisms behind these statistical associations remains challenging. Several methods that integrate methylation, gene expression, and protein quantitative trait loci (QTLs) with GWAS data to determine their causal role in the path from genotype to phenotype have been proposed. Here, we developed and applied a multi-omics Mendelian randomization (MR) framework to study how metabolites mediate the effect of gene expression on complex traits. We identified 206 transcript-metabolite-trait causal triplets for 28 medically relevant phenotypes. Sixty-seven of these associations were missed by classical transcriptome-wide MR, which only uses gene expression and GWAS data. Among these, we identify biologically relevant pathways, such as betweenANKHand calcium levels mediated by citrate andSLC6A12and serum creatinine through modulation of the levels of the renal osmolyte betaine. We show that the signals missed by transcriptome-wide MR are found thanks to the gain in power allowed by integrating multiple omics-layer. Simulation analyses show that with larger molecular QTL studies and in case of mediated effects, our multi-omics MR framework outperforms classical MR approaches designed to detect causal relationships between single molecular traits and complex phenotypes.