A large-scale multivariate pQTL study sheds light on the genetic architecture of obesity

The genetic contribution to obesity has been widely studied, yet the functional mechanisms underlying metabolic states remain elusive. This has prompted analysis of endophenotypes via quantitative trait locus studies, which assess how genetic variants affect intermediate gene (eQTL) or protein (pQTL) expression phenotypes. To leverage shared regulatory patterns, we present the first integrative multivariate pQTL analysis, performed with our scalable Bayesian framework LOCUS on plasma mass-spectrometry and aptamer-based proteomic datasets. We identify 136 pQTL associations in the Ottawa obesity clinical practice, of which > 80% replicate in the DiOGenes obesity cohort and show significant functional enrichments; 16% of the validated hits would be missed by standard univariate methods. By also exploiting extensive clinical data, our methods and results reveal the implication of proteins under genetic control in low-grade inflammation, insulin resistance, and dyslipidemia, thereby opening new perspectives for diagnosing and treating metabolic disorders. All results are freely accessible online from our searchable database.