A cross-platform approach identifies genetic regulators of human metabolism and health

Abstract

In cross-platform analyses of 174 metabolites, we identify 499 associations ( P \u2009<\u20094.9\u2009×\u200910 −10 ) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at GLP2R (p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights. A large-scale genome-wide meta-analysis conducted across different platforms identifies genetic loci regulating levels of circulating metabolites.