In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10−10) 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.

(b) Three-dimensional Manhattan plot displaying chromosomal positions (x axis) of significant associations (P < 4.9 × 10−10, accounting for multiple testing, z axis) across all metabolites (y axis). Colors indicate metabolite groups. P values were obtained from a meta-analysis of genome-wide summary statistics from linear regression models using genetic variants as exposures and metabolite levels as outcomes run within each contributing study. (c) Top view of the three-dimensional Manhattan plot. Dots indicate significantly associated loci. Colors indicate whether the metabolite–locus associations are new. Loci with indication for pleiotropy are annotated.

Data access

Interactive tables with mGWAS summary statistics for individual SNPs, genes, or genetic regions. Results can be sorted, filtered, and exported.