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.

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