Garrod’s concept of “chemical individuality” has contributed to comprehension of molecular origins of human diseases. Untargeted high throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. Here we studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals. We identified 2,599 variant-metabolite associations (P<1.25x10-11) within 330 genomic regions, with rare variants (MAF≤1%) explaining 9.4% of associations. Jointly modelling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters (Genetically Influenced Metabotypes). We assigned causal genes for 62.4% of GIMs, providing new insights into fundamental metabolite physiology and their clinical relevance, including metabolite guided discovery of potential adverse drug effects (DPYD, SRD5A2). We show strong enrichment of Inborn Errors of Metabolism (IEM)-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of IEMs. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential aetiological relationships.