We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified:
2,599 variant-metabolite associations (p < 1.25 × 10−11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations.
Figure 3: variance explained, MAF versus effect size and functional annotation.a, The percentage of phenotypic variance of each metabolite explained by conditionally independent associations. The variance explained is partitioned into that explained by variants within each MAF bin, and indicated by color: rare (purple), low-frequency (pink) and common (orange). 3 groups of metabolites are defined, with rare, low-frequency or common variants explaining the greatest percentage of phenotypic variance of the metabolite. The 5 metabolites with the greatest percentage of phenotypic variance explained by rare, low-frequency or common variants are listed, with the total percentage of variance explained by all variants in that MAF bin shown in parentheses. b, The phenotypic variance of each metabolite explained by variants within each MAF bin as a percentage of the variance explained by all conditionally independent associations. c, MAF versus association effect size for conditionally independent associations, with variants colored by functional annotation class as indicated in d. d, A bar plot of the frequency of variants in each functional class.
Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called ‘genetically influenced metabotypes’. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism.
