Rare and common genetic determinants of metabolic individuality and their effects on human health

Praveen Surendran, Isobel D Stewart, Victoria P W Au Yeung, Maik Pietzner, Johannes Raffler, Maria A Wörheide, Chen Li, Rebecca F Smith, Laura B L Wittemans, Lorenzo Bomba, Cristina Menni, Jonas Zierer, Niccolò Rossi, Patricia A Sheridan, Nicholas A Watkins, Massimo Mangino, Pirro G Hysi, Emanuele Di Angelantonio, Mario Falchi, Tim D SpectorNicole Soranzo, Gregory A Michelotti, Wiebke Arlt, Luca A Lotta, Spiros Denaxas, Harry Hemingway, Eric R Gamazon, Joanna M M Howson, Angela M Wood, John Danesh, Nicholas J Wareham, Gabi Kastenmüller, Eric B Fauman, Karsten Suhre, Adam S Butterworth, Claudia Langenberg

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. 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. 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. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.

Original languageEnglish
Pages (from-to)2321-2332
Number of pages12
JournalNature Medicine
Volume28
Issue number11
DOIs
Publication statusPublished - Nov 2022

Keywords

  • Humans
  • Metabolome/genetics
  • Metabolomics
  • Plasma/metabolism
  • Phenotype
  • Metabolism, Inborn Errors/genetics
  • Membrane Proteins/metabolism
  • 3-Oxo-5-alpha-Steroid 4-Dehydrogenase/genetics

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