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.

Share Rare and common genetic determinants of metabolic individuality and their effects on human health / P. Surendran, I. Stewart, V. Au Yeung, M. Pietzner, J. Raffler, M. Wörheide, C. Li, R. Smith, L. Wittemans, L. Bomba, C. Menni, J. Zierer, N. Rossi, P. Sheridan, N. Watkins, M. Mangino, P. Hysi, E. Di Angelantonio, M. Falchi, T. Spector, N. Soranzo, G. Michelotti, W. Arlt, L. Lotta, S. Denaxas, H. Hemingway, E. Gamazon, J. Howson, A. Wood, J. Danesh, N. Wareham, G. Kastenmüller, E. Fauman, K. Suhre, A. Butterworth, C. Langenberg. - In: NATURE MEDICINE. - ISSN 1078-8956. - 28:11(2022), pp. 2321-2332. [10.1038/s41591-022-02046-0]

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

C. Menni;
2022

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.
Settore MED/01 - Statistica Medica
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1096348
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