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 Spector; Nicole 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

doi:10.1038/s41591-022-02046-0

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 journal › Article › peer-review

39 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 language	English
Pages (from-to)	2321-2332
Number of pages	12
Journal	Nature Medicine
Volume	28
Issue number	11
DOIs	https://doi.org/10.1038/s41591-022-02046-0
Publication status	Published - 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/s41591-022-02046-0

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Surendran, P., Stewart, I. D., Au Yeung, V. P. W., Pietzner, M., Raffler, J., Wörheide, M. A., Li, C., Smith, R. F., Wittemans, L. B. L., Bomba, L., Menni, C., Zierer, J., Rossi, N., Sheridan, P. A., Watkins, N. A., Mangino, M., Hysi, P. G., Di Angelantonio, E., Falchi, M., ... Langenberg, C. (2022). Rare and common genetic determinants of metabolic individuality and their effects on human health. Nature Medicine, 28(11), 2321-2332. https://doi.org/10.1038/s41591-022-02046-0

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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.",

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

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

note = "{\textcopyright} 2022. The Author(s).",

year = "2022",

month = nov,

doi = "10.1038/s41591-022-02046-0",

language = "English",

volume = "28",

pages = "2321--2332",

journal = "Nature Medicine",

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Surendran, P, Stewart, ID, Au Yeung, VPW, Pietzner, M, Raffler, J, Wörheide, MA, Li, C, Smith, RF, Wittemans, LBL, Bomba, L, Menni, C , Zierer, J , Rossi, N, Sheridan, PA, Watkins, NA, Mangino, M, Hysi, PG, Di Angelantonio, E, Falchi, M , Spector, TD, Soranzo, N, Michelotti, GA, Arlt, W, Lotta, LA, Denaxas, S, Hemingway, H, Gamazon, ER, Howson, JMM, Wood, AM, Danesh, J, Wareham, NJ, Kastenmüller, G, Fauman, EB, Suhre, K, Butterworth, AS & Langenberg, C 2022, 'Rare and common genetic determinants of metabolic individuality and their effects on human health', Nature Medicine, vol. 28, no. 11, pp. 2321-2332. https://doi.org/10.1038/s41591-022-02046-0

TY - JOUR

T1 - Rare and common genetic determinants of metabolic individuality and their effects on human health

AU - Surendran, Praveen

AU - Stewart, Isobel D

AU - Au Yeung, Victoria P W

AU - Pietzner, Maik

AU - Raffler, Johannes

AU - Wörheide, Maria A

AU - Li, Chen

AU - Smith, Rebecca F

AU - Wittemans, Laura B L

AU - Bomba, Lorenzo

AU - Menni, Cristina

AU - Zierer, Jonas

AU - Rossi, Niccolò

AU - Sheridan, Patricia A

AU - Watkins, Nicholas A

AU - Mangino, Massimo

AU - Hysi, Pirro G

AU - Di Angelantonio, Emanuele

AU - Falchi, Mario

AU - Spector, Tim D

AU - Soranzo, Nicole

AU - Michelotti, Gregory A

AU - Arlt, Wiebke

AU - Lotta, Luca A

AU - Denaxas, Spiros

AU - Hemingway, Harry

AU - Gamazon, Eric R

AU - Howson, Joanna M M

AU - Wood, Angela M

AU - Danesh, John

AU - Wareham, Nicholas J

AU - Kastenmüller, Gabi

AU - Fauman, Eric B

AU - Suhre, Karsten

AU - Butterworth, Adam S

AU - Langenberg, Claudia

PY - 2022/11

Y1 - 2022/11

N2 - 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.

AB - 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.

KW - Humans

KW - Metabolome/genetics

KW - Metabolomics

KW - Plasma/metabolism

KW - Phenotype

KW - Metabolism, Inborn Errors/genetics

KW - Membrane Proteins/metabolism

KW - 3-Oxo-5-alpha-Steroid 4-Dehydrogenase/genetics

U2 - 10.1038/s41591-022-02046-0

DO - 10.1038/s41591-022-02046-0

M3 - Article

C2 - 36357675

SN - 1078-8956

VL - 28

SP - 2321

EP - 2332

JO - Nature Medicine

JF - Nature Medicine

IS - 11

ER -

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

Abstract

Keywords

UN SDGs

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