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Genetically personalised organ-specific metabolic models in health and disease

Author

Listed:
  • Carles Foguet

    (University of Cambridge
    Wellcome Genome Campus and University of Cambridge
    University of Cambridge
    University of Cambridge)

  • Yu Xu

    (University of Cambridge
    University of Cambridge
    University of Cambridge)

  • Scott C. Ritchie

    (University of Cambridge
    University of Cambridge
    University of Cambridge
    University of Cambridge)

  • Samuel A. Lambert

    (University of Cambridge
    Wellcome Genome Campus and University of Cambridge
    University of Cambridge
    University of Cambridge)

  • Elodie Persyn

    (University of Cambridge
    University of Cambridge
    University of Cambridge)

  • Artika P. Nath

    (University of Cambridge
    Baker Heart and Diabetes Institute)

  • Emma E. Davenport

    (Wellcome Sanger Institute)

  • David J. Roberts

    (John Radcliffe Hospital
    University of Cambridge
    John Radcliffe Hospital)

  • Dirk S. Paul

    (University of Cambridge
    University of Cambridge
    University of Cambridge)

  • Emanuele Angelantonio

    (Wellcome Genome Campus and University of Cambridge
    University of Cambridge
    University of Cambridge
    University of Cambridge)

  • John Danesh

    (Wellcome Genome Campus and University of Cambridge
    University of Cambridge
    University of Cambridge
    University of Cambridge)

  • Adam S. Butterworth

    (Wellcome Genome Campus and University of Cambridge
    University of Cambridge
    University of Cambridge
    University of Cambridge)

  • Christopher Yau

    (University of Oxford
    Health Data Research UK)

  • Michael Inouye

    (University of Cambridge
    Wellcome Genome Campus and University of Cambridge
    University of Cambridge
    University of Cambridge)

Abstract

Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.

Suggested Citation

  • Carles Foguet & Yu Xu & Scott C. Ritchie & Samuel A. Lambert & Elodie Persyn & Artika P. Nath & Emma E. Davenport & David J. Roberts & Dirk S. Paul & Emanuele Angelantonio & John Danesh & Adam S. Butt, 2022. "Genetically personalised organ-specific metabolic models in health and disease," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35017-7
    DOI: 10.1038/s41467-022-35017-7
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    References listed on IDEAS

    as
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