Genetically personalised organ-specific metabolic models in health and disease
Creators: name: Foguet Coll, Carles
name: Xu, Yu
name: Ritchie, Scott
name: Lambert, Samuel
name: Persyn, Elodie
name: Nath, Artika
name: Davenport, Emma E
name: Roberts, David J
name: Paul, Dirk S
name: Di Angelantonio, Emanuele
name: Danesh, John
name: Butterworth, Adam
name: Yau, Christopher
name: Inouye, Michael
Publisher: name: Apollo - University of Cambridge Repository
Year: 2022
Subjects: Article
/631/45/320
/631/553/2710
/692/699/75
/45/43
/141
/119/118
/140/58
/140/131
article

description: Funder: RCUK | Science and Technology Facilities Council (STFC); doi: https://doi.org/10.13039/501100000271
description: Funder: Scottish Government Health and Social Care Directorate (SGHSC); doi: https://doi.org/10.13039/100011529
description: Funder: Wellcome Trust (Wellcome); doi: https://doi.org/10.13039/100010269
description: Funder: NHS Blood and Transplant; doi: https://doi.org/10.13039/100009033
description: Funder: Health Data Research UK Department of Health and Social Care (England) Health and Social Care Research and Development Division (Welsh Government) Public Health Agency (Northern Ireland)
description: 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 4,312 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.
description: Participants in the INTERVAL trial were recruited with the active collaboration of NHS Blood and Transplant (www.nhsbt.nhs.uk), which has supported fieldwork and other elements of the trial. DNA extraction and genotyping were co-funded by the National Institute for Health and Care Research (NIHR), the NIHR BioResource (http://bioresource.nihr.ac.uk) and the NIHR Cambridge Biomedical Research Centre (BRC) (no. BRC-1215-20014)*. Nightingale Health NMR assays were funded by the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). Metabolon Metabolomics assays were funded by the NIHR BioResource and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014)*. The academic coordinating centre for INTERVAL was supported by core funding from the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (no. NIHR BTRU-2014-10024), NIHR BTRU in Donor Health and Behaviour (NIHR203337), UK Medical Research Council (MRC) (no. MR/L003120/1), British Heart Foundation (nos SP/09/002, RG/13/13/30194 and RG/18/13/33946) and the NIHR Cambridge BRC (no. BRC-1215-20014)*. *The views expressed are those of the author(s) and not necessarily those of the NIHR, NHSBT or the Department of Health and Social Care. A complete list of the investigators and contributors to the INTERVAL trial is provided in ref. 36. The academic coordinating centre would like to thank blood donor centre staff and blood donors for participating in the INTERVAL trial. This work was supported by Health Data Research UK, which is funded by the UK MRC, Engineering and Physical Sciences Research Council (EPSRC), Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. The authors are grateful to UK Biobank for access to data to undertake this study (Projects #7439). This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/P020259/1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk). S.R. is funded by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). S.L. is supported by a Canadian Institutes of Health Research postdoctoral fellowship (MFE-171279). E.P. was funded by the EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart grant 116074 and the NIHR BTRU in Donor Health and Genomics (NIHR BTRU-2014-10024) and is funded by the NIHR BTRU in Donor Health and Behaviour (NIHR203337). J.D. holds a British Heart Foundation Professorship and a NIHR Senior Investigator Award. M.I. is supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). M.I. was also supported by the UK Economic and Social Research 878 Council (ES/T013192/1).