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A cross-platform approach identifies genetic regulators of human metabolism and health

Nature Genetics volume 53, pages 54–64 (2021)Cite this article

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Abstract

In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10−10) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at GLP2R (p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.

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Fig. 1: Summary of included metabolites and genetic findings.
Fig. 2: Genetic architecture of metabolite levels.
Fig. 3: Characterization of nonsynonymous SNPs associated with metabolite levels.
Fig. 4: Assignment of putatively causal genes by using two different approaches.
Fig. 5: Statistical and functional follow-up for a missense variant in GLP2R.
Fig. 6: Genetically determined serine and glycine levels are associated with and predict MacTel.
Fig. 7: Common variants at IEM genes mirror disease phenotypes.

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Data availability

All genome-wide summary statistics are made available through an interactive web server (https://omicscience.org/apps/crossplatform/) and were further uploaded to the GWAS catalog (https://www.ebi.ac.uk/gwas/, accession numbers GCST90010722–GCST90010862).

Code availability

Source code was deposited in the following repository: https://github.com/MRC-Epid/crossplatform_mGWAS.

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Acknowledgements

M.P. was supported by a fellowship from the German Research Foundation (DFG PI 1446/2-1). C.O.-W. was funded by an early career fellowship at Homerton College, University of Cambridge. L.B.L.W. acknowledges funding from the Wellcome Trust (WT083442AIA). J.L.G. was supported by grants from the Medical Research Council (MC_UP_A090_1006, MC_PC_13030, MR/P011705/1 and MR/P01836X/1). Work in the Reimann and Gribble laboratories was supported by the Wellcome Trust (106262/Z/14/Z and 106263/Z/14/Z), the UK Medical Research Council (MRC_MC_UU_12012/3) and PhD funding for E.K.B. from MedImmune/AstraZeneca. P.S. is supported by a Rutherford Fund Fellowship from the Medical Research Council (MR/S003746/1). A.M.W. is supported by a BHF-Turing Cardiovascular Data Science Award and by the EC-Innovative Medicines Initiative (BigData@Heart). J.R. is supported by the German Federal Ministry of Education and Research (BMBF) within the framework of e:Med research and funding concept (grant no. 01ZX1912D). J.D. is funded by the National Institute for Health Research (NIHR; Senior Investigator Award) (*). The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) received funding from the Medical Research Council (MR/N003284/1 and MC-UU_12015/1) and Cancer Research UK (C864/A14136). The genetic work in the EPIC-Norfolk study was funded by the Medical Research Council (MC_PC_13048). Metabolite measurements in the EPIC-Norfolk study were supported by the MRC Cambridge Initiative in Metabolic Science (MR/L00002/1) and the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372. The Fenland study is supported by the UK Medical Research Council (MC_UU_12015/1 and MC_PC_13046). Nightingale Health NMR assays were funded by the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). Metabolon Metabolomics assays, the academic coordinating center, DNA extraction and genotyping for INTERVAL were supported by core funding from the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024), the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (SP/09/002, RG/13/13/30194 and RG/18/13/33946), the NIHR (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust) and the NIHR BioResource (http://bioresource.nihr.ac.uk) (*). This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation and Wellcome. We are grateful to all the participants who have been part of the project and to the many members of the study teams at the University of Cambridge who enabled this research. *The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. UK Biobank: this research was conducted using the UK Biobank resource under application no. 44448.

Author information

Author notes

  1. These authors contributed equally: Luca A. Lotta, Maik Pietzner.

Authors and Affiliations

  1. MRC Epidemiology Unit, University of Cambridge, Cambridge, UK

    Luca A. Lotta, Maik Pietzner, Isobel D. Stewart, Laura B. L. Wittemans, Chen Li, Victoria P. W. Auyeung, Jian’an Luan, Eleanor Wheeler, Robert A. Scott, Albert Koulman, Fumiaki Imamura, Nita G. Forouhi, Nicholas J. Wareham & Claudia Langenberg

  2. The Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK

    Laura B. L. Wittemans

  3. Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Roberto Bonelli, Melanie Bahlo & Melanie Bahlo

  4. Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia

    Roberto Bonelli, Melanie Bahlo & Melanie Bahlo

  5. Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany

    Johannes Raffler, Albert Koulman & Gabi Kastenmüller

  6. Metabolic Research Laboratories, University of Cambridge, Cambridge, UK

    Emma K. Biggs, Fiona M. Gribble & Frank Reimann

  7. British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Clare Oliver-Williams, Praveen Surendran, Angela M. Wood, John Danesh & Adam S. Butterworth

  8. Homerton College, University of Cambridge, Cambridge, UK

    Clare Oliver-Williams

  9. National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australia

    Ellie Paige

  10. British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK

    Praveen Surendran, Angela M. Wood, John Danesh & Adam S. Butterworth

  11. Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK

    Praveen Surendran, Angela M. Wood, John Danesh, Adam S. Butterworth & Claudia Langenberg

  12. Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Praveen Surendran

  13. Metabolon Inc, Durham, NC, USA

    Gregory A. Michelotti

  14. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK

    Stephen Burgess & Verena Zuber

  15. Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Stephen Burgess & Kay-Tee Khaw

  16. Department of Epidemiology and Biostatistics, Imperial College London, London, UK

    Verena Zuber

  17. MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK

    Eleanor Sanderson

  18. NIHR BRC Nutritional Biomarker Laboratory, University of Cambridge, Cambridge, UK

    Albert Koulman

  19. Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

    Julian L. Griffin

  20. National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK

    Angela M. Wood, John Danesh & Adam S. Butterworth

  21. The Alan Turing Institute, London, UK

    Angela M. Wood

  22. Cambridge Biomedical Research Centre, National Institute for Health Research, University of Cambridge and Cambridge University Hospitals, Cambridge, UK

    John Danesh & Adam S. Butterworth

  23. Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK

    John Danesh & Adam S. Butterworth

  24. Internal Medicine Research Unit, Pfizer Worldwide Research, Cambridge, MA, USA

    Eric Fauman

  25. Computational Medicine, Berlin Institute of Health (BIH), Charité University Medicine, Berlin, Germany

    Claudia Langenberg

Authors
  1. Luca A. Lotta
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Consortia

MacTel Consortium

  • Melanie Bahlo

Contributions

L.A.L. and C. Langenberg designed the study. L.A.L., M.P. and C. Langenberg drafted the manuscript. L.A.L., M.P., I.D.S., L.B.L.W., C. Li, R.B., C.O.-W, V.P.W.A., J.L., E.W., E.P., P.S., S.B., V.Z. and E.S. analyzed the data. J.R. and G.K. designed and implemented the web server. K.-T.K. and N.J.W. are principal investigators of the EPIC-Norfolk cohort. G.A.M. advised on metabolite mapping across platforms. A.K. and F.I. provided metabolite measurements and quality control in the Fenland study. E.K.B., F.M.G. and F.R. performed all experimental work on GLP2R. M.B. contributed data for MacTel. E.F. performed knowledge-based annotation of genes to variants. J.D. and A.S.B. were responsible for the INTERVAL study. All authors contributed to the interpretation of results and critically reviewed the manuscript.

Corresponding author

Correspondence to Claudia Langenberg.

Ethics declarations

Competing interests

A.S.B. received grants from AstraZeneca, Biogen, Bioverativ, Merck, Novartis and Sanofi. J.D. sits on the International Cardiovascular and Metabolic Advisory Board for Novartis (since 2010), the Steering Committee of UK Biobank (since 2011), is a member of the MRC International Advisory Group, London (since 2013), the MRC High Throughput Science ’Omics Panel, London (since 2013) and serves on the Scientific Advisory Committee for Sanofi (since 2013), the International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis and the AstraZeneca Genomics Advisory Board (since 2018). E.F. is an employee and stock holder of Pfizer. L.A.L. is presently an employee and shareholder of Regeneron Pharmaceuticals Inc. The remaining authors declare no competing interests.

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Supplementary Methods and Figs. 1–5

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Supplementary Tables

Supplementary Tables 1–6

Supplementary Data 1

Sample size for each metabolite.

Supplementary Data 2

Results for MR analysis on MacTel.

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Lotta, L.A., Pietzner, M., Stewart, I.D. et al. A cross-platform approach identifies genetic regulators of human metabolism and health. Nat Genet 53, 54–64 (2021). https://doi.org/10.1038/s41588-020-00751-5

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