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Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases

Nature Genetics volume 55, pages 44–53 (2023)Cite this article

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Abstract

Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.

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Fig. 1: Summary of associations of metabolite levels and genetic loci.
Fig. 2: Genetic architecture of metabolite levels.
Fig. 3: Summary of metabolite ratio GWAS results.
Fig. 4: Assignment of effector genes by using evidence from gene expression and biological knowledge.
Fig. 5: Forest plots showing effects (β coefficient or OR estimates) and 95% confidence intervals from two-sample MR analyses.
Fig. 6: Comparison of estimated BMI-related and non-BMI effects on eBMD and Asthma.

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Data availability

The GWAS summary statistics were deposited to GWAS catalog (https://www.ebi.ac.uk/gwas/). Accession numbers for European GWASs: GCST90199621-90201020; accession numbers for non-European GWASs: GCST90201021-90204063. Individual-level data are available from the Canadian Longitudinal Study on Aging (www.clsa-elcv.ca) for researchers who meet the criteria for access to de-identified CLSA data. HMDB database can be found in http://www.hmdb.ca/system/downloads/current/serum_metabolites.zip. KEGG pathway database can be found in http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/keggPathway.txt.gz. PubChem database can be found in https://pubchem.ncbi.nlm.nih.gov/. GTEx V8 release data can be found in https://www.gtexportal.org/home/datasets. UniProt database can be found in https://www.uniprot.org/uniprot/. GENCODE resource can be found in https://www.gencodegenes.org/. OMIM database can be found in https://www.omim.org/downloads. IMPC database can be found in https://www.mousephenotype.org/data/release. DrugBank database can be found in https://go.drugbank.com.

Code availability

The GWAS was performed using GCTA-fastGWA (v1.93.2 beta). Multi-SNP-based conditional and joint association analysis was performed using GCTA-COJO (v1.93.2 beta). bedtools version v2.29.2 was used. KING package (v2.2.5) was used to remove individuals with first- and second-degree relatives. Snappy (available through Zenodo (https://doi.org/10.5281/zenodo.7328428; ref. 77)) was used to identify LD-proxy SNPs. PLINK 1.9 was used to identify LD-independent SNPs from the trait and disease GWAS. All other data analyses were performed using R (version 4.0.5). R packages including dplyr (1.0.7), data.table (1.14.2), tidyverse (1.2.0), stringr (1.4.1), LDlinkR (1.1.2), TwoSampleMR (0.5.6), coloc (5.1.0), circlize (0.4.13), ComplexHeatmap (2.13.1), RcolorBrewer (1.1–3), ggpubr (0.4.0) and ggplot2 (3.3.5) were used for analysis and plotting. Other analyses and plotting scripts were made available through the GitHub repository (https://github.com/richardslab/metabolomics_GWAS_CLSA) and also through Zenodo (https://doi.org/10.5281/zenodo.7331471) (ref. 79).

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Acknowledgements

This research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation, and the following provinces, Newfoundland, Nova Scotia, Quebec, Ontario, Manitoba, Alberta and British Columbia. This research has been conducted using the CLSA Metabolomics data (v1), CLSA Baseline dataset (v5) and Genomics data (v3), under application number 2006016. The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland. We also acknowledge the effort of the International Parkinson’s Disease Genomics Consortium (IPDGC), the International Multiple Sclerosis Genetics Consortium (IMSGC), the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) consortium and the MEGASTROKE consortium in providing high-quality GWASs data for other researchers.

The Richards research group is supported by the Canadian Institutes of Health Research (CIHR: 365825, 409511 and 100558), the Lady Davis Institute of the Jewish General Hospital, the Canadian Foundation for Innovation, the NIH Foundation, Cancer Research UK (grant number C18281/A29019), Genome Québec, the Public Health Agency of Canada, Genome Québec, McGill University and the Fonds de Recherche Québec Santé (FRQS). J.B.R. is supported by an FRQS Clinical Research Scholarship. Support from Calcul Québec and Compute Canada is acknowledged. TwinsUK is funded by the Welcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Center based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. These funding agencies had no role in the design, implementation or interpretation of this study. Y.C. has been supported by an FRQS. doctoral training fellowship and the Lady Davis Institute/TD Bank Studentship Award. T.L. has been supported by a Vanier Canada Graduate Scholarship, an FRQS. doctoral training fellowship and a McGill University Faculty of Medicine studentship. T.N. is supported by a research fellowship of the Japan Society for the Promotion of Science for Young Scientists. C.-Y.S. has been supported by the Lady Davis Institute/TD Bank Studentship Award. S.Z. has been supported by a CIHR fellowship and an FRQS. fellowship. P.R. holds the Raymond and Margaret Labarge Chair in Optimal Aging and Knowledge Application for Optimal Aging, is the Director of the McMaster Institute for Research on Aging and the Labarge Center for Mobility in Aging and holds a Tier 1 Canada Research Chair in Geroscience.

Author information

Authors and Affiliations

  1. Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada

    Yiheng Chen, Tianyuan Lu, Guillaume Butler-Laporte, Tomoko Nakanishi, Agustin Cerani, Kevin Y. H. Liang, Satoshi Yoshiji, Julian Daniel Sunday Willett, Chen-Yang Su, Celia M. T. Greenwood, Yossi Farjoun, Vincenzo Forgetta, Sirui Zhou & J. Brent Richards

  2. Department of Human Genetics, McGill University, Montreal, Quebec, Canada

    Yiheng Chen, Tomoko Nakanishi, Satoshi Yoshiji, Sirui Zhou & J. Brent Richards

  3. Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada

    Tianyuan Lu, Kevin Y. H. Liang, Julian Daniel Sunday Willett & Celia M. T. Greenwood

  4. 5 Prime Sciences Inc, Montreal, Quebec, Canada

    Tianyuan Lu, Yossi Farjoun, Vincenzo Forgetta & J. Brent Richards

  5. Clinical Pharmacology, Department of Integrative Medical Biology, Umea University, Umea, Sweden

    Ulrika Pettersson-Kymmer

  6. MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK

    Isobel D. Stewart & Claudia Langenberg

  7. Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada

    Guillaume Butler-Laporte, Agustin Cerani, Celia M. T. Greenwood & J. Brent Richards

  8. Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Tomoko Nakanishi & Satoshi Yoshiji

  9. Japan Society for the Promotion of Science, Tokyo, Japan

    Tomoko Nakanishi & Satoshi Yoshiji

  10. McGill Genome Centre, McGill University, Montreal, Quebec, Canada

    Julian Daniel Sunday Willett

  11. Department of Computer Science, McGill University, Montreal, Quebec, Canada

    Chen-Yang Su

  12. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada

    Parminder Raina

  13. McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada

    Parminder Raina

  14. Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada

    Celia M. T. Greenwood

  15. The Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Yossi Farjoun

  16. Fulcrum Genomics, Boulder, CO, USA

    Yossi Farjoun

  17. Precision Healthcare University Research Institute, Queen Mary University of London, London, UK

    Claudia Langenberg

  18. Computational Medicine, Berlin Institute of Health (BIH) at Charité—Universitätsmedizin Berlin, Berlin, Germany

    Claudia Langenberg

  19. Sahlgrenska Osteoporosis Centre, Centre of Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    Claes Ohlsson

  20. Department of Drug Treatment, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden

    Claes Ohlsson

  21. Department of Medicine, McGill University, Montreal, Quebec, Canada

    J. Brent Richards

  22. Department of Twin Research, King’s College London, London, UK

    J. Brent Richards

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  1. Yiheng Chen
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  18. Claes Ohlsson
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  19. J. Brent Richards
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Contributions

Conception and design: Y.C., V.F., S.Z. and J.B.R. Methodology: Y.C., T.L., Y.F., V.F., C.O., S.Z. and J.B.R. Data curation: Y.C. Data Analysis: Y.C., T.L. and U.P.-K. Visualization: Y.C. and T.L. Writing—Original Draft: Y.C. Writing—Review and Editing: Y.C., T.L., U.P.-K., I.D.S., G.B.-L., T.N., A.C., K.Y.H.L., S.Y., J.D.S.W., C.-Y.S., P.R., C.O., C.M.T.G., Y.F., V.F., C.L., S.Z. and J.B.R. Supervision: J.B.R. Project administration: J.B.R. Funding acquisition: J.B.R.

Corresponding author

Correspondence to J. Brent Richards.

Ethics declarations

Ethics declarations

This study was approved by the research ethics boards of the Jewish General Hospital, protocol number 2021–2762.

Competing interests

J.B.R. has served as an advisor to GlaxoSmithKline and Deerfield Capital. J.B.R.’s institution has received investigator-initiated grant funding from Eli Lilly, GlaxoSmithKline and Biogen for projects unrelated to this research. J.B.R. is the CEO of 5 Prime Sciences (http://www.5primesciences.com). T.L. and V.F. are employees of 5 Prime Sciences. Y.F. consults for Fulcrum Genomics, 5 Prime Sciences, and Demetria. T.N. has received speaking fees from Boehringer Ingelheim and AstraZeneca. All other authors declare that there are no conflicts of interest. The opinions expressed in this manuscript are the authors’ own and do not reflect the views of the Canadian Longitudinal Study on Aging.

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Nature Genetics thanks Amalio Telenti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary information

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Supplementary Notes and Supplementary Figs. 1–6.

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Supplementary Tables 1–26.

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Chen, Y., Lu, T., Pettersson-Kymmer, U. et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat Genet 55, 44–53 (2023). https://doi.org/10.1038/s41588-022-01270-1

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