1. Genetics and Genomics

Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation

  1. Courtney J Smith  Is a corresponding author
  2. Nasa Sinnott-Armstrong  Is a corresponding author
  3. Anna Cichońska
  4. Heli Julkunen
  5. Eric B Fauman
  6. Peter Würtz
  7. Jonathan K Pritchard  Is a corresponding author
  1. Stanford University, United States
  2. Fred Hutchinson Cancer Research Center, United States
  3. Nightingale Health Plc, Finland
  4. Pfizer, United States
https://doi.org/10.7554/eLife.79348
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Cite this article
  1. Courtney J Smith
  2. Nasa Sinnott-Armstrong
  3. Anna Cichońska
  4. Heli Julkunen
  5. Eric B Fauman
  6. Peter Würtz
  7. Jonathan K Pritchard
(2022)
Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation
eLife 11:e79348.
https://doi.org/10.7554/eLife.79348
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Abstract

Pleiotropy and genetic correlation are widespread features in GWAS, but they are often difficult to interpret at the molecular level. Here, we perform GWAS of 16 metabolites clustered at the intersection of amino acid catabolism, glycolysis, and ketone body metabolism in a subset of UK Biobank. We utilize the well-documented biochemistry jointly impacting these metabolites to analyze pleiotropic effects in the context of their pathways. Among the 213 lead GWAS hits, we find a strong enrichment for genes encoding pathway-relevant enzymes and transporters. We demonstrate that the effect directions of variants acting on biology between metabolite pairs often contrast with those of upstream or downstream variants as well as the polygenic background. Thus, we find that these outlier variants often reflect biology local to the traits. Finally, we explore the implications for interpreting disease GWAS, underscoring the potential of unifying biochemistry with dense metabolomics data to understand the molecular basis of pleiotropy in complex traits and diseases.

Data availability

The source data and analyzed data have been deposited in Dryad. Code are available at the github link (https://github.com/courtrun/Pleiotropy-of-UKB-Metabolites). The raw individual level data are available through application to UK Biobank.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Courtney J Smith

    Department of Genetics, Stanford University, Stanford, United States
    For correspondence
    courtrun@stanford.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7812-0083

  2. Nasa Sinnott-Armstrong

    Herbold Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, United States
    For correspondence
    nasa@fredhutch.org
    Competing interests
    No competing interests declared.

  3. Anna Cichońska

    Nightingale Health Plc, Helsinki, Finland
    Competing interests
    Anna Cichońska, is a former employee and holds stock options with Nightingale Health Plc..

  4. Heli Julkunen

    Nightingale Health Plc, Helsinki, Finland
    Competing interests
    Heli Julkunen, is an employee and holds stock options with Nightingale Health Plc..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4282-0248

  5. Eric B Fauman

    Internal Medicine Research Unit, Pfizer, Cambridge, United States
    Competing interests
    Eric B Fauman, is affiliated with Pfizer Worldwide Research, has no financial interests to declare, contributed as an individual and the work was not part of a Pfizer collaboration nor was it funded by Pfizer..

  6. Peter Würtz

    Nightingale Health Plc, Helsinki, Finland
    Competing interests
    Peter Würtz, is an employee and shareholder of Nightingale Health Plc..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5832-0221

  7. Jonathan K Pritchard

    Departments of Genetics and Biology, Stanford University, Stanford, United States
    For correspondence
    pritch@stanford.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8828-5236

Funding

Stanford Knight-Hennessy Scholars Program (Graduate Student Fellowship)

  • Courtney J Smith

National Science Foundation (Graduate Student Fellowship)

  • Courtney J Smith

National Institute of Health (5R01HG011432 and 5R01AG066490)

  • Jonathan K Pritchard

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: All participants provided written informed consent and ethical approval was obtained from the North West Multi-Center Research Ethics Committee (11/NW/0382). The current analysis was approved under UK Biobank Project 24983 and 30418.

Copyright

© 2022, Smith et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Courtney J Smith
  2. Nasa Sinnott-Armstrong
  3. Anna Cichońska
  4. Heli Julkunen
  5. Eric B Fauman
  6. Peter Würtz
  7. Jonathan K Pritchard
(2022)
Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation
eLife 11:e79348.
https://doi.org/10.7554/eLife.79348

Share this article

https://doi.org/10.7554/eLife.79348

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