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

View ORCID ProfileCourtney J. Smith, View ORCID ProfileNasa Sinnott-Armstrong, View ORCID ProfileAnna Cichońska, View ORCID ProfileHeli Julkunen, View ORCID ProfileEric Fauman, View ORCID ProfilePeter Würtz, View ORCID ProfileJonathan K. Pritchard
doi: https://doi.org/10.1101/2022.04.02.486791
Courtney J. Smith
1Department of Genetics, Stanford University School of Medicine, United States
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  • For correspondence: courtrun@stanford.edu nasa@fredhutch.org pritch@stanford.edu
Nasa Sinnott-Armstrong
1Department of Genetics, Stanford University School of Medicine, United States
2Herbold Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington
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  • For correspondence: courtrun@stanford.edu nasa@fredhutch.org pritch@stanford.edu
Anna Cichońska
3Nightingale Health Plc, Helsinki, Finland
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Heli Julkunen
3Nightingale Health Plc, Helsinki, Finland
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Eric Fauman
4Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, United States
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Peter Würtz
3Nightingale Health Plc, Helsinki, Finland
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Jonathan K. Pritchard
1Department of Genetics, Stanford University School of Medicine, United States
5Department of Biology, Stanford University, United States Joint First Authors
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  • For correspondence: courtrun@stanford.edu nasa@fredhutch.org pritch@stanford.edu
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Summary

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.

Competing Interest Statement

Anna Cichonska is a former employee and holds stock options with Nightingale Health Plc. Heli Julkunen is an employee and holds stock options with Nightingale Health Plc. Eric 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. Peter Wurtz is an employee and shareholder of Nightingale Health Plc. The other author declares that no competing interests exist.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 04, 2022.
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Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation
Courtney J. Smith, Nasa Sinnott-Armstrong, Anna Cichońska, Heli Julkunen, Eric Fauman, Peter Würtz, Jonathan K. Pritchard
bioRxiv 2022.04.02.486791; doi: https://doi.org/10.1101/2022.04.02.486791
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Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation
Courtney J. Smith, Nasa Sinnott-Armstrong, Anna Cichońska, Heli Julkunen, Eric Fauman, Peter Würtz, Jonathan K. Pritchard
bioRxiv 2022.04.02.486791; doi: https://doi.org/10.1101/2022.04.02.486791

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