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A blood- and brain-based EWAS of smoking

Author

Listed:
  • Aleksandra D. Chybowska

    (University of Edinburgh)

  • Elena Bernabeu

    (University of Edinburgh)

  • Paul Yousefi

    (University of Bristol
    University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol
    University of Bristol)

  • Matthew Suderman

    (University of Bristol
    University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol
    University of Bristol)

  • Robert F. Hillary

    (University of Edinburgh)

  • Richard Clark

    (Western General Hospital)

  • Louise MacGillivray

    (Western General Hospital)

  • Lee Murphy

    (Western General Hospital)

  • Sarah E. Harris

    (The University of Edinburgh)

  • Janie Corley

    (The University of Edinburgh)

  • Archie Campbell

    (University of Edinburgh
    University of Edinburgh)

  • Tara L. Spires-Jones

    (University of Edinburgh
    University of Edinburgh)

  • Daniel L. McCartney

    (University of Edinburgh)

  • Simon R. Cox

    (The University of Edinburgh
    A Platform for Scientific Excellence (SINAPSE) Collaboration)

  • Jackie F. Price

    (University of Edinburgh)

  • Kathryn L. Evans

    (University of Edinburgh)

  • Riccardo E. Marioni

    (University of Edinburgh)

Abstract

DNA methylation offers an objective method to assess the impact of smoking. In this work, we conduct a Bayesian EWAS of smoking pack years (n = 17,865, ~850k sites, Illumina EPIC array) and extend it by analysing whole genome data of smokers and non-smokers from Generation Scotland (n = 46, ~4–21 million sites via TWIST and Oxford Nanopore sequencing). We develop mCigarette, an epigenetic biomarker of smoking, and test it in two British cohorts. Results of brain- and blood-based EWAS (nbrain=14, nblood = 882, >450k sites, Illumina arrays) reveal several loci with near-perfect discrimination of smoking status, but which do not overlap across tissues. Furthermore, we perform a GWAS of epigenetic smoking, identifying several smoking-related loci. Overall, we improve smoking-related biomarker accuracy and enhance the understanding of the effects of smoking by integrating DNA methylation data from multiple tissues and cohorts.

Suggested Citation

  • Aleksandra D. Chybowska & Elena Bernabeu & Paul Yousefi & Matthew Suderman & Robert F. Hillary & Richard Clark & Louise MacGillivray & Lee Murphy & Sarah E. Harris & Janie Corley & Archie Campbell & T, 2025. "A blood- and brain-based EWAS of smoking," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58357-6
    DOI: 10.1038/s41467-025-58357-6
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    References listed on IDEAS

    as
    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Kyoko Watanabe & Erdogan Taskesen & Arjen Bochoven & Danielle Posthuma, 2017. "Functional mapping and annotation of genetic associations with FUMA," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
    3. Daniel Trejo Banos & Daniel L. McCartney & Marion Patxot & Lucas Anchieri & Thomas Battram & Colette Christiansen & Ricardo Costeira & Rosie M. Walker & Stewart W. Morris & Archie Campbell & Qian Zhan, 2020. "Bayesian reassessment of the epigenetic architecture of complex traits," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    4. Daniel Trejo Banos & Daniel L. McCartney & Marion Patxot & Lucas Anchieri & Thomas Battram & Colette Christiansen & Ricardo Costeira & Rosie M. Walker & Stewart W. Morris & Archie Campbell & Qian Zhan, 2020. "Author Correction: Bayesian reassessment of the epigenetic architecture of complex traits," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
    5. Ho, Daniel & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2011. "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i08).
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