IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-33511-6.html
   My bibliography  Save this article

The immune factors driving DNA methylation variation in human blood

Author

Listed:
  • Jacob Bergstedt

    (Human Evolutionary Genetics Unit
    Karolinska Institutet
    Karolinska Institutet)

  • Sadoune Ait Kaci Azzou

    (Human Evolutionary Genetics Unit)

  • Kristin Tsuo

    (Human Evolutionary Genetics Unit)

  • Anthony Jaquaniello

    (Human Evolutionary Genetics Unit)

  • Alejandra Urrutia

    (HI-Bio)

  • Maxime Rotival

    (Human Evolutionary Genetics Unit)

  • David T. S. Lin

    (University of British Columbia)

  • Julia L. MacIsaac

    (University of British Columbia)

  • Michael S. Kobor

    (University of British Columbia)

  • Matthew L. Albert

    (HI-Bio)

  • Darragh Duffy

    (Institut Pasteur, Université Paris Cité, Translational Immunology Unit, Institut Pasteur)

  • Etienne Patin

    (Human Evolutionary Genetics Unit)

  • Lluís Quintana-Murci

    (Human Evolutionary Genetics Unit
    Collège de France)

Abstract

Epigenetic changes are required for normal development, yet the nature and respective contribution of factors that drive epigenetic variation in humans remain to be fully characterized. Here, we assessed how the blood DNA methylome of 884 adults is affected by DNA sequence variation, age, sex and 139 factors relating to life habits and immunity. Furthermore, we investigated whether these effects are mediated or not by changes in cellular composition, measured by deep immunophenotyping. We show that DNA methylation differs substantially between naïve and memory T cells, supporting the need for adjustment on these cell-types. By doing so, we find that latent cytomegalovirus infection drives DNA methylation variation and provide further support that the increased dispersion of DNA methylation with aging is due to epigenetic drift. Finally, our results indicate that cellular composition and DNA sequence variation are the strongest predictors of DNA methylation, highlighting critical factors for medical epigenomics studies.

Suggested Citation

  • Jacob Bergstedt & Sadoune Ait Kaci Azzou & Kristin Tsuo & Anthony Jaquaniello & Alejandra Urrutia & Maxime Rotival & David T. S. Lin & Julia L. MacIsaac & Michael S. Kobor & Matthew L. Albert & Darrag, 2022. "The immune factors driving DNA methylation variation in human blood," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33511-6
    DOI: 10.1038/s41467-022-33511-6
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-33511-6
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-33511-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    2. Darina Czamara & Gökçen Eraslan & Christian M. Page & Jari Lahti & Marius Lahti-Pulkkinen & Esa Hämäläinen & Eero Kajantie & Hannele Laivuori & Pia M. Villa & Rebecca M. Reynolds & Wenche Nystad & Sir, 2019. "Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns," Nature Communications, Nature, vol. 10(1), pages 1-18, December.
    3. 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).
    4. Lucas A. Salas & Ze Zhang & Devin C. Koestler & Rondi A. Butler & Helen M. Hansen & Annette M. Molinaro & John K. Wiencke & Karl T. Kelsey & Brock C. Christensen, 2022. "Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    5. Mathieu Lemire & Syed H.E. Zaidi & Maria Ban & Bing Ge & Dylan Aïssi & Marine Germain & Irfahan Kassam & Mike Wang & Brent W. Zanke & France Gagnon & Pierre-Emmanuel Morange & David-Alexandre Trégouët, 2015. "Long-range epigenetic regulation is conferred by genetic variation located at thousands of independent loci," Nature Communications, Nature, vol. 6(1), pages 1-12, May.
    6. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    7. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    8. Giacomo Cavalli & Edith Heard, 2019. "Advances in epigenetics link genetics to the environment and disease," Nature, Nature, vol. 571(7766), pages 489-499, July.
    9. Laurie A. Boyer & Kathrin Plath & Julia Zeitlinger & Tobias Brambrink & Lea A. Medeiros & Tong Ihn Lee & Stuart S. Levine & Marius Wernig & Adriana Tajonar & Mridula K. Ray & George W. Bell & Arie P. , 2006. "Polycomb complexes repress developmental regulators in murine embryonic stem cells," Nature, Nature, vol. 441(7091), pages 349-353, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andreas Floren & Tobias Müller, 2023. "Using a Machine Learning Approach to Classify the Degree of Forest Management," Sustainability, MDPI, vol. 15(16), pages 1-14, August.
    2. J. K. Wiencke & Annette M. Molinaro & Gayathri Warrier & Terri Rice & Jennifer Clarke & Jennie W. Taylor & Margaret Wrensch & Helen Hansen & Lucie McCoy & Emily Tang & Stan J. Tamaki & Courtney M. Tam, 2022. "DNA methylation as a pharmacodynamic marker of glucocorticoid response and glioma survival," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    3. Heather E Wheeler & Kaanan P Shah & Jonathon Brenner & Tzintzuni Garcia & Keston Aquino-Michaels & GTEx Consortium & Nancy J Cox & Dan L Nicolae & Hae Kyung Im, 2016. "Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues," PLOS Genetics, Public Library of Science, vol. 12(11), pages 1-23, November.
    4. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    5. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    6. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    7. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    8. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    9. Immanuel Bayer & Philip Groth & Sebastian Schneckener, 2013. "Prediction Errors in Learning Drug Response from Gene Expression Data – Influence of Labeling, Sample Size, and Machine Learning Algorithm," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-13, July.
    10. Mostafa Rezaei & Ivor Cribben & Michele Samorani, 2021. "A clustering-based feature selection method for automatically generated relational attributes," Annals of Operations Research, Springer, vol. 303(1), pages 233-263, August.
    11. Gustavo A. Alonso-Silverio & Víctor Francisco-García & Iris P. Guzmán-Guzmán & Elías Ventura-Molina & Antonio Alarcón-Paredes, 2021. "Toward Non-Invasive Estimation of Blood Glucose Concentration: A Comparative Performance," Mathematics, MDPI, vol. 9(20), pages 1-13, October.
    12. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    13. Karim Barigou & Stéphane Loisel & Yahia Salhi, 2020. "Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect," Risks, MDPI, vol. 9(1), pages 1-18, December.
    14. Gurgul Henryk & Machno Artur, 2017. "Trade Pattern on Warsaw Stock Exchange and Prediction of Number of Trades," Statistics in Transition New Series, Statistics Poland, vol. 18(1), pages 91-114, March.
    15. Szefer Elena & Lu Donghuan & Nathoo Farouk & Beg Mirza Faisal & Graham Jinko, 2017. "Multivariate association between single-nucleotide polymorphisms in Alzgene linkage regions and structural changes in the brain: discovery, refinement and validation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(5-6), pages 367-386, December.
    16. Michael Funke & Kadri Männasoo & Helery Tasane, 2023. "Regional Economic Impacts of the Øresund Cross-Border Fixed Link: Cui Bono?," CESifo Working Paper Series 10557, CESifo.
    17. Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Post-Print halshs-00917797, HAL.
    18. Zichen Zhang & Ye Eun Bae & Jonathan R. Bradley & Lang Wu & Chong Wu, 2022. "SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    19. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    20. Peter Bühlmann & Jacopo Mandozzi, 2014. "High-dimensional variable screening and bias in subsequent inference, with an empirical comparison," Computational Statistics, Springer, vol. 29(3), pages 407-430, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33511-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.