DNA methylation signatures associated with bipolar disorder in peripheral blood improve prediction models

BACKGROUND: Bipolar disorder (BD) is a major mood disorder influenced by both genetic and environmental factors. While DNA methylation from peripheral tissues can reflect both genetic and environmental influences and reveal insights into disease biology, it remains understudied in BD. DNA methylation signatures may complement polygenic scores (PGS) and hold potential as biomarkers. Here, we conducted the largest epigenome-wide association study (EWAS) of BD to date and evaluated the predictive value of polymethylation scores (PMS) in classifying case-control status. METHODS: DNA methylation from peripheral blood of 1729 cases and 1747 controls, comprising twelve cohorts, was obtained. We performed meta-analyses for the total sample, male-only, and female-only analyses. Differentially methylated regions (DMRs) were identified using the comb-p method. Polymethylation scores for BD (BD-PMS) were tested for association with BD, and in combination with PGS. FINDINGS: We identified 47 differentially methylated CpG positions (DMPs) in the total and four in the female-only analysis. Ninety, fourteen and six DMRs were identified in the total sample, female-only, and male-only analyses, respectively. Genes annotated to the top DMPs were enriched for immune activation and phosphorylation pathways. DMRs were annotated to genes relevant to neurotransmission, including GABBR1 and CACNA2D4. BD-PMS explained 2% of the variance in BD case-control status, and improved the variance explained from 7.9 to 8.5% when combined with PGS. For bipolar I disorder, BD-PMS explained 4.9% of the variance, and improved the variance explained by PGS from 15.9 to 18.5%. Association of BD with PMS for schizophrenia and major depression suggests pleiotropic epigenetic effects. INTERPRETATION: DNA methylation signatures of BD are detectable in blood using adequately powered data and may reveal novel BD biology that is not captured by genetic studies. PMS from large cohorts have the potential to facilitate the development of prediction tools to aid clinical decision-making. FUNDING: This investigation was primarily funded by the Research Council of Norway (RCN #250299, #273446, #223273) and the University of Bergen. A complete list of funding organisations is provided in the Acknowledgements.

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Journal
EBioMedicine
Published
2026-05-12
DOI
https://doi.org/10.1016/j.ebiom.2026.106284
Primary Topic
Epigenetics and DNA Methylation
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article
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article

DNA methylation signatures associated with bipolar disorder in peripheral blood improve prediction models

Caterina Chillotti, Antoine Weihs, Markos Tesfaye, Kira D. Höffler et al.
EBioMedicine
Epigenetics and DNA Methylation
article

DNA methylation signatures associated with bipolar disorder in peripheral blood improve prediction models

Caterina Chillotti, Antoine Weihs, Markos Tesfaye, Kira D. Höffler, Stéphanie Le Hellard, Luana Spano, Janice M. Fullerton, Trine Vik Lagerberg, Anouar Khayachi, Esteban Sepúlveda, Lavinia Athanasiu, Kyle J. Bourassa, Lourdes Martorell, Melanie E. Garrett, Ole Kristian Drange, Nathan A. Kimbrel, Alessio Squassina, MJ Green, Allison E. Ashley‐Koch, Pasquale Paribello, Giovanni Severino, Claudia Pisanu, M Guadalupe Alda, Gabriel R. Fries, Anne-Kristin Stavrum, Friederike Sophie David, Gunnar Morken, M A Pinna, Sonia Hesam-Shariati, Bronwyn J. Overs, Javier Labad, H J Grabe, Guy Rouleau, Rafaella Ardau, Gabriel R. Fries, Philip B Mitchell, Boris Chaumette, Jair C Soares, Kevin S O'Connell, Srdjan Djurovic, Hans J Grabe, Fabian Streit, Josef Frank, Ingrid Melle, Melissa J Green, Jean C Beckham, Peter R Schofield, Tilo Kircher, Susanne Meinert, Vidar M Steen, Mirko Manchia, Frederike Stein, Alexander Teumer, Leonardo Tondo, Oliver J Watkeys, Gloria Roberts, Udo Dannlowski, Andreas J Forstner, Ole A Andreassen, Martin Alda, Elisabet Vilella, Jair C. Soares, Philip B. Mitchell, Boris Chaumette, Tilo Kircher, Mirko Manchia, Alexander Teumer, Leonardo Tondo, Udo Dannlowski, Andreas J. Forstner, Ole A. Andreassen, Elisabet Vilella
article en

Abstract

BACKGROUND: Bipolar disorder (BD) is a major mood disorder influenced by both genetic and environmental factors. While DNA methylation from peripheral tissues can reflect both genetic and environmental influences and reveal insights into disease biology, it remains understudied in BD. DNA methylation signatures may complement polygenic scores (PGS) and hold potential as biomarkers. Here, we conducted the largest epigenome-wide association study (EWAS) of BD to date and evaluated the predictive value of polymethylation scores (PMS) in classifying case-control status. METHODS: DNA methylation from peripheral blood of 1729 cases and 1747 controls, comprising twelve cohorts, was obtained. We performed meta-analyses for the total sample, male-only, and female-only analyses. Differentially methylated regions (DMRs) were identified using the comb-p method. Polymethylation scores for BD (BD-PMS) were tested for association with BD, and in combination with PGS. FINDINGS: We identified 47 differentially methylated CpG positions (DMPs) in the total and four in the female-only analysis. Ninety, fourteen and six DMRs were identified in the total sample, female-only, and male-only analyses, respectively. Genes annotated to the top DMPs were enriched for immune activation and phosphorylation pathways. DMRs were annotated to genes relevant to neurotransmission, including GABBR1 and CACNA2D4. BD-PMS explained 2% of the variance in BD case-control status, and improved the variance explained from 7.9 to 8.5% when combined with PGS. For bipolar I disorder, BD-PMS explained 4.9% of the variance, and improved the variance explained by PGS from 15.9 to 18.5%. Association of BD with PMS for schizophrenia and major depression suggests pleiotropic epigenetic effects. INTERPRETATION: DNA methylation signatures of BD are detectable in blood using adequately powered data and may reveal novel BD biology that is not captured by genetic studies. PMS from large cohorts have the potential to facilitate the development of prediction tools to aid clinical decision-making. FUNDING: This investigation was primarily funded by the Research Council of Norway (RCN #250299, #273446, #223273) and the University of Bergen. A complete list of funding organisations is provided in the Acknowledgements.

EBioMedicineVol. 128
Oslo University Hospital (NO), Montreal Neurological Institute and Hospital (CA), The University of Sydney (AU), Dalhousie University (CA), Inserm (FR), Philipps University of Marburg (DE), University of Cagliari (IT), Georgetown University (US), University of Oslo (NO), Université Paris Cité (FR), Norwegian University of Science and Technology (NO), Heidelberg University (DE), University of Münster (DE), Universitätsmedizin Greifswald (DE), University Hospital Bonn (DE), Haukeland University Hospital (NO), Instituto de Salud Carlos III (ES), Institut Pere Mata (ES), University Hospital Heidelberg (DE), UNSW Sydney (AU), Maresme Health Consortium (ES), Institut de Recerca Biomèdica Catalunya Sud (ES), St Olav's University Hospital (NO), Central Institute of Mental Health (DE), Durham VA Health Care System (US), Duke Medical Center (US), Institut de Psychiatrie et Neurosciences de Paris (FR), University Hospital Magdeburg (DE), The University of Texas Health Science Center (US), University of Bergen (NO), McGill University (CA), Neuroscience Research Australia (AU), The University of Texas Health Science Center at Houston (US)
Brain and Behavior Research Foundation, John S. Dunn Foundation, Biomedical Laboratory Research and Development, VA Office of Research and Development, Genome Canada, Ministerio de Ciencia, Innovación y Universidades, Deutsche Forschungsgemeinschaft, Agence Nationale de la Recherche, NordForsk, Universitetet i Bergen, Norges Forskningsråd, Institut pour la Recherche en Santé Publique, Canadian Institutes of Health Research, National Health and Medical Research Council, Instituto de Salud Carlos III, Helse Sør-Øst RHF, National Institute of Mental Health
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Openalex Percentile: Top 10%
Epigenetics and DNA Methylation
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