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Epigenetic prediction of major depressive disorder

Variation in DNA methylation (DNAm) is associated with lifestyle factors such as smoking and body mass index (BMI) but there has been little research exploring its ability to identify individuals with major depressive disorder (MDD). Using penalised regression on genome-wide CpG methylation, we test...

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Autores principales: Barbu, Miruna C., Shen, Xueyi, Walker, Rosie M., Howard, David M., Evans, Kathryn L., Whalley, Heather C., Porteous, David J., Morris, Stewart W., Deary, Ian J., Zeng, Yanni, Marioni, Riccardo E., Clarke, Toni-Kim, McIntosh, Andrew M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589651/
https://www.ncbi.nlm.nih.gov/pubmed/32523041
http://dx.doi.org/10.1038/s41380-020-0808-3
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author Barbu, Miruna C.
Shen, Xueyi
Walker, Rosie M.
Howard, David M.
Evans, Kathryn L.
Whalley, Heather C.
Porteous, David J.
Morris, Stewart W.
Deary, Ian J.
Zeng, Yanni
Marioni, Riccardo E.
Clarke, Toni-Kim
McIntosh, Andrew M.
author_facet Barbu, Miruna C.
Shen, Xueyi
Walker, Rosie M.
Howard, David M.
Evans, Kathryn L.
Whalley, Heather C.
Porteous, David J.
Morris, Stewart W.
Deary, Ian J.
Zeng, Yanni
Marioni, Riccardo E.
Clarke, Toni-Kim
McIntosh, Andrew M.
author_sort Barbu, Miruna C.
collection PubMed
description Variation in DNA methylation (DNAm) is associated with lifestyle factors such as smoking and body mass index (BMI) but there has been little research exploring its ability to identify individuals with major depressive disorder (MDD). Using penalised regression on genome-wide CpG methylation, we tested whether DNAm risk scores (MRS), trained on 1223 MDD cases and 1824 controls, could discriminate between cases (n = 363) and controls (n = 1417) in an independent sample, comparing their predictive accuracy to polygenic risk scores (PRS). The MRS explained 1.75% of the variance in MDD (β = 0.338, p = 1.17 × 10(−7)) and remained associated after adjustment for lifestyle factors (β = 0.219, p = 0.001, R(2) = 0.68%). When modelled alongside PRS (β = 0.384, p = 4.69 × 10(−9)) the MRS remained associated with MDD (β = 0.327, p = 5.66 × 10(−7)). The MRS was also associated with incident cases of MDD who were well at recruitment but went on to develop MDD at a later assessment (β = 0.193, p = 0.016, R(2) = 0.52%). Heritability analyses found additive genetic effects explained 22% of variance in the MRS, with a further 19% explained by pedigree-associated genetic effects and 16% by the shared couple environment. Smoking status was also strongly associated with MRS (β = 0.440, p ≤ 2 × 10(−16)). After removing smokers from the training set, the MRS strongly associated with BMI (β = 0.053, p = 0.021). We tested the association of MRS with 61 behavioural phenotypes and found that whilst PRS were associated with psychosocial and mental health phenotypes, MRS were more strongly associated with lifestyle and sociodemographic factors. DNAm-based risk scores of MDD significantly discriminated MDD cases from controls in an independent dataset and may represent an archive of exposures to lifestyle factors that are relevant to the prediction of MDD.
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spelling pubmed-85896512021-11-23 Epigenetic prediction of major depressive disorder Barbu, Miruna C. Shen, Xueyi Walker, Rosie M. Howard, David M. Evans, Kathryn L. Whalley, Heather C. Porteous, David J. Morris, Stewart W. Deary, Ian J. Zeng, Yanni Marioni, Riccardo E. Clarke, Toni-Kim McIntosh, Andrew M. Mol Psychiatry Article Variation in DNA methylation (DNAm) is associated with lifestyle factors such as smoking and body mass index (BMI) but there has been little research exploring its ability to identify individuals with major depressive disorder (MDD). Using penalised regression on genome-wide CpG methylation, we tested whether DNAm risk scores (MRS), trained on 1223 MDD cases and 1824 controls, could discriminate between cases (n = 363) and controls (n = 1417) in an independent sample, comparing their predictive accuracy to polygenic risk scores (PRS). The MRS explained 1.75% of the variance in MDD (β = 0.338, p = 1.17 × 10(−7)) and remained associated after adjustment for lifestyle factors (β = 0.219, p = 0.001, R(2) = 0.68%). When modelled alongside PRS (β = 0.384, p = 4.69 × 10(−9)) the MRS remained associated with MDD (β = 0.327, p = 5.66 × 10(−7)). The MRS was also associated with incident cases of MDD who were well at recruitment but went on to develop MDD at a later assessment (β = 0.193, p = 0.016, R(2) = 0.52%). Heritability analyses found additive genetic effects explained 22% of variance in the MRS, with a further 19% explained by pedigree-associated genetic effects and 16% by the shared couple environment. Smoking status was also strongly associated with MRS (β = 0.440, p ≤ 2 × 10(−16)). After removing smokers from the training set, the MRS strongly associated with BMI (β = 0.053, p = 0.021). We tested the association of MRS with 61 behavioural phenotypes and found that whilst PRS were associated with psychosocial and mental health phenotypes, MRS were more strongly associated with lifestyle and sociodemographic factors. DNAm-based risk scores of MDD significantly discriminated MDD cases from controls in an independent dataset and may represent an archive of exposures to lifestyle factors that are relevant to the prediction of MDD. Nature Publishing Group UK 2020-06-10 2021 /pmc/articles/PMC8589651/ /pubmed/32523041 http://dx.doi.org/10.1038/s41380-020-0808-3 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Barbu, Miruna C.
Shen, Xueyi
Walker, Rosie M.
Howard, David M.
Evans, Kathryn L.
Whalley, Heather C.
Porteous, David J.
Morris, Stewart W.
Deary, Ian J.
Zeng, Yanni
Marioni, Riccardo E.
Clarke, Toni-Kim
McIntosh, Andrew M.
Epigenetic prediction of major depressive disorder
title Epigenetic prediction of major depressive disorder
title_full Epigenetic prediction of major depressive disorder
title_fullStr Epigenetic prediction of major depressive disorder
title_full_unstemmed Epigenetic prediction of major depressive disorder
title_short Epigenetic prediction of major depressive disorder
title_sort epigenetic prediction of major depressive disorder
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589651/
https://www.ncbi.nlm.nih.gov/pubmed/32523041
http://dx.doi.org/10.1038/s41380-020-0808-3
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