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A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes
Highly reproducible smoking-associated DNA methylation changes in whole blood have been reported by many Epigenome-Wide-Association Studies (EWAS). These epigenetic alterations could have important implications for understanding and predicting the risk of smoking-related diseases. To this end, it is...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508850/ https://www.ncbi.nlm.nih.gov/pubmed/32963246 http://dx.doi.org/10.1038/s41467-020-18618-y |
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author | You, Chenglong Wu, Sijie Zheng, Shijie C. Zhu, Tianyu Jing, Han Flagg, Ken Wang, Guangyu Jin, Li Wang, Sijia Teschendorff, Andrew E. |
author_facet | You, Chenglong Wu, Sijie Zheng, Shijie C. Zhu, Tianyu Jing, Han Flagg, Ken Wang, Guangyu Jin, Li Wang, Sijia Teschendorff, Andrew E. |
author_sort | You, Chenglong |
collection | PubMed |
description | Highly reproducible smoking-associated DNA methylation changes in whole blood have been reported by many Epigenome-Wide-Association Studies (EWAS). These epigenetic alterations could have important implications for understanding and predicting the risk of smoking-related diseases. To this end, it is important to establish if these DNA methylation changes happen in all blood cell subtypes or if they are cell-type specific. Here, we apply a cell-type deconvolution algorithm to identify cell-type specific DNA methylation signals in seven large EWAS. We find that most of the highly reproducible smoking-associated hypomethylation signatures are more prominent in the myeloid lineage. A meta-analysis further identifies a myeloid-specific smoking-associated hypermethylation signature enriched for DNase Hypersensitive Sites in acute myeloid leukemia. These results may guide the design of future smoking EWAS and have important implications for our understanding of how smoking affects immune-cell subtypes and how this may influence the risk of smoking related diseases. |
format | Online Article Text |
id | pubmed-7508850 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75088502020-10-08 A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes You, Chenglong Wu, Sijie Zheng, Shijie C. Zhu, Tianyu Jing, Han Flagg, Ken Wang, Guangyu Jin, Li Wang, Sijia Teschendorff, Andrew E. Nat Commun Article Highly reproducible smoking-associated DNA methylation changes in whole blood have been reported by many Epigenome-Wide-Association Studies (EWAS). These epigenetic alterations could have important implications for understanding and predicting the risk of smoking-related diseases. To this end, it is important to establish if these DNA methylation changes happen in all blood cell subtypes or if they are cell-type specific. Here, we apply a cell-type deconvolution algorithm to identify cell-type specific DNA methylation signals in seven large EWAS. We find that most of the highly reproducible smoking-associated hypomethylation signatures are more prominent in the myeloid lineage. A meta-analysis further identifies a myeloid-specific smoking-associated hypermethylation signature enriched for DNase Hypersensitive Sites in acute myeloid leukemia. These results may guide the design of future smoking EWAS and have important implications for our understanding of how smoking affects immune-cell subtypes and how this may influence the risk of smoking related diseases. Nature Publishing Group UK 2020-09-22 /pmc/articles/PMC7508850/ /pubmed/32963246 http://dx.doi.org/10.1038/s41467-020-18618-y Text en © The Author(s) 2020 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/. |
spellingShingle | Article You, Chenglong Wu, Sijie Zheng, Shijie C. Zhu, Tianyu Jing, Han Flagg, Ken Wang, Guangyu Jin, Li Wang, Sijia Teschendorff, Andrew E. A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes |
title | A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes |
title_full | A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes |
title_fullStr | A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes |
title_full_unstemmed | A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes |
title_short | A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes |
title_sort | cell-type deconvolution meta-analysis of whole blood ewas reveals lineage-specific smoking-associated dna methylation changes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508850/ https://www.ncbi.nlm.nih.gov/pubmed/32963246 http://dx.doi.org/10.1038/s41467-020-18618-y |
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