Author
Listed:
- Chenglong You
(University of Chinese Academy of Sciences, Chinese Academy of Sciences)
- Sijie Wu
(University of Chinese Academy of Sciences, Chinese Academy of Sciences
Fudan University
Fudan University)
- Shijie C. Zheng
(University of Chinese Academy of Sciences, Chinese Academy of Sciences
Johns Hopkins Bloomberg School of Public Health)
- Tianyu Zhu
(University of Chinese Academy of Sciences, Chinese Academy of Sciences)
- Han Jing
(University of Chinese Academy of Sciences, Chinese Academy of Sciences)
- Ken Flagg
(Guangzhou Regenerative Medicine Guangdong Laboratory)
- Guangyu Wang
(Tsinghua University)
- Li Jin
(University of Chinese Academy of Sciences, Chinese Academy of Sciences
Fudan University
Fudan University)
- Sijia Wang
(University of Chinese Academy of Sciences, Chinese Academy of Sciences
Chinese Academy of Sciences)
- Andrew E. Teschendorff
(University of Chinese Academy of Sciences, Chinese Academy of Sciences
University College London)
Abstract
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.
Suggested Citation
Chenglong You & Sijie Wu & Shijie C. Zheng & Tianyu Zhu & Han Jing & Ken Flagg & Guangyu Wang & Li Jin & Sijia Wang & Andrew E. Teschendorff, 2020.
"A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes,"
Nature Communications, Nature, vol. 11(1), pages 1-13, December.
Handle:
RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18618-y
DOI: 10.1038/s41467-020-18618-y
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