2016年1月
Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols
PLOS ONE
- 巻
- 11
- 号
- 1
- 開始ページ
- e0147519
- 終了ページ
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1371/journal.pone.0147519
- 出版者・発行元
- PUBLIC LIBRARY SCIENCE
Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre -analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell -type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4 C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P -value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, A = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (A = 1.50) when comparing control and storage conditions. We then considered cell -type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (A adjusted = 1.14) by taking into account a cell -type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell -type composition (A = 1.12-1.45) and no remarkable biases were seen after adjusting for cell -type composition in all four protocols (adjusted = 1.00-1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease -associated DNA methylation markers and the development of DNA methylation profile -based predictive risk models.
- リンク情報
- ID情報
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- DOI : 10.1371/journal.pone.0147519
- ISSN : 1932-6203
- PubMed ID : 26799745
- Web of Science ID : WOS:000368655300118