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Open AccessArticle

Development of Tissue-Specific Age Predictors Using DNA Methylation Data

School of Electrical Engineering and Computer Science, Gwangju Institute of Science of Technology, Gwangju 61005, Korea
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Genes 2019, 10(11), 888; https://doi.org/10.3390/genes10110888
Received: 23 September 2019 / Revised: 1 November 2019 / Accepted: 1 November 2019 / Published: 4 November 2019
DNA methylation patterns have been shown to change throughout the normal aging process. Several studies have found epigenetic aging markers using age predictors, but these studies only focused on blood-specific or tissue-common methylation patterns. Here, we constructed nine tissue-specific age prediction models using methylation array data from normal samples. The constructed models predict the chronological age with good performance (mean absolute error of 5.11 years on average) and show better performance in the independent test than previous multi-tissue age predictors. We also compared tissue-common and tissue-specific aging markers and found that they had different characteristics. Firstly, the tissue-common group tended to contain more positive aging markers with methylation values that increased during the aging process, whereas the tissue-specific group tended to contain more negative aging markers. Secondly, many of the tissue-common markers were located in Cytosine-phosphate-Guanine (CpG) island regions, whereas the tissue-specific markers were located in CpG shore regions. Lastly, the tissue-common CpG markers tended to be located in more evolutionarily conserved regions. In conclusion, our prediction models identified CpG markers that capture both tissue-common and tissue-specific characteristics during the aging process.
Keywords: epigenetics; DNA methylation; age prediction, tissue-specific methylation epigenetics; DNA methylation; age prediction, tissue-specific methylation
MDPI and ACS Style

Choi, H.; Joe, S.; Nam, H. Development of Tissue-Specific Age Predictors Using DNA Methylation Data. Genes 2019, 10, 888.

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