ArticleGenes2019
Development of Tissue-Specific Age Predictors Using DNA Methylation Data.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Consensus Paper: Cerebellum and Ageing.Cerebellum (London, England) · 2024Review
- A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator.BMC bioinformatics · 2023Article
- Refining epigenetic prediction of chronological and biological age.Genome medicine · 2023Article
- PTSD, major depression, and advanced transcriptomic age in brain tissue.Depression and anxiety · 2022Article
- Article
- Review
- The relationship between ageing and changes in the human blood and brain methylomes.NAR genomics and bioinformatics · 2022Article
- Genotoxicity and Epigenotoxicity of Carbazole-Derived Molecules on MCF-7 Breast Cancer Cells.International journal of molecular sciences · 2021Article
- Article
- Epigenetic modifications and the development of kidney graft fibrosis.Current opinion in organ transplantation · 2021Review
- Molecular Aspects of Senescence and Organismal Ageing-DNA Damage Response, Telomeres, Inflammation and Chromatin.International journal of molecular sciences · 2021Review
- Current perspectives on the cellular and molecular features of epigenetic ageing.Experimental biology and medicine (Maywood, N.J.) · 2020Review
- DNA Methylation Clocks and Their Predictive Capacity for Aging Phenotypes and Healthspan.Neuroscience insights · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.