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New targeted approaches for epigenetic age predictions
- Source :
- BMC Biology, Vol 18, Iss 1, Pp 1-15 (2020)
- Publication Year :
- 2020
- Publisher :
- BMC, 2020.
-
Abstract
- Abstract Background Age-associated DNA methylation changes provide a promising biomarker for the aging process. While genome-wide DNA methylation profiles enable robust age-predictors by integration of many age-associated CG dinucleotides (CpGs), there are various alternative approaches for targeted measurements at specific CpGs that better support standardized and cost-effective high-throughput analysis. Results In this study, we utilized 4647 Illumina BeadChip profiles of blood to select CpG sites that facilitate reliable age-predictions based on pyrosequencing. We demonstrate that the precision of DNA methylation measurements can be further increased with droplet digital PCR (ddPCR). In comparison, bisulfite barcoded amplicon sequencing (BBA-seq) gave slightly lower correlation between chronological age and DNA methylation at individual CpGs, while the age-predictions were overall relatively accurate. Furthermore, BBA-seq data revealed that the correlation of methylation levels with age at neighboring CpG sites follows a bell-shaped curve, often associated with a CTCF binding site. We demonstrate that within individual BBA-seq reads the DNA methylation at neighboring CpGs is not coherently modified, but reveals a stochastic pattern. Based on this, we have developed a new approach for epigenetic age predictions based on the binary sequel of methylated and non-methylated sites in individual reads, which reflects heterogeneity in epigenetic aging within a sample. Conclusion Targeted DNA methylation analysis at few age-associated CpGs by pyrosequencing, BBA-seq, and particularly ddPCR enables high precision of epigenetic age-predictions. Furthermore, we demonstrate that the stochastic evolution of age-associated DNA methylation patterns in BBA-seq data enables epigenetic clocks for individual DNA strands.
Details
- Language :
- English
- ISSN :
- 17417007
- Volume :
- 18
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- BMC Biology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.94093329efc745ce9dd69232bf5409dc
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s12915-020-00807-2