bioRxivBioinformaticsPreprint
Improved prediction of chronological age from DNA methylation limits it as a biomarker of ageing
Qian ZhangiD, Costanza Ludovica VallergaiD, Rosie May WalkeriD, Tian LiniD, Anjali K. HendersiD, Grant W. MontgomeryiD, Ji HeiD, Dongsheng FaniD, Javed FowdariD, Martin Alexander KennedyiD, Toni L. PitcheriD, John Pearson, Glenda Margaret HallidayiD, John B. KwokiD, Ian Bernard HickieiD, Simon J.G. LewisiD, Tim AndersoniD, Peter A. Silburn, George D. MellickiD, Sarah E. HarrisiD, Paul H. RedmondiD, Alison D. MurrayiD, David John PorteousiD, Chris HaleyiD, Kathryn Louise EvansiD, , Peter M. VisscheriD
Abstract
DNA methylation is associated with age. The deviation of age predicted from DNA methylation from actual age has been proposed as a biomarker for ageing. However, a better prediction of chronological age implies less opportunity for biological age. Here we used 13,661 samples (from blood and saliva) in the age range of 2 to 104 years from 14 cohorts measured on Illumina HumanMethylation450/EPIC arrays to perform prediction analyses. We show that increasing the sample size achieves a smaller prediction error and higher correlations in test datasets. We demonstrate that smaller prediction errors provide a limit to how much variation in biological ageing can be captured by methylation and provide evidence that age predictors from small samples are prone to confounding by cell composition. Our predictor shows a similar or better performance in non-blood tissues including saliva, endometrium, breast, liver, adipose and muscle, compared with Horvaths across-tissue age predictor.
- organism: human
- assay: DNA methylation
- research focus: differential expression
- approach: statistical model
- Citations
- 16
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