Improved prediction of chronological age from DNA methylation limits it as a biomarker of ageing
By
Qian Zhang,
Costanza L. Vallerga,
Rosie M Walker,
Tian Lin,
Anjali K. Henders,
Grant W Montgomery,
Ji He,
Dongsheng Fan,
Javed Fowdar,
Martin Kennedy,
Toni Pitcher,
John Pearson,
Glenda M. Halliday,
J. B. Kwok,
Ian Hickie,
Simon Lewis,
Tim Anderson,
Peter A. Silburn,
George D. Mellick,
Sarah E. Harris,
Paul Redmond,
Alison D Murray,
David J Porteous,
Christopher S. Haley,
Kathryn Evans,
Andrew McIntosh,
Jian Yang,
Jacob Gratten,
Riccardo E Marioni,
Naomi R. Wray,
Ian J Deary,
Allan F. McRae,
Peter M Visscher
Posted 23 May 2018
bioRxiv DOI: 10.1101/327890
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 in the age range of 2 to 104 years from 14 cohorts measured on Illumina HumanMethylation450/EPIC arrays to perform prediction analyses using Elastic Net and Best Linear Unbiased Prediction. We show that increasing the sample size achieves a smaller prediction error and higher correlations in test datasets. Our predictors achieved prediction errors of about 4.5 years across cohorts, in contrast to >7 years for the widely-used Horvath and Hannum predictors. 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.
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