RT Journal Article
SR Electronic
T1 Improved prediction of chronological age from DNA methylation limits it as a biomarker of ageing
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 327890
DO 10.1101/327890
A1 Qian Zhang
A1 Costanza L. Vallerga
A1 Rosie M Walker
A1 Tian Lin
A1 Anjali K. Henders
A1 Grant W. Montgomery
A1 Ji He
A1 Dongsheng Fan
A1 Javed Fowdar
A1 Martin Kennedy
A1 Toni Pitcher
A1 John Pearson
A1 Glenda Halliday
A1 John B. Kwok
A1 Ian Hickie
A1 Simon Lewis
A1 Tim Anderson
A1 Peter A. Silburn
A1 George D. Mellick
A1 Sarah E. Harris
A1 Paul Redmond
A1 Alison D. Murray
A1 David J. Porteous
A1 Christopher S. Haley
A1 Kathryn L. Evans
A1 Andrew M. McIntosh
A1 Jian Yang
A1 Jacob Gratten
A1 Riccardo E. Marioni
A1 Naomi R. Wray
A1 Ian J. Deary
A1 Allan F. McRae
A1 Peter M. Visscher
YR 2018
UL http://biorxiv.org/content/early/2018/10/28/327890.abstract
AB 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 Horvath’s across-tissue age predictor.