Improved prediction of chronological age from DNA methylation limits it as a biomarker of ageing
Year: 2018
Type: preprint
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 Horvath’s across-tissue age predictor.
DOI: 10.1101/327890
Authors: 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 Halliday, John 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 L. Evans, Andrew M. McIntosh, Jian Yang, Jacob Gratten, Riccardo E. Marioni, Naomi R. Wray, Ian J. Deary, Allan F. McRae, Peter M. Visscher
Institutions: The University of Queensland, Institute for Molecular Bioscience, Institute of Genetics and Cancer, University of Edinburgh, Peking University, Peking University Third Hospital, Griffith University, University of Otago, New Zealand Brain Research Institute, The University of Sydney, University of Aberdeen, MRC Human Genetics Unit, Royal Edinburgh Hospital
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: G2437620908, G259633682, Phenotypic profiling from DNA using genetic and epigenetic information, G3061990085, G3229869697, Complex trait genomics, Using genomics to understand psychiatric disorders, Stratifying Resilience and Depression Longitudinally (STRADL), G4527525017, G5026251150, Understanding the etiology of psychiatric disorders through whole genome analyses, G5834621717, Methods and software tool for complex trait analyses using multi-omics data, Stratifying Resilience and Depression Longitudinally (STRADL), Neurogenetics and Statistical Genomics, G6898834946, G7304791810, Genetic analysis of the relationship between parental age and risk of psychiatric disorders, G839044655, G8724570043, G8812362641
OpenAlex ID: W2804159311