A multi-tissue age prediction model based on DNA methylation analysis

Alsaleh, Hussain and McCallum, Nicola A. and Halligan, Daniel L. and Haddrill, Penelope R. (2017) A multi-tissue age prediction model based on DNA methylation analysis. Forensic Science International: Genetics Supplement Series, 6. e62-e64. ISSN 1875-1768

[img]
Preview
Text (Alsaleh-etal-FSI-2017-A-multi-tissue-age-prediction-model-based-on-DNA-methylation-analysis)
Alsaleh_etal_FSI_2017_A_multi_tissue_age_prediction_model_based_on_DNA_methylation_analysis.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (254kB)| Preview

    Abstract

    Age related tissue-specific DNA methylation markers have been identified in many studies, which can be used to estimate the chronological age of an unknown biological sample’s donor. However, if these markers have been used on the wrong type of tissue, they will give an inaccurate age estimation. This research has therefore examined HumanMethylation450 (HM450) BeadChip-based profiles retrieved from the NCBI repository, with the aim of identifying a set of universal DNA methylation markers across forensically relevant tissues. By using elastic net regression, it was possible to identify 10 age-related (AR) DNA methylation markers across 41 samples coming from five types of tissue (whole blood, saliva, semen, menstrual blood, and vaginal secretions). The average predictive accuracy of the constructed model based on training data is 3.8 years. In an independent dataset of 24 samples from four types of tissues (blood, saliva, menstrual blood, and vaginal secretions), the mean absolute deviation for the menstrual blood and vaginal fluid is 6.9 years, 5.6 years for buccal swabs, and 7.8 years for blood. The overall multi-tissue accuracy rate based on bootstrap analysis was 7.8 years (95% Confidence Interval 6–9.7 years). The identified multi-tissue age prediction model has the potential to assist forensic investigations without the requirement to identify the sample body fluid type.