Abstract
Chronological age estimation may offer valuable investigative leads in human identification cases. Bisulfite pyrosequencing analysis of single CpG sites on five genes (KLF14, ELOVL2, C1orf132, TRIM59, and FHL2) was performed on 264 postmortem blood samples from individuals aged 3 months to 93 years. The goals were to develop age prediction models based on the correlation between the methylation profile and chronological age and to assess the accuracy of the prediction. Linear regression between methylation levels and age at each CpG site revealed that the five markers show a statistically significant correlation with age. The methylation data from a training set of 160 postmortem blood samples were used to develop an age prediction model with a correlation coefficient of 0.65, explaining 73.1% of age variation, with a mean absolute deviation from the chronological age of 7.60 years. The accuracy of the model was evaluated with a test set of 72 samples producing a mean absolute deviation of 7.42 years. The training and test sets were also categorized by specific age groups to assess accuracy and deviation from chronological age. The data for both sets revealed a lower prediction potential as an individual increases in age, particularly for the age categories above 50 years.
Similar content being viewed by others
Data availability
Data is available upon request.
Code availability
Not applicable.
References
Lee H, Jung S, Lee E, Yang W, Shin K (2016) DNA methylation profiling for a confirmatory test for blood, saliva, semen, vaginal fluid and menstrual blood. Forensic Sci Int Genet 24:75–82. https://doi.org/10.1016/j.fsigen.2016.06.007
Vidaki A, Kayser M (2018) Recent progress, methods and perspectives in forensic epigenetics. Forensic Sci Int Genet 37:180–195. https://doi.org/10.1016/j.fsigen.2018.08.008
Unnikrishnan A, Freeman WM, Jackson J, Wren JD, Porter H, Richardson A (2019) The role of DNA methylation in epigenetics of aging. Pharmacol Ther 195:172–185. https://doi.org/10.1016/j.pharmthera.2018.11.001
Richardson B (2003) Impact of aging on DNA methylation. Ageing Res Rev 2(3):245–261. https://doi.org/10.1016/s1568-1637(03)00010-2
Christensen BC, Houseman EA, Marsit CJ, Zheng S, Wrensch MR, Wiemels JL et al (2009) Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PloS Genet 5(8):e1000602. https://doi.org/10.1371/journal.pgen.1000602
Koch CM, Wagner W (2011) Epigenetic-aging-signature to determine age in different tissues. Aging 23(10):1018–1027. https://doi.org/10.18632/aging.100395
Garagnani P, Bacalini MG, Pirazzini C, Gori D, Giuliani C, Mari D et al (2012) Methylation of ELOVL2 gene as a new epigenetic marker of age. Aging Cell 11:1132–1134. https://doi.org/10.1111/acel.12005
Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S et al (2013) Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell 49:359–367. https://doi.org/10.1016/j.molcel.2012.10.016
Weidner C, Lin Q, Koch C, Eisele L, Beier F, Ziegler P et al (2014) Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol 15(2):R24. https://doi.org/10.1186/gb-2014-15-2-r24
Florath I, Butterbach K, Muller H, Bewerunge-Hudler M, Brenner H (2013) Cross-sectional and longitudinal changes in DNA methylation with age: an epigenome-wide analysis revealing over 60 novel age-associated CpG sites. Hum Mol Genet 23(5):1186–1201. https://doi.org/10.1093/hmg/ddt531
Bocklandt S, Lin W, Sehl ME, Sánchez FJ, Sinsheimer JS, Horvath S et al (2011) Epigenetic predictor of age. PLoS One 6:e14821. https://doi.org/10.1371/journal.pone.0014821
Eipel M, Mayer F, Arent T, Ferreira MR, Birkhofer C, Gerstenmaier U et al (2016) Epigenetic age predictions based on buccal swabs are more precise in combination with cell type-specific DNA methylation signatures. Aging 8:1034–1048. https://doi.org/10.18632/aging.100972
Hong S, Jung S, Lee E, Shin K, Yang W, Lee H (2017) DNA methylation-based age prediction from saliva: high age predictability by a combination of 7 CpG markers. Forensic Sci Int Genet 29:118–125. https://doi.org/10.1016/j.fsigen.2017.04.006
Hernandez DG, Nalls MA, Gibbs JR, Arepalli S, van der Brug M, Chong S et al (2011) Distinct DNA methylation changes highly correlated with chronological age in the human brain. Hum Mol Genet 20(6):1164–1172. https://doi.org/10.1093/hmg/ddq561
Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MP, van Eijk K et al (2012) Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol 13(10):R97. https://doi.org/10.1186/gb-2012-13-10-r97
Day K, Waite LL, Thalacker-Mercer A, West A, Bamman MM, Brooks JD et al (2013) Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape. Genome Biol 14:R102. https://doi.org/10.1186/gb-2013-14-9-r102
Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14(10):R115. https://doi.org/10.1186/gb-2013-14-10-r115
Slieker R, Relton C, Gaunt SP, Heijmans B (2018) Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception. Epigenetics Chromatin 11:25. https://doi.org/10.1186/s13072-018-0191-3
Freire-Aradas A, Philips C, Lareu M (2017) Forensic individual age estimation with DNA: from initial approaches to methylation tests. Forensic Sci Rev 29(2):121–144
Ronaghi M, Karamohamed S, Pettersson B, Uhlén M, Nyrén P (1996) Real-time DNA sequencing using detection of pyrophosphate release. Anal Biochem 242:84–89. https://doi.org/10.1006/abio.1996.0432
Colella S, Shen L, Baggerly KA, Issa PJJ, Krahe R (2003) Sensitive and quantitative universal PyrosequencingTM methylation analysis of CpG sites. Biotechniques 35(1):146–150. https://doi.org/10.2144/03351md01
Tost J, Gut IG (2007) DNA methylation analysis by pyrosequencing. Nat Protocols 2:2265–2275. https://doi.org/10.1038/nprot.2007.314
Delaney C, Garg SK, Yung R (2015) Analysis of DNA methylation by pyrosequencing. Methods Mol Biol 1343:249–264. https://doi.org/10.1007/978-1-4939-2963-4_19
Zbieć-Piekarska R, Spólnicka M, Kupiec T, Parys-Proszek A, Makowska Ż, Pałeczka A et al (2015) Development of a forensically useful age prediction method based on DNA methylation analysis. Forensic Sci Int Genet 17:173–179. https://doi.org/10.1016/j.fsigen.2015.05.001
Cho S, Jung SE, Hong SR, Lee EH, Lee JH, Lee SD et al (2017) Independent validation of DNA-based approaches for age prediction in blood. Forensic Sci Int Genet 29:250–256. https://doi.org/10.1016/j.fsigen.2017.04.020
Bekaert B, Kamalandua A, Zapico SC, Van de Voorde W, Decorte R (2015) Improved age determination of blood and teeth samples using a selected set of DNA methylation markers. Epigenetics 10(10):922–930. https://doi.org/10.1080/15592294.2015.1080413
Hamano Y, Manabe S, Morimoto C, Fujimoto S, Ozeki M, Tamaki K (2016) Forensic age prediction for dead or living samples by use of methylation-sensitive high resolution melting. Leg Med 21:5–10. https://doi.org/10.1016/j.legalmed.2016.05.001
Naue J, Hoefsloot H, Mook O, Rijlaarsdam-Hoekstra L, van der Zwalm M, Henneman P et al (2017) Chronological age prediction based on DNA methylation: massive parallel sequencing and random forest regression. Forensic Sci Int Genet 31:19–28. https://doi.org/10.1016/j.fsigen.2017.07.015
Correia Dias H, Cordeiro C, Corte Real F, Cunha E, Manco L (2019) Age estimation based on DNA methylation using blood samples from deceased individuals. J Forensic Sci 65(2):465–470. https://doi.org/10.1111/1556-4029.14185
Chan YH (2003) Biostatistics 104: correlational analysis. Singap Med J 44(12):614–619
Zbieć-Piekarska R, Spólnicka M, Kupiec T, Makowska Ż, Spas A, Parys-Proszek A et al (2015) Examination of DNA methylation status of the ELOVL2 marker may be useful for human age prediction in forensic science. Forensic Sci Int Genet 14:161–167. https://doi.org/10.1016/j.fsigen.2014.10.002
Marioni RE, Shah S, McRae AF, Chen BH, Colicino E, Harris SE et al (2015) DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol 16:25. https://doi.org/10.1186/s13059-015-0584-6
Pavanello S, Campisi M, Tona F, Lin CD, Iliceto S (2019) Exploring epigenetic age in response to intensive relaxing training: a pilot study to slow down biological age. Int J Environ Res Public Health 16(17):3074. https://doi.org/10.3390/ijerph16173074
Jovanovic T, Vance LA, Cross D, Knight AK, Kilaru V, Michopoulos V et al (2017) Exposure to violence accelerates epigenetic aging in children. Sci Rep 7(1):8962. https://doi.org/10.1038/s41598-017-09235-9
Hughes A, Smart M, Gorrie-Stone T, Hannon E, Mill J, Bao Y et al (2018) Socioeconomic position and DNA methylation age acceleration across the life course. Am J Epidemiol 187:2346–2354. https://doi.org/10.1093/aje/kwy155
Austin MK, Chen E, Ross KM, McEwen LM, Maclsaac JL, Kobor MS et al (2018) Early-life socioeconomic disadvantage, not current, predicts accelerated epigenetic aging of monocytes. Psychoneuroendocrinology 97:131–134. https://doi.org/10.1016/j.psyneuen.2018.07.007
Fiorito G, McCrory C, Robinson O, Carmeli C, Rosales CO, Zhang Y et al (2019) Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: a multi-cohort analysis. Aging 11(7):2045–2070. https://doi.org/10.18632/aging.101900
McCartney DL, Stevenson AJ, Walker RM, Gibson J, Morris SW, Campbell A et al (2018) Investigating the relationship between DNA methylation age acceleration and risk factors for Alzheimer’s disease. Alzheimers Dement (Amst) 10:429–437. https://doi.org/10.1016/j.dadn.2018.05.006
Rosen AD, Robertson KD, Hlady RA, Muench C, Lee J, Philibert R et al (2018) DNA methylation age is accelerated in alcohol dependence. Transl Psychiatry 8(1):182. https://doi.org/10.1038/s41398-018-0233-4
Lévesque KF, Casey M, Szyf E, Ismaylova V, Ly MP, Verner M et al (2014) Genome-wide DNA methylation variability in adolescent monozygotic twins followed since birth. Epigenetics 9(10):1410–1421. https://doi.org/10.4161/15592294.2014.970060
Fleckhaus J, Freire-Aradas A, Rothschild M, Schneider P (2017) Impact of genetic ancestry on chronological age prediction using DNA methylation analysis. Forensic Sci Int Genet Suppl Ser 6:e399–e400
Tajuddin SM, Hernandez DG, Chen BH, Noren Hooten N, Mode NA, Nalls MA et al (2019) Novel age-associated DNA methylation changes and epigenetic age acceleration in middle-aged African Americans and whites. Clin Epigenetics 11(1):119. https://doi.org/10.1186/s13148-019-0722-1
Thurston RC, Carroll JE, Levine M, Chang Y, Crandall C, Manson JE et al (2020) Vasomotor symptoms and accelerated epigenetic aging in the Women’s Health Initiative (WHI). J Clin Endocrinol Metab 105(4):1221–1227. https://doi.org/10.1210/clinem/dgaa081
Horvath S, Gurven M, Levine ME, Trumble BC, Kaplan H, Allayee H et al (2016) An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol 17(1):171. https://doi.org/10.1186/s13059-016-1030-0
Spolnicka M, Pospiech E, Peplonska B, Zbiec-Piekarska R, Makowska Z, Pieta A et al (2017) DNA methylation in ELOVL2 and C1orf132 correctly predicted chronological age of individuals from three disease groups. Int J Legal Med 132:1–11. https://doi.org/10.1007/s00414-017-1636-0
Spolnicka M, Zbiec-Piekarska R, Karp M, Machnicki MM, Wlasiuk P, Makowska Z et al (2018) DNA methylation signature in blood does not predict calendar age in patients with chronic lymphocytic leukemia but may alert to the presence of disease. Forensic Sci Int Genet 34:e15–e17. https://doi.org/10.1016/j.fsigen.2018.02.004
Perna L, Zhang Y, Mons U, Holleczek B, Saum KU, Brenner H (2016) Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin Epigenetics 8:64. https://doi.org/10.1186/s13148-016-0228-z
Zheng Y, Joyce BT, Colicino E, Liu L, Zhang W, Dai Q et al (2016) Blood epigenetic age may predict cancer incidence and mortality. EBioMedicine 5:68–73. https://doi.org/10.1016/j.ebiom.2016.02.008
Ambatipudi S, Horvath S, Perrier F, Cuenin C, Hernandez-Vargas H, Le Calvez-Kelm F et al (2017) DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility. Eur J Cancer 75:299–307. https://doi.org/10.1016/j.ejca.2017.01.014
Levine ME, Hosgood HD, Chen B, Absher D, Assimes T, Horvath S (2015) DNA methylation age of blood predicts future onset of lung cancer in the women’s health initiative. Aging 7(9):690–700. https://doi.org/10.18632/aging.100809
Durso DF, Bacalini MG, Sala C, Pirazzini C, Marasco E, Bonafé M et al (2017) Acceleration of leukocytes’ epigenetic age as an early tumor and sex-specific marker of breast and colorectal cancer. Oncotarget 8(14):23237–23245. https://doi.org/10.18632/oncotarget.15573
Acknowledgements
From the Los Angeles County Department of Medical Examiner-Coroner, the authors thank Dr Ruby Javed-Ghaffar, Chief of the Forensic Laboratories; Mr Eric Wahoske, previous supervisor of the Human Genomics Unit; and Sarah de Quintana, Chair of the Research and Publication Committee during this project, for providing approval for the use of postmortem blood samples. We also thank DNA analysts Oscar Pleitez and Naomi Weisz for facilitating the extracted postmortem blood samples. The authors extend thanks to Qiagen for supplying reagents and for the scientific collaboration by Sim Winitz and John Haley in providing training and data review.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethics declarations
The LACDoME-C Research Committee approved this study before initiating the research. The use of decedent samples is permitted to validate new technologies and analysis methods at the LACDoME-C. The results for all samples utilized in this study were reported without personal identifying information (PII) other than the decedent’s age.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yessenia Anaya and Patrick Yew are co-first authors
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Anaya, Y., Yew, P., Roberts, K.A. et al. DNA methylation of decedent blood samples to estimate the chronological age of human remains. Int J Legal Med 135, 2163–2173 (2021). https://doi.org/10.1007/s00414-021-02650-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00414-021-02650-8