Human age prediction using DNA methylation and regression methods

  • Priya Karir
  • Neelam Goel
  • Vivek Kumar GargEmail author
Original Research


Determination of a person’s age can be an important factor in forensic investigation. DNA methylation (DNAm) is a well-known factor signifying change during the aging process but also necessary for the development of mammals. Several studies reported that DNAm can be used as an important marker in predicting the age of a human. This study is carried out to develop the age prediction model using three different regression methods. Multiple linear regression, Support vector regression, and Random forest regression methods are applied using a set of four highly age-correlated CpG sites. For 180 blood samples having age between 2 and 87 years, the mean absolute deviation (MAD) for multiple linear regression method is 8.43 years, for support vector regression is 7.86 years and for random forest regression method is 8.25 years. Further, these models are tested on five different age-groups. The average MAD for multiple linear regression, support vector regression and random forest regression are 3.46, 3.44 and 3.56, respectively. Support vector regression gave the highest accuracy for combined samples as well as for 5 different age groups. It has been concluded from the results that support vector regression is a reliable method for human age prediction.


Age Chronological CpG sites DNA methylation Epigenetic Regression 



There is no funding source.

Compliance with ethical standards

Conflict of interest

All authors of this paper have no actual or potential conflict of interest including any financial, personal or other relationships with other people or organization.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Lim DHK, Maher ER (2010) DNA methylation: a form of epigenetic control of gene expression. Obstet Gynaecol 12:37–42. CrossRefGoogle Scholar
  2. 2.
    Jung M, Pfeifer GP (2015) Aging and DNA methylation. BMC Biol 13:7. CrossRefGoogle Scholar
  3. 3.
    Garg VK, Kashyap D, Tuli HS (2018) Targeting telomerase and topoisomerase-II by natural moieties: an anti-cancer approach. Nov Approach Cancer Study 1:3–4. CrossRefGoogle Scholar
  4. 4.
    Goel N, Karir P, Garg VK (2017) Role of DNA methylation in human age prediction. Mech Ageing Dev 166:33–41. CrossRefGoogle Scholar
  5. 5.
    Stenvinkel P, Karimi M, Johansson S et al (2007) Impact of inflammation on epigenetic DNA methylation: a novel risk factor for cardiovascular disease? J Intern Med 261:488–499. CrossRefGoogle Scholar
  6. 6.
    Jones MJ, Goodman SJ, Kobor MS (2015) DNA methylation and healthy human aging. Aging Cell 14:924–932. CrossRefGoogle Scholar
  7. 7.
    Jin B, Li Y, Robertson KD (2011) DNA methylation: superior or subordinate in the epigenetic hierarchy? Genes Cancer 2:607–617. CrossRefGoogle Scholar
  8. 8.
    Li E, Zhang Y (2014) DNA methylation in mammals. Cold Spring Harb Perspect Biol 6:2014. CrossRefGoogle Scholar
  9. 9.
    Goel N, Garg VK (2018) Aging in humans and role of DNA methylation. EC Pharmacol Toxicol 6:891–892Google Scholar
  10. 10.
    Singh S, Kaur S, Goel N (2015) A review of computational intelligence methods for eukaryotic promoter prediction. Nucleosides Nucleotides Nucl Acids 34:449–462. CrossRefGoogle Scholar
  11. 11.
    Leung C, Tsai K (2013) DNA methylation in aggressive gastric carcinoma. Gastric Carcinoma-New Insights into Curr Manag. CrossRefGoogle Scholar
  12. 12.
    He X-J, Chen T, Zhu J-K (2011) Regulation and function of DNA methylation in plants and animals. Cell Res 54:442–465. CrossRefGoogle Scholar
  13. 13.
    Zampieri M, Ciccarone F, Calabrese R et al (2015) Reconfiguration of DNA methylation in aging. Mech Ageing Dev 151:60–70. CrossRefGoogle Scholar
  14. 14.
    Moore LD, Le T, Fan G (2013) DNA methylation and its basic function. Neuropsychopharmacology 38:23–38. CrossRefGoogle Scholar
  15. 15.
    Robertson KD (2005) DNA methylation and human disease. Nat Rev Genet 6:597–610. CrossRefGoogle Scholar
  16. 16.
    Papin C, Ibrahim A, Le Gras S et al (2017) Combinatorial DNA methylation codes at repetitive elements. Genome Res 27:934–946. CrossRefGoogle Scholar
  17. 17.
    McClintock B (1956) Controlling elements and the gene. Cold Spring Harb Symp Quant Biol 21:197–216. CrossRefGoogle Scholar
  18. 18.
    Shapiro JA, von Sternberg R (2005) Why repetitive DNA is essential to genome function. Biol Rev Camb Philos Soc 80:227–250. CrossRefGoogle Scholar
  19. 19.
    Xu C, Qu H, Wang G et al (2015) A novel strategy for forensic age prediction by DNA methylation and support vector regression model. Nat Publ Gr 5:1–10. CrossRefGoogle Scholar
  20. 20.
    Mikeska T, Craig JM (2014) DNA methylation biomarkers: cancer and beyond. Genes (Basel) 5:821–864. CrossRefGoogle Scholar
  21. 21.
    Kohli RM, Zhang Y (2013) TET enzymes, TDG and the dynamics of DNA demethylation. Nature 502:472–479. CrossRefGoogle Scholar
  22. 22.
    Chen Z, Riggs AD (2011) DNA methylation and demethylation in mammals. J Biol Chem 286:18347–18353. CrossRefGoogle Scholar
  23. 23.
    Wilson VL, Smith RA, Ma S, Cutler RG (1987) Genomic 5-methyldeoxycytidine decreases with age. J Biol Chem 262:9948–9951Google Scholar
  24. 24.
    Weidner CI, Lin Q, Koch CM et al (2014) Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol 15:R24. CrossRefGoogle Scholar
  25. 25.
    Zbieć-Piekarska R, Spólnicka M, Kupiec T et al (2015) Development of a forensically useful age prediction method based on DNA methylation analysis. Forensic Sci Int Genet 17:173–179. CrossRefGoogle Scholar
  26. 26.
    Marioni RE, Shah S, McRae AF et al (2015) DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol 16:25. CrossRefGoogle Scholar
  27. 27.
    Charlesworth B, Sniegowski P, Stephan W (1994) The evolutionary dynamics of repetitive DNA in eukaryotes. Nature 371:215–220. CrossRefGoogle Scholar
  28. 28.
    Huang Y-W, Huang TH-M, Wang L-S (2010) Profiling DNA methylomes from microarray to genome-scale sequencing. Technol Cancer Res Treat 9:139–147. CrossRefGoogle Scholar
  29. 29.
    Zeilinger S, Kühnel B, Klopp N et al (2013) Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLoS ONE 8:e63812. CrossRefGoogle Scholar
  30. 30.
    Lin Q, Wagner W (2015) Epigenetic aging signatures are coherently modified in cancer. PLoS Genet 11:e1005334. CrossRefGoogle Scholar
  31. 31.
    Xu Z, Taylor JA (2014) Genome-wide age-related DNA methylation changes in blood and other tissues relate to histone modification, expression and cancer. Carcinogenesis 35:356–364. CrossRefGoogle Scholar
  32. 32.
    Mukaka MM (2012) Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24:69–71. CrossRefGoogle Scholar
  33. 33.
    Friendly M, Denis D (2005) The early origins and development of the scatterplot. J Hist Behav Sci 41:103–130. CrossRefGoogle Scholar
  34. 34.
    Habib EAE (2012) Mean absolute deviation about median as a tool of explanatory data analysis. Int J Res Rev Appl Sci 11:517–523Google Scholar
  35. 35.
    Ngo HT (2012) The steps to follow in a multiple regression analysis. SAS Glob Forum 2012:1–12Google Scholar
  36. 36.
    Goel N, Singh S, Chand T (2015) An improved method for splice site prediction in DNA sequences using support vector machines. Procedia Comput Sci 57:358–367. CrossRefGoogle Scholar
  37. 37.
    Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Inf Process 11:203–224Google Scholar
  38. 38.
    Hofmann M (2006) Support vector machines: kernels and the kernel trick. pp 1–16Google Scholar
  39. 39.
    Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2:18–22Google Scholar
  40. 40.
    Breiman L (2001) random forests. In: Random forests. pp 1–33Google Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyUIET, Panjab UniversityChandigarhIndia
  2. 2.Department of BiochemistryGovernment Medical College and Hospital (GMCH)ChandigarhIndia

Personalised recommendations