Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Inter-laboratory adaption of age estimation models by DNA methylation analysis—problems and solutions

Abstract

In recent years, a lot of age prediction models based on different CpG motives in different cell types were published determining the biological age of a person by DNA methylation. For a general employment of this technique, maybe even as a routine method, the cross-laboratory application of such models has to be examined. Therefore, we tested two different published age prediction models for blood and mouth swab samples with regard to prediction accuracy (Bekaert et al Epigenetics 10:922–930, 2015a; Bekaert et al Forensic Sci Int Genet Suppl Ser 5:e144–e145, 2015b). Both models are based on CpG sites of four genes (ASPA, EDARADD, PDE4-C, and ELOVL2), but with a different combination of CpGs for the two tissue types. A mean absolute difference (MAD) between chronological and predicted age of 9.84 and 8.32 years for blood and buccal swab models could be demonstrated, respectively, which is significantly worse than the published data, probably due to higher DNA methylation variances in some CpGs. By retraining both prediction models, the prediction accuracy could be improved to a MAD of 5.55 and 4.65 years for the renewed blood and buccal swab model, respectively. This study demonstrates the usefulness of effective DNA standards to normalize DNA methylation data for better comparison of study results.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. 1.

    Jones MJ, Goodman SJ, Kobor MS (2015) DNA methylation and healthy human aging. Aging Cell 14:924–932. https://doi.org/10.1111/acel.12349

  2. 2.

    Boyd-Kirkup JD, Green CD, Wu G, Wang D, Han JD (2013) Epigenomics and the regulation of aging. Epigenomics 5:205–227. https://doi.org/10.2217/epi.13.5

  3. 3.

    Johnson AA, Akman K, Calimport SRG, Wuttke D, Stolzing A, de Magalhães JP (2012) The role of DNA methylation in aging, rejuvenation, and age-related disease. Rejuvenation Res 15:483–494. https://doi.org/10.1089/rej.2012.1324

  4. 4.

    Fraga MF (2009) Genetic and epigenetic regulation of aging. Curr Opin Immunol 21:446–453. https://doi.org/10.1016/j.coi.2009.04.003

  5. 5.

    Sedivy JM, Banumathy G, Adams PD (2008) Aging by epigenetics-a consequence of chromatin damage? Exp Cell Res 314:1909–1917. https://doi.org/10.1016/j.yexcr.2008.02.023

  6. 6.

    Zampieri M, Ciccarone F, Calabrese R, Franceschi C, Bürkle A, Caiafa P (2015) Reconfiguration of DNA methylation in aging. Mech Ageing Dev 151:60–70. https://doi.org/10.1016/j.mad.2015.02.002

  7. 7.

    Weber M, Schübeler D (2007) Genomic patterns of DNA methylation: targets and function of an epigenetic mark. Curr Opin Cell Biol 19:273–280. https://doi.org/10.1016/j.ceb.2007.04.011

  8. 8.

    Xu C, Qu H, Wang G et al (2015) A novel strategy for forensic age prediction by DNA methylation and support vector regression model. Sci Rep 5:1–10. https://doi.org/10.1038/srep17788

  9. 9.

    Bekaert B, Kamalandua A, Zapico SC et al (2015) A selective set of DNA-methylation markers for age determination of blood, teeth and buccal samples. Forensic Sci Int Genet Suppl Ser 5:e144–e145. https://doi.org/10.1016/j.fsigss.2015.09.058

  10. 10.

    Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, Deconde R, Chen M, Rajapakse I, Friend S, Ideker T, Zhang K (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

  11. 11.

    Johansson Å, Enroth S, Gyllensten U (2013) Continuous aging of the human DNA methylome throughout the human lifespan. PLoS One 8:e67378. https://doi.org/10.1371/journal.pone.0067378

  12. 12.

    Zbieć-Piekarska R, Spólnicka M, Kupiec T, Parys-Proszek A, Makowska Ż, Pałeczka A, Kucharczyk K, Płoski R, Branicki W (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

  13. 13.

    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. https://doi.org/10.1186/gb-2014-15-2-r24

  14. 14.

    Endo K, Li J, Nakanishi M, Asada T, Ikesue M, Goto Y, Fukushima Y, Iwai N et al (2015) Establishment of the MethyLight assay for assessing aging, cigarette smoking, and alcohol consumption. Biomed Res Int 2015:451981. https://doi.org/10.1155/2015/451981

  15. 15.

    Koch CM, Wagner W (2011) Epigenetic-aging-signature to determine age in different tissues. Aging (Albany NY) 3:1018–1027. https://doi.org/10.18632/aging.100395

  16. 16.

    Bocklandt S, Lin W, Sánchez F et al (2011) No title. PLoS One 6:e14821. https://doi.org/10.1371/journal.pone.0014821

  17. 17.

    Alisch RS, Barwick BG, Chopra P, Myrick LK, Satten GA, Conneely KN, Warren ST (2012) Age-associated DNA methylation in pediatric populations. Genome Res 22:623–632. https://doi.org/10.1101/gr.125187.111

  18. 18.

    Garagnani P, Bacalini MG, Pirazzini C, Gori D, Giuliani C, Mari D, di Blasio AM, Gentilini D, Vitale G, Collino S, Rezzi S, Castellani G, Capri M, Salvioli S, Franceschi C (2012) Methylation of ELOVL2 gene as a new epigenetic marker of age. Aging Cell 11:1132–1134. https://doi.org/10.1111/acel.12005

  19. 19.

    Bell JT, Tsai PC, Yang TP et al (2012) Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population. PLoS Genet 8:e1002629. https://doi.org/10.1371/journal.pgen.1002629

  20. 20.

    Salpea P, Russanova VR, Hirai TH, Sourlingas TG, Sekeri-Pataryas KE, Romero R, Epstein J, Howard BH (2012) Postnatal development- and age-related changes in DNA-methylation patterns in the human genome. Nucleic Acids Res 40:6477–6494. https://doi.org/10.1093/nar/gks312

  21. 21.

    Byun HM, Nordio F, Coull BA et al (2012) Temporal stability of epigenetic markers: sequence characteristics and predictors of short-term DNA methylation variations. PLoS One 7:1–9. https://doi.org/10.1371/journal.pone.0039220

  22. 22.

    Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14:R115. https://doi.org/10.1186/gb-2013-14-10-r115

  23. 23.

    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 10:922–930. https://doi.org/10.1080/15592294.2015.1080413

  24. 24.

    Daunay A, Baudrin LG, Deleuze JF, How-Kit A (2019) Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing. Sci Rep 9:1–10. https://doi.org/10.1038/s41598-019-45197-w

  25. 25.

    Declerck K, Vanden Berghe W (2018) Back to the future: epigenetic clock plasticity towards healthy aging. Mech Ageing Dev 174:18–29. https://doi.org/10.1016/j.mad.2018.01.002

  26. 26.

    Eipel M, Mayer F, Arent T et al (2016) Epigenetic age predictions based on buccal swabs are more precise in combination with cell type-specific DNA methylation signatures. Aging (Albany NY) 8:1034–1048. https://doi.org/10.18632/aging.100972

  27. 27.

    Freire-Aradas A, Phillips C, Lareu MV (2017) Forensic individual age estimation with DNA: from initial approaches to methylation tests. Forensic Sci Rev 29:121–144. https://doi.org/10.1016/j.fsigen.2017.04.020

  28. 28.

    Heinze G, Wallisch C, Dunkler D (2018) Variable selection – a review and recommendations for the practicing statistician. Biom J 60:431–449. https://doi.org/10.1002/bimj.201700067

  29. 29.

    Cho S, Jung SE, Hong SR, Lee EH, Lee JH, Lee SD, Lee HY 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

  30. 30.

    Park J, Hwan J, Seo E et al (2016) Forensic Science International: genetics identification and evaluation of age-correlated DNA methylation markers for forensic use. Forensic Sci Int Genet 23:64–70. https://doi.org/10.1016/j.fsigen.2016.03.005

  31. 31.

    Affinito O, Palumbo D, Fierro A, et al (2019) Nucleotide distance influences co-methylation between nearby CpG sites. Genomics 0–1. https://doi.org/10.1016/j.ygeno.2019.05.007

  32. 32.

    Saito D, Suyama M (2015) Linkage disequilibrium analysis of allelic heterogeneity in DNA methylation. Epigenetics 10:1093–1098. https://doi.org/10.1080/15592294.2015.1115176

  33. 33.

    Eckhardt F, Lewin J, Cortese R et al (2006) DNA methylation profiling of human chromosomes 6, 20 and 22. Nat Genet. https://doi.org/10.1038/ng1909

  34. 34.

    Zhang Y, Rohde C, Tieriing S et al (2009) DNA methylation analysis of chromosome 21 gene promoters at single base pair and single allele resolution. PLoS Genet. https://doi.org/10.1371/journal.pgen.1000438

  35. 35.

    Lövkvist C, Dodd IB, Sneppen K, Haerter JO (2016) DNA methylation in human epigenomes depends on local topology of CpG sites. Nucleic Acids Res. https://doi.org/10.1093/nar/gkw124

  36. 36.

    Haerter JO, Lövkvist C, Dodd IB, Sneppen K (2014) Collaboration between CpG sites is needed for stable somatic inheritance of DNA methylation states. Nucleic Acids Res. https://doi.org/10.1093/nar/gkt1235

Download references

Acknowledgments

The authors would like to thank Prof. Dr. Winfried Siffert (Institute of Pharmacogenetics, University Hospital Essen, Germany) for providing the pyrosequencing device and Dr. Birte Möhlendick, Iris Manthey, Stephanie Büscher, and Grit Müller for excellent technical assistance. Additionally, we thank all the donors of blood and buccal swab samples.

Author information

Correspondence to Micaela Poetsch.

Ethics declarations

All samples were obtained with approval of the Medical Ethics Committee at the University of Duisburg-Essen in accordance with the Declaration of Helsinki and national laws and with informed consent and approval of the prosecution for blood samples of corpses.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 16 kb)

ESM 2

(XLSX 78 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pfeifer, M., Bajanowski, T., Helmus, J. et al. Inter-laboratory adaption of age estimation models by DNA methylation analysis—problems and solutions. Int J Legal Med (2020). https://doi.org/10.1007/s00414-020-02263-7

Download citation

Keywords

  • Estimation of biological age
  • DNA methylation
  • Pyrosequencing
  • CpG maker