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Impact of genetic ancestry on chronological age prediction using DNA methylation analysis

Published:September 20, 2017DOI:https://doi.org/10.1016/j.fsigss.2017.09.162

      Abstract

      Several forensic age predictors based on DNA methylation have been developed, reporting a considerable number of CpG loci highly correlated with chronological age. Most of the published age prediction models so far were based on European samples, and therefore there is a need to explore additional worldwide populations. We performed a comprehensive analysis of methylation profiles from buccal mucosa samples of three independent population groups from Middle East, West Africa and Central Europe. For each population approx. 50 samples with an equal distribution of donor ages were collected. Using bisulfite pyrosequencing we analyzed the age dependent methylation changes at five CpG sites in the genes of ASPA, ITGA2B, PDE4C and ELOVL2 and predicted the donor ages using two previously reported age prediction models. Our results display significantly lower dispersions of methylations and thus errors of predicted ages in the Middle East population compared to the Central Europe and West African populations. Interestingly, the overall methylation variation was highly similar between the three populations at all investigated CpGs.

      Keywords

      1. Introduction

      Due to its variable characteristics DNA methylation is a promising marker candidate for the prediction of variable traits like human aging. Several studies developed highly accurate age prediction models based on DNA methylation markers with accuracies of predicted ages between 3 and 5 years mean absolute deviation [
      • Eipel M.
      • Mayer F.
      • Arent T.
      • et al.
      Epigenetic age predictions based on buccal swabs are more precise in combination with cell type-specific DNA methylation signatures.
      ,
      • Zbieć-Piekarska R.
      • Spólnicka M.
      • Kupiec T.
      • et al.
      Development of a forensically useful age prediction method based on DNA methylation analysis.
      ,
      • Freire-Aradas A.
      • Phillips C.
      • Mosquera-Miguel A.
      • et al.
      Development of a methylation marker set for forensic age estimation using analysis of public methylation data and the Agena Bioscience EpiTYPER system.
      ]. Nevertheless the variability of DNA methylation may also be a major factor causing deviations since numerous environmental factors may alter DNA methylation patterns in unexpected ways. To define the suitability and robustness of methylation based age prediction models it is thus highly important to collect the most representative data about the CpG markers as well as to identify any impacts on their methylation status. To this end most of the studies already considered the effects of common lifestyle parameters and diseases [
      • Weidner C.I.
      • Lin Q.
      • Koch C.M.
      • et al.
      Aging of blood can be tracked by DNA methylation changes at just three CpG sites.
      ]. However, the influence of biogeographic origin on DNA methylation has not been considered yet.

      2. Material and methods

      All analyzed human samples originated from buccal mucosa swabs from routine paternity work obtained with informed consent. DNA was extracted using the EZ1 or QIAsymphony instruments and kits (Qiagen, Hilden, Germany) and stored at −20 °C. 100 ng of DNA was bisulfite converted using the Bisulfite Fast Conversion Kit (Qiagen) and separated in equal quantities into four PCR assays. The methylation levels at CpG1 in ASPA (cg02228185), CpG2 in ITGA2B (cg25809905), CpG1 in PDE4C (one CpG position upstream cg17861230) and CpG5 & CpG7 in ELOVL2 (GRCh38: 11044642 & 11044634) were analyzed via pyrosequencing using the PyroMark Q48 instrument and reagents (Qiagen). For the prediction of donor ages we applied our methylation data to the prediction models from Eipel at al. [
      • Eipel M.
      • Mayer F.
      • Arent T.
      • et al.
      Epigenetic age predictions based on buccal swabs are more precise in combination with cell type-specific DNA methylation signatures.
      ] including the CpG sites in ASPA, ITGA2B and PDE4C and Zbieć-Piekarska et al. [
      • Zbieć-Piekarska R.
      • Spólnicka M.
      • Kupiec T.
      • et al.
      Development of a forensically useful age prediction method based on DNA methylation analysis.
      ] including both CpG sites in ELOVL2. Mentionable, the model data from Zbieć-Piekarska et al. are based on data from blood samples. However, in a previous unpublished study we found that this model predicts donor ages from buccal mucosa samples sufficiently well albeit with a constant overestimation of donor ages. Since this study only focused on correlations between actual ages and predicted ages and not on the predicted age values itself, this deviation doesn’t affect the results. Spearman correlations (=corr.) were analyzed using the statistical software package R.

      3. Results and discussion

      In the present study we analyzed the correlations between DNA methylation and chronological age for Middle Eastern, Central European and West African population groups. Conforming to the results of previous studies we observed relatively low correlations for the CpG sites in ASPA and ITGA2B (around 0.5) but high correlations for the three CpG sites in PDE4C and ELOVL2 with values of around 0.8 or even 0.9 (Table 1). Mentionable, the overall changes in terms of methylation levels and variations were highly similar between the populations at all investigated CpG sites. Interestingly, at all CpG sites the dispersion of the methylation values constantly increased starting with the Middle East, followed by the central European and then the West African population (from low to high) (shown for CpG7 in ELOVL2 in Fig. 1). Consequently, the predicted ages showed the same trend. Since the dispersion of the methylation values is directly linked to the accuracy of the age prediction models our results indicate that age predictions might be performed with higher accuracies for individuals from the Middle East compared to individuals from Central Europe and West Africa. However, with only 50 samples per population group our sample sets are most likely too small to sufficiently model the age dependent methylation for whole populations. Thus, further studies on larger datasets have to validate the population specific dispersions of methylation that are indicated in the present study.
      Table 1CpG methylation and predicted age correlations with actual ages.
      Methylation site (corr.)Age predictions
      PopulationASPA CpG1ITGA2B CpG2PDE4C CpG1ELOVL2 CpG5ELOVL2 CpG7Model [1]Model [2]
      Middle East−0.49−0.310.940.890.930.950.93
      Central Europe−0.6−0.310.880.880.840.880.89
      West Africa−0.47−0.280.830.780.780.830.84
      Fig. 1
      Fig. 1Dispersion diagrams of methylations at CpG 7 in ELOVL2 for the three populations under analysis. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
      The plots show the dispersion diagrams for CpG 7 in ELOVL2. X-axes represent the actual ages while the Y-axes plot the methylation values. Data of the Middle East population (black), Central Europe (red) and West African (green) populations were plotted separately. The corresponding Spearman correlation coefficients and sample sizes are depicted inside each plot.

      4. Conclusion

      The results of our study give first evidence that the strength of correlation between methylation and chronological age and thus the accuracy of age predictions might vary between populations. It would further mean that the age prediction models may have to be adjusted for different populations of origin. Consequently it might be required to include ancestry informative markers into the analysis as an additional factor for age prediction models. For chronological age predictions those CpG markers can be then incorporated that show the strongest correlation within the determined population. These adjustments would indeed result in an increased complexity of the models but might also raise the accuracy of age predictions which is the overall goal.

      Conflict of interest statement

      The authors have nothing to disclose.

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