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@ARTICLE{Mayer:907806,
      author       = {Mayer, F. and Becker, J. and Reinauer, C. and Böhme, P.
                      and Eickhoff, S. B. and Koop, B. and Gündüz, T. and Blum,
                      J. and Wagner, W. and Ritz-Timme, S.},
      title        = {{A}ltered {DNA} methylation at age-associated {C}p{G} sites
                      in children with growth disorders: impact on age
                      estimation?},
      journal      = {International journal of legal medicine},
      volume       = {136},
      number       = {4},
      issn         = {0044-3433},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {FZJ-2022-02225},
      pages        = {987-996},
      year         = {2022},
      abstract     = {Age estimation based on DNA methylation (DNAm) can be
                      applied to children, adolescents and adults, but many CG
                      dinucleotides (CpGs) exhibit different kinetics of
                      age-associated DNAm across these age ranges. Furthermore, it
                      is still unclear how growth disorders impact epigenetic age
                      predictions, and this may be particularly relevant for a
                      forensic application. In this study, we analyzed buccal
                      mucosa samples from 95 healthy children and 104 children
                      with different growth disorders. DNAm was analysed by
                      pyrosequencing for 22 CpGs in the genes PDE4C, ELOVL2, RPA2,
                      EDARADD and DDO. The relationship between DNAm and age in
                      healthy children was tested by Spearman's rank correlation.
                      Differences in DNAm between the groups "healthy children"
                      and the (sub-)groups of children with growth disorders were
                      tested by ANCOVA. Models for age estimation were trained (1)
                      based on the data from 11 CpGs with a close correlation
                      between DNAm and age (R ≥ 0.75) and (2) on five CpGs that
                      also did not present significant differences in DNAm between
                      healthy and diseased children. Statistical analysis revealed
                      significant differences between the healthy group and the
                      group with growth disorders (11 CpGs), the subgroup with a
                      short stature (12 CpGs) and the non-short stature subgroup
                      (three CpGs). The results are in line with the assumption of
                      an epigenetic regulation of height-influencing genes. Age
                      predictors trained on 11 CpGs with high correlations between
                      DNAm and age revealed higher mean absolute errors (MAEs) in
                      the group of growth disorders (mean MAE 2.21 years versus
                      MAE 1.79 in the healthy group) as well as in the short
                      stature (sub-)groups; furthermore, there was a clear
                      tendency for overestimation of ages in all growth disorder
                      groups (mean age deviations: total growth disorder group
                      1.85 years, short stature group 1.99 years). Age estimates
                      on samples from children with growth disorders were more
                      precise when using a model containing only the five CpGs
                      that did not present significant differences in DNAm between
                      healthy and diseased children (mean age deviations: total
                      growth disorder group 1.45 years, short stature group 1.66
                      years). The results suggest that CpGs in genes involved in
                      processes relevant for growth and development should be
                      avoided in age prediction models for children since they may
                      be sensitive for alterations in the DNAm pattern in cases of
                      growth disorders.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35551445},
      UT           = {WOS:000799080600001},
      doi          = {10.1007/s00414-022-02826-w},
      url          = {https://juser.fz-juelich.de/record/907806},
}