Altered DNA methylation at age-associated CpG sites in children with growth disorders: impact on age estimation?
Children with growth disorders
DNA methylation
Epigenetic age estimation
Forensic age estimation
Journal
International journal of legal medicine
ISSN: 1437-1596
Titre abrégé: Int J Legal Med
Pays: Germany
ID NLM: 9101456
Informations de publication
Date de publication:
Jul 2022
Jul 2022
Historique:
received:
29
11
2021
accepted:
07
04
2022
pubmed:
14
5
2022
medline:
9
6
2022
entrez:
13
5
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 35551445
doi: 10.1007/s00414-022-02826-w
pii: 10.1007/s00414-022-02826-w
pmc: PMC9170667
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
987-996Informations de copyright
© 2022. The Author(s).
Références
Böhme P, Reckert A, Becker J, Ritz-Timme S (2021) Molecular methods for age estimation. Rechtsmedizin. https://doi.org/10.1007/s00194-021-00490-9
doi: 10.1007/s00194-021-00490-9
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
pubmed: 28691915
Hanafi M, Soedarsono N, Auerkari E (2021) Biological age estimation using DNA methylation analysis: a systematic review. Sci Dent J 5:1–11. https://doi.org/10.4103/sdj.Sdj_27_20
doi: 10.4103/sdj.Sdj_27_20
Han Y, Franzen J, Stiehl T et al (2020) New targeted approaches for epigenetic age predictions. BMC Biol 18:71. https://doi.org/10.1186/s12915-020-00807-2
doi: 10.1186/s12915-020-00807-2
pmcid: 7315536
pubmed: 32580727
Koop BE, Reckert A, Becker J, Han Y, Wagner W, Ritz-Timme S (2020) Epigenetic clocks may come out of rhythm-implications for the estimation of chronological age in forensic casework. Int J Legal Med 134:2215–2228. https://doi.org/10.1007/s00414-020-02375-0
doi: 10.1007/s00414-020-02375-0
pmcid: 7578121
pubmed: 32661599
Pfeifer M, Bajanowski T, Helmus J, Poetsch M (2020) Inter-laboratory adaption of age estimation models by DNA methylation analysis-problems and solutions. Int J Legal Med 134:953–961. https://doi.org/10.1007/s00414-020-02263-7
doi: 10.1007/s00414-020-02263-7
pubmed: 32055939
Freire-Aradas A, Phillips C, Giron-Santamaria L et al (2018) Tracking age-correlated DNA methylation markers in the young. Forensic Sci Int Genet 36:50–59. https://doi.org/10.1016/j.fsigen.2018.06.011
doi: 10.1016/j.fsigen.2018.06.011
pubmed: 29933125
Wu X, Chen W, Lin F et al (2019) DNA methylation profile is a quantitative measure of biological aging in children. Aging (Albany NY) 11: 10031–51. https://doi.org/10.18632/aging.102399
Alisch RS, Barwick BG, Chopra P et al (2012) Age-associated DNA methylation in pediatric populations. Genome Res 22:623–632. https://doi.org/10.1101/gr.125187.111
doi: 10.1101/gr.125187.111
pmcid: 3317145
pubmed: 22300631
Almstrup K, Lindhardt Johansen M, Busch AS et al (2016) Pubertal development in healthy children is mirrored by DNA methylation patterns in peripheral blood. Sci Rep 6:28657. https://doi.org/10.1038/srep28657
doi: 10.1038/srep28657
pmcid: 4923870
pubmed: 27349168
Binder AM, Corvalan C, Mericq V et al (2018) Faster ticking rate of the epigenetic clock is associated with faster pubertal development in girls. Epigenetics 13:85–94. https://doi.org/10.1080/15592294.2017.1414127
doi: 10.1080/15592294.2017.1414127
pmcid: 5836971
pubmed: 29235933
Muthuirulan P, Capellini TD (2019) Complex phenotypes: mechanisms underlying variation in human stature. Curr Osteoporos Rep 17:301–323. https://doi.org/10.1007/s11914-019-00527-9
doi: 10.1007/s11914-019-00527-9
pmcid: 6819265
pubmed: 31441021
Barstow C, Rerucha C (2015) Evaluation of short and tall stature in children. Am Fam Physician 92:43–50
pubmed: 26132126
AW Root 2020 Genetic Regulation of adult stature in humans J ClinEndocrinol Metab 105. https://doi.org/10.1210/clinem/dgaa210
Yengo L, Sidorenko J, Kemper KE et al (2018) Meta-analysis of genome-wide association studies for height and body mass index in approximately 700000 individuals of European ancestry. Hum Mol Genet 27:3641–3649. https://doi.org/10.1093/hmg/ddy271
doi: 10.1093/hmg/ddy271
pmcid: 6488973
pubmed: 30124842
M Guo Z Liu J Willen et al 2017 Epigenetic profiling of growth plate chondrocytes sheds insight into regulatory genetic variation influencing height Elife 6. https://doi.org/10.7554/eLife.29329
Jee YH, Andrade AC, Baron J, Nilsson O (2017) Genetics of short stature. Endocrinol Metab Clin North Am 46:259–281. https://doi.org/10.1016/j.ecl.2017.01.001
doi: 10.1016/j.ecl.2017.01.001
pmcid: 5424617
pubmed: 28476223
Ouni M, Castell AL, Rothenbuhler A, Linglart A, Bougneres P (2016) Higher methylation of the IGF1 P2 promoter is associated with idiopathic short stature. Clin Endocrinol (Oxf) 84:216–221. https://doi.org/10.1111/cen.12867
doi: 10.1111/cen.12867
S Peeters K Declerck M Thomas et al 2020 DNA methylation profiling and genomic analysis in 20 children with short stature who were born small for gestational age J ClinEndocrinol Metab 105. https://doi.org/10.1210/clinem/dgaa465
Aref-Eshghi E, Rodenhiser DI, Schenkel LC et al (2018) Genomic DNA methylation signatures enable concurrent diagnosis and clinical genetic variant classification in neurodevelopmental syndromes. Am J Human Gene 102:156–174. https://doi.org/10.1016/j.ajhg.2017.12.008
doi: 10.1016/j.ajhg.2017.12.008
Sadikovic B, Aref-Eshghi E, Levy MA, Rodenhiser D (2019) DNA methylation signatures in mendelian developmental disorders as a diagnostic bridge between genotype and phenotype. Epigenomics 11:563–575. https://doi.org/10.2217/epi-2018-0192
doi: 10.2217/epi-2018-0192
pubmed: 30875234
Hood RL, Schenkel LC, Nikkel SM et al (2016) The defining DNA methylation signature of Floating-Harbor syndrome. Sci Rep 6:38803. https://doi.org/10.1038/srep38803
doi: 10.1038/srep38803
pmcid: 5146968
pubmed: 27934915
Naue J, Hoefsloot HCJ, Kloosterman AD, Verschure PJ (2018) Forensic DNA methylation profiling from minimal traces: how low can we go? Forensic Sci Int Genet 33:17–23. https://doi.org/10.1016/j.fsigen.2017.11.004
doi: 10.1016/j.fsigen.2017.11.004
pubmed: 29175600
Becker J, Böhme P, Reckert A et al (2021) Evidence for differences in DNA methylation between Germans and Japanese. Int J Legal Med. https://doi.org/10.1007/s00414-021-02736-3
doi: 10.1007/s00414-021-02736-3
pmcid: 8847189
pubmed: 34739581
Naue J, Hoefsloot HCJ, Mook ORF 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
doi: 10.1016/j.fsigen.2017.07.015
pubmed: 28841467
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
doi: 10.1186/gb-2014-15-2-r24
pmcid: 4053864
pubmed: 24490752
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
doi: 10.1080/15592294.2015.1080413
pmcid: 4844214
pubmed: 26280308
Sun D, Zhang T, Su S et al (2019) Body Mass Index Drives Changes in DNA Methylation: A Longitudinal Study. Circ Res 125:824–833. https://doi.org/10.1161/CIRCRESAHA.119.315397
doi: 10.1161/CIRCRESAHA.119.315397
pmcid: 6786955
pubmed: 31510868
Fan X, Zhao S, Yu C et al (2021) Exome sequencing reveals genetic architecture in patients with isolated or syndromic short stature. J Genet Genomics 48:396–402. https://doi.org/10.1016/j.jgg.2021.02.008
doi: 10.1016/j.jgg.2021.02.008
pubmed: 34006472
Azevedo MF, Faucz FR, Bimpaki E et al (2014) Clinical and molecular genetics of the phosphodiesterases (PDEs). Endocr Rev 35:195–233. https://doi.org/10.1210/er.2013-1053
doi: 10.1210/er.2013-1053
pubmed: 24311737
Jakobsson A, Westerberg R, Jacobsson A (2006) Fatty acid elongases in mammals: their regulation and roles in metabolism. Prog Lipid Res 45:237–249. https://doi.org/10.1016/j.plipres.2006.01.004
doi: 10.1016/j.plipres.2006.01.004
pubmed: 16564093
Dueva R, Iliakis G (2020) Replication protein A: a multifunctional protein with roles in DNA replication, repair and beyond. NAR Cancer 2: zcaa022. https://doi.org/10.1093/narcan/zcaa022
E Keller W Kiess R Pfäffle A Keller 2007 Kleinwuchs Kinder- und Jugendmedizin 7 209 216. https://doi.org/10.1055/s-0038-1625650