Literature DB >> 29172065

DNA methylation markers in combination with skeletal and dental ages to improve age estimation in children.

Lei Shi1, Fan Jiang2, Fengxiu Ouyang3, Jun Zhang3, Zhimin Wang4, Xiaoming Shen5.   

Abstract

Age estimation is critical in forensic science, in competitive sports and games and in other age-related fields, but the current methods are suboptimal. The combination of age-associated DNA methylation markers with skeletal age (SA) and dental age (DA) may improve the accuracy and precision of age estimation, but no study has examined this topic. In the current study, we measured SA (GP, TW3-RUS, and TW3-Carpal methods) and DA (Demirjian and Willems methods) by X-ray examination in 124 Chinese children (78 boys and 46 girls) aged 6-15 years. To identify age-associated CpG sites, we analyzed methylome-wide DNA methylation profiling by using the Illumina HumanMethylation450 BeadChip system in 48 randomly selected children. Five CpG sites were identified as associated with chronologic age (CA), with an absolute value of Pearson's correlation coefficient (r)>0.5 (p<0.01) and a false discovery rate<0.01. The validation of age-associated CpG sites was performed using droplet digital PCR techniques in all 124 children. After validation, four CpG sites for boys and five CpG sites for girls were further adopted to build the age estimation model with SA and DA using multivariate linear stepwise regressions. These CpG sites were located at 4 known genes: DDO, PRPH2, DHX8, and ITGA2B and at one unknown gene with the Illumina ID number of 22398226. The accuracy of age estimation methods was compared according to the mean absolute error (MAE) and root mean square error (RMSE). The best single measure for SA was the TW3-RUS method (MAE=0.69years, RMSE=0.95years) in boys, and the GP method (MAE=0.74years, RMSE=0.94years) in girls. For DA, the Willems method was the best single measure for both boys (MAE=0.63years, RMSE=0.78years) and girls (MAE=0.54years, RMSE=0.68years). The models that incorporated SA and DA with the methylation levels of age-associated CpG sites provided the highest accuracy of age estimation in both boys (MAE=0.47years, R2=0.886) and girls (MAE=0.33years, R2=0.941). Cross validation of the results confirmed the reliability and validity of the models. In conclusion, age-associated DNA methylation markers in combination with SA and DA greatly improve the accuracy of age estimation in Chinese children. This method may be applied in forensic science, in competitive sports and games and in other age-related fields.
Copyright © 2017. Published by Elsevier B.V.

Entities:  

Keywords:  Age estimation; Combined methods; DNA methylation markers; Dental development; Hand-wrist bones

Mesh:

Substances:

Year:  2017        PMID: 29172065     DOI: 10.1016/j.fsigen.2017.11.005

Source DB:  PubMed          Journal:  Forensic Sci Int Genet        ISSN: 1872-4973            Impact factor:   4.882


  13 in total

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Journal:  Am J Hum Genet       Date:  2019-03-28       Impact factor: 11.025

2.  Age estimation combining radiographic information of two dental and four skeletal predictors in children and subadults.

Authors:  Akiko Kumagai; Guy Willems; Ademir Franco; Patrick Thevissen
Journal:  Int J Legal Med       Date:  2018-08-11       Impact factor: 2.686

3.  Age estimation based on different molecular clocks in several tissues and a multivariate approach: an explorative study.

Authors:  Julia Becker; Nina Sophia Mahlke; A Reckert; S B Eickhoff; S Ritz-Timme
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4.  DNA methylation levels and telomere length in human teeth: usefulness for age estimation.

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Review 5.  The Continuum of Aging and Age-Related Diseases: Common Mechanisms but Different Rates.

Authors:  Claudio Franceschi; Paolo Garagnani; Cristina Morsiani; Maria Conte; Aurelia Santoro; Andrea Grignolio; Daniela Monti; Miriam Capri; Stefano Salvioli
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6.  Transcriptome analysis identifies a robust gene expression program in the mouse intestinal epithelium on aging.

Authors:  Juri Kazakevych; Elena Stoyanova; Anke Liebert; Patrick Varga-Weisz
Journal:  Sci Rep       Date:  2019-07-18       Impact factor: 4.379

7.  New targeted approaches for epigenetic age predictions.

Authors:  Yang Han; Julia Franzen; Thomas Stiehl; Michael Gobs; Chao-Chung Kuo; Miloš Nikolić; Jan Hapala; Barbara Elisabeth Koop; Klaus Strathmann; Stefanie Ritz-Timme; Wolfgang Wagner
Journal:  BMC Biol       Date:  2020-06-24       Impact factor: 7.431

8.  DNA methylation-based age estimation in pediatric healthy tissues and brain tumors.

Authors:  Teresia Kling; Anna Wenger; Helena Carén
Journal:  Aging (Albany NY)       Date:  2020-11-09       Impact factor: 5.682

9.  Equivalent DNA methylation variation between monozygotic co-twins and unrelated individuals reveals universal epigenetic inter-individual dissimilarity.

Authors:  Benjamin Planterose Jiménez; Fan Liu; Amke Caliebe; Diego Montiel González; Jordana T Bell; Manfred Kayser; Athina Vidaki
Journal:  Genome Biol       Date:  2021-01-05       Impact factor: 13.583

10.  BAFopathies' DNA methylation epi-signatures demonstrate diagnostic utility and functional continuum of Coffin-Siris and Nicolaides-Baraitser syndromes.

Authors:  Erfan Aref-Eshghi; Eric G Bend; Rebecca L Hood; Laila C Schenkel; Deanna Alexis Carere; Rana Chakrabarti; Sandesh C S Nagamani; Sau Wai Cheung; Philippe M Campeau; Chitra Prasad; Victoria Mok Siu; Lauren Brady; Mark A Tarnopolsky; David J Callen; A Micheil Innes; Susan M White; Wendy S Meschino; Andrew Y Shuen; Guillaume Paré; Dennis E Bulman; Peter J Ainsworth; Hanxin Lin; David I Rodenhiser; Raoul C Hennekam; Kym M Boycott; Charles E Schwartz; Bekim Sadikovic
Journal:  Nat Commun       Date:  2018-11-20       Impact factor: 14.919

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