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Platform-independent models for age prediction using DNA methylation data

Authors
 Sae Rom Hong  ;  Kyoung-Jin Shin  ;  Sang-Eun Jung  ;  Eun Hee Lee  ;  Hwan Young Lee 
Citation
 FORENSIC SCIENCE INTERNATIONAL-GENETICS, Vol.38 : 39-47, 2019 
Journal Title
 FORENSIC SCIENCE INTERNATIONAL-GENETICS 
ISSN
 1872-4973 
Issue Date
2019
MeSH
Adolescent ; Adult ; Aged ; Aging/genetics* ; CpG Islands/genetics ; DNA Methylation* ; Female ; Forensic Genetics/methods* ; Genotyping Techniques/instrumentation ; High-Throughput Nucleotide Sequencing* ; Humans ; Male ; Middle Aged ; Models, Genetic ; Multivariate Analysis ; Neural Networks (Computer) ; Saliva/chemistry ; Sequence Analysis, DNA* ; Sulfites ; Young Adult
Keywords
Age prediction ; DNA methylation ; MPS ; Methylation SNaPshot ; Neural network
Abstract
Age prediction has been in the spotlight recently because it can provide an important information about the contributors of biological evidence left at crime scenes. Specifically, many researchers have actively suggested age-prediction models using DNA methylation at several CpG sites and tested the candidates using platforms such as the HumanMethylation 450 array and pyrosequencing. With DNA methylation data obtained from each platform, age prediction models were constructed using diverse statistical methods typically with multivariate linear regression. However, because each developed model is based on single-platform data, the prediction accuracy is reduced when applying DNA methylation data obtained from other platforms. In this study, bisulfite sequencing data for 95 saliva samples were generated using massively parallel sequencing (MPS) and compared with methylation SNaPshot data from the same 95 individuals. The predicted age obtained by applying MPS data to an age-prediction model built for methylation SNaPshot data differed greatly from the chronological age due to platform differences. Therefore, novel variables were introduced to indicate the platform type, and construct platform-independent age predictive models using a neural network and multivariate linear regression. The final neural network model had a mean absolute deviation (MAD) of 3.19 years between the predicted and chronological age, and the mean absolute percentage error (MAPE) was 8.89% in the test set. Similarly, the linear regression model showed 3.69 years of MAD and 10.44% of MAPE in the same test set. The platform-independent age-prediction model was made extensible to an increasing number of platforms by introducing platform variables, and the idea of platform variables can be applied to age prediction models for other body fluids.
Full Text
https://www.sciencedirect.com/science/article/pii/S1872497318302412
DOI
10.1016/j.fsigen.2018.10.005
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Forensic Medicine (법의학과) > 1. Journal Papers
Yonsei Authors
Shin, Kyoung Jin(신경진) ORCID logo https://orcid.org/0000-0002-1059-9665
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/169895
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