Literature DB >> 26010003

Evaluation of DNA methylation markers and their potential to predict human aging.

Deborah Soares Bispo Santos Silva1,2, Joana Antunes1, Kuppareddi Balamurugan3, George Duncan4, Clarice Sampaio Alho2, Bruce McCord1.   

Abstract

We present epigenetic methylation data for two genetic loci, GRIA2, and NPTX2, which were tested for prediction of age from different donors of biofluids. We analyzed 44 saliva samples and 23 blood samples from volunteers with ages ranging from 5 to 72 years. DNA was extracted and bisulfite modified using commercial kits. Specific primers were used for amplification and methylation profiles were determined by pyrosequencing. Methylation data from both markers and their relationship with age were determined using linear regression analysis, which indicates a positive correlation between methylation and age. Older individuals tend to have increased methylation in both markers compared to younger individuals and this trend was more pronounced in the GRIA2 locus when compared to NPTX2. The epigenetic predicted age, calculated using a GRIA2 regression analysis model, was strongly correlated to chronological age (R(2) = 0.801), with an average difference of 6.9 years between estimated and observed ages. When using a NPTX2 regression model, we observed a lower correlation between predicted and chronological age (R(2) = 0.654), with an average difference of 9.2 years. These data indicate these loci can be used as a novel tool for age prediction with potential applications in many areas, including clinical and forensic investigations.
© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Age prediction; Biofluids; DNA methylation; Pyrosequencing

Mesh:

Substances:

Year:  2015        PMID: 26010003     DOI: 10.1002/elps.201500137

Source DB:  PubMed          Journal:  Electrophoresis        ISSN: 0173-0835            Impact factor:   3.535


  11 in total

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Authors:  Bram Bekaert; Aubeline Kamalandua; Sara C Zapico; Wim Van de Voorde; Ronny Decorte
Journal:  Epigenetics       Date:  2015-08-17       Impact factor: 4.528

2.  DNA methylation analysis from saliva samples for epidemiological studies.

Authors:  Shota Nishitani; Sasha E Parets; Brian W Haas; Alicia K Smith
Journal:  Epigenetics       Date:  2018-08-01       Impact factor: 4.528

3.  Predicting Chronological Age from DNA Methylation Data: A Machine Learning Approach for Small Datasets and Limited Predictors.

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Journal:  Methods Mol Biol       Date:  2022

4.  Forensic age prediction for saliva samples using methylation-sensitive high resolution melting: exploratory application for cigarette butts.

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Journal:  Sci Rep       Date:  2017-09-05       Impact factor: 4.379

5.  From forensic epigenetics to forensic epigenomics: broadening DNA investigative intelligence.

Authors:  Athina Vidaki; Manfred Kayser
Journal:  Genome Biol       Date:  2017-12-21       Impact factor: 13.583

6.  Analysis of DNA methylation of potential age-related methylation sites in canine peripheral blood leukocytes.

Authors:  Genta Ito; Kuniko Yoshimura; Yasuyuki Momoi
Journal:  J Vet Med Sci       Date:  2017-03-04       Impact factor: 1.267

7.  DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing.

Authors:  Athina Vidaki; David Ballard; Anastasia Aliferi; Thomas H Miller; Leon P Barron; Denise Syndercombe Court
Journal:  Forensic Sci Int Genet       Date:  2017-02-28       Impact factor: 4.882

8.  Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor.

Authors:  Xingyan Li; Weidong Li; Yan Xu
Journal:  Genes (Basel)       Date:  2018-08-21       Impact factor: 4.096

9.  MapReduce-Based Parallel Genetic Algorithm for CpG-Site Selection in Age Prediction.

Authors:  Zahra Momeni; Mohammad Saniee Abadeh
Journal:  Genes (Basel)       Date:  2019-11-25       Impact factor: 4.096

10.  Combinatorial identification of DNA methylation patterns over age in the human brain.

Authors:  Behrooz Torabi Moghadam; Michal Dabrowski; Bozena Kaminska; Manfred G Grabherr; Jan Komorowski
Journal:  BMC Bioinformatics       Date:  2016-09-23       Impact factor: 3.169

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