Literature DB >> 32325350

DNA methylation age estimation in blood samples of living and deceased individuals using a multiplex SNaPshot assay.

Helena Correia Dias1, Cristina Cordeiro2, Janet Pereira3, Catarina Pinto3, Francisco Corte Real2, Eugénia Cunha4, Licínio Manco5.   

Abstract

Many studies in the forensic field have reported that analysis of DNA methylation is the most reliable method of predicting age. In a previous study, 5 CpG sites located in ELOVL2, FHL2, KLF14, C1orf132 and TRIM59 genes were tested for age prediction purposes in blood, saliva and buccal swab samples from Korean individuals using a multiplex methylation SNaPshot assay. The main goals of the present study were i) to replicate the same multiplex SNaPshot assay in blood samples from Portuguese individuals, ii) to compare DNA methylation status between two different populations and iii) to address putative differences in the methylation status between blood from living and deceased individuals. Blood samples from 59 living individuals (37 females, 22 males; aged 1-94 years-old) and from 62 deceased individuals (13 females, 49 males; aged 28-86 years-old) were evaluated. The specific primers were those previously described. Linear regression models were used to analyse relationships between methylation levels and chronological age using IBM SPSS software v.24. Our results allowed to build a final age prediction model (APM) for blood samples of living individuals with 3 CpG sites, at ELOVL2, FHL2 and C1orf132 genes, explaining 96.3% of age variation, with a mean absolute deviation (MAD) from chronological age of 4.25 years. Some differences were found in the extent of the age association in the targeted loci comparing Portuguese with Korean individuals. The final APM built for deceased individuals included 4 CpG sites, at ELOVL2, FHL2, C1orf132 and TRIM59 genes, explaining 79.3% of age variation, with a MAD of 5.36 years. Combining both sets of samples from living and deceased individuals, the most accurate APM with 4 CpGs, at ELOVL2, FHL2, C1orf132 and TRIM59 genes, explained 92.5% of variation in age, with a MAD of 4.97 years. In conclusion, our study replicated in blood samples of Portuguese living individuals a previous SNaPshot assay for age estimation. The possibility that age markers might be population specific and that postmortem changes can alter the methylation status among specific loci was suggested by our data. Our study showed the usefulness of the multiplex methylation SNaPshot assay for forensic analysis in blood samples of living and deceased individuals.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Age prediction; Blood samples; Living and deceased individuals; Methylation SNaPshot; Replication study

Year:  2020        PMID: 32325350     DOI: 10.1016/j.forsciint.2020.110267

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  9 in total

1.  Accurate age estimation from blood samples of Han Chinese individuals using eight high-performance age-related CpG sites.

Authors:  Xueli Han; Chao Xiao; Shaohua Yi; Ya Li; Maomin Chen; Daixin Huang
Journal:  Int J Legal Med       Date:  2022-07-11       Impact factor: 2.791

2.  Novel Subgroups of Type 2 Diabetes Display Different Epigenetic Patterns That Associate With Future Diabetic Complications.

Authors:  Silja Schrader; Alexander Perfilyev; Emma Ahlqvist; Leif Groop; Allan Vaag; Mats Martinell; Sonia García-Calzón; Charlotte Ling
Journal:  Diabetes Care       Date:  2022-07-07       Impact factor: 17.152

3.  Postmortem age estimation via DNA methylation analysis in buccal swabs from corpses in different stages of decomposition-a "proof of principle" study.

Authors:  Barbara Elisabeth Koop; Felix Mayer; Tanju Gündüz; Jacqueline Blum; Julia Becker; Judith Schaffrath; Wolfgang Wagner; Yang Han; Petra Boehme; Stefanie Ritz-Timme
Journal:  Int J Legal Med       Date:  2020-07-07       Impact factor: 2.686

4.  Identifying Methylation Patterns in Dental Pulp Aging: Application to Age-at-Death Estimation in Forensic Anthropology.

Authors:  Sara C Zapico; Quentin Gauthier; Aleksandra Antevska; Bruce R McCord
Journal:  Int J Mol Sci       Date:  2021-04-02       Impact factor: 5.923

5.  Chronological Age Prediction: Developmental Evaluation of DNA Methylation-Based Machine Learning Models.

Authors:  Haoliang Fan; Qiqian Xie; Zheng Zhang; Junhao Wang; Xuncai Chen; Pingming Qiu
Journal:  Front Bioeng Biotechnol       Date:  2022-01-24

Review 6.  Epigenetic age prediction.

Authors:  Daniel J Simpson; Tamir Chandra
Journal:  Aging Cell       Date:  2021-08-20       Impact factor: 9.304

Review 7.  How (Epi)Genetic Regulation of the LIM-Domain Protein FHL2 Impacts Multifactorial Disease.

Authors:  Jayron J Habibe; Maria P Clemente-Olivo; Carlie J de Vries
Journal:  Cells       Date:  2021-10-01       Impact factor: 6.600

8.  Circular RNA as a Potential Biomarker for Forensic Age Prediction.

Authors:  Junyan Wang; Chunyan Wang; Yangyan Wei; Yanhao Zhao; Can Wang; Chaolong Lu; Jin Feng; Shujin Li; Bin Cong
Journal:  Front Genet       Date:  2022-02-07       Impact factor: 4.599

9.  A Blood-Bone-Tooth Model for Age Prediction in Forensic Contexts.

Authors:  Helena Correia Dias; Licínio Manco; Francisco Corte Real; Eugénia Cunha
Journal:  Biology (Basel)       Date:  2021-12-10
  9 in total

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