Literature DB >> 27288716

Human age estimation from blood using mRNA, DNA methylation, DNA rearrangement, and telomere length.

Dmitry Zubakov1, Fan Liu2, Iris Kokmeijer1, Ying Choi1, Joyce B J van Meurs3, Wilfred F J van IJcken4, André G Uitterlinden5, Albert Hofman6, Linda Broer3, Cornelia M van Duijn6, Jörn Lewin7, Manfred Kayser8.   

Abstract

Establishing the age of unknown persons, or persons with unknown age, can provide important leads in police investigations, disaster victim identification, fraud cases, and in other legal affairs. Previous methods mostly relied on morphological features available from teeth or skeletal parts. The development of molecular methods for age estimation allowing to use human specimens that possess no morphological age information, such as bloodstains, is extremely valuable as this type of samples is commonly found at crime scenes. Recently, we introduced a DNA-based approach for human age estimation from blood based on the quantification of T-cell specific DNA rearrangements (sjTRECs), which achieves accurate assignment of blood DNA samples to one of four 20-year-interval age categories. Aiming at improving the accuracy of molecular age estimation from blood, we investigated different types of biomarkers. We started out by systematic genome-wide surveys for new age-informative mRNA and DNA methylation markers in blood from the same young and old individuals using microarray technologies. The obtained candidate markers were validated in independent samples covering a wide age range using alternative technologies together with previously proposed DNA methylation, sjTREC, and telomere length markers. Cross-validated multiple regression analysis was applied for estimating and validating the age predictive power of various sets of biomarkers within and across different marker types. We found that DNA methylation markers outperformed mRNA, sjTREC, and telomere length in age predictive power. The best performing model included 8 DNA methylation markers derived from 3 CpG islands reaching a high level of accuracy (cross-validated R(2)=0.88, SE±6.97 years, mean absolute deviation 5.07 years). However, our data also suggest that mRNA markers can provide independent age information: a model using a combined set of 5 DNA methylation markers and one mRNA marker could provide similarly high accuracy (cross-validated R(2)=0.86, SE±7.62 years, mean absolute deviation 4.60 years). Overall, our study provides new and confirms previously suggested molecular biomarkers for age estimation from blood. Moreover, our comparative study design revealed that DNA methylation markers are superior for this purpose over other types of molecular biomarkers tested. While the new and some previous findings are highly promising, before molecular age estimation can eventually meet forensic practice, the proposed biomarkers should be tested further in larger sets of blood samples from both healthy and unhealthy individuals, and markers and genotyping methods shall be validated to meet forensic standards.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Blood biomarkers; DNA methylation; Forensic; Gene expression; Human age estimation; Telomere length; mRNA; sjTREC

Mesh:

Substances:

Year:  2016        PMID: 27288716     DOI: 10.1016/j.fsigen.2016.05.014

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


  26 in total

1.  Influence of immunologic status on age prediction using signal joint T cell receptor excision circles.

Authors:  Sohee Cho; Hee Jin Seo; Ji Hyun Lee; Moon Young Kim; Soong Deok Lee
Journal:  Int J Legal Med       Date:  2017-02-01       Impact factor: 2.686

2.  Usefulness of telomere length in DNA from human teeth for age estimation.

Authors:  Ana Belén Márquez-Ruiz; Lucas González-Herrera; Aurora Valenzuela
Journal:  Int J Legal Med       Date:  2017-04-24       Impact factor: 2.686

3.  The Rotterdam Study: 2018 update on objectives, design and main results.

Authors:  M Arfan Ikram; Guy G O Brusselle; Sarwa Darwish Murad; Cornelia M van Duijn; Oscar H Franco; André Goedegebure; Caroline C W Klaver; Tamar E C Nijsten; Robin P Peeters; Bruno H Stricker; Henning Tiemeier; André G Uitterlinden; Meike W Vernooij; Albert Hofman
Journal:  Eur J Epidemiol       Date:  2017-10-24       Impact factor: 8.082

4.  DNA methylation levels and telomere length in human teeth: usefulness for age estimation.

Authors:  Ana Belén Márquez-Ruiz; Lucas González-Herrera; Juan de Dios Luna; Aurora Valenzuela
Journal:  Int J Legal Med       Date:  2020-01-02       Impact factor: 2.686

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

Authors:  Anastasia Aliferi; David Ballard
Journal:  Methods Mol Biol       Date:  2022

6.  Epigenetic clock measuring age acceleration via DNA methylation levels in blood is associated with decreased oocyte yield.

Authors:  Brent Monseur; Gayathree Murugappan; Jason Bentley; Nelson Teng; Lynn Westphal
Journal:  J Assist Reprod Genet       Date:  2020-04-13       Impact factor: 3.412

7.  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

8.  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

9.  Estimating the survival advantage based on telomere length and serum biomarkers of aging.

Authors:  Yilin Zhao; Shijun Li; Hui Liu
Journal:  J Transl Med       Date:  2017-08-01       Impact factor: 5.531

10.  A SNP panel for identification of DNA and RNA specimens.

Authors:  Soheil Yousefi; Tooba Abbassi-Daloii; Thirsa Kraaijenbrink; Martijn Vermaat; Hailiang Mei; Peter van 't Hof; Maarten van Iterson; Daria V Zhernakova; Annique Claringbould; Lude Franke; Leen M 't Hart; Roderick C Slieker; Amber van der Heijden; Peter de Knijff; Peter A C 't Hoen
Journal:  BMC Genomics       Date:  2018-01-25       Impact factor: 3.969

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