Journal Article10.1016/J.FSIGEN.2016.05.014
Human age estimation from blood using mRNA, DNA methylation, DNA rearrangement, and telomere length
Dmitry Zubakov,Fan Liu,Fan Liu,Iris Kokmeijer,Ying Choi,Joyce B. J. van Meurs,Wilfred F. J. van IJcken,André G. Uitterlinden,Albert Hofman,Linda Broer,Cornelia M. van Duijn,Jörn Lewin,Manfred Kayser +12 more
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TL;DR: Before molecular age estimation from blood 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.
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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.
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Citations
The Rotterdam Study: 2018 update on objectives, design and main results.
M. Arfan Ikram,Guy Brusselle,Guy Brusselle,Sarwa Darwish Murad,Cornelia M. van Duijn,Oscar H. Franco,André Goedegebure,Caroline C W Klaver,Tamar Nijsten,Robin P. Peeters,Bruno H. Stricker,Henning Tiemeier,André G. Uitterlinden,Meike W. Vernooij,Albert Hofman,Albert Hofman +15 more
TL;DR: The rationale of the study and its design is given, a summary of the major findings and an update of the objectives and methods are presented and the cohort is being expanded by persons aged 40 years and over.
DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing
Athina Vidaki,David Ballard,Anastasia Aliferi,Thomas H. Miller,Leon Barron,Denise Syndercombe Court +5 more
TL;DR: A new NGS-based method was combined with machine learning for age prediction and the model predicted age successfully for twins and ‘diseased’ individuals.
Chronological age prediction based on DNA methylation: Massive parallel sequencing and random forest regression
Jana Naue,Huub C. J. Hoefsloot,Olaf R.F. Mook,Laura Rijlaarsdam-Hoekstra,Marloes C.H. van der Zwalm,Peter Henneman,Ate D. Kloosterman,Ate D. Kloosterman,Pernette J. Verschure +8 more
TL;DR: This work presents an age-prediction tool for whole blood based on massive parallel sequencing (MPS) and a random forest machine learning algorithm that uncovered well-known DNAm age-dependent markers, as well as additional new age- dependent sites for improvement of the model, and allowed the creation of a reliable and accurate epigenetic tool for age-Prediction without restriction to a linear change in DNAm with age.
Recent progress, methods and perspectives in forensic epigenetics.
Athina Vidaki,Manfred Kayser +1 more
TL;DR: The most recent literature on these three main topics of current forensic epigenetic investigations are summarized, which provides future perspectives with regard to new research questions, new epigenetic markers and recent technological advances that will move the field towards forensic epigenomics in the near future.
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Independent validation of DNA-based approaches for age prediction in blood.
TL;DR: This study demonstrates the usefulness of the proposed markers and the genotyping method in an independent dataset, and suggests the possibility of combining different types of DNA markers to improve prediction accuracy.
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