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      Collaborative exercise: analysis of age estimation using a QIAGEN protocol and the PyroMark Q48 platform

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          Abstract

           

          Human age estimation from trace samples may give important leads early in a police investigation by contributing to the description of the perpetrator. Several molecular biomarkers are available for the estimation of chronological age, and currently, DNA methylation patterns are the most promising. In this study, a QIAGEN age protocol for age estimation was tested by five forensic genetic laboratories. The assay comprised bisulfite treatment of the extracted DNA, amplification of five CpG loci (in the genes of ELOVL2, C1orf132, TRIM59, KLF14, and FHL2), and sequencing of the amplicons using the PyroMark Q48 platform. Blood samples from 49 individuals with ages ranging from 18 to 64 years as well as negative and methylation controls were analyzed. An existing age estimation model was applied to display a mean absolute deviation of 3.62 years within the reference data set.

          Key points
          • Age determination as an intelligence tool during investigations can be a powerful tool in forensic genetics.

          • In this study, five laboratories ran 49 samples and obtained a mean absolute deviation of 3.62 years.

          • Five markers were analyzed on a PyroMark Q48 platform.

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          Most cited references32

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          DNA methylation age of human tissues and cell types

          Background It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure. Results I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance. Conclusions I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
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            Genome-wide methylation profiles reveal quantitative views of human aging rates.

            The ability to measure human aging from molecular profiles has practical implications in many fields, including disease prevention and treatment, forensics, and extension of life. Although chronological age has been linked to changes in DNA methylation, the methylome has not yet been used to measure and compare human aging rates. Here, we build a quantitative model of aging using measurements at more than 450,000 CpG markers from the whole blood of 656 human individuals, aged 19 to 101. This model measures the rate at which an individual's methylome ages, which we show is impacted by gender and genetic variants. We also show that differences in aging rates help explain epigenetic drift and are reflected in the transcriptome. Moreover, we show how our aging model is upheld in other human tissues and reveals an advanced aging rate in tumor tissue. Our model highlights specific components of the aging process and provides a quantitative readout for studying the role of methylation in age-related disease. Copyright © 2013 Elsevier Inc. All rights reserved.
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              Aging of blood can be tracked by DNA methylation changes at just three CpG sites

              Background Human aging is associated with DNA methylation changes at specific sites in the genome. These epigenetic modifications may be used to track donor age for forensic analysis or to estimate biological age. Results We perform a comprehensive analysis of methylation profiles to narrow down 102 age-related CpG sites in blood. We demonstrate that most of these age-associated methylation changes are reversed in induced pluripotent stem cells (iPSCs). Methylation levels at three age-related CpGs - located in the genes ITGA2B, ASPA and PDE4C - were subsequently analyzed by bisulfite pyrosequencing of 151 blood samples. This epigenetic aging signature facilitates age predictions with a mean absolute deviation from chronological age of less than 5 years. This precision is higher than age predictions based on telomere length. Variation of age predictions correlates moderately with clinical and lifestyle parameters supporting the notion that age-associated methylation changes are associated more with biological age than with chronological age. Furthermore, patients with acquired aplastic anemia or dyskeratosis congenita - two diseases associated with progressive bone marrow failure and severe telomere attrition - are predicted to be prematurely aged. Conclusions Our epigenetic aging signature provides a simple biomarker to estimate the state of aging in blood. Age-associated DNA methylation changes are counteracted in iPSCs. On the other hand, over-estimation of chronological age in bone marrow failure syndromes is indicative for exhaustion of the hematopoietic cell pool. Thus, epigenetic changes upon aging seem to reflect biological aging of blood.
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                Author and article information

                Contributors
                Journal
                Forensic Sci Res
                Forensic Sci Res
                fsr
                Forensic Sciences Research
                Oxford University Press
                2096-1790
                2471-1411
                March 2024
                05 January 2024
                05 January 2024
                : 9
                : 1
                : owad055
                Affiliations
                Section of Forensic Genetics , Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen Copenhagen, Denmark
                Institute of Legal Medicine , Faculty of Medicine and University Clinic, University of Cologne , Cologne, Germany
                Section of Forensic Genetics , Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen Copenhagen, Denmark
                Institute of Molecular and Translational Medicine , Faculty of Medicine and Dentistry, Palacky University Olomouc and the University Hospital Olomouc , Olomouc, the Czech Republic
                Laboratoire d’Hématologie Médico-Légale , Bordeaux Cedex, France
                Laboratoire d’Hématologie Médico-Légale , Bordeaux Cedex, France
                Department of Chemistry and Biochemistry, Florida International University , Miami, FL, USA
                Department of Chemistry and Biochemistry, Florida International University , Miami, FL, USA
                Laboratoire d’Hématologie Médico-Légale , Bordeaux Cedex, France
                Department of Chemistry and Biochemistry, Florida International University , Miami, FL, USA
                Institute of Legal Medicine , Faculty of Medicine and University Clinic, University of Cologne , Cologne, Germany
                Institute of Molecular and Translational Medicine , Faculty of Medicine and Dentistry, Palacky University Olomouc and the University Hospital Olomouc , Olomouc, the Czech Republic
                Section of Forensic Genetics , Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen Copenhagen, Denmark
                Author notes
                Corresponding author. E-mail: marie.louise.kampmann@ 123456sund.ku.dk
                Article
                owad055
                10.1093/fsr/owad055
                10986743
                38567377
                a37ced17-54b6-46fb-872c-820cbf5982fa
                © The Author(s) 2024. Published by OUP on behalf of the Academy of Forensic Science.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 January 2023
                : 26 September 2023
                Page count
                Pages: 6
                Funding
                Funded by: Palacky University;
                Award ID: LM2018125
                Funded by: National Institute of Justice, DOI 10.13039/100005289;
                Funded by: Department of Justice, DOI 10.13039/100000074;
                Award ID: 2017-NE-BX-0001
                Funded by: Department of Chemistry and Biochemistry;
                Funded by: Florida International University, DOI 10.13039/100007681;
                Categories
                Research Article
                AcademicSubjects/MED00010
                AcademicSubjects/MED00430
                AcademicSubjects/MED00820
                AcademicSubjects/SOC00010
                AcademicSubjects/SOC00020

                forensic genetics,age estimation,dna methylation,pyrosequencing,trace sample

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