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Ji Z, Xing Y, Li J, Feng X, Yang F, Zhu B, Yan J. Male-specific age prediction based on Y-chromosome DNA methylation with blood using pyrosequencing. Forensic Sci Int Genet 2024; 71:103050. [PMID: 38703560 DOI: 10.1016/j.fsigen.2024.103050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/14/2024] [Accepted: 04/19/2024] [Indexed: 05/06/2024]
Abstract
Age prediction is an important aspect of forensic science that offers valuable insight into identification. In recent years, extensive studies have been conducted on age prediction based on DNA methylation, and numerous studies have demonstrated that DNA methylation is a reliable biomarker for age prediction. However, almost all studies on age prediction based on DNA methylation have focused on age-related CpG sites in autosomes, which are concentrated on single-source DNA samples. Mixed samples, especially male-female mixed samples, are common in forensic casework. The application of Y-STRs and Y-SNPs can provide clues for the genetic typing of male individuals in male-female mixtures, but they cannot provide the age information of male individuals. Studies on Y-chromosome DNA methylation can address this issue. In this study, we identified five age-related CpG sites on the Y chromosome (Y-CpGs) and developed a male-specific age prediction model using pyrosequencing combined with a support vector machine algorithm. The mean absolute deviation of the model was 5.50 years in the training set and 6.74 years in the testing set. When we used a male blood sample to predict age, the deviation between the predicted and chronological age was 1.18 years. Then, we mixed the genomic DNA of the male and a female at ratios of 1:1, 1:5, 1:10, and 1:50, the range of deviation between the predicted and chronological age of the male in the mixture was 1.16-1.74 years. In addition, there was no significant difference between the methylation values of bloodstains and blood in the same sample, which indicates that our model is also suitable for bloodstain samples. Overall, our results show that age prediction using DNA methylation of the Y chromosome has potential applications in forensic science and can be of great help in predicting the age of males in male-female mixtures. Furthermore, this work lays the foundation for future research on age-related applications of Y-CpGs.
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Affiliation(s)
- Zhimin Ji
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China
| | - Yangfeng Xing
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China
| | - Junli Li
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China
| | - Xiaoxiao Feng
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China
| | - Fenglong Yang
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China.
| | - Bofeng Zhu
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China; Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, PR China.
| | - Jiangwei Yan
- School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030009, PR China.
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2
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Yuen ZWS, Shanmuganandam S, Stanley M, Jiang S, Hein N, Daniel R, McNevin D, Jack C, Eyras E. Profiling age and body fluid DNA methylation markers using nanopore adaptive sampling. Forensic Sci Int Genet 2024; 71:103048. [PMID: 38640705 DOI: 10.1016/j.fsigen.2024.103048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 04/07/2024] [Accepted: 04/11/2024] [Indexed: 04/21/2024]
Abstract
DNA methylation plays essential roles in regulating physiological processes, from tissue and organ development to gene expression and aging processes and has emerged as a widely used biomarker for the identification of body fluids and age prediction. Currently, methylation markers are targeted independently at specific CpG sites as part of a multiplexed assay rather than through a unified assay. Methylation detection is also dependent on divergent methodologies, ranging from enzyme digestion and affinity enrichment to bisulfite treatment, alongside various technologies for high-throughput profiling, including microarray and sequencing. In this pilot study, we test the simultaneous identification of age-associated and body fluid-specific methylation markers using a single technology, nanopore adaptive sampling. This innovative approach enables the profiling of multiple CpG marker sites across entire gene regions from a single sample without the need for specialized DNA preparation or additional biochemical treatments. Our study demonstrates that adaptive sampling achieves sufficient coverage in regions of interest to accurately determine the methylation status, shows a robust consistency with whole-genome bisulfite sequencing data, and corroborates known CpG markers of age and body fluids. Our work also resulted in the identification of new sites strongly correlated with age, suggesting new possible age methylation markers. This study lays the groundwork for the systematic development of nanopore-based methodologies in both age prediction and body fluid identification, highlighting the feasibility and potential of nanopore adaptive sampling while acknowledging the need for further validation and expansion in future research.
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Affiliation(s)
- Zaka Wing-Sze Yuen
- EMBL Australia Partner Laboratory Network, John Curtin School of Medical Research, The Australian National University, Canberra, Australia; The Shine-Dalgarno Centre for RNA Innovation, John Curtin School of Medical Research, The Australian National University, Canberra, Australia; The Centre for Computational Biomedical Sciences, John Curtin School of Medical Research, The Australian National University, Canberra, Australia
| | - Somasundhari Shanmuganandam
- Department of Immunity, Inflammation and Infection, The John Curtin School of Medical Research, Australian National University, Canberra, ACT 2601, Australia; Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Australian National University, Canberra, ACT 2601, Australia
| | - Maurice Stanley
- Department of Immunity, Inflammation and Infection, The John Curtin School of Medical Research, Australian National University, Canberra, ACT 2601, Australia; Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Australian National University, Canberra, ACT 2601, Australia
| | - Simon Jiang
- Department of Immunity, Inflammation and Infection, The John Curtin School of Medical Research, Australian National University, Canberra, ACT 2601, Australia; Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Australian National University, Canberra, ACT 2601, Australia; Department of Renal Medicine, The Canberra Hospital, Canberra, ACT 2605, Australia
| | - Nadine Hein
- ACRF Department of Cancer Biology and Therapeutics and Division of Genome Sciences and Cancer, John Curtin School of Medical Research, Australian National University, Acton, Canberra, Australia
| | - Runa Daniel
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, Queensland, Australia
| | - Dennis McNevin
- Centre for Forensic Science, School of Mathematical & Physical Sciences, Faculty of Science, University of Technology Sydney, Sydney, Australia
| | - Cameron Jack
- ANU Bioinformatics Consultancy, John Curtin School of Medical Research, The Australian National University, Canberra, Australia
| | - Eduardo Eyras
- EMBL Australia Partner Laboratory Network, John Curtin School of Medical Research, The Australian National University, Canberra, Australia; The Shine-Dalgarno Centre for RNA Innovation, John Curtin School of Medical Research, The Australian National University, Canberra, Australia; The Centre for Computational Biomedical Sciences, John Curtin School of Medical Research, The Australian National University, Canberra, Australia.
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3
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Gutiérrez-Hurtado IA, Sánchez-Méndez AD, Becerra-Loaiza DS, Rangel-Villalobos H, Torres-Carrillo N, Gallegos-Arreola MP, Aguilar-Velázquez JA. Loss of the Y Chromosome: A Review of Molecular Mechanisms, Age Inference, and Implications for Men's Health. Int J Mol Sci 2024; 25:4230. [PMID: 38673816 PMCID: PMC11050192 DOI: 10.3390/ijms25084230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 03/29/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Until a few years ago, it was believed that the gradual mosaic loss of the Y chromosome (mLOY) was a normal age-related process. However, it is now known that mLOY is associated with a wide variety of pathologies in men, such as cardiovascular diseases, neurodegenerative disorders, and many types of cancer. Nevertheless, the mechanisms that generate mLOY in men have not been studied so far. This task is of great importance because it will allow focusing on possible methods of prophylaxis or therapy for diseases associated with mLOY. On the other hand, it would allow better understanding of mLOY as a possible marker for inferring the age of male samples in cases of human identification. Due to the above, in this work, a comprehensive review of the literature was conducted, presenting the most relevant information on the possible molecular mechanisms by which mLOY is generated, as well as its implications for men's health and its possible use as a marker to infer age.
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Affiliation(s)
- Itzae Adonai Gutiérrez-Hurtado
- Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Guadalajara 44340, Jalisco, Mexico
| | - Astrid Desireé Sánchez-Méndez
- Laboratorio de Ciencias Morfológico Forenses y Medicina Molecular, Departamento de Morfología, Centro Universitario de Ciencias de la Salud, Guadalajara 44340, Jalisco, Mexico
- Doctorado en Genética Humana, Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
| | | | - Héctor Rangel-Villalobos
- Instituto de Investigación en Genética Molecular, Departamento de Ciencias Médicas y de la Vida, Centro Universitario de la Ciénega, Universidad de Guadalajara, Ocotlán 47820, Jalisco, Mexico
| | - Norma Torres-Carrillo
- Departamento de Microbiología y Patología, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
| | - Martha Patricia Gallegos-Arreola
- División de Genética, Centro de Investigación Biomédica de Occidente (CIBO), Instituto Mexicano del Seguro Social (IMSS), Guadalajara 44340, Jalisco, Mexico
| | - José Alonso Aguilar-Velázquez
- Laboratorio de Ciencias Morfológico Forenses y Medicina Molecular, Departamento de Morfología, Centro Universitario de Ciencias de la Salud, Guadalajara 44340, Jalisco, Mexico
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4
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Kampmann ML, Fleckhaus J, Børsting C, Jurtikova H, Piters A, Papin J, Gauthier Q, Ghemrawi M, Doutremepuich C, McCord B, Schneider PM, Drabek J, Morling N. Collaborative exercise: analysis of age estimation using a QIAGEN protocol and the PyroMark Q48 platform. Forensic Sci Res 2024; 9:owad055. [PMID: 38567377 PMCID: PMC10986743 DOI: 10.1093/fsr/owad055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 09/26/2023] [Indexed: 04/04/2024] Open
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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Affiliation(s)
- Marie-Louise Kampmann
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagen, Denmark
| | - Jan Fleckhaus
- Institute of Legal Medicine, Faculty of Medicine and University Clinic, University of Cologne, Cologne, Germany
| | - Claus Børsting
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagen, Denmark
| | - Helena Jurtikova
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University Olomouc and the University Hospital Olomouc, Olomouc, the Czech Republic
| | - Alice Piters
- Laboratoire d’Hématologie Médico-Légale, Bordeaux Cedex, France
| | - Julien Papin
- Laboratoire d’Hématologie Médico-Légale, Bordeaux Cedex, France
| | - Quentin Gauthier
- Department of Chemistry and Biochemistry, Florida International University, Miami, FL, USA
| | - Mirna Ghemrawi
- Department of Chemistry and Biochemistry, Florida International University, Miami, FL, USA
| | | | - Bruce McCord
- Department of Chemistry and Biochemistry, Florida International University, Miami, FL, USA
| | - Peter M Schneider
- Institute of Legal Medicine, Faculty of Medicine and University Clinic, University of Cologne, Cologne, Germany
| | - Jiri Drabek
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University Olomouc and the University Hospital Olomouc, Olomouc, the Czech Republic
| | - Niels Morling
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagen, Denmark
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5
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Prosz A, Pipek O, Börcsök J, Palla G, Szallasi Z, Spisak S, Csabai I. Biologically informed deep learning for explainable epigenetic clocks. Sci Rep 2024; 14:1306. [PMID: 38225268 PMCID: PMC10789766 DOI: 10.1038/s41598-023-50495-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 12/20/2023] [Indexed: 01/17/2024] Open
Abstract
Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model.
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Affiliation(s)
- Aurel Prosz
- Danish Cancer Institute, Copenhagen, Denmark
| | - Orsolya Pipek
- Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Judit Börcsök
- Danish Cancer Institute, Copenhagen, Denmark
- Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
| | - Gergely Palla
- Department of Biological Physics, ELTE Eötvös Loránd University, Budapest, Hungary
- Health Services Management Training Centre, Semmelweis University, Budapest, Hungary
| | | | - Sandor Spisak
- Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary.
| | - István Csabai
- Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary
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6
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Marcante B, Delicati A, Onofri M, Tozzo P, Caenazzo L. Estimation of Human Chronological Age from Buccal Swab Samples through a DNA Methylation Analysis Approach of a Five-Locus Multiple Regression Model. Int J Mol Sci 2024; 25:935. [PMID: 38256009 PMCID: PMC10815300 DOI: 10.3390/ijms25020935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/05/2024] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
Recent advancements in forensic genetics have facilitated the extraction of additional characteristics from unidentified samples. This study delves into the predictive potential of a five-gene (ELOVL2, FHL2, KLF14, C1orf132, and TRIM59) methylation rate analysis for human age estimation using buccal swabs collected from 60 Italian volunteers. The methylation levels of specific CpG sites in the five genes were analyzed through bisulfite conversion, single-base extension, and capillary electrophoresis. A multivariate linear regression model was crafted on the training set, then the test set was employed to validate the predictive model. The multivariate predictive model revealed a mean absolute deviation of 3.49 years in the test set of our sample. While limitations include a modest sample size, the study provides valuable insights into the potential of buccal swab-based age prediction, aiding in criminal investigations where accurate age determination is crucial. Our results also highlight that it is necessary to investigate the effectiveness of predictive models specific to biological tissues and individual populations, since models already proven effective for other populations or different tissues did not show the same effectiveness in our study.
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Affiliation(s)
- Beatrice Marcante
- Legal Medicine Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy; (B.M.); (A.D.); (P.T.)
| | - Arianna Delicati
- Legal Medicine Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy; (B.M.); (A.D.); (P.T.)
| | - Martina Onofri
- Section of Legal Medicine, Department of Medicine and Surgery, Santa Maria Hospital, University of Perugia, 05100 Terni, Italy;
| | - Pamela Tozzo
- Legal Medicine Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy; (B.M.); (A.D.); (P.T.)
| | - Luciana Caenazzo
- Legal Medicine Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy; (B.M.); (A.D.); (P.T.)
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7
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Refn MR, Andersen MM, Kampmann ML, Tfelt-Hansen J, Sørensen E, Larsen MH, Morling N, Børsting C, Pereira V. Longitudinal changes and variation in human DNA methylation analysed with the Illumina MethylationEPIC BeadChip assay and their implications on forensic age prediction. Sci Rep 2023; 13:21658. [PMID: 38066081 PMCID: PMC10709620 DOI: 10.1038/s41598-023-49064-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 12/04/2023] [Indexed: 12/18/2023] Open
Abstract
DNA methylation, a pivotal epigenetic modification, plays a crucial role in regulating gene expression and is known to undergo dynamic changes with age. The present study investigated epigenome-wide methylation profiles in 64 individuals over two time points, 15 years apart, using the Illumina EPIC850k arrays. A mixed-effects model identified 2821 age-associated differentially methylated CpG positions (aDMPs) with a median rate of change of 0.18% per year, consistent with a 10-15% change during a human lifespan. Significant variation in the baseline DNA methylation levels between individuals of similar ages as well as inconsistent direction of change with time across individuals were observed for all the aDMPs. Twenty-three of the 2821 aDMPs were previously incorporated into forensic age prediction models. These markers displayed larger changes in DNA methylation with age compared to all the aDMPs and less variation among individuals. Nevertheless, the forensic aDMPs also showed inter-individual variations in the direction of DNA methylation changes. Only cg16867657 in ELOVL2 exhibited a uniform direction of the age-related change among the investigated individuals, which supports the current knowledge that CpG sites in ELOVL2 are the best markers for age prediction.
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Affiliation(s)
- Mie Rath Refn
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark.
| | - Mikkel Meyer Andersen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
- The Department of Mathematical Sciences, Aalborg University, 9220, Aalborg, Denmark
| | - Marie-Louise Kampmann
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Jacob Tfelt-Hansen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
- The Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark
| | - Margit Hørup Larsen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark
| | - Niels Morling
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Claus Børsting
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Vania Pereira
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
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Kotková L, Drábek J. Age-related changes in sperm DNA methylation and their forensic and clinical implications. Epigenomics 2023; 15:1157-1173. [PMID: 38031735 DOI: 10.2217/epi-2023-0307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023] Open
Abstract
As a link between a stable genome and a dynamic environment, epigenetics is a promising tool for mapping age-related changes in human DNA. Methylated cytosine changes at specific loci are generally less studied in sperm DNA than in somatic cell DNA. Age-related methylation changes can be connected to various reproductive health problems and multiple disorders in offspring. In addition, they can be helpful in forensic fields, where testing of specific loci in semen samples found at sexual assault crime scenes can predict a perpetrator's age and narrow down the police investigation. This review focuses on age-related methylation changes in sperm. It covers the biological role of methylation, methylation testing techniques and the implications of methylation changes in forensics and clinical practice.
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Affiliation(s)
- Lucie Kotková
- Institute of Molecular & Translational Medicine, Faculty of Medicine & Dentistry, Palacky University Olomouc and University Hospital Olomouc, 77900, Czech Republic
| | - Jiří Drábek
- Institute of Molecular & Translational Medicine, Faculty of Medicine & Dentistry, Palacky University Olomouc and University Hospital Olomouc, 77900, Czech Republic
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Xiao C, Li Y, Chen M, Yi S, Huang D. Improved age estimation from semen using sperm-specific age-related CpG markers. Forensic Sci Int Genet 2023; 67:102941. [PMID: 37820545 DOI: 10.1016/j.fsigen.2023.102941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/25/2023] [Accepted: 10/02/2023] [Indexed: 10/13/2023]
Abstract
Accurate age estimation from semen has the potential to greatly narrow the pool of unidentified suspects in sexual assault investigations. However, previous efforts utilizing semen age-related CpG (AR-CpG) markers have shown lower accuracy compared to blood AR-CpG-based methods. This discrepancy may be attributed to DNA methylation (DNAm) interferences from "round cells" such as leukocytes and immature sperm cells in semen. This study aimed to develop age calculators based on sperm-specific AR-CpG markers and to achieve performance-improved age estimates from sperm DNA. Through an analysis of publicly available MethylationEPIC microarray data from 90 sperm samples of healthy males aged 22-51 years, we identified 31 sperm-specific AR-CpG markers with absolute Pearson's R values > 0.5 and Benjamini-Hochberg adjusted p values < 0.013. The top 19 AR-CpG markers with the largest absolute R values and beta ranges > 0.10, along with 3 reported semen AR-CpG markers (cg06304190, cg06979108, and cg12837463), were integrated into two methylation SNaPshot panels (Ⅰ and Ⅱ), each containing 11 markers. The 21 qualified AR-CpG markers showed absolute R values ≥ 0.427 in an independent validation cohort of 253 sperm DNA samples (22-67 years), with cg21843517 exhibiting the strongest age correlation (R = 0.853). The optimal models, constructed using sperm DNAm data of the training set (n = 214, 22-67 years) and markers from panel Ⅰ (n = 11), panel Ⅱ (n = 10), or both panels, achieved mean absolute errors (MAEs) of 2.526-4.746, 3.890-5.715, and > 9.800 years on the test sets of sperm (n = 39, 23-64 years), semen (same donors as the sperm test set), and whole blood (n = 40, 22-65 years), respectively. The simplified models incorporating 3, 5, 9, or 14 AR-CpG markers (MAE = 2.918-4.139 years for sperm) still outperformed the Lee et al. original model (MAE = 6.444 years for semen) and the reconstructed panel Lee model (MAE = 6.011 years for sperm). The final models, utilizing all sperm DNAm data (n = 253) and markers from panel Ⅰ, panel Ⅱ, or both panels, yielded mean MAEs of 2.587, 2.766, and 2.200 years, respectively, on the 50 test sets generated by 5 repeats of 10-fold cross-validations. Additionally, multiple markers in both panels demonstrated the ability to discern sperm or semen from blood with 100% accuracy. In summary, our study substantiates the potential of sperm-specific AR-CpG markers for precise age estimation from sperm DNA, providing an improved toolset for forensic investigations.
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Affiliation(s)
- Chao Xiao
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China; Hubei Key Laboratory of the Forensic Science, Hubei University of Police, Wuhan, Hubei 430035, PR China.
| | - Ya Li
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Maomin Chen
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Shaohua Yi
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Daixin Huang
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China.
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10
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Waziry R, Gu Y, Williams O, Hägg S. Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults. EPIGENETICS COMMUNICATIONS 2023; 3:7. [PMID: 38037563 PMCID: PMC10688599 DOI: 10.1186/s43682-023-00022-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 09/28/2023] [Indexed: 12/02/2023]
Abstract
Background Saliva measures are generally more accessible than blood, especially in vulnerable populations. However, connections between aging biology biomarkers in different body tissues remain unknown. Methods The present study included individuals (N = 2406) who consented for saliva and blood draw in the Health and Retirement Telomere length study in 2008 and the Venous blood study in 2016 who had complete data for both tissues. We assessed biological aging based on telomere length in saliva and DNA methylation and physiology measures in blood. DNA methylation clocks combine information from CpGs to produce the aging measures representative of epigenetic aging in humans. We analyzed DNA methylation clocks proposed by Horvath (353 CpG sites), Hannum (71 CpG sites), Levine or PhenoAge, (513 CpG sites), GrimAge, (epigenetic surrogate markers for select plasma proteins), Horvath skin and blood (391 CpG sites), Lin (99 CpG sites), Weidner (3 CpG sites), and VidalBralo (8 CpG sites). Physiology measures (referred to as phenotypic age) included albumin, creatinine, glucose, [log] C-reactive protein, lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count. The phenotypic age algorithm is based on parametrization of Gompertz proportional hazard models. Average telomere length was assayed using quantitative PCR (qPCR) by comparing the telomere sequence copy number in each patient's sample (T) to a single-copy gene copy number (S). The resulting T/S ratio was proportional to telomere length, mean. Within individual, relationships between aging biology measures in blood and saliva and variations according to sex were assessed. Results Saliva-based telomere length showed inverse associations with both physiology-based and DNA methylation-based aging biology biomarkers in blood. Longer saliva-based telomere length was associated with 1 to 4 years slower biological aging based on blood-based biomarkers with the highest magnitude being Weidner (β = - 3.97, P = 0.005), GrimAge (β = - 3.33, P < 0.001), and Lin (β = - 3.45, P = 0.008) biomarkers of DNA methylation. Conclusions There are strong connections between aging biology biomarkers in saliva and blood in older adults. Changes in telomere length vary with changes in DNA methylation and physiology biomarkers of aging biology. We observed variations in the relationship between each body system represented by physiology biomarkers and biological aging, particularly at the DNA methylation level. These observations provide novel opportunities for integration of both blood-based and saliva-based biomarkers in clinical care of vulnerable and clinically difficult to reach populations where either or both tissues would be accessible for clinical monitoring purposes.
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Affiliation(s)
- R. Waziry
- Department of Neurology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Y. Gu
- Department of Neurology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
- The Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, USA
- G.H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
- The Department of Epidemiology, Joseph P. Mailman School of Public Health, Columbia University, New York, NY, USA
| | - O. Williams
- Department of Neurology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - S. Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
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11
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Naue J. Getting the chronological age out of DNA: using insights of age-dependent DNA methylation for forensic DNA applications. Genes Genomics 2023; 45:1239-1261. [PMID: 37253906 PMCID: PMC10504122 DOI: 10.1007/s13258-023-01392-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/15/2023] [Indexed: 06/01/2023]
Abstract
BACKGROUND DNA analysis for forensic investigations has a long tradition with important developments and optimizations since its first application. Traditionally, short tandem repeats analysis has been the most powerful method for the identification of individuals. However, in addition, epigenetic changes, i.e., DNA methylation, came into focus of forensic DNA research. Chronological age prediction is one promising application to allow for narrowing the pool of possible individuals who caused a trace, as well as to support the identification of unknown bodies and for age verification of living individuals. OBJECTIVE This review aims to provide an overview of the current knowledge, possibilities, and (current) limitations about DNA methylation-based chronological age prediction with emphasis on forensic application. METHODS The development, implementation and application of age prediction tools requires a deep understanding about the biological background, the analysis methods, the age-dependent DNA methylation markers, as well as the mathematical models for age prediction and their evaluation. Furthermore, additional influences can have an impact. Therefore, the literature was evaluated in respect to these diverse topics. CONCLUSION The numerous research efforts in recent years have led to a rapid change in our understanding of the application of DNA methylation for chronological age prediction, which is now on the way to implementation and validation. Knowledge of the various aspects leads to a better understanding and allows a more informed interpretation of DNAm quantification results, as well as the obtained results by the age prediction tools.
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Affiliation(s)
- Jana Naue
- Institute of Forensic Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
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12
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Fang C, Zhou P, Li R, Guo J, Qiu H, Zhang J, Li M, Yu C, Meng D, Xu X, Liu X, Guan D, Yan J. Development of a novel forensic age estimation strategy for aged blood samples by combining piRNA and miRNA markers. Int J Legal Med 2023; 137:1327-1335. [PMID: 37264192 DOI: 10.1007/s00414-023-03028-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 05/22/2023] [Indexed: 06/03/2023]
Abstract
In forensic investigations, age estimation is vital for determining whether a suspect is under or over the legally defined adult age. With breakthroughs in RNA sequencing technology, small noncoding RNAs have provided new ways to solve problems related to the age estimation of trace or aged samples, owing to their small molecular weight and better stability. In our previous study, we had applied miRNAs for the age estimation of bloodstains; however, further improvement of the existing model is needed. PIWI-interacting RNAs (PiRNAs), which are 24-32 nt noncoding small RNA molecules involved in the PIWI-piRNA pathway, play an important role in the aging process. In this study, we explored the possibility of simultaneously analyzing piRNAs and miRNAs for better age estimation purpose. Through massively parallel sequencing, five age-related piRNAs were identified in blood samples that had been stored for eight years. Further real-time PCR analysis revealed that two piRNAs (piR-000753 and piR-020548) showed relatively higher efficiency in age estimation. Additionally, two age-related miRNAs (miR-324-3p and miR-330-5p) were used to build the estimation model. Among all algorithms tested, gradient boosting showed the lowest mean absolute error (MAE) and root mean square error (RMSE) values (3.171 and 4.403 years, respectively) for the validation dataset (n = 110). The errors of the model were less than 5 years and 10 years for 81.82% and 96.36% of the samples, respectively. The results suggest that the combined use of piRNA and miRNA markers may increase the accuracy of age estimation, and our new model has great potential for application in forensic casework.
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Affiliation(s)
- Chen Fang
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, People's Republic of China.
- Department of Pathology and Forensic Medicine, School of Clinical and Basic Medical Sciences, Shandong First Medical University& Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China.
| | - Peng Zhou
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, People's Republic of China
- Department of Pathology and Forensic Medicine, School of Clinical and Basic Medical Sciences, Shandong First Medical University& Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China
| | - Ran Li
- Department of Pathology and Forensic Medicine, School of Clinical and Basic Medical Sciences, Shandong First Medical University& Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China
| | - Jinghan Guo
- Department of Pathology and Forensic Medicine, School of Clinical and Basic Medical Sciences, Shandong First Medical University& Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China
| | - Huixian Qiu
- Department of Pathology and Forensic Medicine, School of Clinical and Basic Medical Sciences, Shandong First Medical University& Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China
| | - Jingjuan Zhang
- Department of Pathology and Forensic Medicine, School of Clinical and Basic Medical Sciences, Shandong First Medical University& Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China
| | - Min Li
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, People's Republic of China
- Department of Pathology and Forensic Medicine, School of Clinical and Basic Medical Sciences, Shandong First Medical University& Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China
| | - Chunjiang Yu
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, People's Republic of China
- Department of Pathology and Forensic Medicine, School of Clinical and Basic Medical Sciences, Shandong First Medical University& Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China
| | - Deping Meng
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, People's Republic of China
- Department of Pathology and Forensic Medicine, School of Clinical and Basic Medical Sciences, Shandong First Medical University& Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China
| | - Xiaoqun Xu
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, People's Republic of China
- Department of Pathology and Forensic Medicine, School of Clinical and Basic Medical Sciences, Shandong First Medical University& Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China
| | - Xu Liu
- Institute of Analysis and Testing, Beijing Academy of Science and Technology (Beijing Center for Physical and Chemical Analysis), Beijing, 100094, People's Republic of China
| | - Di Guan
- Institute of Analysis and Testing, Beijing Academy of Science and Technology (Beijing Center for Physical and Chemical Analysis), Beijing, 100094, People's Republic of China
| | - Jiangwei Yan
- Shanxi Medical University, 030001, Taiyuan, People's Republic of China.
- Department of Pathology and Forensic Medicine, School of Clinical and Basic Medical Sciences, Shandong First Medical University& Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China.
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Refn MR, Kampmann ML, Morling N, Tfelt-Hansen J, Børsting C, Pereira V. Prediction of chronological age and its applications in forensic casework: methods, current practices, and future perspectives. Forensic Sci Res 2023; 8:85-97. [PMID: 37621446 PMCID: PMC10445583 DOI: 10.1093/fsr/owad021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/28/2023] [Indexed: 08/26/2023] Open
Abstract
Estimating an individual's age can be relevant in several areas primarily related to the clinical and forensic fields. In the latter, estimation of an individual's chronological age from biological material left by the perpetrator at a crime scene may provide helpful information for police investigation. Estimation of age is also beneficial in immigration cases, where age can affect the person's protection status under the law, or in disaster victim identification to narrow the list of potential missing persons. In the last decade, research has focused on establishing new approaches for age prediction in the forensic field. From the first forensic age estimations based on morphological inspections of macroscopic changes in bone and teeth, the focus has shifted to molecular methods for age estimation. These methods allow the use of samples from human biological material that does not contain morphological age features and can, in theory, be investigated in traces containing only small amounts of biological material. Molecular methods involving DNA analyses are the primary choice and estimation of DNA methylation levels at specific sites in the genome is the most promising tool. This review aims to provide an overview of the status of forensic age prediction using molecular methods, with particular focus in DNA methylation. The frequent challenges that impact forensic age prediction model development will be addressed, together with the importance of validation efforts within the forensic community.
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Affiliation(s)
- Mie Rath Refn
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marie-Louise Kampmann
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Morling
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jacob Tfelt-Hansen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen , Denmark
| | - Claus Børsting
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Vania Pereira
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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14
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Kayser M, Branicki W, Parson W, Phillips C. Recent advances in Forensic DNA Phenotyping of appearance, ancestry and age. Forensic Sci Int Genet 2023; 65:102870. [PMID: 37084623 DOI: 10.1016/j.fsigen.2023.102870] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 04/04/2023] [Indexed: 04/09/2023]
Abstract
Forensic DNA Phenotyping (FDP) comprises the prediction of a person's externally visible characteristics regarding appearance, biogeographic ancestry and age from DNA of crime scene samples, to provide investigative leads to help find unknown perpetrators that cannot be identified with forensic STR-profiling. In recent years, FDP has advanced considerably in all of its three components, which we summarize in this review article. Appearance prediction from DNA has broadened beyond eye, hair and skin color to additionally comprise other traits such as eyebrow color, freckles, hair structure, hair loss in men, and tall stature. Biogeographic ancestry inference from DNA has progressed from continental ancestry to sub-continental ancestry detection and the resolving of co-ancestry patterns in genetically admixed individuals. Age estimation from DNA has widened beyond blood to more somatic tissues such as saliva and bones as well as new markers and tools for semen. Technological progress has allowed forensically suitable DNA technology with largely increased multiplex capacity for the simultaneous analysis of hundreds of DNA predictors with targeted massively parallel sequencing (MPS). Forensically validated MPS-based FDP tools for predicting from crime scene DNA i) several appearance traits, ii) multi-regional ancestry, iii) several appearance traits together with multi-regional ancestry, and iv) age from different tissue types, are already available. Despite recent advances that will likely increase the impact of FDP in criminal casework in the near future, moving reliable appearance, ancestry and age prediction from crime scene DNA to the level of detail and accuracy police investigators may desire, requires further intensified scientific research together with technical developments and forensic validations as well as the necessary funding.
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Affiliation(s)
- Manfred Kayser
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Wojciech Branicki
- Institute of Zoology and Biomedical Research, Jagiellonian University, Kraków, Poland,; Institute of Forensic Research, Kraków, Poland
| | - Walther Parson
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria; Forensic Science Program, The Pennsylvania State University, PA, USA
| | - Christopher Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
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15
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Galante N, Cotroneo R, Furci D, Lodetti G, Casali MB. Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives. Int J Legal Med 2023; 137:445-458. [PMID: 36507961 DOI: 10.1007/s00414-022-02928-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 12/04/2022] [Indexed: 12/14/2022]
Abstract
In recent years, new studies based on artificial intelligence (AI) have been conducted in the forensic field, posing new challenges and demonstrating the advantages and disadvantages of using AI methodologies to solve forensic well-known problems. Specifically, AI technology has tried to overcome the human subjective bias limitations of the traditional approach of the forensic sciences, which include sex prediction and age estimation from morphometric measurements in forensic anthropology or evaluating the third molar stage of development in forensic odontology. Likewise, AI has been studied as an assisting tool in forensic pathology for a quick and easy identification of the taxonomy of diatoms. The present systematic review follows the PRISMA 2020 statements and aims to explore an emerging topic that has been poorly analyzed in the forensic literature. Benefits, limitations, and forensic implications concerning AI are therefore highlighted, by providing an extensive critical review of its current applications on forensic sciences as well as its future directions. Results are divided into 5 subsections which included forensic anthropology, forensic odontology, forensic pathology, forensic genetics, and other forensic branches. The discussion offers a useful instrument to investigate the potential benefits of AI in the forensic fields as well as to point out the existing open questions and issues concerning its application on real-life scenarios. Procedural notes and technical aspects are also provided to the readers.
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Affiliation(s)
- Nicola Galante
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy.
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy.
| | - Rosy Cotroneo
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
| | - Domenico Furci
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
| | - Giorgia Lodetti
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
| | - Michelangelo Bruno Casali
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Oncology and Hemato-Oncology (DIPO), University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
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16
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Yang F, Qian J, Qu H, Ji Z, Li J, Hu W, Cheng F, Fang X, Yan J. DNA methylation-based age prediction with bloodstains using pyrosequencing and random forest regression. Electrophoresis 2023; 44:835-844. [PMID: 36739525 DOI: 10.1002/elps.202200250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/08/2022] [Accepted: 01/24/2023] [Indexed: 02/06/2023]
Abstract
The use of DNA methylation to predict chronological age has shown promising potential for obtaining additional information in forensic investigations. To date, several studies have reported age prediction models based on DNA methylation in body fluids with high DNA content. However, it is often difficult to apply these existing methods in practice due to the low amount of DNA present in stains of body fluids that are part of a trace material. In this study, we present a sensitive and rapid test for age prediction with bloodstains based on pyrosequencing and random forest regression. This assay requires only 0.1 ng of genomic DNA and the entire procedure can be completed within 10 h, making it practical for forensic investigations that require a short turnaround time. We examined the methylation levels of 46 CpG sites from six genes using bloodstain samples from 128 males and 113 females aged 10-79 years. A random forest regression model was then used to construct an age prediction model for males and females separately. The final age prediction models were developed with seven CpG sites (three for males and four for females) based on the performance of the random forest regression. The mean absolute deviation was less than 3 years for each model. Our results demonstrate that DNA methylation-based age prediction using pyrosequencing and random forest regression has potential applications in forensics to accurately predict the biological age of a bloodstain donor.
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Affiliation(s)
- Fenglong Yang
- School of Forensic Medicine, Shanxi Medical University, Shanxi, P. R. China
| | - Jialin Qian
- Beijing Center for Physical and Chemical Analysis, Beijing, P. R. China
| | - Hongzhu Qu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing, P. R. China
| | - Zhimin Ji
- School of Forensic Medicine, Shanxi Medical University, Shanxi, P. R. China
| | - Junli Li
- School of Forensic Medicine, Shanxi Medical University, Shanxi, P. R. China
| | - Wenjing Hu
- School of Forensic Medicine, Shanxi Medical University, Shanxi, P. R. China
| | - Feng Cheng
- School of Forensic Medicine, Shanxi Medical University, Shanxi, P. R. China
| | - Xiangdong Fang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing, P. R. China
| | - Jiangwei Yan
- School of Forensic Medicine, Shanxi Medical University, Shanxi, P. R. China
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17
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Jiang L, Zhang K, Wei X, Li J, Wang S, Wang Z, Zhou Y, Zha L, Luo H, Song F. Developing a male-specific age predictive model based on Y-CpGs for forensic analysis. Forensic Sci Int 2023; 343:111566. [PMID: 36640536 DOI: 10.1016/j.forsciint.2023.111566] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/22/2022] [Accepted: 01/09/2023] [Indexed: 01/13/2023]
Abstract
In forensic work, predicting the age of the criminal suspect or victim could provide beneficial clues for investigation. Epigenetic age estimation based on age-correlated DNA methylation has been one of the most widely studied methods of age estimation. However, almost all available epigenetic age prediction models are based on autosomal CpGs, which are only applicable to single-source DNA samples. In this study, we screened the available methylation data sets to identify loci with potential to meet the objectives of this study and then established a male-specific age prediction model based on 2 SNaPshot systems that contain 13 Y-CpGs and the mean absolute deviation (MAD) values were 4-6 years. The multiplex methylation SNaPshot systems and age-predictive model have been validated for sensitivity (the DNA input could be as low as 0.5 ng) and male specificity. They are supposed to have feasibility in forensic practice. In addition, it demonstrated that the method was also applicable to bloodstains, which were commonly found at crime scenes. The results showed good performance (the training set: R2 = 0.9341, MAD = 4.65 years; the test set: R2 = 0.8952, MAD = 5.73 years) in case investigation for predicting male age. For mixtures, when the male to female DNA ratio is 1:1, 1:10, the deviation between the actual age and the predicted age obtained by the model was less than 8 years, which offers great hope for future prediction of the age of males in mixtures and will be a powerful tool for special cases, such as sexual assault. Furthermore, the work provides a basis for the application of Y-CpGs in forensic science.
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Affiliation(s)
- Lanrui Jiang
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Ke Zhang
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China; Public Security Bureau of Zhengzhou City, Zhengzhou, Henan Province 450003, China
| | - Xiaowen Wei
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Jiahang Li
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Shuangshuang Wang
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Zefei Wang
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Yuxiang Zhou
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Lagabaiyila Zha
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, No172. Tongzipo Road, Changsha, Hunan Province 410013, China
| | - Haibo Luo
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China.
| | - Feng Song
- Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan Province 610041, China.
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18
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An ELOVL2-Based Epigenetic Clock for Forensic Age Prediction: A Systematic Review. Int J Mol Sci 2023; 24:ijms24032254. [PMID: 36768576 PMCID: PMC9916975 DOI: 10.3390/ijms24032254] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/13/2023] [Accepted: 01/18/2023] [Indexed: 01/26/2023] Open
Abstract
The prediction of chronological age from methylation-based biomarkers represents one of the most promising applications in the field of forensic sciences. Age-prediction models developed so far are not easily applicable for forensic caseworkers. Among the several attempts to pursue this objective, the formulation of single-locus models might represent a good strategy. The present work aimed to develop an accurate single-locus model for age prediction exploiting ELOVL2, a gene for which epigenetic alterations are most highly correlated with age. We carried out a systematic review of different published pyrosequencing datasets in which methylation of the ELOVL2 promoter was analysed to formulate age prediction models. Nine of these, with available datasets involving 2298 participants, were selected. We found that irrespective of which model was adopted, a very strong relationship between ELOVL2 methylation levels and age exists. In particular, the model giving the best age-prediction accuracy was the gradient boosting regressor with a prediction error of about 5.5 years. The findings reported here strongly support the use of ELOVL2 for the formulation of a single-locus epigenetic model, but the inclusion of additional, non-redundant markers is a fundamental requirement to apply a molecular model to forensic applications with more robust results.
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19
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Hong SR, Shin KJ. Can we integrate method-specific age-predictive models?: Analysis method-induced differences in detected DNA methylation status. Forensic Sci Int Genet 2023; 62:102805. [PMID: 36379153 DOI: 10.1016/j.fsigen.2022.102805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 11/02/2022] [Accepted: 11/08/2022] [Indexed: 11/11/2022]
Abstract
Forensic research surrounding the use of DNA methylation (DNAm) markers to predict age suggests that accurate prediction of chronological age can be achieved with just several DNAm markers. Several age-prediction models are based on DNAm levels that are detectable by a diverse range of DNAm analysis methods. Among the many DNAm analysis methods, targeted amplicon-based massively parallel sequencing (MPS) and single-base extension (SBE) methods have been widely studied owing to their practicality, including their multiplex capabilities. Since these two DNAm analysis methods share an identical amplification step during their experimental processes, several studies have compared the differences between the methods to construct integrated age-prediction models based on both MPS and SBE data. In this study, we compared the specific differences in DNAm levels between these two commonly exploited analysis methods by analyzing the identical PCR amplicons from the same samples and quantifying the actual bisulfite-converted DNA amount involved in the PCR step. The DNAm levels of five well-studied age-associated markers-CpGs on the ELOVL2, FHL2, KLF14, MIR29B2CHG, and TRIM59 genes-were obtained from blood samples of 250 Koreans using both DNAm analysis methods. The results showed that only ELOVL2 is interchangeable between the MPS and SBE methods, while the rest of the markers showed significant differences in DNAm values. These differences may result in high errors and consequential lowered accuracy in age estimates. Therefore, a DNAm analysis method-specific approach that considers method-induced DNAm differences is recommended to improve the overall accuracy and reliability of age-prediction methods.
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Affiliation(s)
- Sae Rom Hong
- Department of Forensic Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, 50-1 Yonsei-ro, 03722 Seoul, Republic of Korea
| | - Kyoung-Jin Shin
- Department of Forensic Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, 50-1 Yonsei-ro, 03722 Seoul, Republic of Korea.
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20
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Ambroa-Conde A, Girón-Santamaría L, Mosquera-Miguel A, Phillips C, Casares de Cal M, Gómez-Tato A, Álvarez-Dios J, de la Puente M, Ruiz-Ramírez J, Lareu M, Freire-Aradas A. Epigenetic age estimation in saliva and in buccal cells. Forensic Sci Int Genet 2022; 61:102770. [DOI: 10.1016/j.fsigen.2022.102770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/22/2022] [Accepted: 08/24/2022] [Indexed: 11/04/2022]
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21
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Daunay A, Hardy LM, Bouyacoub Y, Sahbatou M, Touvier M, Blanché H, Deleuze JF, How-Kit A. Centenarians consistently present a younger epigenetic age than their chronological age with four epigenetic clocks based on a small number of CpG sites. Aging (Albany NY) 2022; 14:7718-7733. [PMID: 36202132 PMCID: PMC9596211 DOI: 10.18632/aging.204316] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/23/2022] [Indexed: 11/25/2022]
Abstract
Aging is a progressive time-dependent biological process affecting differentially individuals, who can sometimes present exceptional longevity. Epigenetic alterations are one of the hallmarks of aging, which comprise the epigenetic drift and clock at DNA methylation level. In the present study, we estimated the DNA methylation-based age (DNAmage) using four epigenetic clocks based on a small number of CpGs in French centenarians and semi-supercentenarians (CSSC, n=214) as well as nonagenarians' and centenarians' offspring (NCO, n=143) compared to individuals from the French general population (CG, n=149). DNA methylation analysis of the nine CpGs included in the epigenetic clocks showed high correlation with chronological age (-0.66>R>0.54) and also the presence of an epigenetic drift for four CpGs that was only visible in CSSC. DNAmage analysis showed that CSSC and to a lesser extend NCO present a younger DNAmage than their chronological age (15-28.5 years for CSSC, 4.4-11.5 years for NCO and 4.2-8.2 years for CG), which were strongly significant in CSSC compared to CG (p-values<2.2e-16). These differences suggest that epigenetic aging and potentially biological aging are slowed in exceptionally long-lived individuals and that epigenetic clocks based on a small number of CpGs are sufficient to reveal alterations of the global epigenetic clock.
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Affiliation(s)
- Antoine Daunay
- Laboratory for Genomics, Foundation Jean Dausset - CEPH, Paris, France
| | - Lise M Hardy
- Laboratory for Genomics, Foundation Jean Dausset - CEPH, Paris, France.,Laboratory of Excellence GenMed, Paris, France
| | - Yosra Bouyacoub
- Laboratory for Genomics, Foundation Jean Dausset - CEPH, Paris, France.,Laboratory of Excellence GenMed, Paris, France
| | - Mourad Sahbatou
- Laboratory for Genomics, Foundation Jean Dausset - CEPH, Paris, France
| | - Mathilde Touvier
- Sorbonne Paris Nord University, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center Inserm U1153, Inrae U1125, Cnam, University of Paris (CRESS), Bobigny, France
| | - Hélène Blanché
- Laboratory of Excellence GenMed, Paris, France.,Centre de Ressources Biologiques, CEPH Biobank, Foundation Jean Dausset - CEPH, Paris, France
| | - Jean-François Deleuze
- Laboratory for Genomics, Foundation Jean Dausset - CEPH, Paris, France.,Laboratory of Excellence GenMed, Paris, France.,Centre de Ressources Biologiques, CEPH Biobank, Foundation Jean Dausset - CEPH, Paris, France.,Centre National de Recherche en Génomique Humaine, CEA, Institut François Jacob, Evry, France
| | - Alexandre How-Kit
- Laboratory for Genomics, Foundation Jean Dausset - CEPH, Paris, France
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22
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Carratto TMT, Moraes VMS, Recalde TSF, Oliveira MLGD, Teixeira Mendes-Junior C. Applications of massively parallel sequencing in forensic genetics. Genet Mol Biol 2022; 45:e20220077. [PMID: 36121926 PMCID: PMC9514793 DOI: 10.1590/1678-4685-gmb-2022-0077] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/15/2022] [Indexed: 11/22/2022] Open
Abstract
Massively parallel sequencing, also referred to as next-generation sequencing, has positively changed DNA analysis, allowing further advances in genetics. Its capability of dealing with low quantity/damaged samples makes it an interesting instrument for forensics. The main advantage of MPS is the possibility of analyzing simultaneously thousands of genetic markers, generating high-resolution data. Its detailed sequence information allowed the discovery of variations in core forensic short tandem repeat loci, as well as the identification of previous unknown polymorphisms. Furthermore, different types of markers can be sequenced in a single run, enabling the emergence of DIP-STRs, SNP-STR haplotypes, and microhaplotypes, which can be very useful in mixture deconvolution cases. In addition, the multiplex analysis of different single nucleotide polymorphisms can provide valuable information about identity, biogeographic ancestry, paternity, or phenotype. DNA methylation patterns, mitochondrial DNA, mRNA, and microRNA profiling can also be analyzed for different purposes, such as age inference, maternal lineage analysis, body-fluid identification, and monozygotic twin discrimination. MPS technology also empowers the study of metagenomics, which analyzes genetic material from a microbial community to obtain information about individual identification, post-mortem interval estimation, geolocation inference, and substrate analysis. This review aims to discuss the main applications of MPS in forensic genetics.
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Affiliation(s)
- Thássia Mayra Telles Carratto
- Universidade de São Paulo, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Ribeirão Preto, SP, Brazil
| | - Vitor Matheus Soares Moraes
- Universidade de São Paulo, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Ribeirão Preto, SP, Brazil
| | | | | | - Celso Teixeira Mendes-Junior
- Universidade de São Paulo, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Ribeirão Preto, SP, Brazil
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23
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Accurate age estimation from blood samples of Han Chinese individuals using eight high-performance age-related CpG sites. Int J Legal Med 2022; 136:1655-1665. [PMID: 35819508 DOI: 10.1007/s00414-022-02865-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 07/04/2022] [Indexed: 10/17/2022]
Abstract
Age-related CpG sites (AR-CpGs) are currently the most promising biomarkers for forensic age estimation. In our previous studies, we first validated the age correlation of seven reported AR-CpGs in blood samples of Chinese Han population. Subsequently, we screened some good age predictors from blood samples of Chinese Han population, and built pyrosequencing-based age prediction models. However, it is still important to select a set of high-performance AR-CpGs in a specific racial group and establish a simple and efficient method for accurate age estimation for forensic purpose. In this study, eight AR-CpGs, namely chr6: 11,044,628 (ELOVL2), cg06639320 (FHL2), chr1: 207,823,723 (C1orf132), cg19283806 (CCDC102B), cg14361627 (KLF14), cg17740900 (SYNE2), cg07553761 (TRIM59), and cg26947034, were selected based on our previous studies, and a multiplex methylation SNaPshot assay was developed to investigate DNA methylation levels at these AR-CpGs in 529 blood samples (aged 2-82 years) from Han Chinese population. All selected CpG sites showed strong age correlation with the correlation coefficient (r) from 0.8363 to 0.9251. Multiple linear regression (MLR) and support vector regression (SVR) age prediction models were simultaneously established to fit change characteristics of DNA methylation levels of eight AR-CpGs with the age in 374 donors' blood samples. The MLR model enabled age prediction with R2 = 0.923, mean absolute error (MAE) = 3.52, while the SVR model enabled age prediction with R2 = 0.935, MAE = 2.88. One hundred fifty-five independent samples were used as a validation set to test the two models' performance, and the prediction MAE for the validation set was 3.71 and 3.34 for the MLR and SVR models, respectively. For the MLR and SVR models, the correct prediction rate at ± 5 years reached a high level of 79.35% and 83.23%, respectively. In general, these statistical parameters indicated that the SVR model outperformed the MLR model in age prediction of the Han Chinese population. In addition, our method provides sufficient sensitivity in forensic applications and allows for 100% efficiency when examining bloodstains kept in room conditions for up to 43 days. These results indicate that our multiplex methylation SNaPshot assay is a reliable, effective, and accurate method for age prediction in blood samples from the Chinese Han population.
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24
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Unlocking the potential of forensic traces: Analytical approaches to generate investigative leads. Sci Justice 2022; 62:310-326. [PMID: 35598924 DOI: 10.1016/j.scijus.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 03/17/2022] [Accepted: 03/19/2022] [Indexed: 11/21/2022]
Abstract
Forensic investigation involves gathering the information necessary to understand the criminal events as well as linking objects or individuals to an item, location or other individual(s) for investigative purposes. For years techniques such as presumptive chemical tests, DNA profiling or fingermark analysis have been of great value to this process. However, these techniques have their limitations, whether it is a lack of confidence in the results obtained due to cross-reactivity, subjectivity and low sensitivity; or because they are dependent on holding reference samples in a pre-existing database. There is currently a need to devise new ways to gather as much information as possible from a single trace, particularly from biological traces commonly encountered in forensic casework. This review outlines the most recent advancements in the forensic analysis of biological fluids, fingermarks and hair. Special emphasis is placed on analytical methods that can expand the information obtained from the trace beyond what is achieved in the usual practices. Special attention is paid to those methods that accurately determine the nature of the sample, as well as how long it has been at the crime scene, along with individualising information regarding the donor source of the trace.
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25
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Li L, Zhang C, Liu S, Guan H, Zhang Y. Age Prediction by DNA Methylation in Neural Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1393-1402. [PMID: 34048347 DOI: 10.1109/tcbb.2021.3084596] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Aging is traditionally thought to be caused by complex and interacting factors such as DNA methylation. The traditional formula of DNA methylation aging is based on linear models and little work has explored the effectiveness of neural networks, which can learn non-linear relationships. DNA methylation data typically consists of hundreds of thousands of feature space and a much less number of biological samples. This leads to overfitting and a poor generalization of neural networks. We propose Correlation Pre-Filtered Neural Network (CPFNN) that uses Spearman Correlation to pre-filter the input features before feeding them into neural networks. We compare CPFNN with the statistical regressions (i.e., Horvath's and Hannum's formulas), the neural networks with LASSO regularization and elastic net regularization, and the Dropout Neural Networks. CPFNN outperforms these models by at least 1 year in term of Mean Absolute Error (MAE), with a MAE of 2.7 years. We also test for association between the epigenetic age with Schizophrenia and Down Syndrome ( p=0.024 and , respectively). We discover that for a large number of candidate features, such as genome-wide DNA methylation data, a key factor in improving prediction accuracy is to appropriately weight features that are highly correlated with the outcome of interest.
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26
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Adaptive feature selection framework for DNA methylation-based age prediction. Soft comput 2022. [DOI: 10.1007/s00500-022-06844-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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27
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DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8393498. [PMID: 35111213 PMCID: PMC8803417 DOI: 10.1155/2022/8393498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 11/20/2021] [Accepted: 12/22/2021] [Indexed: 12/28/2022]
Abstract
Purpose. Age can be an important clue in uncovering the identity of persons that left biological evidence at crime scenes. With the availability of DNA methylation data, several age prediction models are developed by using statistical and machine learning methods. From epigenetic studies, it has been demonstrated that there is a close association between aging and DNA methylation. Most of the existing studies focused on healthy samples, whereas diseases may have a significant impact on human age. Therefore, in this article, an age prediction model is proposed using DNA methylation biomarkers for healthy and diseased samples. Methods. The dataset contains 454 healthy samples and 400 diseased samples from publicly available sources with age (1–89 years old). Six CpG sites are identified from this data having a high correlation with age using Pearson’s correlation coefficient. In this work, the age prediction model is developed using four different machine learning techniques, namely, Multiple Linear Regression, Support Vector Regression, Gradient Boosting Regression, and Random Forest Regression. Separate models are designed for healthy and diseased data. The data are split randomly into 80 : 20 ratios for training and testing, respectively. Results. Among all the techniques, the model designed using Random Forest Regression shows the best performance, and Gradient Boosting Regression is the second best model. In the case of healthy samples, the model achieved a MAD of 2.51 years for training data and 4.85 for testing data. Also, for diseased samples, a MAD of 3.83 years is obtained for training and 9.53 years for testing. Conclusion. These results showed that the proposed model can predict age for healthy and diseased samples.
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28
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Fan H, Xie Q, Zhang Z, Wang J, Chen X, Qiu P. Chronological Age Prediction: Developmental Evaluation of DNA Methylation-Based Machine Learning Models. Front Bioeng Biotechnol 2022; 9:819991. [PMID: 35141217 PMCID: PMC8819006 DOI: 10.3389/fbioe.2021.819991] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 12/21/2021] [Indexed: 11/13/2022] Open
Abstract
Epigenetic clock, a highly accurate age estimator based on DNA methylation (DNAm) level, is the basis for predicting mortality/morbidity and elucidating the molecular mechanism of aging, which is of great significance in forensics, justice, and social life. Herein, we integrated machine learning (ML) algorithms to construct blood epigenetic clock in Southern Han Chinese (CHS) for chronological age prediction. The correlation coefficient (r) meta-analyses of 7,084 individuals were firstly implemented to select five genes (ELOVL2, C1orf132, TRIM59, FHL2, and KLF14) from a candidate set of nine age-associated DNAm biomarkers. The DNAm-based profiles of the CHS cohort (240 blood samples differing in age from 1 to 81 years) were generated by the bisulfite targeted amplicon pyrosequencing (BTA-pseq) from 34 cytosine-phosphate-guanine sites (CpGs) of five selected genes, revealing that the methylation levels at different CpGs exhibit population specificity. Furthermore, we established and evaluated four chronological age prediction models using distinct ML algorithms: stepwise regression (SR), support vector regression (SVR-eps and SVR-nu), and random forest regression (RFR). The median absolute deviation (MAD) values increased with chronological age, especially in the 61–81 age category. No apparent gender effect was found in different ML models of the CHS cohort (all p > 0.05). The MAD values were 2.97, 2.22, 2.19, and 1.29 years for SR, SVR-eps, SVR-nu, and RFR in the CHS cohort, respectively. Eventually, compared to the MAD range of the meta cohort (2.53–5.07 years), a promising RFR model (ntree = 500 and mtry = 8) was optimized with an MAD of 1.15 years in the 1–60 age categories of the CHS cohort, which could be regarded as a robust epigenetic clock in blood for age-related issues.
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Affiliation(s)
- Haoliang Fan
- *Correspondence: Haoliang Fan, ; Xuncai Chen, ; Pingming Qiu,
| | | | | | | | - Xuncai Chen
- *Correspondence: Haoliang Fan, ; Xuncai Chen, ; Pingming Qiu,
| | - Pingming Qiu
- *Correspondence: Haoliang Fan, ; Xuncai Chen, ; Pingming Qiu,
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29
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Aliferi A, Ballard D. Predicting Chronological Age from DNA Methylation Data: A Machine Learning Approach for Small Datasets and Limited Predictors. Methods Mol Biol 2022; 2432:187-200. [PMID: 35505216 DOI: 10.1007/978-1-0716-1994-0_14] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Recent research studies using epigenetic data have been exploring whether it is possible to estimate how old someone is using only their DNA. This application stems from the strong correlation that has been observed in humans between the methylation status of certain DNA loci and chronological age. While genome-wide methylation sequencing has been the most prominent approach in epigenetics research, recent studies have shown that targeted sequencing of a limited number of loci can be successfully used for the estimation of chronological age from DNA samples, even when using small datasets. Following this shift, the need to investigate further into the appropriate statistics behind the predictive models used for DNA methylation-based prediction has been identified in multiple studies. This chapter will look into an example of basic data manipulation and modeling that can be applied to small DNA methylation datasets (100-400 samples) produced through targeted methylation sequencing for a small number of predictors (10-25 methylation sites). Data manipulation will focus on converting the obtained methylation values for the different predictors to a statistically meaningful dataset, followed by a basic introduction into importing such datasets in R, as well as randomizing and splitting into appropriate training and test sets for modeling. Finally, a basic introduction to R modeling will be outlined, starting with feature selection algorithms and continuing with a simple modeling example (linear model) as well as a more complex algorithm (Support Vector Machine).
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Affiliation(s)
- Anastasia Aliferi
- King's Forensics, Department of Analytical, Environmental and Forensic Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
| | - David Ballard
- King's Forensics, Department of Analytical, Environmental and Forensic Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
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30
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A collaborative exercise on DNA methylation-based age prediction and body fluid typing. Forensic Sci Int Genet 2021; 57:102656. [PMID: 34973557 DOI: 10.1016/j.fsigen.2021.102656] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/09/2021] [Accepted: 12/14/2021] [Indexed: 11/20/2022]
Abstract
DNA methylation has become one of the most useful biomarkers for age prediction and body fluid identification in the forensic field. Therefore, several assays have been developed to detect age-associated and body fluid-specific DNA methylation changes. Among the many methods developed, SNaPshot-based assays should be particularly useful in forensic laboratories, as they permit multiplex analysis and use the same capillary electrophoresis instrumentation as STR analysis. However, technical validation of any developed assays is crucial for their proper integration into routine forensic workflow. In the present collaborative exercise, two SNaPshot multiplex assays for age prediction and a SNaPshot multiplex for body fluid identification were tested in twelve laboratories. The experimental set-up of the exercise was designed to reflect the entire workflow of SNaPshot-based methylation analysis and involved four increasingly complex tasks designed to detect potential factors influencing methylation measurements. The results of body fluid identification from each laboratory provided sufficient information to determine appropriate age prediction methods in subsequent analysis. In age prediction, systematic measurement differences resulting from the type of genetic analyzer used were identified as the biggest cause of DNA methylation variation between laboratories. Also, the use of a buffer that ensures a high ratio of specific to non-specific primer binding resulted in changes in DNA methylation measurement, especially when using degenerate primers in the PCR reaction. In addition, high input volumes of bisulfite-converted DNA often caused PCR failure, presumably due to carry-over of PCR inhibitors from the bisulfite conversion reaction. The proficiency of the analysts and experimental conditions for efficient SNaPshot reactions were also important for consistent DNA methylation measurement. Several bisulfite conversion kits were used for this study, but differences resulting from the use of any specific kit were not clearly discerned. Even when different experimental settings were used in each laboratory, a positive outcome of the study was a mean absolute age prediction error amongst participant's data of only 2.7 years for semen, 5.0 years for blood and 3.8 years for saliva.
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31
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Aliferi A, Sundaram S, Ballard D, Freire-Aradas A, Phillips C, Lareu MV, Court DS. Combining current knowledge on DNA methylation-based age estimation towards the development of a superior forensic DNA intelligence tool. Forensic Sci Int Genet 2021; 57:102637. [PMID: 34852982 DOI: 10.1016/j.fsigen.2021.102637] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 10/19/2021] [Accepted: 11/17/2021] [Indexed: 01/09/2023]
Abstract
The estimation of chronological age from biological fluids has been an important quest for forensic scientists worldwide, with recent approaches exploiting the variability of DNA methylation patterns with age in order to develop the next generation of forensic 'DNA intelligence' tools for this application. Drawing from the conclusions of previous work utilising massively parallel sequencing (MPS) for this analysis, this work introduces a DNA methylation-based age estimation method for blood that exhibits the best combination of prediction accuracy and sensitivity reported to date. Statistical evaluation of markers from 51 studies using microarray data from over 4000 individuals, followed by validation using in-house generated MPS data, revealed a final set of 11 markers with the greatest potential for accurate age estimation from minimal DNA material. Utilising an algorithm based on support vector machines, the proposed model achieved an average error (MAE) of 3.3 years, with this level of accuracy retained down to 5 ng of starting DNA input (~ 1 ng PCR input). The accuracy of the model was retained (MAE = 3.8 years) in a separate test set of 88 samples of Spanish origin, while predictions for donors of greater forensic interest (< 55 years of age) displayed even higher accuracy (MAE = 2.6 years). Finally, no sex-related bias was observed for this model, while there were also no signs of variation observed between control and disease-associated populations for schizophrenia, rheumatoid arthritis, frontal temporal dementia and progressive supranuclear palsy in microarray data relating to the 11 markers.
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Affiliation(s)
- Anastasia Aliferi
- King's Forensics, Department of Analytical, Environmental and Forensic Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Sudha Sundaram
- King's Forensics, Department of Analytical, Environmental and Forensic Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - David Ballard
- King's Forensics, Department of Analytical, Environmental and Forensic Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom.
| | - Ana Freire-Aradas
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Galicia, Spain
| | - Christopher Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Galicia, Spain
| | - Maria Victoria Lareu
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Galicia, Spain
| | - Denise Syndercombe Court
- King's Forensics, Department of Analytical, Environmental and Forensic Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
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32
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Heidegger A, Pisarek A, de la Puente M, Niederstätter H, Pośpiech E, Woźniak A, Schury N, Unterländer M, Sidstedt M, Junker K, Ventayol Garcia M, Laurent FX, Ulus A, Vannier J, Bastisch I, Hedman J, Sijen T, Branicki W, Xavier C, Parson W. Development and inter-laboratory validation of the VISAGE enhanced tool for age estimation from semen using quantitative DNA methylation analysis. Forensic Sci Int Genet 2021; 56:102596. [PMID: 34763164 DOI: 10.1016/j.fsigen.2021.102596] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/07/2021] [Accepted: 09/08/2021] [Indexed: 12/27/2022]
Abstract
The analysis of DNA methylation has become an established method for chronological age estimation. This has triggered interest in the forensic community to develop new methods for age estimation from biological crime scene material. Various assays are available for age estimation from somatic tissues, the majority from blood. Age prediction from semen requires different DNA methylation markers and the only assays currently developed for forensic analysis are based on SNaPshot or pyrosequencing. Here, we describe a new assay using massively parallel sequencing to analyse 13 candidate CpG sites targeted in two multiplex PCRs. The assay has been validated by five consortium laboratories of the VISible Attributes through GEnomics (VISAGE) project within a collaborative exercise and was tested for reproducible quantification of DNA methylation levels and sensitivity with DNA methylation controls. Furthermore, DNA extracts and stains on Whatman FTA cards from two semen samples were used to evaluate concordance and mimic casework samples. Overall, the assay yielded high read depths (> 1000 reads) at all 13 marker positions. The methylation values obtained indicated robust quantification with an average standard deviation of 2.8% at the expected methylation level of 50% across the 13 markers and a good performance with 50 ng DNA input into bisulfite conversion. The absolute difference of quantifications from one participating laboratory to the mean quantifications of concordance and semen stains of remaining laboratories was approximately 1%. These results demonstrated the assay to be robust and suitable for age estimation from semen in forensic investigations. In addition to the 13-marker assay, a more streamlined protocol combining only five age markers in one multiplex PCR was developed. Preliminary results showed no substantial differences in DNA methylation quantification between the two assays, indicating its applicability with the VISAGE age model for semen developed with data from the complete 13-marker tool.
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Affiliation(s)
- A Heidegger
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - A Pisarek
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
| | - M de la Puente
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria; Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
| | - H Niederstätter
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - E Pośpiech
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
| | - A Woźniak
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | - N Schury
- Federal Criminal Police Office, Wiesbaden, Germany
| | | | - M Sidstedt
- National Forensic Centre (NFC), Swedish Police Authority, Linköping, Sweden
| | - K Junker
- National Forensic Centre (NFC), Swedish Police Authority, Linköping, Sweden
| | - M Ventayol Garcia
- Biological Traces, Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB The Hague, The Netherlands
| | - F X Laurent
- Institut National de Police Scientifique, Laboratoire de Police Scientifique de Lyon, Ecully Cedex, France
| | - A Ulus
- Institut National de Police Scientifique, Laboratoire de Police Scientifique de Lyon, Ecully Cedex, France
| | - J Vannier
- Institut National de Police Scientifique, Laboratoire de Police Scientifique de Lyon, Ecully Cedex, France
| | - I Bastisch
- Federal Criminal Police Office, Wiesbaden, Germany
| | - J Hedman
- National Forensic Centre (NFC), Swedish Police Authority, Linköping, Sweden; Applied Microbiology, Department of Chemistry, Lund University, Lund, Sweden
| | - T Sijen
- Biological Traces, Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB The Hague, The Netherlands; University of Amsterdam, Swammerdam Institute of Life Sciences, Science Park 904, 1098XH Amsterdam, The Netherlands
| | - W Branicki
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland; Central Forensic Laboratory of the Police, Warsaw, Poland
| | - C Xavier
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria.
| | - W Parson
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria; Forensic Science Program, The Pennsylvania State University, State College, PA, USA.
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Simpson DJ, Chandra T. Epigenetic age prediction. Aging Cell 2021; 20:e13452. [PMID: 34415665 PMCID: PMC8441394 DOI: 10.1111/acel.13452] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 07/21/2021] [Accepted: 07/27/2021] [Indexed: 12/14/2022] Open
Abstract
Advanced age is the main common risk factor for cancer, cardiovascular disease and neurodegeneration. Yet, more is known about the molecular basis of any of these groups of diseases than the changes that accompany ageing itself. Progress in molecular ageing research was slow because the tools predicting whether someone aged slowly or fast (biological age) were unreliable. To understand ageing as a risk factor for disease and to develop interventions, the molecular ageing field needed a quantitative measure; a clock for biological age. Over the past decade, a number of age predictors utilising DNA methylation have been developed, referred to as epigenetic clocks. While they appear to estimate biological age, it remains unclear whether the methylation changes used to train the clocks are a reflection of other underlying cellular or molecular processes, or whether methylation itself is involved in the ageing process. The precise aspects of ageing that the epigenetic clocks capture remain hidden and seem to vary between predictors. Nonetheless, the use of epigenetic clocks has opened the door towards studying biological ageing quantitatively, and new clocks and applications, such as forensics, appear frequently. In this review, we will discuss the range of epigenetic clocks available, their strengths and weaknesses, and their applicability to various scientific queries.
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Affiliation(s)
- Daniel J. Simpson
- MRC Human Genetics UnitMRC Institute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Tamir Chandra
- MRC Human Genetics UnitMRC Institute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
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Kukla-Bartoszek M, Teisseyre P, Pośpiech E, Karłowska-Pik J, Zieliński P, Woźniak A, Boroń M, Dąbrowski M, Zubańska M, Jarosz A, Płoski R, Grzybowski T, Spólnicka M, Mielniczuk J, Branicki W. Searching for improvements in predicting human eye colour from DNA. Int J Legal Med 2021; 135:2175-2187. [PMID: 34259936 PMCID: PMC8523394 DOI: 10.1007/s00414-021-02645-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/17/2021] [Indexed: 01/29/2023]
Abstract
Increasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.
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Affiliation(s)
- Magdalena Kukla-Bartoszek
- Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Kraków, Poland. .,Malopolska Centre of Biotechnology of the Jagiellonian University, Kraków, Poland.
| | - Paweł Teisseyre
- Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland.,Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Ewelina Pośpiech
- Malopolska Centre of Biotechnology of the Jagiellonian University, Kraków, Poland
| | - Joanna Karłowska-Pik
- Faculty of Mathematics and Computer Science, Nicolaus Copernicus University in Toruń, Toruń, Poland
| | - Piotr Zieliński
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Kraków, Poland
| | - Anna Woźniak
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | - Michał Boroń
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | - Michał Dąbrowski
- Laboratory of Bioinformatics, Neurobiology Centre, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
| | - Magdalena Zubańska
- Faculty of Law and Administration, Department of Criminology and Forensic Sciences, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland.,Unit of Forensic Sciences, Faculty of Internal Security, Police Academy, Szczytno, Poland
| | - Agata Jarosz
- Malopolska Centre of Biotechnology of the Jagiellonian University, Kraków, Poland
| | - Rafał Płoski
- Department of Medical Genetics, Warsaw Medical University, Warsaw, Poland
| | - Tomasz Grzybowski
- Division of Molecular and Forensic Genetics, Department of Forensic Medicine, Nicolaus Copernicus University in Toruń, Collegium Medicum in Bydgoszcz, Bydgoszcz, Poland
| | | | - Jan Mielniczuk
- Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland.,Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Wojciech Branicki
- Malopolska Centre of Biotechnology of the Jagiellonian University, Kraków, Poland. .,Central Forensic Laboratory of the Police, Warsaw, Poland.
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Xiao C, Yi S, Huang D. Genome-wide identification of age-related CpG sites for age estimation from blood DNA of Han Chinese individuals. Electrophoresis 2021; 42:1488-1496. [PMID: 33978960 DOI: 10.1002/elps.202000367] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 02/07/2021] [Accepted: 05/05/2021] [Indexed: 11/11/2022]
Abstract
Age-related CpG (AR-CpG) sites are currently the most promising molecular markers for forensic age estimation. However, the AR-CpG sites of Han Chinese population remains to be systematically characterized. In this study, we performed genome-wide methylation analyses on 42 whole blood DNA from healthy Han Chinese volunteers (aged from 18 to 62 years) using the Illumina MethylationEPIC BeadChip microarray. As expected, both known and novel AR-CpG sites were identified. Considering the sex difference in aging rate, we then separately selected AR-CpG candidates and built pyrosequencing-based multiple linear regression models for age estimation of males and females. The model constructed from the male sample group (n = 167, aged from 1.50 to 85.71 years) explained 95.22% of variation in age using five AR-CpG sites (chr6:11044864 ELOVL2, chr1:207997068 C1orf132, cg19283806 CCDC102B, cg17740900, and chr10:73740306 CHST3) and yielded a mean absolute error (MAE) of 2.79 years. The model constructed from the female sample group (n = 141, aged from 3.33 to 80.38 years) explained 94.90% of variation in age with six AR-CpG sites (chr6:11044867 ELOVL2, chr1:207997060 C1orf132, chr2:106015757 FHL2, cg26947034, chr16: 67184108 B3GNT9, and chr20:44658203 SLC12A5) and yielded an MAE of 2.53 years. Besides, the estimated age was highly correlated with the actual age (R > 0.97). The robustness of these AR-CpG markers was demonstrated by 10-fold cross-validations. In conclusion, we updated the AR-CpG sites of Han Chinese population and provided two sets of AR-CpG sites for accurate age estimation.
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Affiliation(s)
- Chao Xiao
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei, 430030, P. R. China
| | - Shaohua Yi
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei, 430030, P. R. China
| | - Daixin Huang
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei, 430030, P. R. China
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Zhang J, Fu H, Xu Y. Age Prediction of Human Based on DNA Methylation by Blood Tissues. Genes (Basel) 2021; 12:genes12060870. [PMID: 34204075 PMCID: PMC8228382 DOI: 10.3390/genes12060870] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/27/2021] [Accepted: 06/05/2021] [Indexed: 12/14/2022] Open
Abstract
In recent years, scientists have found a close correlation between DNA methylation and aging in epigenetics. With the in-depth research in the field of DNA methylation, researchers have established a quantitative statistical relationship to predict the individual ages. This work used human blood tissue samples to study the association between age and DNA methylation. We built two predictors based on healthy and disease data, respectively. For the health data, we retrieved a total of 1191 samples from four previous reports. By calculating the Pearson correlation coefficient between age and DNA methylation values, 111 age-related CpG sites were selected. Gradient boosting regression was utilized to build the predictive model and obtained the R2 value of 0.86 and MAD of 3.90 years on testing dataset, which were better than other four regression methods as well as Horvath’s results. For the disease data, 354 rheumatoid arthritis samples were retrieved from a previous study. Then, 45 CpG sites were selected to build the predictor and the corresponded MAD and R2 were 3.11 years and 0.89 on the testing dataset respectively, which showed the robustness of our predictor. Our results were better than the ones from other four regression methods. Finally, we also analyzed the twenty-four common CpG sites in both healthy and disease datasets which illustrated the functional relevance of the selected CpG sites.
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Jin X, Liu Y, Zhang Y, Li Y, Chen C, Wang H. Autosomal deletion/insertion polymorphisms for global stratification analyses and ancestry origin inferences of different continental populations by machine learning methods. Electrophoresis 2021; 42:1473-1479. [PMID: 33948979 DOI: 10.1002/elps.202100044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 03/19/2021] [Accepted: 04/12/2021] [Indexed: 11/11/2022]
Abstract
A lot of population data of 30 deletion/insertion polymorphisms (DIPs) of the Investigator DIPplex kit in different continental populations have been reported. Here, we assessed genetic distributions of these 30 DIPs in different continental populations to pinpoint candidate ancestry informative DIPs. Besides, the effectiveness of machine learning methods for ancestry analysis was explored. Pairwise informativeness (In) values of 30 DIPs revealed that six loci displayed relatively high In values (>0.1) among different continental populations. Besides, more loci showed high population-specific divergence (PSD) values in African population. Based on the pairwise In and PSD values of 30 DIPs, 17 DIPs in the Investigator DIPplex kit were selected to ancestry analyses of African, European, and East Asian populations. Even though 30 DIPs provided better ancestry resolution of these continental populations based on the results of PCA and population genetic structure, we found that 17 DIPs could also distinguish these continental populations. More importantly, these 17 DIPs possessed more balanced cumulative PSD distributions in these populations. Six machine learning methods were used to perform ancestry analyses of these continental populations based on 17 DIPs. Obtained results revealed that naïve Bayes manifested the greatest performance; whereas, k nearest neighbor showed relatively low performance. To sum up, these machine learning methods, especially for naïve Bayes, could be used as the valuable tool for ancestry analysis.
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Affiliation(s)
- Xiaoye Jin
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, P. R. China.,Medical Genetics Institute of Henan Province, Henan Provincial People's Hospital,Zhengzhou University People's Hospital, Zhengzhou, P. R. China.,National Health Commission Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Zhengzhou, P. R. China
| | - Yuluo Liu
- Department of Forensic Science, Guangdong Police College, Guangzhou, P. R. China
| | - Yuanyuan Zhang
- School of Foreign Languages, Zhengzhou University of Industrial Technology, Zhengzhou, P. R. China
| | - Yongle Li
- National Health Commission Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Zhengzhou, P. R. China
| | - Chuanliang Chen
- Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, P. R. China
| | - Hongdan Wang
- Medical Genetics Institute of Henan Province, Henan Provincial People's Hospital,Zhengzhou University People's Hospital, Zhengzhou, P. R. China.,National Health Commission Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Zhengzhou, P. R. China
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Nutzung von Altersinformationen aus posttranslationalen Proteinmodifikationen und DNA-Methylierung zur postmortalen Lebensaltersschätzung. Rechtsmedizin (Berl) 2021. [DOI: 10.1007/s00194-021-00489-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
ZusammenfassungMit der Identifikation und Beschreibung „molekularer Uhren“ (posttranslationale Proteinmodifikationen, DNA-Methylierung) eröffnen sich neue Möglichkeiten zur Entwicklung von Verfahren zur postmortalen Lebensaltersschätzung. Bislang werden diese Ansätze aber nur unabhängig voneinander eingesetzt. Ihre Verknüpfung verspricht eine bessere Erfassung hochkomplexer Alterungsprozesse und damit die Möglichkeit zur Entwicklung optimierter Verfahren zur Altersschätzung für verschiedenste Szenarien der forensischen Praxis.In Vorbereitung umfangreicher Untersuchungen zur Überprüfung dieser Hypothese wurden verschiedene molekulare Uhren (Akkumulation von D‑Asparaginsäure, Akkumulation von Pentosidin und DNA-Methylierungsmarker [RPA2, ZYG11A, F5, HOXC4, NKIRAS2, TRIM59, ELOVL2, DDO, KLF14 und PDE4C]) in 4 fäulnisresistenten Geweben (Knochen, Sehne, Bandscheibe, Epiglottis) von 15 Individuen untersucht.In allen untersuchten Geweben fand sich eine starke Korrelation beider Proteinmarker sowie jeweils mehrerer DNA-Methylierungsmarker mit dem Lebensalter. Dabei zeigten die untersuchten Parameter gewebsspezifische Veränderungen mit dem Alter.Die Ergebnisse der Pilotstudie belegen das Potenzial der Verknüpfung molekularer Verfahren für die postmortale Altersschätzung. Weitere Untersuchungen werden zeigen, wie genau postmortale Altersschätzungen sein können, wenn Altersinformationen aus posttranslationalen Proteinmodifikationen und DNA-Methylierung aus verschiedenen Geweben in multivariaten Modellen verknüpft werden.
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Woźniak A, Heidegger A, Piniewska-Róg D, Pośpiech E, Xavier C, Pisarek A, Kartasińska E, Boroń M, Freire-Aradas A, Wojtas M, de la Puente M, Niederstätter H, Płoski R, Spólnicka M, Kayser M, Phillips C, Parson W, Branicki W. Development of the VISAGE enhanced tool and statistical models for epigenetic age estimation in blood, buccal cells and bones. Aging (Albany NY) 2021; 13:6459-6484. [PMID: 33707346 PMCID: PMC7993733 DOI: 10.18632/aging.202783] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 02/16/2021] [Indexed: 01/07/2023]
Abstract
DNA methylation is known as a biomarker for age with applications in forensics. Here we describe the VISAGE (VISible Attributes through GEnomics) Consortium's enhanced tool for epigenetic age estimation in somatic tissues. The tool is based on eight DNA methylation markers (44 CpGs), bisulfite multiplex PCR followed by sequencing on the MiSeq FGx platform, and three statistical prediction models for blood, buccal cells and bones. The model for blood is based on six CpGs from ELOVL2, MIR29B2CHG, KLF14, FHL2, TRIM59 and PDE4C, and predicts age with a mean absolute error (MAE) of 3.2 years, while the model for buccal cells includes five CpGs from PDE4C, MIR29B2CHG, ELOVL2, KLF14 and EDARADD and predicts age with MAE of 3.7 years, and the model for bones has six CpGs from ELOVL2, KLF14, PDE4C and ASPA and predicts age with MAE of 3.4 years. The VISAGE enhanced tool for age estimation in somatic tissues enables reliable collection of DNA methylation data from small amounts of DNA using a sensitive multiplex MPS assay that provides accurate estimation of age in blood, buccal swabs, and bones using the statistical model tailored to each tissue.
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Affiliation(s)
- Anna Woźniak
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | - Antonia Heidegger
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Danuta Piniewska-Róg
- Department of Forensic Medicine, Jagiellonian University Medical College, Krakow, Poland
| | - Ewelina Pośpiech
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
| | - Catarina Xavier
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Aleksandra Pisarek
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
| | | | - Michał Boroń
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | - Ana Freire-Aradas
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Marta Wojtas
- Department of Forensic Medicine, Jagiellonian University Medical College, Krakow, Poland
| | - Maria de la Puente
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria.,Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Harald Niederstätter
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Rafał Płoski
- Department Medical Genetics, Warsaw Medical University, Warsaw, Poland
| | | | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Christopher Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Walther Parson
- Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria.,Forensic Science Program, The Pennsylvania State University, University Park, PA 16802, USA
| | - Wojciech Branicki
- Central Forensic Laboratory of the Police, Warsaw, Poland.,Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
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Hao T, Guo J, Liu J, Wang J, Liu Z, Cheng X, Li J, Ren J, Li Z, Yan J, Zhang G. Predicting human age by detecting DNA methylation status in hair. Electrophoresis 2021; 42:1255-1261. [PMID: 33629357 DOI: 10.1002/elps.202000349] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/07/2021] [Accepted: 02/07/2021] [Indexed: 12/29/2022]
Abstract
Age prediction is of great importance for criminal investigation and judicial expertise. DNA methylation status is considered a promising method to infer tissue age by virtue of age-dependent changes on methylation sites. In recent years, forensic scientists have established various models to predict the chronological age of blood, saliva, and semen based on DNA methylation status. However, hair-inferred age has not been studied in the field of forensic science. In this study, we measured the methylation statuses of potential age-related CpG sites by using the multiplex methylation SNaPshot method. A total of 10 CpG sites from the LAG3, SCGN, ELOVL2, KLF14, C1orf132, SLC12A5, GRIA2, and PDE4C genes were found to be tightly associated with age in hair follicles. A correlation coefficient above 0.7 was found for four CpG sites (cg24724428 and Chr6:11044628 in ELOVL2, cg25148589 in GRIA2, and cg07547549 in SLC12A5). Among four age-prediction models, the multiple linear regression model consisting of 10 CpG sites provided the best-fitting results, with a median absolute deviation of 3.68 years. It is feasible to obtain both human identification and age information from a single scalp hair follicle. No significant differences in methylation degree were found between different sexes, hair types, or hair colors. In conclusion, we established a method to evaluate chronological age by assessing DNA methylation status in hair follicles.
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Affiliation(s)
- Ting Hao
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, P. R. China
| | - Jiangling Guo
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, P. R. China
| | - Jinding Liu
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, P. R. China
| | - Jiaqi Wang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, P. R. China
| | - Zidong Liu
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, P. R. China
| | - Xiaojuan Cheng
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, P. R. China
| | - Jintao Li
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, P. R. China
| | - Jianbo Ren
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, P. R. China
| | - Zeqin Li
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, P. R. China
| | - Jiangwei Yan
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, P. R. China
| | - Gengqian Zhang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, P. R. China
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Thong Z, Tan JYY, Loo ES, Phua YW, Chan XLS, Syn CKC. Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples. Sci Rep 2021; 11:1744. [PMID: 33462351 PMCID: PMC7814006 DOI: 10.1038/s41598-021-81556-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 12/29/2020] [Indexed: 12/21/2022] Open
Abstract
Regression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.
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Affiliation(s)
- Zhonghui Thong
- DNA Profiling Laboratory, Biology Division, Health Sciences Authority, 11 Outram Road, Singapore, 169078, Singapore.
| | - Jolena Ying Ying Tan
- DNA Profiling Laboratory, Biology Division, Health Sciences Authority, 11 Outram Road, Singapore, 169078, Singapore
| | - Eileen Shuzhen Loo
- DNA Profiling Laboratory, Biology Division, Health Sciences Authority, 11 Outram Road, Singapore, 169078, Singapore
| | - Yu Wei Phua
- DNA Profiling Laboratory, Biology Division, Health Sciences Authority, 11 Outram Road, Singapore, 169078, Singapore
| | - Xavier Liang Shun Chan
- DNA Profiling Laboratory, Biology Division, Health Sciences Authority, 11 Outram Road, Singapore, 169078, Singapore
| | - Christopher Kiu-Choong Syn
- DNA Profiling Laboratory, Biology Division, Health Sciences Authority, 11 Outram Road, Singapore, 169078, Singapore
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Soedarsono N, Hanafi MS, Auerkari E. Biological age estimation using DNA methylation analysis: A systematic review. SCIENTIFIC DENTAL JOURNAL 2021. [DOI: 10.4103/sdj.sdj_27_20] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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Maulani C, Auerkari EI. Age estimation using DNA methylation technique in forensics: a systematic review. EGYPTIAN JOURNAL OF FORENSIC SCIENCES 2020. [DOI: 10.1186/s41935-020-00214-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
AbstractBackgroundIn addition to the DNA sequence, epigenetic markers have become substantial forensic tools during the last decade. Estimating the age of an individual from human biological remains may provide information for a forensic investigation. Age estimation in molecular strategies can be obtained by telomere length, mRNa mutation, or by sjTRECs but the accuracy is not sufficient in forensic practice because of high margin error.Main bodyOne solution to this problem is to use DNA methylation methods. DNA methylation markers for tissue identification at age-associated CpG sites have been suggested as the most informative biomarkers for estimating the age of an unknown donor. This review aims to give an overview of DNA methylation profiling for estimating the age in cases of forensic relevance and the important aspects in determining the mean absolute deviation (MAD) or mean absolute error (MAE) of the estimated age. Online database searching was performed through PubMed, Scopus, and Google Scholar with keywords selected for forensic age estimation. Thirty-two studies were included in the review, with variable DNA samples but blood commonly as a source. Pyrosequencing and EpiTYPER were methods mostly used in DNA analysis. The MAD in the estimates from DNA methylation was about 3 to 5 years, which was better than other methods such as those based on telomere length or signal-joint T-cell receptor excision circles. The ELOVL2 gene was a commonly used DNA methylation marker in age estimation.ConclusionDNA methylation is a favorable candidate for estimating the age at the time of death in forensic profiling, with an uncertainty mean absolute deviation of about 3 to 5 years in the predicted age. The sample type, platform techniques used, and methods to construct age predictive models were important in determining the accuracy in mean absolute deviation or mean absolute error. The DNA methylation outcome suggests good potential to support conventional STR profiling in forensic cases.
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Garali I, Sahbatou M, Daunay A, Baudrin LG, Renault V, Bouyacoub Y, Deleuze JF, How-Kit A. Improvements and inter-laboratory implementation and optimization of blood-based single-locus age prediction models using DNA methylation of the ELOVL2 promoter. Sci Rep 2020; 10:15652. [PMID: 32973211 PMCID: PMC7515898 DOI: 10.1038/s41598-020-72567-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 09/02/2020] [Indexed: 01/21/2023] Open
Abstract
Several blood-based age prediction models have been developed using less than a dozen to more than a hundred DNA methylation biomarkers. Only one model (Z-P1) based on pyrosequencing has been developed using DNA methylation of a single locus located in the ELOVL2 promoter, which is considered as one of the best age-prediction biomarker. Although multi-locus models generally present better performances compared to the single-locus model, they require more DNA and present more inter-laboratory variations impacting the predictions. Here we developed 17,018 single-locus age prediction models based on DNA methylation of the ELOVL2 promoter from pooled data of four different studies (training set of 1,028 individuals aged from 0 and 91 years) using six different statistical approaches and testing every combination of the 7 CpGs, aiming to improve the prediction performances and reduce the effects of inter-laboratory variations. Compared to Z-P1 model, three statistical models with the optimal combinations of CpGs presented improved performances (MAD of 4.41–4.77 in the testing set of 385 individuals) and no age-dependent bias. In an independent testing set of 100 individuals (19–65 years), we showed that the prediction accuracy could be further improved by using different CpG combinations and increasing the number of technical replicates (MAD of 4.17).
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Affiliation(s)
- Imene Garali
- Laboratory for Bioinformatics, Foundation Jean Dausset-CEPH, Paris, France.,Laboratory of Excellence GenMed, Paris, France
| | - Mourad Sahbatou
- Laboratory for Human Genetics, Foundation Jean Dausset-CEPH, Paris, France
| | - Antoine Daunay
- Laboratory for Genomics, Foundation Jean Dausset-CEPH, 75010, Paris, France
| | - Laura G Baudrin
- Laboratory of Excellence GenMed, Paris, France.,Laboratory for Genomics, Foundation Jean Dausset-CEPH, 75010, Paris, France
| | - Victor Renault
- Laboratory for Bioinformatics, Foundation Jean Dausset-CEPH, Paris, France
| | - Yosra Bouyacoub
- Laboratory of Excellence GenMed, Paris, France.,Laboratory for Genomics, Foundation Jean Dausset-CEPH, 75010, Paris, France
| | - Jean-François Deleuze
- Laboratory for Bioinformatics, Foundation Jean Dausset-CEPH, Paris, France.,Laboratory of Excellence GenMed, Paris, France.,Laboratory for Human Genetics, Foundation Jean Dausset-CEPH, Paris, France.,Laboratory for Genomics, Foundation Jean Dausset-CEPH, 75010, Paris, France.,Centre National de Recherche en Génomique Humaine, CEA, Institut François Jacob, Evry, France
| | - Alexandre How-Kit
- Laboratory for Genomics, Foundation Jean Dausset-CEPH, 75010, Paris, France.
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Freire-Aradas A, Pośpiech E, Aliferi A, Girón-Santamaría L, Mosquera-Miguel A, Pisarek A, Ambroa-Conde A, Phillips C, Casares de Cal MA, Gómez-Tato A, Spólnicka M, Woźniak A, Álvarez-Dios J, Ballard D, Court DS, Branicki W, Carracedo Á, Lareu MV. A Comparison of Forensic Age Prediction Models Using Data From Four DNA Methylation Technologies. Front Genet 2020; 11:932. [PMID: 32973877 PMCID: PMC7466768 DOI: 10.3389/fgene.2020.00932] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 07/27/2020] [Indexed: 12/20/2022] Open
Abstract
Individual age estimation can be applied to criminal, legal, and anthropological investigations. DNA methylation has been established as the biomarker of choice for age prediction, since it was observed that specific CpG positions in the genome show systematic changes during an individual’s lifetime, with progressive increases or decreases in methylation levels. Subsequently, several forensic age prediction models have been reported, providing average age prediction error ranges of ±3–4 years, using a broad spectrum of technologies and underlying statistical analyses. DNA methylation assessment is not categorical but quantitative. Therefore, the detection platform used plays a pivotal role, since quantitative and semi-quantitative technologies could potentially result in differences in detected DNA methylation levels. In the present study, we analyzed as a shared sample pool, 84 blood-based DNA controls ranging from 18 to 99 years old using four different technologies: EpiTYPER®, pyrosequencing, MiSeq, and SNaPshotTM. The DNA methylation levels detected for CpG sites from ELOVL2, FHL2, and MIR29B2 with each system were compared. A restricted three CpG-site age prediction model was rebuilt for each system, as well as for a combination of technologies, based on previous training datasets, and age predictions were calculated accordingly for all the samples detected with the previous technologies. While the DNA methylation patterns and subsequent age predictions from EpiTYPER®, pyrosequencing, and MiSeq systems are largely comparable for the CpG sites studied, SNaPshotTM gives bigger differences reflected in higher predictive errors. However, these differences can be reduced by applying a z-score data transformation.
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Affiliation(s)
- A Freire-Aradas
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Galicia, Spain
| | - E Pośpiech
- Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| | - A Aliferi
- King's Forensics, Department of Analytical, Environmental and Forensic Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - L Girón-Santamaría
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Galicia, Spain
| | - A Mosquera-Miguel
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Galicia, Spain
| | - A Pisarek
- Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| | - A Ambroa-Conde
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Galicia, Spain
| | - C Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Galicia, Spain
| | - M A Casares de Cal
- Faculty of Mathematics, University of Santiago de Compostela, Galicia, Spain
| | - A Gómez-Tato
- Faculty of Mathematics, University of Santiago de Compostela, Galicia, Spain
| | - M Spólnicka
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | - A Woźniak
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | - J Álvarez-Dios
- Faculty of Mathematics, University of Santiago de Compostela, Galicia, Spain
| | - D Ballard
- King's Forensics, Department of Analytical, Environmental and Forensic Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - D Syndercombe Court
- King's Forensics, Department of Analytical, Environmental and Forensic Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - W Branicki
- Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland.,Central Forensic Laboratory of the Police, Warsaw, Poland
| | - Ángel Carracedo
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Galicia, Spain.,Fundación Pública Galega de Medicina Xenómica - CIBERER-IDIS, Santiago de Compostela, Spain
| | - M V Lareu
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Galicia, Spain
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47
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Correia Dias H, Corte-Real F, Cunha E, Manco L. DNA methylation age estimation from human bone and teeth. AUST J FORENSIC SCI 2020. [DOI: 10.1080/00450618.2020.1805011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Helena Correia Dias
- Research Centre for Anthropology and Health (CIAS), Department of Life Sciences, University of Coimbra, Coimbra, Portugal
- Centre for Functional Ecology (CEF), Laboratory of Forensic Anthropology, Department of Life Sciences, University of Coimbra, Coimbra, Portugal
- National Institute of Legal Medicine and Forensic Sciences, Coimbra, Portugal
| | - Francisco Corte-Real
- National Institute of Legal Medicine and Forensic Sciences, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Eugénia Cunha
- Centre for Functional Ecology (CEF), Laboratory of Forensic Anthropology, Department of Life Sciences, University of Coimbra, Coimbra, Portugal
- National Institute of Legal Medicine and Forensic Sciences, Coimbra, Portugal
| | - Licínio Manco
- Research Centre for Anthropology and Health (CIAS), Department of Life Sciences, University of Coimbra, Coimbra, Portugal
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48
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Lau PY, Fung WK. Evaluation of marker selection methods and statistical models for chronological age prediction based on DNA methylation. Leg Med (Tokyo) 2020; 47:101744. [PMID: 32659707 DOI: 10.1016/j.legalmed.2020.101744] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 06/02/2020] [Accepted: 06/26/2020] [Indexed: 12/16/2022]
Abstract
In forensic investigation, retrieving biological information from DNA evidence is a promising field of interest. One of the applications is on the estimation of the age of the donor based on DNA methylation. A large number of studies focused on age prediction using the 450 K Human Methylation Beadchip. Various marker selection methods and prediction models have been considered. However, there is a lack of research evaluating different high-dimensional variable selection methods of CpG sites with various models for age prediction. The aim of this study is to evaluate four variable selection methods (forward selection, LASSO, elastic net and SCAD) combined with a classical statistical model and sophisticated machine learning models based on the mean absolute deviation (MAD) and the root-mean-square error (RMSE). We used publicly available 450 K data set containing 991 whole blood samples (age 19-101 years). We found that the multiple linear regression model with 16 markers selected from the forward selection method performed very well in age prediction (MAD = 3.76 years and RMSE = 5.01 years). On the other hand, the highly advanced ultrahigh dimensional variable selection methods and sophisticated machine learning algorithms appeared unnecessary for age prediction based on DNA methylation.
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Affiliation(s)
- Pui Yin Lau
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Wing Kam Fung
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
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49
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Koop BE, Mayer F, Gündüz T, Blum J, Becker J, Schaffrath J, Wagner W, Han Y, Boehme P, Ritz-Timme S. Postmortem age estimation via DNA methylation analysis in buccal swabs from corpses in different stages of decomposition-a "proof of principle" study. Int J Legal Med 2020; 135:167-173. [PMID: 32632799 PMCID: PMC7782454 DOI: 10.1007/s00414-020-02360-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 06/22/2020] [Indexed: 12/13/2022]
Abstract
Age estimation based on the analysis of DNA methylation patterns has become a focus of forensic research within the past few years. However, there is little data available regarding postmortem DNA methylation analysis yet, and literature mainly encompasses analysis of blood from corpses without any signs of decomposition. It is not entirely clear yet which other types of specimen are suitable for postmortem epigenetic age estimation, and if advanced decomposition may affect methylation patterns of CpG sites. In living persons, buccal swabs are an easily accessible source of DNA for epigenetic age estimation. In this work, the applicability of this approach (buccal swabs as source of DNA) under different postmortem conditions was tested. Methylation levels of PDE4C were investigated in buccal swab samples collected from 73 corpses (0–90 years old; mean: 51.2) in different stages of decomposition. Moreover, buccal swab samples from 142 living individuals (0–89 years old; mean 41.2) were analysed. As expected, methylation levels exhibited a high correlation with age in living individuals (training set: r2 = 0.87, validation set: r2 = 0.85). This was also the case in postmortem samples (r2 = 0.90), independent of the state of decomposition. Only in advanced putrified cases with extremely low DNA amounts, epigenetic age estimation was not possible. In conclusion, buccal swabs are a suitable and easy to collect source for DNA methylation analysis as long as sufficient amounts of DNA are present.
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Affiliation(s)
- Barbara Elisabeth Koop
- Institute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany.
| | - Felix Mayer
- Institute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany
| | - Tanju Gündüz
- Institute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany
| | - Jacqueline Blum
- Institute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany
| | - Julia Becker
- Institute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany
| | - Judith Schaffrath
- Institute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany
| | - Wolfgang Wagner
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen Faculty of Medicine, Aachen, Germany
| | - Yang Han
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen Faculty of Medicine, Aachen, Germany
| | - Petra Boehme
- Institute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany
| | - Stefanie Ritz-Timme
- Institute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany
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50
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Han Y, Franzen J, Stiehl T, Gobs M, Kuo CC, Nikolić M, Hapala J, Koop BE, Strathmann K, Ritz-Timme S, Wagner W. New targeted approaches for epigenetic age predictions. BMC Biol 2020; 18:71. [PMID: 32580727 PMCID: PMC7315536 DOI: 10.1186/s12915-020-00807-2] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 06/08/2020] [Indexed: 12/16/2022] Open
Abstract
Background Age-associated DNA methylation changes provide a promising biomarker for the aging process. While genome-wide DNA methylation profiles enable robust age-predictors by integration of many age-associated CG dinucleotides (CpGs), there are various alternative approaches for targeted measurements at specific CpGs that better support standardized and cost-effective high-throughput analysis. Results In this study, we utilized 4647 Illumina BeadChip profiles of blood to select CpG sites that facilitate reliable age-predictions based on pyrosequencing. We demonstrate that the precision of DNA methylation measurements can be further increased with droplet digital PCR (ddPCR). In comparison, bisulfite barcoded amplicon sequencing (BBA-seq) gave slightly lower correlation between chronological age and DNA methylation at individual CpGs, while the age-predictions were overall relatively accurate. Furthermore, BBA-seq data revealed that the correlation of methylation levels with age at neighboring CpG sites follows a bell-shaped curve, often associated with a CTCF binding site. We demonstrate that within individual BBA-seq reads the DNA methylation at neighboring CpGs is not coherently modified, but reveals a stochastic pattern. Based on this, we have developed a new approach for epigenetic age predictions based on the binary sequel of methylated and non-methylated sites in individual reads, which reflects heterogeneity in epigenetic aging within a sample. Conclusion Targeted DNA methylation analysis at few age-associated CpGs by pyrosequencing, BBA-seq, and particularly ddPCR enables high precision of epigenetic age-predictions. Furthermore, we demonstrate that the stochastic evolution of age-associated DNA methylation patterns in BBA-seq data enables epigenetic clocks for individual DNA strands.
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Affiliation(s)
- Yang Han
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany.,Institute for Biomedical Engineering - Cell Biology, University Hospital of RWTH Aachen, Aachen, Germany
| | - Julia Franzen
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany.,Institute for Biomedical Engineering - Cell Biology, University Hospital of RWTH Aachen, Aachen, Germany
| | - Thomas Stiehl
- Interdisciplinary Center for Scientific Computing (IWR), Institute of Applied Mathematics, University of Heidelberg, Heidelberg, Germany
| | - Michael Gobs
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany.,Institute for Biomedical Engineering - Cell Biology, University Hospital of RWTH Aachen, Aachen, Germany
| | - Chao-Chung Kuo
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany.,Institute for Biomedical Engineering - Cell Biology, University Hospital of RWTH Aachen, Aachen, Germany
| | - Miloš Nikolić
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany.,Institute for Biomedical Engineering - Cell Biology, University Hospital of RWTH Aachen, Aachen, Germany
| | - Jan Hapala
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany.,Institute for Biomedical Engineering - Cell Biology, University Hospital of RWTH Aachen, Aachen, Germany
| | | | - Klaus Strathmann
- Institute for Transfusion Medicine, RWTH Aachen University Medical School, Aachen, Germany
| | - Stefanie Ritz-Timme
- Institute for Legal Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Wolfgang Wagner
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Pauwelsstraße 20, 52074, Aachen, Germany. .,Institute for Biomedical Engineering - Cell Biology, University Hospital of RWTH Aachen, Aachen, Germany.
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