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Grignani P, Bertoglio B, Monti MC, Cuoghi Costantini R, Ricci U, Onofri M, Fattorini P, Previderè C. Age estimation of burnt human remains through DNA methylation analysis. Int J Legal Med 2024:10.1007/s00414-024-03320-1. [PMID: 39266801 DOI: 10.1007/s00414-024-03320-1] [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: 06/14/2024] [Accepted: 08/28/2024] [Indexed: 09/14/2024]
Abstract
The identification of human fire victims is a challenging task in forensic medicine. The heat-induced alterations of biological tissues can make the conventional anthropological analyses difficult. Even if the DNA profile of the victim is achieved, it is possible that no match can be found in a forensic DNA database, thus hindering positive identification. In such cases, any information useful to nail down a possible identity should be collected, such as DNA methylation analysis which could provide useful investigative leads. In the present study, five age-related epigenetic markers (ELOVL2, FHL2, KLF14, C1orf132, and TRIM59) were initially analysed in blood samples of 72 living Italian individuals of known age, using a Single Base Extension (SBE) assay. An age prediction model was built by multiple linear regression including all the markers (Mean Absolute Error, MAE: 3.15 years). This model was tested on 29 blood samples collected during autopsies from burnt human remains, already identified through DNA analysis, providing a MAE of 6.92 years. The model allowed a correct prediction in 79.3% of the cases (95% prediction interval), while six cases were associated with inaccurate predictions (min-max prediction error: 9.8-37.3 years). Among the different sample variables considered to explain these results, only the DNA degradation index was a relevant factor affecting the reliability of the predictions. In conclusion, the SBE typing of blood from burnt remains proved to be a reliable tool to estimate chronological age of most of the samples, also in consideration of its cost-effectiveness and the availability of CE sequencers in every forensic genetics laboratory.
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Affiliation(s)
- Pierangela Grignani
- Dipartimento di Sanità Pubblica, Medicina Sperimentale e Forense, Università di Pavia, Pavia, Italy
| | - Barbara Bertoglio
- Dipartimento di Sanità Pubblica, Medicina Sperimentale e Forense, Università di Pavia, Pavia, Italy.
| | - Maria Cristina Monti
- Dipartimento di Sanità Pubblica, Medicina Sperimentale e Forense, Università di Pavia, Pavia, Italy
| | - Riccardo Cuoghi Costantini
- Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Università di Modena e Reggio Emilia, Modena, Italy
| | - Ugo Ricci
- AOU Careggi SOD Diagnostica Genetica Equipe Genetica Forense, Firenze, Italy
| | - Martina Onofri
- Dipartimento di Medicina e Chirurgia, Azienda Ospedaliera S. Maria, Università di Perugia, Terni, Italy
| | - Paolo Fattorini
- Dipartimento Clinico di Scienze mediche, chirurgiche e della salute, Università di Trieste, Trieste, Italy
| | - Carlo Previderè
- Dipartimento di Sanità Pubblica, Medicina Sperimentale e Forense, Università di Pavia, Pavia, Italy
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2
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Tang XE, Lu T, Zhou YC, Zhan MJ, Chen W, Peng Z, Liu JH, Gui YF, Deng ZH, Fan F. Adult age estimation from the sternum using maximum intensity projection images of CT and data mining in a Chinese population. Int J Legal Med 2024; 138:961-970. [PMID: 38240839 DOI: 10.1007/s00414-024-03161-y] [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: 09/05/2023] [Accepted: 01/08/2024] [Indexed: 04/11/2024]
Abstract
This study aimed to explore and develop data mining models for adult age estimation based on CT reconstruction images from the sternum. Maximum intensity projection (MIP) images of chest CT were retrospectively collected from a modern Chinese population, and data from 2700 patients (1349 males and 1351 females) aged 20 to 70 years were obtained. A staging technique within four indicators was applied. Several data mining models were established, and mean absolute error (MAE) was the primary comparison parameter. The intraobserver and interobserver agreement levels were good. Within internal validation, the optimal data mining model obtained the lowest MAE of 9.08 in males and 10.41 in females. For the external validation (N = 200), MAEs were 7.09 in males and 7.15 in females. In conclusion, the accuracy of our model for adult age estimation was among similar studies. MIP images of the sternum could be a potential age indicator. However, it should be combined with other indicators since the accuracy level is still unsatisfactory.
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Affiliation(s)
- Xian-E Tang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Ting Lu
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Yu-Chi Zhou
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Meng-Jun Zhan
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Wang Chen
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Zhao Peng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Jun-Hong Liu
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Yu-Fan Gui
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Zhen-Hua Deng
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China.
| | - Fei Fan
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China.
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3
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Shiga M, Asari M, Takahashi Y, Isozaki S, Hoshina C, Mori K, Namba R, Okuda K, Shimizu K. DNA methylation-based age estimation and quantification of the degradation levels of bisulfite-converted DNA. Leg Med (Tokyo) 2024; 67:102336. [PMID: 37923589 DOI: 10.1016/j.legalmed.2023.102336] [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: 07/07/2023] [Revised: 10/12/2023] [Accepted: 10/18/2023] [Indexed: 11/07/2023]
Abstract
DNA methylation modifications are known to influence epigenetic phenomena and have been a focus of forensic science research for some time. Degraded DNA after bisulfite treatment is widely used in DNA methylation analysis. In this study, we analyzed methylation levels at 12 CpG sites of four selected genomic regions by pyrosequencing after bisulfite treatment. DNA was extracted from buccal swab samples collected from 102 Japanese individuals who were 21-77 years old. We also developed a simple method to quantify the degradation levels of bisulfite-converted DNA by real-time PCR, and evaluated the effect of DNA degradation on age estimation. We found that the methylation levels and chronological ages were highly correlated in the four selected regions, and the mean absolute deviation (MAD) between chronological and estimated ages was low at 3.88 years. These results indicated that pyrosequencing analysis at the 12 CpGs was useful for age estimation in the Japanese population. To develop a sensitive quantification method, we analyzed the amplification efficiency of short and long fragments from 10 regions by real-time PCR. The amplification efficiency was highest for CCDC102B, and the degradation levels of bisulfite-converted DNA for the 102 samples were categorized as moderately or heavily degraded. For the younger age groups (20-49 years), the MADs were lower for moderately degraded DNA than they were for heavily degraded DNA. This finding indicates that degradation levels affected the accuracy of age estimation in most of the samples; the exception was the samples from the 50-77 years age group.
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Affiliation(s)
- Mihiro Shiga
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan; Department of Orthopaedic Surgery, Keiyukai Medical Foundation Yoshida Hospital, Asahikawa 070-0054, Japan
| | - Masaru Asari
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan.
| | - Yuta Takahashi
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan
| | - Shotaro Isozaki
- Department of Forensic Medicine, Tokai University School of Medicine, Isehara 259-1193, Japan
| | - Chisato Hoshina
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan
| | - Kanae Mori
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan
| | - Ryo Namba
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan
| | - Katsuhiro Okuda
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan
| | - Keiko Shimizu
- Department of Legal Medicine, Asahikawa Medical University, Asahikawa 078-8510, Japan
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Yamagishi T, Sakurai W, Watanabe K, Toyomane K, Akutsu T. Development and comparison of forensic interval age prediction models by statistical and machine learning methods based on the methylation rates of ELOVL2 in blood DNA. Forensic Sci Int Genet 2024; 69:103004. [PMID: 38160598 DOI: 10.1016/j.fsigen.2023.103004] [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: 10/05/2023] [Revised: 12/06/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024]
Abstract
Age estimation can be useful information for narrowing down candidates of unidentified donors in criminal investigations. Various age estimation models based on DNA methylation biomarkers have been developed for forensic usage in the past decade. However, many of these models using ordinary least squares regression cannot generate an appropriate estimation due to the deterioration in prediction accuracy caused by an increased prediction error in older age groups. In the present study, to address this problem, we developed age estimation models that set an appropriate prediction interval for all age groups by two approaches: a statistical method using quantile regression (QR) and a machine learning method using an artificial neural network (ANN). Methylation datasets (n = 1280, age 0-91 years) of the promoter for the gene encoding ELOVL fatty acid elongase 2 were used to develop the QR and ANN models. By validation using several test datasets, both models were shown to enlarge prediction intervals in accordance with aging and have a high level of correct prediction (>90 %) for older age groups. The QR and ANN models also generated a point age prediction with high accuracy. The ANN model enabled a prediction with a mean absolute error (MAE) of 5.3 years and root mean square error (RMSE) of 7.3 years for the test dataset (n = 549), which were comparable to those of the QR model (MAE = 5.6 years, RMSE = 7.8 years). Their applicability to casework was also confirmed using bloodstain samples stored for various periods of time (1-14 years), indicating the stability of the models for aged bloodstain samples. From these results, it was considered that the proposed models can provide more useful and effective age estimation in forensic settings.
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Affiliation(s)
- Takayuki Yamagishi
- National Research Institute of Police Science, 6-3-1 Kashiwanoha, Kashiwa, Chiba 277-0882, Japan.
| | - Wataru Sakurai
- National Research Institute of Police Science, 6-3-1 Kashiwanoha, Kashiwa, Chiba 277-0882, Japan
| | - Ken Watanabe
- National Research Institute of Police Science, 6-3-1 Kashiwanoha, Kashiwa, Chiba 277-0882, Japan
| | - Kochi Toyomane
- National Research Institute of Police Science, 6-3-1 Kashiwanoha, Kashiwa, Chiba 277-0882, Japan
| | - Tomoko Akutsu
- National Research Institute of Police Science, 6-3-1 Kashiwanoha, Kashiwa, Chiba 277-0882, Japan
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5
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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: 5] [Impact Index Per Article: 5.0] [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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6
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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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Romeo G, Buonaccorsi JP, Thoresen M. Detecting and correcting for heteroscedasticity in the presence of measurement error. COMMUN STAT-SIMUL C 2023. [DOI: 10.1080/03610918.2023.2190061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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8
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Carlsen L, Holländer O, Danzer MF, Vennemann M, Augustin C. DNA methylation-based age estimation for adults and minors: considering sex-specific differences and non-linear correlations. Int J Legal Med 2023; 137:635-643. [PMID: 36811674 PMCID: PMC10085938 DOI: 10.1007/s00414-023-02967-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 01/31/2023] [Indexed: 02/24/2023]
Abstract
DNA methylation patterns change during human lifetime; thus, they can be used to estimate an individual's age. It is known, however, that correlation between DNA methylation and aging might not be linear and that the sex might influence the methylation status. In this study, we conducted a comparative evaluation of linear and several non-linear regressions, as well as sex-specific versus unisex models. Buccal swab samples from 230 donors aged 1 to 88 years were analyzed using a minisequencing multiplex array. Samples were divided into a training set (n = 161) and a validation set (n = 69). The training set was used for a sequential replacement regression and a simultaneous 10-fold cross-validation. The resulting model was improved by including a cut-off of 20 years, dividing the younger individuals with non-linear from the older individuals with linear dependence between age and methylation status. Sex-specific models were developed and improved prediction accuracy in females but not in males, which might be explained by a small sample set. We finally established a non-linear, unisex model combining the markers EDARADD, KLF14, ELOVL2, FHL2, C1orf132, and TRIM59. While age- and sex-adjustments did not generally improve the performance of our model, we discuss how other models and large cohorts might benefit from such adjustments. Our model showed a cross-validated MAD and RMSE of 4.680 and 6.436 years in the training set and of 4.695 and 6.602 years in the validation set, respectively. We briefly explain how to apply the model for age prediction.
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Affiliation(s)
- Laura Carlsen
- Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Olivia Holländer
- Institute of Legal Medicine, University of Münster, Röntgenstraße 23, 48149, Münster, Germany
| | - Moritz Fabian Danzer
- Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany
| | - Marielle Vennemann
- Institute of Legal Medicine, University of Münster, Röntgenstraße 23, 48149, Münster, Germany
| | - Christa Augustin
- Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany.
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9
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Fokias K, Dierckx L, Van de Voorde W, Bekaert B. Age determination through DNA methylation patterns in fingernails and toenails. Forensic Sci Int Genet 2023; 64:102846. [PMID: 36867979 DOI: 10.1016/j.fsigen.2023.102846] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/05/2022] [Accepted: 02/09/2023] [Indexed: 02/18/2023]
Abstract
Over the past decade, age prediction based on DNA methylation has become a vastly investigated topic; many age prediction models have been developed based on different DNAm markers and using various tissues. However, the potential of using nails to this end has not yet been explored. Their inherent resistance to decay and ease of sampling would offer an advantage in cases where post-mortem degradation poses challenges concerning sample collection and DNA-extraction. In the current study, clippings from both fingernails and toenails were collected from 108 living test subjects (age range: 0-96 years). The methylation status of 15 CpGs located in 4 previously established age-related markers (ASPA, EDARADD, PDE4C, ELOVL2) was investigated through pyrosequencing of bisulphite converted DNA. Significant dissimilarities in methylation levels were observed between all four limbs, hence both limb-specific age prediction models and prediction models combining multiple sampling locations were developed. When applied to their respective test sets, these models yielded a mean absolute deviation between predicted and chronological age ranging from 5.48 to 9.36 years when using ordinary least squares regression. In addition, the assay was tested on methylation data derived from 5 nail samples collected from deceased individuals, demonstrating its feasibility for application in post-mortem cases. In conclusion, this study provides the first proof that chronological age can be assessed through DNA methylation patterns in nails.
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Affiliation(s)
- Kristina Fokias
- KU Leuven, Forensic Biomedical Sciences, Department of Imaging & Pathology, Leuven, Belgium
| | - Lotte Dierckx
- KU Leuven, Forensic Biomedical Sciences, Department of Imaging & Pathology, Leuven, Belgium
| | - Wim Van de Voorde
- KU Leuven, Forensic Biomedical Sciences, Department of Imaging & Pathology, Leuven, Belgium; UZ Leuven, Laboratory of Forensic Genetics, Leuven, Belgium
| | - Bram Bekaert
- KU Leuven, Forensic Biomedical Sciences, Department of Imaging & Pathology, Leuven, Belgium; UZ Leuven, Laboratory of Forensic Genetics, Leuven, Belgium.
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10
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Ghemrawi M, Tejero NF, Duncan G, McCord B. Pyrosequencing: Current forensic methodology and future applications-a review. Electrophoresis 2023; 44:298-312. [PMID: 36168852 DOI: 10.1002/elps.202200177] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 02/01/2023]
Abstract
The recent development of small, single-amplicon-based benchtop systems for pyrosequencing has opened up a host of novel procedures for applications in forensic science. Pyrosequencing is a sequencing by synthesis technique, based on chemiluminescent inorganic pyrophosphate detection. This review explains the pyrosequencing workflow and illustrates the step-by-step chemistry, followed by a description of the assay design and factors to keep in mind for an exemplary assay. Existing and potential forensic applications are highlighted using this technology. Current applications include identifying species, identifying bodily fluids, and determining smoking status. We also review progress in potential applications for the future, including research on distinguishing monozygotic twins, detecting alcohol and drug abuse, and other phenotypic characteristics such as diet and body mass index. Overall, the versatility of the pyrosequencing technologies renders it a useful tool in forensic genomics.
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Affiliation(s)
- Mirna Ghemrawi
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida, USA
| | - Nicole Fernandez Tejero
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida, USA
| | - George Duncan
- Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Dania Beach, Florida, USA
| | - Bruce McCord
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida, USA
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11
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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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12
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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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13
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Guan X, Ohuchi T, Hashiyada M, Funayama M. Age-related DNA methylation analysis for forensic age estimation using post-mortem blood samples from Japanese individuals. Leg Med (Tokyo) 2021; 53:101917. [PMID: 34126371 DOI: 10.1016/j.legalmed.2021.101917] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 05/28/2021] [Accepted: 05/31/2021] [Indexed: 01/21/2023]
Abstract
As one of external visible characteristics (EVCs) in forensic phenotyping, age estimation is essential to providing additional information about a sample donor. With the development of epigenetics, age-related DNA methylation may be used as a reliable predictor of age estimation. With the aim of building a feasible age estimation model for Japanese individuals, 53 CpG sites distributed between 11 candidate genes were selected from previous studies. The DNA methylation level of each target CpG site was identified and measured on a massive parallel platform (synthesis by sequencing, Illumina, California, United States) from 60 forensic blood samples during the initial training phase. Multiple linear regression and quantile regression analyses were later performed to build linear and quantile age estimation models, respectively. Four CpG sites on four genes- ASPA, ELOVL2, ITGA2B, and PDE4C -, were found to be highly correlated with chronological age in DNA samples from Japanese individuals (|R| > 0.75). Subsequently, an independent validation dataset (n = 30) was used to verify and evaluate the performance of the two models. Comparison of mean absolute deviation (MAD) with other indicators showed that both models provide accurate age predictions (MAD: linear = 6.493 years; quantile = 6.243 years). The quantile model, however, can provide the changeable prediction intervals that grow wider with increasing age, and this tendency is consistent with the natural aging process in humans. Hence, the quantile model is recommended in this study.
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Affiliation(s)
- X Guan
- Tohoku University, Graduate School of Medicine, Department of Forensic Medicine, Japan.
| | - T Ohuchi
- Tohoku University, Graduate School of Medicine, Department of Forensic Medicine, Japan
| | - M Hashiyada
- Department of Legal Medicine, Kansai Medical University, Japan
| | - M Funayama
- Tohoku University, Graduate School of Medicine, Department of Forensic Medicine, Japan
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14
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Vidaki A, Montiel González D, Planterose Jiménez B, Kayser M. Male-specific age estimation based on Y-chromosomal DNA methylation. Aging (Albany NY) 2021; 13:6442-6458. [PMID: 33744870 PMCID: PMC7993701 DOI: 10.18632/aging.202775] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 02/25/2021] [Indexed: 11/29/2022]
Abstract
Although DNA methylation variation of autosomal CpGs provides robust age predictive biomarkers, no male-specific age predictor exists based on Y-CpGs yet. Since sex chromosomes play an important role in aging, a Y-chromosome-based age predictor would allow studying male-specific aging effects and would also be useful in forensics. Here, we used blood-based DNA methylation microarray data of 1,057 males from six cohorts aged 15-87 and identified 75 Y-CpGs with an interquartile range of ≥0.1. Of these, 22 and six were significantly hyper- and hypomethylated with age (p(cor)<0.05, Bonferroni), respectively. Amongst several machine learning algorithms, a model based on support vector machines with radial kernel performed best in male-specific age prediction. We achieved a mean absolute deviation (MAD) between true and predicted age of 7.54 years (cor=0.81, validation) when using all 75 Y-CpGs, and a MAD of 8.46 years (cor=0.73, validation) based on the most predictive 19 Y-CpGs. The accuracies of both age predictors did not worsen with increased age, in contrast to autosomal CpG-based age predictors that are known to predict age with reduced accuracy in the elderly. Overall, we introduce the first-of-its-kind male-specific epigenetic age predictor for future applications in aging research and forensics.
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Affiliation(s)
- Athina Vidaki
- Department of Genetic Identification, Erasmus University Medical Center Rotterdam, Rotterdam 3000, CA, The Netherlands
| | - Diego Montiel González
- Department of Genetic Identification, Erasmus University Medical Center Rotterdam, Rotterdam 3000, CA, The Netherlands
| | - Benjamin Planterose Jiménez
- Department of Genetic Identification, Erasmus University Medical Center Rotterdam, Rotterdam 3000, CA, The Netherlands
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus University Medical Center Rotterdam, Rotterdam 3000, CA, The Netherlands
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15
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Haas C, Neubauer J, Salzmann AP, Hanson E, Ballantyne J. Forensic transcriptome analysis using massively parallel sequencing. Forensic Sci Int Genet 2021; 52:102486. [PMID: 33657509 DOI: 10.1016/j.fsigen.2021.102486] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/11/2021] [Accepted: 02/12/2021] [Indexed: 12/15/2022]
Abstract
The application of transcriptome analyses in forensic genetics has experienced tremendous growth and development in the past decade. The earliest studies and main applications were body fluid and tissue identification, using targeted RNA transcripts and a reverse transcription endpoint PCR method. A number of markers have been identified for the forensically most relevant body fluids and tissues and the method has been successfully used in casework. The introduction of Massively Parallel Sequencing (MPS) opened up new perspectives and opportunities to advance the field. Contrary to genomic DNA where two copies of an autosomal DNA segment are present in a cell, abundant RNA species are expressed in high copy numbers. Even whole transcriptome sequencing (RNA-Seq) of forensically relevant body fluids and of postmortem material was shown to be possible. This review gives an overview on forensic transcriptome analyses and applications. The methods cover whole transcriptome as well as targeted MPS approaches. High resolution forensic transcriptome analyses using MPS are being applied to body fluid/ tissue identification, determination of the age of stains and the age of the donor, the estimation of the post-mortem interval and to post mortem death investigations.
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Affiliation(s)
- Cordula Haas
- University of Zurich, Zurich Institute of Forensic Medicine, Forensic Genetics, Winterthurerstrasse 190/52, CH-8057 Zurich, Switzerland.
| | - Jacqueline Neubauer
- University of Zurich, Zurich Institute of Forensic Medicine, Forensic Genetics, Winterthurerstrasse 190/52, CH-8057 Zurich, Switzerland
| | - Andrea Patrizia Salzmann
- University of Zurich, Zurich Institute of Forensic Medicine, Forensic Genetics, Winterthurerstrasse 190/52, CH-8057 Zurich, Switzerland
| | - Erin Hanson
- National Center for Forensic Science, University of Central Florida, 12354 Research Parkway, Suite 225, Orlando, FL 32826, USA
| | - Jack Ballantyne
- National Center for Forensic Science, University of Central Florida, 12354 Research Parkway, Suite 225, Orlando, FL 32826, USA; Department of Chemistry, National Center for Forensic Science, University of Central Florida, 12354 Research Parkway, Suite 225, Orlando, FL 32826, USA
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16
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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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17
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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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18
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Fan F, Dong X, Wu X, Li R, Dai X, Zhang K, Huang F, Deng Z. An evaluation of statistical models for age estimation and the assessment of the 18-year threshold using conventional pelvic radiographs. Forensic Sci Int 2020; 314:110350. [PMID: 32650207 DOI: 10.1016/j.forsciint.2020.110350] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 05/24/2020] [Accepted: 05/25/2020] [Indexed: 11/30/2022]
Abstract
The developmental patterns of the pelvic epiphyses are one of the anatomical markers used in the assessment of skeletal age and the legally relevant age threshold. In this study, four regression models and five classification models were developed for forensic age estimation and the determination of the 18-year threshold, respectively. A total of 2137 conventional pelvic radiographs (1215 males and 922 females) aged 10.00-25.99 years were analyzed, and the ossification and fusion of the iliac crest and ischial tuberosity epiphyses were scored separately. The epiphyses on both sides were used as inputs for all models. The accuracy of the regression models was compared using the mean absolute error (MAE) and root mean square error. The percentages of correct classifications were evaluated for the determination of the 18-year threshold. Support vector regression (SVR) and gradient boosting regression (GBR) showed higher accuracy for age estimation in both sexes. The lowest MAE was 1.38 years in males when using SVR and 1.16 years in females when using GBR. In the demarcation of minors and adults, the percentage of correct classification was over 92%, and the area under the receiver operating characteristic curves was over 0.91 in all models, except the Bernoulli naive Bayes classifier. This study demonstrated that the present models may be helpful for age estimation and the determination of the 18-year threshold. However, owing to the high effective dose of ionizing radiation used during conventional radiography of the pelvis, it is expected that these models will be tested with pelvic MRI for age estimation.
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Affiliation(s)
- Fei Fan
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Xiaoai Dong
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Xuemei Wu
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Rui Li
- College of Computer Science, Sichuan University, Chengdu, 610064, China
| | - Xinhua Dai
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Kui Zhang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Feijun Huang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China.
| | - Zhenhua Deng
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China.
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19
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DNA methylation-based age prediction using cell separation algorithm. Comput Biol Med 2020; 121:103747. [PMID: 32339093 DOI: 10.1016/j.compbiomed.2020.103747] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 04/03/2020] [Accepted: 04/03/2020] [Indexed: 11/23/2022]
Abstract
The age of each individual can be predicted based on the alteration rule of DNA methylation with age. In this paper, an age prediction method is developed in order to solve multivariate regression problems from DNA methylation data, by optimizing the artificial neural network (ANN) model using a new proposed algorithm named the Cell Separation Algorithm (CSA). The CSA imitates cell separation action by using a differential centrifugation process involving multiple centrifugation steps and increasing the rotor speed in each step. The CSA performs similar to the centrifugal force in separating the solutions based on their objective function in different steps, with velocity increasing in each step. Firstly, 25 test functions are used to test the CSA. Secondly, the CSA is examined on three forms of age prediction problems from two body fluids (blood and saliva). The healthy blood samples, diseased blood samples and saliva samples are used to test the method's capability. The results of the CSA are compared not only with other methods proposed in previous studies, but also with the results from stochastic gradient descent (SGD), ADAM, and genetic algorithm (GA). The model results of CSA are extremely better than the four methods proposed in previous works that have not used ANN training process. The CSA also outperformed SGD, ADAM that employ the ANN model without ANN optimization by meta-heuristics. The CSA results are comparable (even superior) to the GA model which takes the advantages of both ANN and meta-heuristics.
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20
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Márquez-Ruiz AB, González-Herrera L, Luna JDD, Valenzuela A. DNA methylation levels and telomere length in human teeth: usefulness for age estimation. Int J Legal Med 2020; 134:451-459. [PMID: 31897670 DOI: 10.1007/s00414-019-02242-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 12/19/2019] [Indexed: 01/26/2023]
Abstract
In the last decade, increasing knowledge of epigenetics has led to the development of DNA methylation-based models to predict age, which have shown high predictive accuracy. However, despite the value of teeth as forensic samples, few studies have focused on this source of DNA. This study used bisulfite pyrosequencing to measure the methylation levels of specific CpG sites located in the ELOVL2, ASPA, and PDE4C genes, with the aim of selecting the most age-informative genes and determining their associations with age, in 65 tooth samples from individuals 15 to 85 years old. As a second aim, methylation data and measurements of relative telomere length in the same set of samples were used to develop preliminary age prediction models to evaluate the accuracy of both biomarkers together and separately in estimating age from teeth for forensic purposes. In our sample, several CpG sites from ELOVL2 and PDE4C genes, as well as telomere length, were significantly associated with chronological age. We developed age prediction quantile regression models based on DNA methylation levels, with and without telomere length as an additional variable, and adjusted for type of tooth and sex. Our results suggest that telomere length may have limited usefulness as a supplementary marker for DNA methylation-based age estimation in tooth samples, given that it contributed little improvement in the prediction errors of the models. In addition, even at older ages, DNA methylation appeared to be more informative in predicting age than telomere length when both biomarkers were evaluated separately.
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Affiliation(s)
- Ana Belén Márquez-Ruiz
- Department of Forensic Medicine, Faculty of Medicine, University of Granada, Avda. de la Investigación, 11, 18016, Granada, Spain.
| | - Lucas González-Herrera
- Department of Forensic Medicine, Faculty of Medicine, University of Granada, Avda. de la Investigación, 11, 18016, Granada, Spain
| | - Juan de Dios Luna
- Department of Statistics, Faculty of Medicine, University of Granada, Avda. de la Investigación, 11, 18016, Granada, Spain
| | - Aurora Valenzuela
- Department of Forensic Medicine, Faculty of Medicine, University of Granada, Avda. de la Investigación, 11, 18016, Granada, Spain
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21
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Fan F, Tu M, Li R, Dai X, Zhang K, Chen H, Huang F, Deng Z. Age estimation by multidetector computed tomography of cranial sutures in Chinese male adults. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2019; 171:550-558. [PMID: 31891181 DOI: 10.1002/ajpa.23998] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 09/21/2019] [Accepted: 12/17/2019] [Indexed: 11/08/2022]
Affiliation(s)
- Fei Fan
- West China School of Basic Medical Sciences & Forensic MedicineSichuan University Chengdu China
| | - Meng Tu
- West China School of Basic Medical Sciences & Forensic MedicineSichuan University Chengdu China
| | - Rui Li
- College of Computer ScienceSichuan University Chengdu China
| | - Xinhua Dai
- West China School of Basic Medical Sciences & Forensic MedicineSichuan University Chengdu China
| | - Kui Zhang
- West China School of Basic Medical Sciences & Forensic MedicineSichuan University Chengdu China
| | - Hu Chen
- College of Computer ScienceSichuan University Chengdu China
| | - Feijun Huang
- West China School of Basic Medical Sciences & Forensic MedicineSichuan University Chengdu China
| | - Zhenhua Deng
- West China School of Basic Medical Sciences & Forensic MedicineSichuan University Chengdu China
- Key Laboratory of Evidence Science (China University of Political Science and Law)Ministry of Education
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22
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MapReduce-Based Parallel Genetic Algorithm for CpG-Site Selection in Age Prediction. Genes (Basel) 2019; 10:genes10120969. [PMID: 31775313 PMCID: PMC6947642 DOI: 10.3390/genes10120969] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Revised: 11/12/2019] [Accepted: 11/15/2019] [Indexed: 11/23/2022] Open
Abstract
Genomic biomarkers such as DNA methylation (DNAm) are employed for age prediction. In recent years, several studies have suggested the association between changes in DNAm and its effect on human age. The high dimensional nature of this type of data significantly increases the execution time of modeling algorithms. To mitigate this problem, we propose a two-stage parallel algorithm for selection of age related CpG-sites. The algorithm first attempts to cluster the data into similar age ranges. In the next stage, a parallel genetic algorithm (PGA), based on the MapReduce paradigm (MR-based PGA), is used for selecting age-related features of each individual age range. In the proposed method, the execution of the algorithm for each age range (data parallel), the evaluation of chromosomes (task parallel) and the calculation of the fitness function (data parallel) are performed using a novel parallel framework. In this paper, we consider 16 different healthy DNAm datasets that are related to the human blood tissue and that contain the relevant age information. These datasets are combined into a single unioned set, which is in turn randomly divided into two sets of train and test data with a ratio of 7:3, respectively. We build a Gradient Boosting Regressor (GBR) model on the selected CpG-sites from the train set. To evaluate the model accuracy, we compared our results with state-of-the-art approaches that used these datasets, and observed that our method performs better on the unseen test dataset with a Mean Absolute Deviation (MAD) of 3.62 years, and a correlation (R2) of 95.96% between age and DNAm. In the train data, the MAD and R2 are 1.27 years and 99.27%, respectively. Finally, we evaluate our method in terms of the effect of parallelization in computation time. The algorithm without parallelization requires 4123 min to complete, whereas the parallelized execution on 3 computing machines having 32 processing cores each, only takes a total of 58 min. This shows that our proposed algorithm is both efficient and scalable.
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23
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Bell CG, Lowe R, Adams PD, Baccarelli AA, Beck S, Bell JT, Christensen BC, Gladyshev VN, Heijmans BT, Horvath S, Ideker T, Issa JPJ, Kelsey KT, Marioni RE, Reik W, Relton CL, Schalkwyk LC, Teschendorff AE, Wagner W, Zhang K, Rakyan VK. DNA methylation aging clocks: challenges and recommendations. Genome Biol 2019; 20:249. [PMID: 31767039 PMCID: PMC6876109 DOI: 10.1186/s13059-019-1824-y] [Citation(s) in RCA: 455] [Impact Index Per Article: 91.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 09/16/2019] [Indexed: 12/15/2022] Open
Abstract
Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.
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Affiliation(s)
- Christopher G Bell
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - Robert Lowe
- The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - Peter D Adams
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
- Beatson Institute for Cancer Research and University of Glasgow, Glasgow, UK.
| | - Andrea A Baccarelli
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
| | - Stephan Beck
- Medical Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, London, UK.
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Department of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.
| | - Steve Horvath
- Department of Human Genetics, Gonda Research Center, David Geffen School of Medicine, Los Angeles, CA, USA.
- Department of Biostatistics, School of Public Health, University of California-Los Angeles, Los Angeles, CA, USA.
| | - Trey Ideker
- San Diego Center for Systems Biology, University of California-San Diego, San Diego, CA, USA.
| | - Jean-Pierre J Issa
- Fels Institute for Cancer Research, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA.
| | - Karl T Kelsey
- Department of Epidemiology, Brown University, Providence, RI, USA.
- Department of Pathology and Laboratory Medicine, Brown University, Providence, RI, USA.
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
| | - Wolf Reik
- Epigenetics Programme, The Babraham Institute, Cambridge, UK.
- The Wellcome Trust Sanger Institute, Cambridge, UK.
| | - Caroline L Relton
- Medical Research Council Integrative Epidemiology Unit (MRC IEU), School of Social and Community Medicine, University of Bristol, Bristol, UK.
| | | | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
- UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London, WC1E 6BT, UK.
| | - Wolfgang Wagner
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen Faculty of Medicine, Aachen, Germany.
| | - Kang Zhang
- Faculty of Medicine, Macau University of Science and Technology, Taipa, Macau.
| | - Vardhman K Rakyan
- The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
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Novel multiplex strategy for DNA methylation-based age prediction from small amounts of DNA via Pyrosequencing. Forensic Sci Int Genet 2019; 44:102189. [PMID: 31648151 DOI: 10.1016/j.fsigen.2019.102189] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 10/11/2019] [Accepted: 10/12/2019] [Indexed: 11/20/2022]
Abstract
DNA methylation-based age estimation is a promising new tool for forensic molecular biology. There is growing understanding of the best predictive CpG loci and their performance in various sample types. Since forensic samples usually provide only small amounts of DNA, the sensitivity of the method is crucial. Pyrosequencing is one of the most sensitive methods but only capable to analyze different target regions separately. Thus, multiple input DNA samples are required for investigations of different target regions, which is required for all current age estimation models. To overcome this limitation, we developed a novel multiplex strategy for Pyrosequencing, which allows the investigation of different target regions from a single small amount of input DNA. A pre-amplification step was introduced to increase the amount of target-specific template for the subsequent sequencing PCR step. We tested this multiplex strategy for eight target regions including 15 age CpGs associated with the genes of ELOVL2, FHL2, CCDC102B, C1orf132, KLF14, EDARADD, PDE4C and SST. Except for FHL2, all target regions were successfully sequenced with the multiplex strategy and the precision in terms of reproducibility of the measurements was equal to the singleplex strategy. The measured methylation values at the age CpGs displayed borderline significant differences between both analytical strategies for six out of 14 CpG sites whereas both strategies delivered equal methylation values for the remaining eight age CpGs. In total, our results indicate that the multiplex strategy can act as a promising alternative for age estimation studies in cases when only limited amounts of DNA samples are available.
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Mansour H, Sperhake JP, Bekaert B, Krebs O, Friedrich P, Fuhrmann A, Püschel K. New aspects of dental implants and DNA technology in human identification. Forensic Sci Int 2019; 302:109926. [PMID: 31444040 DOI: 10.1016/j.forsciint.2019.109926] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/11/2019] [Accepted: 08/05/2019] [Indexed: 01/29/2023]
Abstract
Missing, ineligible or delayed reference data to establish conventional dental or DNA identification are common scenarios in forensic practice. Therefore, it is worthwhile to explore new avenues that facilitate human identification. Due to the recent remarkable evolution in the prosthetic dental restorations based on dental implants and the emergence of novel DNA technologies utilized to infer the biological profile, the identification process has become easier than ever before. We report on a characteristic case, which highlights the particular importance of dental implants and DNA approaches in the prospective investigations for human identification. The aim of this publication is to focus on the possibility of identifying the batch numbers, even if they were not engraved in dental implants, making antemortem dental records of dental implants more easily accessible to establish a comparative dental identification. In addition, the reported case presents the supplementary data yielded through estimating the epigenetic age using DNA methylation as well as the biogeographical origin using Y-Haplotype and mitochondrial DNA analyses. Our results demonstrate that expanded oral implant investigations that also include implants extraction and comprehensive microscopic measurements can lead to identifying their batch numbers despite the numerous number of implants systems manufactured and distributed worldwide. Data saved by dental implant manufacturers can be very supportive and represent additional reference data for dental identification, when antemortem dental records are still missing. Furthermore, DNA methylation and mitochondrial DNA analyses can support the progress of investigation.
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Affiliation(s)
- Hussam Mansour
- University Medical Center Hamburg-Eppendorf, Institute of Legal Medicine, Butenfeld34, 22529 Hamburg, Germany.
| | - Jan Peter Sperhake
- University Medical Center Hamburg-Eppendorf, Institute of Legal Medicine, Butenfeld34, 22529 Hamburg, Germany.
| | - Bram Bekaert
- KU Leuven - University of Leuven, Department of Imaging & Pathology, Campus St-Rafaël, Kapucijnenvoer 33, Leuven, Belgium; KU-Leuven - University of Leuven, University Hospitals Leuven, Department of Forensic Medicine, Laboratory of Forensic Genetics and Molecular Archeology, Campus St-Rafaël, Kapucijnenvoer 33, Leuven, Belgium.
| | - Oliver Krebs
- University Medical Center Hamburg-Eppendorf, Institute of Legal Medicine, Butenfeld34, 22529 Hamburg, Germany.
| | - Peter Friedrich
- State Criminal Investigation Department of the City of Hamburg (LKA 41), Bruno-Georges-Platz 1, 22297 Hamburg, Germany.
| | - Andreas Fuhrmann
- University Medical Center Hamburg-Eppendorf, Institute of Legal Medicine, Butenfeld34, 22529 Hamburg, Germany.
| | - Klaus Püschel
- University Medical Center Hamburg-Eppendorf, Institute of Legal Medicine, Butenfeld34, 22529 Hamburg, Germany.
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McCord BR, Gauthier Q, Cho S, Roig MN, Gibson-Daw GC, Young B, Taglia F, Zapico SC, Mariot RF, Lee SB, Duncan G. Forensic DNA Analysis. Anal Chem 2019; 91:673-688. [PMID: 30485738 DOI: 10.1021/acs.analchem.8b05318] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Bruce R McCord
- Department of Chemistry , Florida International University , Miami , Florida 33199 , United States
| | - Quentin Gauthier
- Department of Chemistry , Florida International University , Miami , Florida 33199 , United States
| | - Sohee Cho
- Department of Forensic Medicine , Seoul National University , Seoul , 08826 , South Korea
| | - Meghan N Roig
- Department of Chemistry , Florida International University , Miami , Florida 33199 , United States
| | - Georgiana C Gibson-Daw
- Department of Chemistry , Florida International University , Miami , Florida 33199 , United States
| | - Brian Young
- Niche Vision, Inc. , Akron , Ohio 44311 , United States
| | - Fabiana Taglia
- Department of Chemistry , Florida International University , Miami , Florida 33199 , United States
| | - Sara C Zapico
- Department of Chemistry , Florida International University , Miami , Florida 33199 , United States
| | - Roberta Fogliatto Mariot
- Department of Chemistry , Florida International University , Miami , Florida 33199 , United States
| | - Steven B Lee
- Forensic Science Program, Justice Studies Department , San Jose State University , San Jose , California 95192 , United States
| | - George Duncan
- Department of Chemistry , Florida International University , Miami , Florida 33199 , United States
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27
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Recent progress, methods and perspectives in forensic epigenetics. Forensic Sci Int Genet 2018; 37:180-195. [PMID: 30176440 DOI: 10.1016/j.fsigen.2018.08.008] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 08/15/2018] [Indexed: 01/19/2023]
Abstract
Forensic epigenetics, i.e., investigating epigenetics variation to resolve forensically relevant questions unanswerable with standard forensic DNA profiling has been gaining substantial ground over the last few years. Differential DNA methylation among tissues and individuals has been proposed as useful resource for three forensic applications i) determining the tissue type of a human biological trace, ii) estimating the age of an unknown trace donor, and iii) differentiating between monozygotic twins. Thus far, forensic epigenetic investigations have used a wide range of methods for CpG marker discovery, prediction modelling and targeted DNA methylation analysis, all coming with advantages and disadvantages when it comes to forensic trace analysis. In this review, we summarize the most recent literature on these three main topics of current forensic epigenetic investigations and discuss limitations and practical considerations in experimental design and data interpretation, such as technical and biological biases. Moreover, we provide future perspectives with regard to new research questions, new epigenetic markers and recent technological advances that - as we envision - will move the field towards forensic epigenomics in the near future.
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28
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Freire-Aradas A, Phillips C, Girón-Santamaría L, Mosquera-Miguel A, Gómez-Tato A, Casares de Cal MÁ, Álvarez-Dios J, Lareu MV. Tracking age-correlated DNA methylation markers in the young. Forensic Sci Int Genet 2018; 36:50-59. [PMID: 29933125 DOI: 10.1016/j.fsigen.2018.06.011] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 06/08/2018] [Accepted: 06/11/2018] [Indexed: 01/03/2023]
Abstract
DNA methylation is the most extensively studied epigenetic signature, with a large number of studies reporting age-correlated CpG sites in overlapping genes. However, most of these studies lack sample coverage of individuals under 18 years old and therefore little is known about the progression of DNA methylation patterns in children and adolescents. In the present study we aimed to select candidate age-correlated DNA methylation markers based on public datasets from Illumina BeadChip arrays and previous publications, then to explore the resulting markers in 209 blood samples from donors aged between 2 to 18 years old using the EpiTYPER® DNA methylation analysis system. Results from our analyses identified six genes highly correlated with age in the young, in particular the gene KCNAB3, which indicates its potential as a highly informative and specific age biomarker for childhood and adolescence. We outline a preliminary age prediction model based on quantile regression that uses data from the six CpG sites most strongly correlated with age ranges extended to include children and adolescents.
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Affiliation(s)
- Ana Freire-Aradas
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain.
| | - Christopher Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
| | - Lorena Girón-Santamaría
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
| | - Ana Mosquera-Miguel
- Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain
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