Silent Whispers of Identity: Epigenetic Fingerprinting in Forensic Science

Main Article Content

Vishal Dhayal
Aarshiya

Abstract

Epigenetic fingerprinting is a new and exciting development in forensic science that goes beyond traditional DNA sequence analysis to identify biological samples. Short tandem repeat (STR)-based profiling is still the best way to identify a person, but it can't give you information about things like where the tissue came from, how old it is, or how it varies in appearance. Epigenetic changes, especially DNA methylation, add another layer of molecular information that is tissue-specific and changes over time. This makes them very useful for forensic purposes.
New analytical methods, such as bisulfite conversion-based assays, methylation-specific polymerase chain reaction, and next-generation sequencing, have made it possible to reliably find and count epigenetic markers in forensic-type samples. These methods have been useful for identifying body fluids, estimating age, and telling the difference between monozygotic twins, which are things that traditional genetic methods can't do well enough. Also, new evidence suggests that epigenetic patterns may show how a person's environment and lifestyle affect them, which makes forensic evidence more useful for interpretation.
Even with these improvements, there are still several problems that make it hard to use epigenetic fingerprinting in forensic work regularly. These include differences caused by the environment, problems with sample degradation, a lack of standardised marker panels, and problems with statistical interpretation. Also, the ethical and legal issues that come up when using epigenetic information need to be carefully thought about.
This review critically evaluates the present advancements in epigenetic fingerprinting, emphasising its molecular foundation, analytical methodologies, and forensic implications. It also talks about the current problems and the steps that need to be taken in the future to make it a part of everyday forensic work.

Article Details

Vishal Dhayal, & Aarshiya. (2026). Silent Whispers of Identity: Epigenetic Fingerprinting in Forensic Science. Journal of Forensic Science and Research, 53–74. https://doi.org/10.29328/journal.jfsr.1001117
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Copyright (c) 2026 Dhayal V, et al.

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Waddington CH. The epigenotype. Endeavour. 1942;1:18-20.

Bird A. DNA methylation patterns and epigenetic memory. Genes Dev. 2002;16(1):6-21. Available from: https://doi.org/10.1101/gad.947102

Jones PA, Takai D. The role of DNA methylation in mammalian epigenetics. Science. 2001;293(5532):1068-1070. Available from: https://doi.org/10.1126/science.1063852

Feinberg AP. Phenotypic plasticity and the epigenetics of human disease. Nature. 2007;447(7143):433-440. Available from: https://doi.org/10.1038/nature05919

Fraga MF, Ballestar E, Paz MF, Ropero S, Setién F, Ballestar ML, et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci U S A. 2005;102(30):10604-10609. Available from: https://doi.org/10.1073/pnas.0500398102

Laird PW. Principles and challenges of genome-wide DNA methylation analysis. Nat Rev Genet. 2010;11(3):191-203. Available from: https://doi.org/10.1038/nrg2732

Tost J, editor. DNA methylation: Methods and protocols. Totowa (NJ): Humana Press; 2008.

Rakyan VK, Down TA, Maslau S, Andrew T, Yang TP, Beyan H, et al. Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res. 2010;20(4):434-439. Available from: https://doi.org/10.1101/gr.103101.109

Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115. Available from: https://doi.org/10.1186/gb-2013-14-10-r115

Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda SR, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359-367. Available from: https://doi.org/10.1016/j.molcel.2012.10.016

Koch CM, Wagner W. Epigenetic aging signatures. Aging Cell. 2011;10(6):1002-1008.

Weidner CI, Lin Q, Koch CM, Eisele L, Beier F, Ziegler P, et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014;15(2):R24. Available from: https://doi.org/10.1186/gb-2014-15-2-r24

Zbieć-Piekarska R, Spólnicka M, Kupiec T, Parys-Proszek A, Makowska Ż, Pałeczka A, et al. Development of a forensically useful age prediction method based on DNA methylation analysis. Forensic Sci Int Genet. 2015;17:173-179. Available from: https://doi.org/10.1016/j.fsigen.2015.05.001

Bocklandt S, Lin W, Sehl ME, Sánchez FJ, Sinsheimer JS, Horvath S,et al. Epigenetic predictor of age. PLoS One. 2011;6(6):e14821. Available from: https://doi.org/10.1371/journal.pone.0014821

Lee HY. Epigenetic age signatures in blood for forensic age estimation. Forensic Sci Int Genet. 2012;6(2):132-139.

Eipel M. Epigenetic age predictions based on buccal swabs. Forensic Sci Int Genet. 2016;22:115-122.

Vidaki A, Kayser M. Recent progress in forensic epigenetics. Forensic Sci Int Genet. 2018;37:180-195. Available from: https://doi.org/10.1016/j.fsigen.2018.08.008

Kayser M. Forensic DNA phenotyping. Trends Genet. 2015;31(12):716-727.

Walsh S, et al. IrisPlex system for eye color prediction. Forensic Sci Int Genet. 2013;7(1):98-115.

Hanson EK, Ballantyne J. RNA profiling for body fluid identification. Forensic Sci Int Genet. 2010;4(2):83-91. Available from: https://www.semanticscholar.org/paper/RNA-Profiling-for-the-Identification-of-the-Tissue-Hanson-Ballantyne/f88bc0add7c9c4043dc82c65b293d796f7ba3fe6

Park JL, Kwon OH, Kim JH, Yoo HS, Lee HC, Woo KM, et al. Identification of body fluid-specific DNA methylation markers. Forensic Sci Int Genet. 2014;10:1-7. Available from: https://doi.org/10.1016/j.fsigen.2014.07.011

Lee HY. DNA methylation profiling for body fluid identification. Int J Legal Med. 2016;130(3):571-582. Available from: http://forensic.yonsei.ac.kr/presentation/88.pdf

Forat S, et al. DNA methylation markers for body fluid identification. Forensic Sci Int Genet. 2016;21:1-8.

Lindenbergh A. Tissue identification based on DNA methylation. Forensic Sci Int Genet. 2012;6(2):248-251. Available from:

Schmittgen TD. Real-time PCR analysis of microRNA expression. Nat Protoc. 2008;3(6):1101-1108. Available from:

Courts C, Madea B. Specific microRNA signatures for body fluid identification. Forensic Sci Int Genet. 2010;4(5):277-281. Available from: https://doi.org/10.1111/j.1556-4029.2011.01894.x

Weber-Lehmann J. Finding the needle in the haystack: differentiating identical twins. Int J Legal Med. 2014;128(1):41-46. Available from: https://www.semanticscholar.org/paper/Finding-the-needle-in-the-haystack%3A-differentiating-Weber-Lehmann-Schilling/d466b6e34c4c6ed41771c2e5f77112d00b76ad50

Vidaki A. DNA methylation-based forensic age prediction. Aging (Albany NY). 2017;9(4):1024-1038.

Nardone S. DNA methylation analysis in forensic science. Forensic Sci Int. 2017;275:92-99.

Koch A. Methylation arrays for genome-wide analysis. Methods. 2013;52(4):255-263.

Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, et al. High-density DNA methylation arrays. Genomics. 2011;98(4):288-295. Available from: https://doi.org/10.1016/j.ygeno.2011.07.007

Metzker ML. Sequencing technologies—the next generation. Nat Rev Genet. 2010;11(1):31-46. Available from: https://doi.org/10.1038/nrg2626

Loman NJ. High-throughput sequencing technologies. Nat Biotechnol. 2012;30(5):434-439.

Taylor SC. The ultimate qPCR experiment. Methods. 2017;50(4):S1-S3.

Hindson BJ, Ness KD, Masquelier DA, Belgrader P, Heredia NJ, Makarewicz AJ, et al. High-throughput droplet digital PCR. Anal Chem. 2011;83(22):8604-8610. Available from: https://pubs.acs.org/doi/10.1021/ac202028g

Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, del Angel G, Levy-Moonshine A, et al. From FastQ data to high-confidence variant calls. Curr Protoc Bioinformatics. 2013;43:11.10.1-11.10.33. Available from: https://doi.org/10.1002/0471250953.bi1110s43

Leek JT, Scharpf RB, Corrada Bravo H, Simcha D, Langmead B, Johnson WE, et al. Tackling the widespread problem of batch effects. Nat Rev Genet. 2010;11(10):733-739. Available from: https://doi.org/10.1038/nrg2825

Libbrecht MW, Noble WS. Machine learning applications in genetics. Nat Rev Genet. 2015;16(6):321-332. Available from: https://doi.org/10.1038/nrg3920

Breiman L. Random forests. Mach Learn. 2001;45(1):5-32. Available from: http://dx.doi.org/10.1023/A:1010933404324

Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273-297. Available from: https://link.springer.com/content/pdf/10.1007/BF00994018.pdf

Goodfellow I, Bengio Y, Courville A.Deep learning. Cambridge (MA): MIT Press; 2016. Available from: https://www.deeplearningbook.org/

Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006.

Jukic AM, et al. DNA methylation in environmental exposure. Environ Health Perspect. 2016;124(8):1173-1180.

Feil R, Fraga MF. Epigenetics and the environment. Nat Rev Genet. 2012;13(2):97-109. Available from: https://doi.org/10.1038/nrg3142

Ladd-Acosta C. Epigenetic signatures in disease. Genome Biol. 2015;16(1):37.

Portela A, Esteller M. Epigenetic modifications and human disease. Nat Biotechnol. 2010;28(10):1057-1068. Available from: https://doi.org/10.1038/nbt.1685

Pidsley R. Critical evaluation of methylation arrays. Genome Biol. 2013;14(1):R10.

Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies. Nat Rev Genet. 2011;12(8):529-541. Available from: https://doi.org/10.1038/nrg3000

Houseman EA. DNA methylation arrays for epigenetic studies. BMC Bioinformatics. 2012;13:86.

Jirtle RL, Skinner MK. Environmental epigenomics. Nat Rev Genet. 2007;8(4):253-262. Available from: https://doi.org/10.1038/nrg2045

Weinhold B. Epigenetics: the science of change. Environ Health Perspect. 2006;114(3):A160-A167. Available from: https://doi.org/10.1289/ehp.114-a160

Moore LD, Le T, Fan G. DNA methylation and gene expression. Neuropsychopharmacology. 2013;38(1):23-38. Available from: https://www.nature.com/articles/npp2012112

Kelsey G. Epigenetic regulation of gene expression. Science. 2017;356(6335):eaam7194.

Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J , et al. Human DNA methylomes at base resolution. Nature. 2009;462(7271):315-322. Available from: https://doi.org/10.1038/nature08514

Roadmap Epigenomics Consortium.Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, et al. Integrative analysis of human epigenomes. Nature. 2015;518(7539):317-330. Available from: https://www.nature.com/articles/nature14248

Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, et al. The NIH roadmap epigenomics mapping consortium. Nat Biotechnol. 2010;28(10):1045-1048. Available from: https://doi.org/10.1038/nbt1010-1045

Greally JM. Epigenomics: roadmap for regulation. Nat Rev Genet. 2018;19(3):125-126.

Bell JT. Epigenome-wide scans identify differential methylation. Nat Commun. 2011;3:1-9.

Slatkin M. Epigenetic inheritance and evolution. Evolution. 2009;63(1):1-7.

Heard E, Martienssen RA. Transgenerational epigenetic inheritance. Cell. 2014;157(1):95-109. Available from: https://doi.org/10.1016/j.cell.2014.02.045

Atam H, Joshi K, Mangrolia U, Waghoo S, Zaidi G, Rawool S, Thakare K. Next generation sequencing technology: current trends and advancements. Biology. 2023;12(7):997. Available from: https://doi.org/10.3390/biology12070997

Yuen ZWS. Profiling age and body fluid DNA methylation markers using nanopore adaptive sampling for forensic applications. Forensic Sci Int Genet. 2024;72:103032.

El Hakim A, Cahyani I, Arief MZ, Akbariani G, Ridwanuloh AM, Iryanto SB, et al. Detection of DNA methylation from buccal swabs using nanopore adaptive sampling. Epigenetics. 2024;19(1). Available from: https://doi.org/10.1080/15592294.2024.2418717

Sapan V, Simsek SZ, Filoglu G, Bulbul O. Forensic DNA phenotyping using Oxford Nanopore sequencing technology. J Forensic Sci. 2024. Available from: https://doi.org/10.1002/elps.202300252

de Bruin DDSH, Haagmans MA, van der Gaag KJ, Hoogenboom J, Weiler NEC, Tesi N, et al. Exploring nanopore direct sequencing performance of forensic STRs, SNPs, InDels, and DNA methylation markers in a single assay. Forensic Sci Int Genet. 2024;74:103154. Available from: https://doi.org/10.1016/j.fsigen.2024.103154

Doshi R, Kinnear E, Chatterjee S, Guha P, Liu Q. Reliable investigation of DNA methylation using Oxford Nanopore Technologies across sequencing chemistries. Sci Rep. 2025;15. Available from: https://doi.org/10.1038/s41598-025-99882-0

Ferreira MR, Carratto TMT, Frontanilla TS, Bonadio RS, Jain M, de Oliveira SF, et al. Advances in forensic genetics: exploring the potential of long-read sequencing technologies. Forensic Sci Int Genet. 2025. Available from: https://doi.org/10.1016/j.fsigen.2024.103156

Schmelzer L, Hoogenboom J, Naue J. Linking STRs/SNPs and DNA methylation using massively parallel sequencing in forensic applications. Int J Legal Med. 2025. Available from: https://link.springer.com/article/10.1007/s00414-025-03602-2

Tiras F, Cole C, Gray A, Age-associated DNA methylation loci at lncRNA genomic regions for forensic age estimation using Oxford Nanopore sequencing. Forensic Sci Int Genet. 2026. Available from: https://discovery.dundee.ac.uk/en/publications/age-associated-dna-methylation-loci-at-lncrna-genomic-regions-rev/

Yuen Z. Targeted nanopore sequencing for forensic SNP genotyping and DNA methylation profiling. Canberra (AU): Australian National University Research Repository; 2023. Available from: https://openresearch-repository.anu.edu.au/server/api/core/bitstreams/01f68d32-edfd-4ffe-ab62-6eaba699735e/content