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Epigenetic Predictor of Age
Open accessAbstract
From the moment of conception, we begin to age. A decay of cellular structures, gene regulation, and DNA sequence ages cells and organisms. DNA methylation patterns change with increasing age and contribute to age related disease. Here we identify 88 sites in or near 80 genes for which the degree of cytosine methylation is significantly correlated with age in saliva of 34 male identical twin pairs between 21 and 55 years of age. Furthermore, we validated sites in the promoters of three genes and replicated our results in a general population sample of 31 males and 29 females between 18 and 70 years of age. The methylation of three sites--in the promoters of the EDARADD, TOM1L1, and NPTX2 genes--is linear with age over a range of five decades. Using just two cytosines from these loci, we built a regression model that explained 73% of the variance in age, and is able to predict the age of an individual with an average accuracy of 5.2 years. In forensic science, such a model could estimate the age of a person, based on a biological sample alone. Furthermore, a measurement of relevant sites in the genome could be a tool in routine medical screening to predict the risk of age-related diseases and to tailor interventions based on the epigenetic bio-age instead of the chronological age.
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“The first DNA methylation clock was an elastic net model (BOX 1) built in 2011 by Bocklandt et al.53, who demonstrated highly accurate chronological age prediction (within ~5 years) using only about 100 saliva samples.”
From Measuring biological age using omics data (2022) · cites this paper as [53]
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Cites
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
Self-citationStatistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Self-citationEpigenetic differences arise during the lifetime of monozygotic twins
Fraga, M. F., Ballestar, E., Paz, M. F., Ropero, S., Setien, F., Ballestar, M. L., Heine-Suñer, D., Cigudosa, J. C., Urioste, M., Benitez, J., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500398102 · Cited by 3,602
Marks, P. A., & Breslow, R. (2007) · Nature Biotechnology · doi:10.1038/nbt1272 · Cited by 1,198
Geometric Interpretation of Gene Coexpression Network Analysis
Horvath, S., & Dong, J. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000117 · Cited by 867
Self-citationAge-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer
Teschendorff, A. E., Menon, U., Gentry-Maharaj, A., Ramus, S. J., Weisenberger, D. J., Shen, H., Campan, M., Noushmehr, H., Bell, C. G., Maxwell, A. P., et al. (2010) · Genome Research · doi:10.1101/gr.103606.109 · Cited by 855
Genome-wide association analysis by lasso penalized logistic regression
Wu, T. T., Chen, Y. F., Hastie, T., Sobel, E., & Lange, K. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp041 · Cited by 785
Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domainsPreprint
Rakyan, V. K., Down, T. A., Maslau, S., Andrew, T., Yang, T.-P., Beyan, H., Whittaker, P., McCann, O. T., Finer, S., Valdes, A. M., et al. (2010) · Genome Research · doi:10.1101/gr.103101.109 · Cited by 746
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Yuen, Z. W.-S., Shanmuganandam, S., Stanley, M., Jiang, S., Hein, N., Daniel, R., McNevin, D. B., Jack, C., & Eyras, E. (2024) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2024.103048 · Cited by 32
Quantification of Epigenetic Aging in Public Health
Kusters, C., & Horvath, S. (2024) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-060222-015657 · Cited by 35
Self-citationEpigenetic clocks and DNA methylation biomarkers of brain health and disease
Conole, E. L. S., Robertson, J. A., Smith, H. M., Cox, S. R., & Marioni, R. E. (2025) · Nature Reviews Neurology · doi:10.1038/s41582-025-01105-7 · Cited by 38
Todhunter, M. E., Sayaman, R. W., Miyano, M., & LaBarge, M. A. (2018) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2018.05.016 · Cited by 32
Li, X., Zhang, Y., Gao, X. Y., Holleczek, B., Schöttker, B., & Brenner, H. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103686 · Cited by 33
Milicic, L., Vacher, M. A., Porter, T., Doré, V., Burnham, S. C., Bourgeat, P., Shishegar, R., Doecke, J. D., Armstrong, N. J., Tankard, R. M., et al. (2022) · GeroScience · doi:10.1007/s11357-022-00558-8 · Cited by 33
Insights into the Ecology and Evolution of Polyploid Plants through Network Analysis
Gallagher, J. P., Grover, C. E., Hu, G., & Wendel, J. F. (2016) · Molecular Ecology · doi:10.1111/mec.13626 · Cited by 34
Chen, Q., Dwaraka, V. B., Carreras-Gallo, N., Mendez, K. M., Chen, Y., Begum, S., Kachroo, P., Prince, N., Went, H., Mendez, T. L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.16.562114 · Cited by 37
Guan, X., Ohuchi, T., Hashiyada, M., & Funayama, M. (2021) · Legal Medicine · doi:10.1016/j.legalmed.2021.101917 · Cited by 31
Lawrence, M. G., Pidsley, R., Niranjan, B., Papargiris, M., Pereira, B., Richards, M. G., Teng, L., Norden, S., Ryan, A., Frydenberg, M., et al. (2020) · Clinical Epigenetics · doi:10.1186/s13148-020-00836-2 · Cited by 31
Biological aging in schizophrenia and psychosis severity: DNA methylation analysis
Dada, O. O., Adanty, C., Dai, N., Jeremian, R., Alli, S. R., Gerretsen, P., Graff, A., Strauss, J., & Luca, V. D. (2020) · Psychiatry Research · doi:10.1016/j.psychres.2020.113646 · Cited by 30
Reece, A. S., & Hulse, G. K. (2021) · BMC Cancer · doi:10.1186/s12885-021-07924-3 · Cited by 31
Agudelo, J., Halámková, L., Brunelle, E., Rodrigues, R. M., Huynh, C., & Halámek, J. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b01169 · Cited by 31
Nwanaji‐Enwerem, J. C., Weisskopf, M. G., & Baccarelli, A. (2018) · Ageing Research Reviews · doi:10.1016/j.arr.2018.04.005 · Cited by 29
Refn, M. R., Kampmann, M., Morling, N., Tfelt‐Hansen, J., Børsting, C., & Pereira, V. (2023) · Forensic Sciences Research · doi:10.1093/fsr/owad021 · Cited by 36
DNA methylation rates scale with maximum lifespan across mammals
Crofts, S. J. C., Latorre-Crespo, E., & Chandra, T. (2023) · Nature Aging · doi:10.1038/s43587-023-00535-6 · Cited by 28
Conserved epigenetic hallmarks of T cell aging during immunity and malignancy
Tian, M., Soerens, A. G., Alli, S., Kang, T. G., Vasandan, A. B., Wang, Z., Vezys, V., Kimura, S., Iacobucci, I., Baylin, S. B., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00649-5 · Cited by 32
Fluctuating DNA methylation tracks cancer evolution at clinical scale
Gabbutt, C., Duran‐Ferrer, M., Grant, H. E., Mallo, D., Nadeu, F., Househam, J., Villamor, N., Müller, M., Heath, S. C., Raineri, E., et al. (2025) · Nature · doi:10.1038/s41586-025-09374-4 · Cited by 36
Longevity: epigenetic and biomolecular aspects
Taormina, G., & Mirisola, M. G. (2015) · BioMolecular Concepts · doi:10.1515/bmc-2014-0038 · Cited by 31
Environmental Challenge, Epigenetic Plasticity and the Induction of Altered Phenotypes in Mammals
Lillycrop, K. A., & Burdge, G. C. (2014) · Epigenomics · doi:10.2217/epi.14.51 · Cited by 30
Epigenetic age acceleration and clinical outcomes in gliomas
Zheng, C., Berger, N. A., Li, L., & Xu, R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0236045 · Cited by 31
Epigenetic Clock and Circadian Rhythms in Stem Cell Aging and Rejuvenation
Samoilova, E. M., Викторович, Б. В., Ekusheva, E. V., Zhang, C., Troitskiy, A. V., & Baklaushev, V. P. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11111050 · Cited by 32
Epigenetic models developed for plains zebras predict age in domestic horses and endangered equids
Larison, B., de Pinho, G. M., Haghani, A., Zoller, J. A., Li, C. Z., Finno, C. J., Farrell, C. P., Kaelin, C. B., Barsh, G. S., Wooding, B., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02935-z · Cited by 32
Self-citationDevelopment of Tissue-Specific Age Predictors Using DNA Methylation Data
Choi, H., Joe, S., & Nam, H. (2019) · Genes · doi:10.3390/genes10110888 · Cited by 28
Naue, J. (2023) · Genes & Genomics · doi:10.1007/s13258-023-01392-8 · Cited by 34
Contextualizing aging clocks and properly describing biological age
Johnson, A. A., & Shokhirev, M. N. (2024) · Aging Cell · doi:10.1111/acel.14377 · Cited by 30
Ali, O., Cerjak, D., Kent, J. W., James, R. G., Blangero, J., Carless, M. A., & Zhang, Y. (2015) · Clinical Epigenetics · doi:10.1186/s13148-015-0048-6 · Cited by 32
Watanabe, K., Taniguchi, K., & Akutsu, T. (2018) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2018.09.004 · Cited by 29
Zapico, S. C., Gauthier, Q. T., Antevska, A., & McCord, B. R. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22073717 · Cited by 29
Smit, D. J. A., Andreassen, O. A., Boomsma, D. I., Burwell, S. J., Chorlian, D. B., de Geus, E. J. C., Elvsåshagen, T., Gordon, R. L., Harper, J., Hegerl, U., et al. (2021) · Brain and Behavior · doi:10.1002/brb3.2188 · Cited by 34
Fairfield, E. A., Richardson, D. S., Daniels, C. L., Butler, C. L., Bell, E., & Taylor, M. I. (2021) · Evolutionary Applications · doi:10.1111/eva.13296 · Cited by 29
Reece, A. S., & Hulse, G. K. (2022) · Current Zoology · doi:10.1093/eep/dvab015 · Cited by 30
An ELOVL2-Based Epigenetic Clock for Forensic Age Prediction: A Systematic Review
Paparazzo, E., Lagani, V., Geracitano, S., Citrigno, L., Aceto, M. A., Malvaso, A., Bruno, F., Passarino, G., & Montesanto, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032254 · Cited by 31
Weidner, C. I., Ziegler, P., Hahn, M., Brümmendorf, T. H., Ho, A. D., Dreger, P., & Wagner, W. (2014) · Leukemia · doi:10.1038/leu.2014.323 · Cited by 31
DNA Methylation Signature of Aging: Potential Impact on the Pathogenesis of Parkinson’s Disease
Yazar, V., Dawson, V. L., Dawson, T. M., & Kang, S.-U. (2023) · Journal of Parkinson s Disease · doi:10.3233/jpd-223517 · Cited by 29
Biomarkers of Aging and Relevant Evaluation Techniques: A Comprehensive Review
Xue, T., Zhu, Z., Wang, L., Li, C., Sun, L., Wang, W., & Gong, W. (2023) · Aging and Disease · doi:10.14336/ad.2023.00808-1 · Cited by 37
Ndhlovu, L. C., Bendall, M. L., Dwaraka, V. B., Pang, A. P. S., Dopkins, N., Carreras-Gallo, N., Smith, R. C., Nixon, D. F., & Corley, M. J. (2024) · Aging Cell · doi:10.1111/acel.14288 · Cited by 38
Johnson, A. A., & Shokhirev, M. N. (2025) · Biogerontology · doi:10.1007/s10522-025-10265-4 · Cited by 30
In vivo biomarkers of structural and functional brain development and aging in humans
Franke, K., Bublak, P., Hoyer, D., Billiet, T., Gaser, C., Witte, O. W., & Schwab, M. G. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.11.002 · Cited by 29
Beydoun, M. A., Beydoun, H. A., Hooten, N. N., Maldonado, A. I., Weiss, J., Evans, M. K., & Zonderman, A. B. (2022) · Aging · doi:10.18632/aging.204150 · Cited by 27
Mo, C., Wang, J., Ye, Z., Ke, H., Liu, S., Hatch, K. S., Gao, S., Magidson, J. F., Chen, C., Mitchell, B. D., et al. (2022) · Addiction · doi:10.1111/add.16088 · Cited by 27
Reliable Hallmarks and Biomarkers of Senescent Lymphocytes
Сергеевна, М. Ю., Tereshchenko, V., Bogdanova, D. A., & Александрович, Р. С. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242115653 · Cited by 28
The EpiTect Methyl qPCR Assay as novel age estimation method in forensic biology
Mawlood, S. K., Dennany, L., Watson, N. D., & Pickard, B. S. (2016) · Forensic Science International · doi:10.1016/j.forsciint.2016.03.047 · Cited by 28
Lung transcriptomic clock predicts premature aging in cigarette smoke-exposed mice
Choukrallah, M.-A., Hoeng, J., Peitsch, M. C., & Martin, F. (2020) · BMC Genomics · doi:10.1186/s12864-020-6712-z · Cited by 27
Smurfness‐based two‐phase model of ageing helps deconvolve the ageing transcriptional signature
Zane, F., Bouzid, H., Marmol, S. S., Brazane, M., Besse, S., Molina, J. L., Cansell, C., Aprahamian, F., Durand, S., Ayache, J., et al. (2023) · Aging Cell · doi:10.1111/acel.13946 · Cited by 31
Translational geroscience: From invertebrate models to companion animal and human interventions
Lee, M. B., & Kaeberlein, M. R. (2018) · Translational Medicine of Aging · doi:10.1016/j.tma.2018.08.002 · Cited by 28
A minimal number CpGs of ELOVL2 gene for a chronological age estimation using pyrosequencing
Sukawutthiya, P., Sathirapatya, T., & Vongpaisarnsin, K. (2021) · Forensic Science International · doi:10.1016/j.forsciint.2020.110631 · Cited by 27
Dental age estimation based on DNA methylation using real-time methylation-specific PCR
Ogata, A., Kondo, M., Yoshikawa, M., Okano, M., Tsutsumi, T., & Aboshi, H. (2022) · Forensic Science International · doi:10.1016/j.forsciint.2022.111445 · Cited by 30
Impacts of Chromatin States and Long-Range Genomic Segments on Aging and DNA Methylation
Sun, D., & Yi, S. V. (2015) · PLoS ONE · doi:10.1371/journal.pone.0128517 · Cited by 30
Gutiérrez-Hurtado, I. A., Sánchez-Méndez, A. D., Becerra‐Loaiza, D. S., Rangel‐Villalobos, H., Torres‐Carrillo, N., Gallegos‐Arreola, M. P., & Aguilar‐Velázquez, J. A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25084230 · Cited by 26
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Abyzov, A. S., & Vaccarino, F. M. (2020) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083118-015241 · Cited by 28
Age estimation based on blood DNA methylation levels in brown bears
Nakamura, S., Yamazaki, J., Matsumoto, N., Inoue‐Murayama, M., Qi, H., Yamanaka, M., Nakanishi, M., Yanagawa, Y., Sashika, M., Tsubota, T., et al. (2023) · Molecular Ecology Resources · doi:10.1111/1755-0998.13788 · Cited by 27
Wang, Y., Zhang, J., Xiao, X., Liu, H., Wang, F., Li, S., Wen, Y., Wei, Y., Su, J., Zhang, Y., et al. (2016) · Scientific Reports · doi:10.1038/srep22722 · Cited by 27
On epigenetic stochasticity, entropy and cancer risk
Teschendorff, A. E. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0054 · Cited by 24
Schmunk, L. J., Call, T. P., McCartney, D. L., Javaid, H., Hastings, W. J., Jovičević, V., Kojadinović, D., Tomkinson, N., Zlamalova, E., McGee, K. C., et al. (2025) · Aging Cell · doi:10.1111/acel.14444 · Cited by 25
Crime investigation through DNA methylation analysis: methods and applications in forensics
Rana, A. K. (2018) · Egyptian Journal of Forensic Sciences · doi:10.1186/s41935-018-0042-1 · Cited by 30
DNA methylation in genes associated with the evolution of ageing and disease: A critical review
Auley, M. T. M. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101488 · Cited by 25
Wang, Z., Hui, Q., Goldberg, J. H., Smith, N. L., Kaseer, B., Murrah, N. V., Levantsevych, O. M., Shallenberger, L. H., Diggers, E., Bremner, J. D., et al. (2021) · Psychosomatic Medicine · doi:10.1097/psy.0000000000001028 · Cited by 27
Transcriptome-wide association study of inflammatory biologic age
Lin, H., Lunetta, K. L., Zhao, Q., Rong, J., Benjamin, E. J., Mendelson, M., Joehanes, R., Levy, D. A., Larson, M. G., & Murabito, J. M. (2017) · Aging · doi:10.18632/aging.101321 · Cited by 24
Pfeifer, M., Bajanowski, T., Helmus, J., & Poetsch, M. (2020) · International Journal of Legal Medicine · doi:10.1007/s00414-020-02263-7 · Cited by 25
Marcante, B., Marino, L., Cattaneo, N. E., Delicati, A., Tozzo, P., & Caenazzo, L. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26073158 · Cited by 28
Aging epigenetics: Accumulation of errors or realization of a specific program?
Ashapkin, V. V., Kutueva, L. I., & Vanyushin, B. F. (2015) · Biochemistry (Moscow) · doi:10.1134/s0006297915110024 · Cited by 25
Recent Advances in the Systems Biology of Aging
McCormick, M. A., & Promislow, D. (2017) · Antioxidants and Redox Signaling · doi:10.1089/ars.2017.7367 · Cited by 25
Mills, J., Beach, S., Dogan, M. V., Simons, R. L., Gibbons, F., Long, J., & Philibert, R. (2019) · Genes · doi:10.3390/genes10010051 · Cited by 20
McKennan, C. G., Naughton, K. A., Stanhope, C. T., Kattan, M., O’Connor, G. T., Sandel, M. T., Visness, C. M., Wood, R. A., Bacharier, L. B., Beigelman, A., et al. (2020) · Epigenetics · doi:10.1080/15592294.2020.1817290 · Cited by 23
Carlsen, L., Holländer, O., Danzer, M. F., Vennemann, M., & Augustin, C. (2023) · International Journal of Legal Medicine · doi:10.1007/s00414-023-02967-6 · Cited by 28
The nucleic acid revolution continues – will forensic biology become forensic molecular biology?
Gunn, P. R., Walsh, S. J., & Roux, C. P. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00044 · Cited by 29
Xiao, C., Yi, S., & Huang, D. (2021) · Electrophoresis · doi:10.1002/elps.202000367 · Cited by 24
Epigenetic Age Acceleration and Hearing: Observations From the Baltimore Longitudinal Study of Aging
Kuo, P.-L., Moore, A. Z., Lin, F. R., & Ferrucci, L. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.790926 · Cited by 23
A Review of the Epigenetic Clock: Emerging Biomarkers for Asthma and Allergic Disease
Daley, D., Vasileva, D., & Greenwood, C. M. T. (2023) · Genes · doi:10.3390/genes14091724 · Cited by 25
Neale, R. Ε., Clark, P. J., Fawcett, J., Fritschi, L., Nagler, B., Risch, H. A., Walters, R., Crawford, W., Webb, P. M., Whiteman, D. C., et al. (2014) · Cancer Epidemiology · doi:10.1016/j.canep.2014.08.006 · Cited by 23
Endo, K., Li, J., Nakanishi, M., Asada, T., Ikesue, M., Goto, Y., Fukushima, Y., & Iwai, N. (2015) · BioMed Research International · doi:10.1155/2015/451981 · Cited by 23
Zupkovitz, G., Kabiljo, J., Kothmayer, M., Schlick, K., Schöfer, C., Lagger, S., & Pusch, O. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.627143 · Cited by 24
DNA methylation of decedent blood samples to estimate the chronological age of human remains
Anaya, Y., Yew, P., Roberts, K. A., & Hardy, W. R. (2021) · International Journal of Legal Medicine · doi:10.1007/s00414-021-02650-8 · Cited by 24
McIntyre, R. L., Rahman, M., Vanapalli, S. A., Houtkooper, R. H., & Janssens, G. E. (2021) · Frontiers in Aging · doi:10.3389/fragi.2021.708680 · Cited by 23
Turning back time with epigenetic clocks
Drew, L. (2022) · Nature · doi:10.1038/d41586-022-00077-8 · Cited by 22
Safaee, M., Lin, J., Smith, D. L., Fury, M., Scheer, J. K., Burke, J. F., Bravate, C., Lambert, D., & Ames, C. P. (2022) · Journal of Neurosurgery Spine · doi:10.3171/2022.10.spine22605 · Cited by 23
Silveira, P. P., & Meaney, M. J. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106008 · Cited by 26
Yang, G., Yu, X., Weisenberger, D. J., Lü, T., & Liang, G. (2023) · Cancers · doi:10.3390/cancers15112934 · Cited by 25
Do age‐associated DNA methylation changes increase the risk of malignant transformation?
Wagner, W., Weidner, C. I., & Lin, Q. (2014) · BioEssays · doi:10.1002/bies.201400063 · Cited by 23
Improved Human Age Prediction by Using Gene Expression Profiles From Multiple Tissues
Wang, F., Yang, J., Lin, H., Li, Q., Ye, Z., Lu, Q., Chen, L., Tu, Z., & Tian, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.01025 · Cited by 24
Epigenetic Clocks: Beyond Biological Age, Using the Past to Predict the Present and Future
Liang, R., Tang, Q., Chen, J., & Zhu, L. (2024) · Aging and Disease · doi:10.14336/ad.2024.1495 · Cited by 28
Market Drivers and Discovering Technologies in Meat Species Identification
Rao, M. S., Chakraborty, G., & Murthy, K. S. (2019) · Food Analytical Methods · doi:10.1007/s12161-019-01591-8 · Cited by 25
Recent Advances in Noninvasive Biosensors for Forensics, Biometrics, and Cybersecurity
McGoldrick, L. K., & Halámek, J. (2020) · Sensors · doi:10.3390/s20215974 · Cited by 27
An update in toxicology of ageing
Račková, L., Mach, M., & Brnoliaková, Z. (2021) · Environmental Toxicology and Pharmacology · doi:10.1016/j.etap.2021.103611 · Cited by 22
So, M. H., & Lee, H. Y. (2021) · Electrophoresis · doi:10.1002/elps.202000312 · Cited by 22
Qi, H., Kinoshita, K., Mori, T., Matsumoto, K., Matsui, Y., & Inoue‐Murayama, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-99424-4 · Cited by 22
Perez-Correa, J.-F., Tharmapalan, V., Geiger, H., & Wagner, W. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.902857 · Cited by 23
Novel feature selection methods for construction of accurate epigenetic clocks
Li, A., Kane, A. E., Mueller, A. L., English, B. W., Arena, A., Vera, D. L., & Sinclair, D. A. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009938 · Cited by 22
Phan, T. H., Nguyen, V. T. C., Pham, T. T. T., Nguyen, C. V., Ho, T. D., Pham, T. M. Q., Tran, T.-H., Nguyen, T. D., Le, N. D. K., Nguyen, T.-H., et al. (2022) · Future Oncology · doi:10.2217/fon-2022-1218 · Cited by 22
Kim, J., Kim, K., Kim, H., Yoon, G., & Lee, K. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-997 · Cited by 22
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Metadata
- Authors
- Bocklandt, S., Lin, W., Sehl, M. E., Sánchez, F. J., Sinsheimer, J. S., Horvath, S., & Vilain, E.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Vilain, Eric
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-06-22
- Volume
- 6
- Issue
- 6
- Pages
- e14821
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3120753
- OpenAlex
- W2080644309
- Cited by
- 1,012
- References
- 22
- Citations by year
- 2026: 56 · 2025: 71 · 2024: 83 · 2023: 92 · 2022: 95 · 2021: 108 · 2020: 87 · 2019: 64 · 2018: 52 · 2017: 65 · 2016: 58 · 2015: 63 · 2014: 43 · 2013: 40 · 2012: 30
- FWCI
- 14.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Telomeres, Telomerase, and Senescence
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Syndromes and Imprinting
- Keywords
- DNA methylation, age prediction, epigenetic clocks, saliva, EDARADD, NPTX2, promoter methylation, forensic age estimation, age-related disease risk
- MeSH terms
- Adolescent, Adult, Aged, Aging, Female, Humans, Male, Middle Aged, Models, Genetic, Saliva, Twins, Reproducibility of Results, DNA Probes, DNA Methylation, Oligonucleotide Array Sequence Analysis, Epigenesis, Genetic, Young Adult, Genetic Loci
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