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General methodology of epiphenotyping
This figure depicts the general workflow for epiphenotyping, focusing on the use of machine learning to generate valid models for predicting phenotypes from DNA methylation patterns.

Epiphenotyping involves studying the relationship between DNA methylation patterns and phenotypic traits in individuals and populations to be able to predict a phenotype from a DNA methylation profile. In the following sections, the background of epiphenotyping, an overview of a general methodology, its applications, advantages, and limitations are covered.

Epigenetics refers to heritable changes in gene expression that are not changes in the underlying DNA sequence...[1] DNA methylation is a key epigenetic mechanism, involving the addition of methyl (-CH3) groups to specific DNA regions, almost always at cytosine-guanine dinucleotides (CpG sites). CpG sites are DNA sequences where a cytosine nucleotide is followed by a guanine nucleotide connected by a phosphate group.[2]

Background

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The term epiphenotyping comes from integrating the two words "epigenetics" and "phenotyping". Epiphenotyping is the process of using genome-wide DNA methylation patterns to predict phenotypes.[3][4] Through computational methods, epiphenotyping utilizes DNA methylation data to infer information about phenotypic traits such as gestational age, sex, cell composition, and genetic ancestry.[3][4][5][6]

Importantly, epiphenotyping and epigenome-wide association studies (EWAS) are both approaches used in epigenetics research, however they focus on distinct aspects of epigenetic data analysis. Epiphenotyping is focused on inferring phenotypic information from epigenetic data and understanding the biological implications of epigenetic patterns.[3] Whereas EWAS is a hypothesis-driven approach that is centered around identifying specific epigenetic markers associated with a particular disease status, environmental factor, and/or phenotype.[7][8]

The term "epiphenotyping" was first introduced in a paper in 2023 that evaluated the use of epiphenotyping in DNA methylation array studies of the human placenta.[3] It is worth noting that although this specific term may be recent, the method itself of using DNA methylation data to predict phenotypes has been utilized since 2011.[9]

Epiphenotyping workflow

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The following section goes through the general methodology employed by studies that generate epiphenotyping models.

Data collection

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Firstly, researchers extract and purify DNA from samples of interest (e.g., blood or placental tissues). The DNA samples are then assayed on high-throughput technologies such as DNA methylation arrays (e.g., Illumina Infinium MethylationEPIC (850K) array) or with whole genome bisulfite sequencing (WGBS) to collect DNA methylation data. In addition to collecting biological samples, key biological variables (e.g., gestational age at birth, sex, and self-reported ethnicity) and technical variables (e.g., processing time and temperature at which samples are stored) are also collected.[3]

Preprocessing

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The raw DNA methylation data undergoes preprocessing steps to address technical variation and filter out noise or low-quality methylation probes. Normalization and batch correction also happen at this stage. R packages such as minfi, wateRmelon, and ewastools facilitate data quality control checks.[10][11][12] For example, bisulfite conversion efficiency, array run quality, and average total fluorescence intensity are crucial measures to assess between samples.[3][13] The use of data normalization algorithms (e.g., functional or quantile) and probe filtering have been shown to reduce variability in biological and technical variables between sets of DNA methylation data.[14][15][16] Principal component analysis (PCA) is often applied to reduce the dimensionality of the data before proceeding to analyze it further.[17]

Training model

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In this stage a model is developed that tries to predict epiphenotypes from the DNA methylation data. The model predictions are compared with known phenotypic information for validation. From the preprocessed and normalized data, a portion of the dataset is used for training the model known as the training dataset. Epiphenotyping models have been developed that use linear regression to identify CpGs that are predictors of the phenotype of interest, while newer models have used machine learning techniques.[4][5] Machine learning algorithms such as random forests or support vector machines are used to train models on the preprocessed DNA methylation data.[18]

Applying the model

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Once the models have been tested and shown to have high predictive power, they can be applied to new DNA methylation data to infer epiphenotypes. Sometimes generating the epiphenotypes is the final step of a study, but other times the epiphenotypes are generated to be used for further analysis and association studies. Epiphenotypes can be included as covariates in other models that look for associations between phenotypes and DNA methylation patterns.[3][19]

Further analysis can also be done on the estimated epiphenotypes to look for potential associations between epiphenotypes and specific biological functions or disease processes. For instance, there may be CpGs that are highly predictive of a phenotype which could indicate which genes are important for the development of that phenotype. An example of this is examining how blood or placental cell composition relates to preeclampsia, or how certain CpGs predictive of epigenetic age correlate with gestational age discrepancies.[3]

Applications

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The figure shows examples of applications of epiphenotyping and some different phenotypes that models can predict from DNA methylation data.

Epigenetic clocks

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The most common use of epiphenotyping are epigenetic clocks that predict the age of a biological sample based on DNA methylation.[20][21] Epigenetic changes, including changes in DNA methylation (overall global hypomethylation), are associated with cellular aging.[22]

Epigenetic predicted chronological age generally increases with actual chronological age but the rate of epigenetic aging can vary across tissues and between individuals.[20] Deviation of the predicted age and actual age is known as epigenetic age acceleration.[23] Epigenetic age acceleration has been associated with several phenotypes including obesity,[24] lung cancer incidence,[25] and traumatic stress[26] among others.

First-Generation Epigenetic clocks

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The first published epigenetic clock that used DNA methylation to predict chronological age came from the Bocklandt group in 2011.[9] This clock was generated with saliva samples and was based on 3 CpGs found in 3 gene promoters (EDARADD, TOM1L1, and NPTX2). Two years later, Steve Horvath generated epigenetic clocks for 51 different tissues and cell-types using 353 CpGs.[4]

Second-Generation Epigenetic clocks

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Since 2013 there has been an explosion of new epigenetic clocks with newer clocks including more CpG sites in their models and having higher predictive accuracy.[20][21] Additionally, there have been both intrinsic and extrinsic epigenetic clocks developed.[27]

Newer epigenetic clocks known as the "second generation" were developed that used additional clinical biomarkers (e.g. White blood cell count) to more accurately predict phenotypic age rather than chronological age of a sample from DNA methylation.[21][28] The PhenoAge model trained on whole blood samples predicted phenotypic age using 513 CpGs, with improved prediction of mortality compared to "first generation" clocks like Horvath's.[28] GrimAge is another model trained to use DNA methylation to predict the levels of 7 plasma proteins and lifespan/time-to-death.[29] GrimAge has also been shown to be predictive of other phenotypes such as an individual's time-to-coronary heart disease, time-to-cancer, time-to-menopause, and cognitive performance.[5][29] Both PhenoAge and GrimAge were developed for whole blood, whereas other clocks have been developed for other tissues such as the brain[30] and skeletal muscle.[31]

Epigenetic clocks have also been developed to predict the gestational age based on DNA methylation from the cord blood or placenta.[32][33][34] Epigenetic age acceleration measured from placental samples has been associated with preeclampsia and maternal dislipidemia.[35][36]

Epigenetic Clock Number of CpGs Tissue Publication
Epigenetic Predictor of Age 3 Saliva (white blood cells and epithelial cells) Bocklandt et al., 2011[9]
Multi-tissue epigenetic clock 353 51 tissues and cell types Horvath, 2013[4]
Hannum epigenetic clock 71 Whole blood Hannum et al., 2013[37]
PhenoAge 513 Whole blood Levine et al., 2018[28]
GrimAge 1030 Whole blood Lu et al., 2019[5]

Forensic applications

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Epigenetic clocks have the potential to be used in forensic science applications for estimating the chronological age of a biological sample.[38][39][40] Epigenetic clocks could be used to estimate the age of an unidentified body, a perpetrators biological sample, or settle a legal dispute about an individual's age. Aside from age estimates, there are a number of other potential phenotypes that could be useful in a forensic setting that could be elucidated from DNA methylation: genetic ancestry, smoking, alcohol consumption, body size, socioeconomic status, and more.[40]

Some limitations in the application of the technology to forensic science is that generating comprehensive methylome profiles is technically difficult for most forensic laboratories, individuals of young and old ages are poorly represented in most model datasets, ethical and legal issues, and low-quantity/quality DNA samples.[39][40][41]

Epiphenotyping in oncology

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It is known that cancer cells have altered DNA methylation patterns compared to normal cells.[21] One application of epiphenotyping is to use models to predict an individual's risk of cancer, as well as in some cases DNA methylation patterns are used to diagnose certain cancer types.[42] A variety of epiphenotyping models for oncology use have been developed for various cancers including breast, prostate, neurological, and lung among others.[21] Another oncological application of epiphenotyping is for predicting the tissue-of-origin of a cancer from DNA methylation.[43][44]

Other applications

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Aside from cancer, epiphenotyping has been done to predict an individual's risk for other diseases including Alzheimer's disease and cardiovascular disease.[45][46]

Another application of epiphenotyping includes predicting cell-type compositions within bulk tissue samples.[47][48] There have also been some epiphenotyping models that predict genetic ancestry or sex from global DNA methylation patterns [49][50][51]

Advantages and limitations

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Epiphenotyping models are only as good as the data they used to train their models. Cohort age range, genetic ancestry make-up, and sex ratio can all affect how predictive the model will be on other data, their external validity.[21] Sometimes the epiphenotyping models only work, or work better on certain age ranges, or for certain genetic ancestries,[52] this is generally a result of the epiphenotyping model being overfit.

Methylation Risk scores (MRS)

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While polygenic risk scores (PRS) represent an estimate of an individual's phenotype based on many genetic variants, methylation risk scores (MRS) similarly provide an estimate of an individual's phenotype based on the methylation at many CpGs.[53] PRS are generated through Genome Wide Association Studies (GWAS) and analogously MRS are generated by EWAS. Some use the term MRS in a similar way to the term epiphenotyping, to predict a phenotype from DNA methylation data [53]

Machine learning classification models like random forests identify discriminatory features in a dataset to generate MRS. For example, MRS have been developed to identify smokers versus non-smokers.[54] Alternatively, MRS can be used as a covariate in other analyses to adjust for that phenotype and reduce confounding effects. For example, the predicted smoking status based on DNA methylation was used as a covariate in an EWAS study that identified associations between DNA methylation and schizophrenia [55]

References

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  1. ^ Gibney, E. R.; Nolan, C. M. (July 2010). "Epigenetics and gene expression". Heredity. 105 (1): 4–13. doi:10.1038/hdy.2010.54. ISSN 1365-2540. PMID 20461105.
  2. ^ Moore, Lisa D.; Le, Thuc; Fan, Guoping (January 2013). "DNA Methylation and Its Basic Function". Neuropsychopharmacology. 38 (1): 23–38. doi:10.1038/npp.2012.112. ISSN 1740-634X. PMC 3521964. PMID 22781841.
  3. ^ a b c d e f g h Khan, A.; Inkster, A. M.; Peñaherrera, M. S.; King, S.; Kildea, S.; Oberlander, T. F.; Olson, D. M.; Vaillancourt, C.; Brain, U.; Beraldo, E. O.; Beristain, A. G.; Clifton, V. L.; Del Gobbo, G. F.; Lam, W. L.; Metz, G. A. S. (2023-10-04). "The application of epiphenotyping approaches to DNA methylation array studies of the human placenta". Epigenetics & Chromatin. 16 (1): 37. doi:10.1186/s13072-023-00507-5. ISSN 1756-8935. PMC 10548571. PMID 37794499.
  4. ^ a b c d e Horvath, Steve (2013-12-10). "DNA methylation age of human tissues and cell types". Genome Biology. 14 (10): 3156. doi:10.1186/gb-2013-14-10-r115. ISSN 1474-760X. PMC 4015143. PMID 24138928. (Erratum: doi:10.1186/s13059-015-0649-6, PMID 25968125,  Retraction Watch. If the erratum has been checked and does not affect the cited material, please replace {{erratum|...}} with {{erratum|...|checked=yes}}.)
  5. ^ a b c d Lu, Ake T.; Quach, Austin; Wilson, James G.; Reiner, Alex P.; Aviv, Abraham; Raj, Kenneth; Hou, Lifang; Baccarelli, Andrea A.; Li, Yun; Stewart, James D.; Whitsel, Eric A.; Assimes, Themistocles L.; Ferrucci, Luigi; Horvath, Steve (2019-01-21). "DNA methylation GrimAge strongly predicts lifespan and healthspan". Aging. 11 (2): 303–327. doi:10.18632/aging.101684. ISSN 1945-4589. PMC 6366976. PMID 30669119.
  6. ^ Titus, Alexander J.; Gallimore, Rachel M.; Salas, Lucas A.; Christensen, Brock C. (2017-07-19). "Cell-type deconvolution from DNA methylation: a review of recent applications". Human Molecular Genetics. 26 (R2): R216–R224. doi:10.1093/hmg/ddx275. ISSN 0964-6906. PMC 5886462. PMID 28977446.
  7. ^ Campagna, Maria Pia; Xavier, Alexandre; Lechner-Scott, Jeannette; Maltby, Vicky; Scott, Rodney J.; Butzkueven, Helmut; Jokubaitis, Vilija G.; Lea, Rodney A. (2021-12-04). "Epigenome-wide association studies: current knowledge, strategies and recommendations". Clinical Epigenetics. 13 (1): 214. doi:10.1186/s13148-021-01200-8. ISSN 1868-7083. PMC 8645110. PMID 34863305.
  8. ^ Flanagan, James M. (2015). "Epigenome-wide association studies (EWAS): past, present, and future". Cancer Epigenetics. Methods in Molecular Biology (Clifton, N.J.). Vol. 1238. pp. 51–63. doi:10.1007/978-1-4939-1804-1_3. ISBN 978-1-4939-1803-4. ISSN 1940-6029. PMID 25421654.
  9. ^ a b c Bocklandt, Sven; Lin, Wen; Sehl, Mary E.; Sánchez, Francisco J.; Sinsheimer, Janet S.; Horvath, Steve; Vilain, Eric (2011-06-22). "Epigenetic Predictor of Age". PLOS ONE. 6 (6): e14821. Bibcode:2011PLoSO...614821B. doi:10.1371/journal.pone.0014821. ISSN 1932-6203. PMC 3120753. PMID 21731603.
  10. ^ Aryee, Martin J.; Jaffe, Andrew E.; Corrada-Bravo, Hector; Ladd-Acosta, Christine; Feinberg, Andrew P.; Hansen, Kasper D.; Irizarry, Rafael A. (2014-05-15). "Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays". Bioinformatics. 30 (10): 1363–1369. doi:10.1093/bioinformatics/btu049. ISSN 1367-4811. PMC 4016708. PMID 24478339.
  11. ^ Pidsley, Ruth; Y Wong, Chloe C.; Volta, Manuela; Lunnon, Katie; Mill, Jonathan; Schalkwyk, Leonard C. (2013-05-01). "A data-driven approach to preprocessing Illumina 450K methylation array data". BMC Genomics. 14 (1): 293. doi:10.1186/1471-2164-14-293. ISSN 1471-2164. PMC 3769145. PMID 23631413.
  12. ^ Murat, Katarzyna; Grüning, Björn; Poterlowicz, Paulina Wiktoria; Westgate, Gillian; Tobin, Desmond J; Poterlowicz, Krzysztof (2020-05-01). "Ewastools: Infinium Human Methylation BeadChip pipeline for population epigenetics integrated into Galaxy". GigaScience. 9 (5). doi:10.1093/gigascience/giaa049. ISSN 2047-217X. PMC 7219210. PMID 32401319.
  13. ^ Triche, Timothy J.; Weisenberger, Daniel J.; Van Den Berg, David; Laird, Peter W.; Siegmund, Kimberly D. (2013-04-01). "Low-level processing of Illumina Infinium DNA Methylation BeadArrays". Nucleic Acids Research. 41 (7): e90. doi:10.1093/nar/gkt090. ISSN 1362-4962. PMC 3627582. PMID 23476028.
  14. ^ Fortin, Jean-Philippe; Labbe, Aurélie; Lemire, Mathieu; Zanke, Brent W.; Hudson, Thomas J.; Fertig, Elana J.; Greenwood, Celia MT; Hansen, Kasper D. (2014-12-03). "Functional normalization of 450k methylation array data improves replication in large cancer studies". Genome Biology. 15 (11): 503. doi:10.1186/s13059-014-0503-2. ISSN 1474-760X. PMC 4283580. PMID 25599564.
  15. ^ Maksimovic, Jovana; Gordon, Lavinia; Oshlack, Alicia (2012-06-15). "SWAN: Subset-quantile Within Array Normalization for Illumina Infinium HumanMethylation450 BeadChips". Genome Biology. 13 (6): R44. doi:10.1186/gb-2012-13-6-r44. ISSN 1474-760X. PMC 3446316. PMID 22703947.
  16. ^ Van der Most, Peter J; Küpers, Leanne K; Snieder, Harold; Nolte, Ilja (2017-01-24). "QCEWAS: automated quality control of results of epigenome-wide association studies". Bioinformatics. 33 (8): 1243–1245. doi:10.1093/bioinformatics/btw766. hdl:1983/d96c2caa-6e07-431a-932b-6d44ceeecd7c. ISSN 1367-4803. PMID 28119308.
  17. ^ Miao, Rui; Dang, Qi; Cai, Jie; Huang, Hai-Hui; Xie, Sheng-Li; Liang, Yong (September 2022). "Sparse principal component analysis based on genome network for correcting cell type heterogeneity in epigenome-wide association studies". Medical & Biological Engineering & Computing. 60 (9): 2601–2618. doi:10.1007/s11517-022-02599-9. ISSN 1741-0444. PMID 35789457. S2CID 250282262.
  18. ^ Li, Dong-Dong; Chen, Ting; Ling, You-Liang; Jiang, YongAn; Li, Qiu-Gen (2022). "A Methylation Diagnostic Model Based on Random Forests and Neural Networks for Asthma Identification". Computational and Mathematical Methods in Medicine. 2022: 2679050. doi:10.1155/2022/2679050. ISSN 1748-6718. PMC 9534672. PMID 36213574.
  19. ^ Guintivano, Jerry; Shabalin, Andrey A; Chan, Robin F.; Rubinow, David R.; Sullivan, Patrick F.; Meltzer-Brody, Samantha; Aberg, Karolina a; van den Oord, Edwin J. C. G. (2020-11-01). "Test-statistic inflation in methylome-wide association studies". Epigenetics. 15 (11): 1163–1166. doi:10.1080/15592294.2020.1758382. ISSN 1559-2294. PMC 7595582. PMID 32425094.
  20. ^ a b c Bergsma, Tessa; Rogaeva, Ekaterina (January 2020). "DNA Methylation Clocks and Their Predictive Capacity for Aging Phenotypes and Healthspan". Neuroscience Insights. 15: 263310552094222. doi:10.1177/2633105520942221. ISSN 2633-1055. PMC 7376380. PMID 32743556.
  21. ^ a b c d e f Yousefi, Paul D.; Suderman, Matthew; Langdon, Ryan; Whitehurst, Oliver; Davey Smith, George; Relton, Caroline L. (June 2022). "DNA methylation-based predictors of health: applications and statistical considerations". Nature Reviews Genetics. 23 (6): 369–383. doi:10.1038/s41576-022-00465-w. ISSN 1471-0064. PMID 35304597. S2CID 247548403.
  22. ^ Johnson, Adiv A.; Akman, Kemal; Calimport, Stuart R.G.; Wuttke, Daniel; Stolzing, Alexandra; de Magalhães, João Pedro (October 2012). "The Role of DNA Methylation in Aging, Rejuvenation, and Age-Related Disease". Rejuvenation Research. 15 (5): 483–494. doi:10.1089/rej.2012.1324. ISSN 1549-1684. PMC 3482848. PMID 23098078.
  23. ^ Bozack, Anne K.; Rifas-Shiman, Sheryl L.; Gold, Diane R.; Laubach, Zachary M.; Perng, Wei; Hivert, Marie-France; Cardenas, Andres (2023-04-12). "DNA methylation age at birth and childhood: performance of epigenetic clocks and characteristics associated with epigenetic age acceleration in the Project Viva cohort". Clinical Epigenetics. 15 (1): 62. doi:10.1186/s13148-023-01480-2. ISSN 1868-7083. PMC 10099681. PMID 37046280.
  24. ^ Horvath, Steve; Erhart, Wiebke; Brosch, Mario; Ammerpohl, Ole; von Schönfels, Witigo; Ahrens, Markus; Heits, Nils; Bell, Jordana T.; Tsai, Pei-Chien; Spector, Tim D.; Deloukas, Panos; Siebert, Reiner; Sipos, Bence; Becker, Thomas; Röcken, Christoph (2014-10-28). "Obesity accelerates epigenetic aging of human liver". Proceedings of the National Academy of Sciences. 111 (43): 15538–15543. Bibcode:2014PNAS..11115538H. doi:10.1073/pnas.1412759111. ISSN 0027-8424. PMC 4217403. PMID 25313081.
  25. ^ Levine, Morgan E.; Hosgood, H. Dean; Chen, Brian; Absher, Devin; Assimes, Themistocles; Horvath, Steve (2015-09-24). "DNA methylation age of blood predicts future onset of lung cancer in the women's health initiative". Aging. 7 (9): 690–700. doi:10.18632/aging.100809. ISSN 1945-4589. PMC 4600626. PMID 26411804.
  26. ^ Boks, Marco P.; Mierlo, Hans C. van; Rutten, Bart P. F.; Radstake, Timothy R. D. J.; De Witte, Lot; Geuze, Elbert; Horvath, Steve; Schalkwyk, Leonard C.; Vinkers, Christiaan H.; Broen, Jasper C. A.; Vermetten, Eric (2015-01-01). "Longitudinal changes of telomere length and epigenetic age related to traumatic stress and post-traumatic stress disorder" (PDF). Psychoneuroendocrinology. This issue includes a Special Section on Biomarkers in the Military - New Findings from Prospective Studies. 51: 506–512. doi:10.1016/j.psyneuen.2014.07.011. ISSN 0306-4530. PMID 25129579. S2CID 8699936.
  27. ^ Bell, Christopher G.; Lowe, Robert; Adams, Peter D.; Baccarelli, Andrea A.; Beck, Stephan; Bell, Jordana T.; Christensen, Brock C.; Gladyshev, Vadim N.; Heijmans, Bastiaan T.; Horvath, Steve; Ideker, Trey; Issa, Jean-Pierre J.; Kelsey, Karl T.; Marioni, Riccardo E.; Reik, Wolf (2019-11-25). "DNA methylation aging clocks: challenges and recommendations". Genome Biology. 20 (1): 249. doi:10.1186/s13059-019-1824-y. ISSN 1474-760X. PMC 6876109. PMID 31767039.
  28. ^ a b c Levine, Morgan E.; Lu, Ake T.; Quach, Austin; Chen, Brian H.; Assimes, Themistocles L.; Bandinelli, Stefania; Hou, Lifang; Baccarelli, Andrea A.; Stewart, James D.; Li, Yun; Whitsel, Eric A.; Wilson, James G; Reiner, Alex P; Aviv, Abraham; Lohman, Kurt (2018-04-18). "An epigenetic biomarker of aging for lifespan and healthspan". Aging. 10 (4): 573–591. doi:10.18632/aging.101414. ISSN 1945-4589. PMC 5940111. PMID 29676998.
  29. ^ a b McCrory, Cathal; Fiorito, Giovanni; Hernandez, Belinda; Polidoro, Silvia; O'Halloran, Aisling M; Hever, Ann; Ni Cheallaigh, Cliona; Lu, Ake T; Horvath, Steve; Vineis, Paolo; Kenny, Rose Anne (2020-11-19). "GrimAge Outperforms Other Epigenetic Clocks in the Prediction of Age-Related Clinical Phenotypes and All-Cause Mortality". The Journals of Gerontology: Series A. 76 (5): 741–749. doi:10.1093/gerona/glaa286. ISSN 1079-5006. PMC 8087266. PMID 33211845.
  30. ^ Grodstein, Francine; Lemos, Bernardo; Yu, Lei; Klein, Hans-Ulrich; Iatrou, Artemis; Buchman, Aron S.; Shireby, Gemma L.; Mill, Jonathan; Schneider, Julie A.; De Jager, Philip L.; Bennett, David A. (2021-09-01). "The association of epigenetic clocks in brain tissue with brain pathologies and common aging phenotypes". Neurobiology of Disease. 157: 105428. doi:10.1016/j.nbd.2021.105428. ISSN 0969-9961. PMC 8373772. PMID 34153464.
  31. ^ Voisin, Sarah; Harvey, Nicholas R.; Haupt, Larisa M.; Griffiths, Lyn R.; Ashton, Kevin J.; Coffey, Vernon G.; Doering, Thomas M.; Thompson, Jamie-Lee M.; Benedict, Christian; Cedernaes, Jonathan; Lindholm, Malene E.; Craig, Jeffrey M.; Rowlands, David S.; Sharples, Adam P.; Horvath, Steve (August 2020). "An epigenetic clock for human skeletal muscle". Journal of Cachexia, Sarcopenia and Muscle. 11 (4): 887–898. doi:10.1002/jcsm.12556. ISSN 2190-5991. PMC 7432573. PMID 32067420.
  32. ^ Bohlin, J.; Håberg, S. E.; Magnus, P.; Reese, S. E.; Gjessing, H. K.; Magnus, M. C.; Parr, C. L.; Page, C. M.; London, S. J.; Nystad, W. (2016-10-07). "Prediction of gestational age based on genome-wide differentially methylated regions". Genome Biology. 17 (1): 207. doi:10.1186/s13059-016-1063-4. ISSN 1474-760X. PMC 5054559. PMID 27717397.
  33. ^ Knight, Anna K.; Craig, Jeffrey M.; Theda, Christiane; Bækvad-Hansen, Marie; Bybjerg-Grauholm, Jonas; Hansen, Christine S.; Hollegaard, Mads V.; Hougaard, David M.; Mortensen, Preben B.; Weinsheimer, Shantel M.; Werge, Thomas M.; Brennan, Patricia A.; Cubells, Joseph F.; Newport, D. Jeffrey; Stowe, Zachary N. (2016-10-07). "An epigenetic clock for gestational age at birth based on blood methylation data". Genome Biology. 17 (1): 206. doi:10.1186/s13059-016-1068-z. ISSN 1474-760X. PMC 5054584. PMID 27717399.
  34. ^ Lee, Yunsung; Choufani, Sanaa; Weksberg, Rosanna; Wilson, Samantha L.; Yuan, Victor; Burt, Amber; Marsit, Carmen; Lu, Ake T.; Ritz, Beate; Bohlin, Jon; Gjessing, Håkon K.; Harris, Jennifer R.; Magnus, Per; Binder, Alexandra M.; Robinson, Wendy P. (2019-06-24). "Placental epigenetic clocks: estimating gestational age using placental DNA methylation levels". Aging. 11 (12): 4238–4253. doi:10.18632/aging.102049. ISSN 1945-4589. PMC 6628997. PMID 31235674.
  35. ^ Suvakov, Sonja; Ghamrawi, Ranine; Cubro, Hajrunisa; Tu, Haitao; White, Wendy M.; Tobah, Yvonne S. Butler; Milic, Natasa M.; Grande, Joseph P.; Cunningham, Julie M.; Chebib, Fouad T.; Prata, Larissa G.P. Langhi; Zhu, Yi; Tchkonia, Tamara; Kirkland, James L.; Nath, Karl A. (August 2021). "Epigenetic and senescence markers indicate an accelerated ageing-like state in women with preeclamptic pregnancies". eBioMedicine. 70: 103536. doi:10.1016/j.ebiom.2021.103536. ISSN 2352-3964. PMC 8365351. PMID 34391091.
  36. ^ Shrestha, Deepika; Workalemahu, Tsegaselassie; Tekola-Ayele, Fasil (2019-10-03). "Maternal dyslipidemia during early pregnancy and epigenetic ageing of the placenta". Epigenetics. 14 (10): 1030–1039. doi:10.1080/15592294.2019.1629234. ISSN 1559-2294. PMC 6691987. PMID 31179827.
  37. ^ Hannum, Gregory; Guinney, Justin; Zhao, Ling; Zhang, Li; Hughes, Guy; Sadda, SriniVas; Klotzle, Brandy; Bibikova, Marina; Fan, Jian-Bing; Gao, Yuan; Deconde, Rob; Chen, Menzies; Rajapakse, Indika; Friend, Stephen; Ideker, Trey (January 2013). "Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates". Molecular Cell. 49 (2): 359–367. doi:10.1016/j.molcel.2012.10.016. ISSN 1097-2765. PMC 3780611. PMID 23177740.
  38. ^ Aanes, Håvard; Bleka, Øyvind; Dahlberg, Pål Skage; Carm, Kristina Totland; Lehtimäki, Terho; Raitakari, Olli; Kähönen, Mika; Hurme, Mikko; Rolseth, Veslemøy (2023-02-09). "A new blood based epigenetic age predictor for adolescents and young adults". Scientific Reports. 13 (1): 2303. Bibcode:2023NatSR..13.2303A. doi:10.1038/s41598-023-29381-7. ISSN 2045-2322. PMC 9911637. PMID 36759656.
  39. ^ a b Montesanto, Alberto; D'Aquila, Patrizia; Lagani, Vincenzo; Paparazzo, Ersilia; Geracitano, Silvana; Formentini, Laura; Giacconi, Robertina; Cardelli, Maurizio; Provinciali, Mauro; Bellizzi, Dina; Passarino, Giuseppe (September 2020). "A New Robust Epigenetic Model for Forensic Age Prediction". Journal of Forensic Sciences. 65 (5): 1424–1431. doi:10.1111/1556-4029.14460. ISSN 0022-1198. PMID 32453457. S2CID 218894971.
  40. ^ a b c Vidaki, Athina; Kayser, Manfred (2017-12-21). "From forensic epigenetics to forensic epigenomics: broadening DNA investigative intelligence". Genome Biology. 18 (1): 238. doi:10.1186/s13059-017-1373-1. ISSN 1474-760X. PMC 5738715. PMID 29268765.
  41. ^ Freire-Aradas, A.; Girón-Santamaría, L.; Mosquera-Miguel, A.; Ambroa-Conde, A.; Phillips, C.; Casares de Cal, M.; Gómez-Tato, A.; Álvarez-Dios, J.; Pospiech, E.; Aliferi, A.; Syndercombe Court, D.; Branicki, W.; Lareu, M.V. (September 2022). "A common epigenetic clock from childhood to old age". Forensic Science International: Genetics. 60: 102743. doi:10.1016/j.fsigen.2022.102743. hdl:10347/29147. ISSN 1872-4973. PMID 35777225.
  42. ^ Pan, Yunbao; Liu, Guohong; Zhou, Fuling; Su, Bojin; Li, Yirong (2018-02-01). "DNA methylation profiles in cancer diagnosis and therapeutics". Clinical and Experimental Medicine. 18 (1): 1–14. doi:10.1007/s10238-017-0467-0. ISSN 1591-9528. PMID 28752221. S2CID 3232298.
  43. ^ Zheng, Chunlei; Xu, Rong (2020-05-08). "Predicting cancer origins with a DNA methylation-based deep neural network model". PLOS ONE. 15 (5): e0226461. Bibcode:2020PLoSO..1526461Z. doi:10.1371/journal.pone.0226461. ISSN 1932-6203. PMC 7209244. PMID 32384093.
  44. ^ Kang, Shuli; Li, Qingjiao; Chen, Quan; Zhou, Yonggang; Park, Stacy; Lee, Gina; Grimes, Brandon; Krysan, Kostyantyn; Yu, Min; Wang, Wei; Alber, Frank; Sun, Fengzhu; Dubinett, Steven M.; Li, Wenyuan; Zhou, Xianghong Jasmine (2017-03-24). "CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA". Genome Biology. 18 (1): 53. doi:10.1186/s13059-017-1191-5. ISSN 1474-760X. PMC 5364586. PMID 28335812.
  45. ^ Park, Chihyun; Ha, Jihwan; Park, Sanghyun (2020-02-01). "Prediction of Alzheimer's disease based on deep neural network by integrating gene expression and DNA methylation dataset". Expert Systems with Applications. 140: 112873. doi:10.1016/j.eswa.2019.112873. ISSN 0957-4174. S2CID 201888983.
  46. ^ Westerman, Kenneth; Fernández-Sanlés, Alba; Patil, Prasad; Sebastiani, Paola; Jacques, Paul; Starr, John M.; J. Deary, Ian; Liu, Qing; Liu, Simin; Elosua, Roberto; DeMeo, Dawn L.; Ordovás, José M. (2020-04-21). "Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics". Journal of the American Heart Association. 9 (8): e015299. doi:10.1161/JAHA.119.015299. ISSN 2047-9980. PMC 7428544. PMID 32308120.
  47. ^ Zhu, Tianyu; Liu, Jacklyn; Beck, Stephan; Pan, Sun; Capper, David; Lechner, Matt; Thirlwell, Chrissie; Breeze, Charles E.; Teschendorff, Andrew E. (March 2022). "A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution". Nature Methods. 19 (3): 296–306. doi:10.1038/s41592-022-01412-7. ISSN 1548-7105. PMC 8916958. PMID 35277705.
  48. ^ Houseman, Eugene Andres; Accomando, William P.; Koestler, Devin C.; Christensen, Brock C.; Marsit, Carmen J.; Nelson, Heather H.; Wiencke, John K.; Kelsey, Karl T. (2012-05-08). "DNA methylation arrays as surrogate measures of cell mixture distribution". BMC Bioinformatics. 13 (1): 86. doi:10.1186/1471-2105-13-86. ISSN 1471-2105. PMC 3532182. PMID 22568884.
  49. ^ Yuan, Victor; Price, E. Magda; Del Gobbo, Giulia; Mostafavi, Sara; Cox, Brian; Binder, Alexandra M.; Michels, Karin B.; Marsit, Carmen; Robinson, Wendy P. (2019-08-09). "Accurate ethnicity prediction from placental DNA methylation data". Epigenetics & Chromatin. 12 (1): 51. doi:10.1186/s13072-019-0296-3. ISSN 1756-8935. PMC 6688210. PMID 31399127.
  50. ^ Zhang, Fang Fang; Cardarelli, Roberto; Carroll, Joan; Fulda, Kimberly G.; Kaur, Manleen; Gonzalez, Karina; Vishwanatha, Jamboor K.; Santella, Regina M.; Morabia, Alfredo (May 2011). "Significant differences in global genomic DNA methylation by gender and race/ethnicity in peripheral blood". Epigenetics. 6 (5): 623–629. doi:10.4161/epi.6.5.15335. ISSN 1559-2294. PMC 3230547. PMID 21739720.
  51. ^ Wang, Yucheng; Hannon, Eilis; Grant, Olivia A.; Gorrie-Stone, Tyler J.; Kumari, Meena; Mill, Jonathan; Zhai, Xiaojun; McDonald-Maier, Klaus D.; Schalkwyk, Leonard C. (2021-06-28). "DNA methylation-based sex classifier to predict sex and identify sex chromosome aneuploidy". BMC Genomics. 22 (1): 484. doi:10.1186/s12864-021-07675-2. ISSN 1471-2164. PMC 8240370. PMID 34182928.
  52. ^ Thong, Zhonghui; Tan, Jolena Ying Ying; Loo, Eileen Shuzhen; Phua, Yu Wei; Chan, Xavier Liang Shun; Syn, Christopher Kiu-Choong (2021-01-18). "Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples". Scientific Reports. 11 (1): 1744. Bibcode:2021NatSR..11.1744T. doi:10.1038/s41598-021-81556-2. ISSN 2045-2322. PMC 7814006. PMID 33462351.
  53. ^ a b Hüls, Anke; Czamara, Darina (2020-02-01). "Methodological challenges in constructing DNA methylation risk scores". Epigenetics. 15 (1–2): 1–11. doi:10.1080/15592294.2019.1644879. ISSN 1559-2294. PMC 6961658. PMID 31318318.
  54. ^ Elliott, Hannah R.; Tillin, Therese; McArdle, Wendy L.; Ho, Karen; Duggirala, Aparna; Frayling, Tim M.; Davey Smith, George; Hughes, Alun D.; Chaturvedi, Nish; Relton, Caroline L. (2014-02-03). "Differences in smoking associated DNA methylation patterns in South Asians and Europeans". Clinical Epigenetics. 6 (1): 4. doi:10.1186/1868-7083-6-4. ISSN 1868-7083. PMC 3915234. PMID 24485148.
  55. ^ Hannon, Eilis; Dempster, Emma; Viana, Joana; Burrage, Joe; Smith, Adam R.; Macdonald, Ruby; St Clair, David; Mustard, Colette; Breen, Gerome; Therman, Sebastian; Kaprio, Jaakko; Toulopoulou, Timothea; Pol, Hilleke E. Hulshoff; Bohlken, Marc M.; Kahn, Rene S. (2016-08-30). "An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation". Genome Biology. 17 (1): 176. doi:10.1186/s13059-016-1041-x. ISSN 1474-760X. PMC 5004279. PMID 27572077.
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