Biology:Epiphenotyping

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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

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

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

Data collection

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

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

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

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

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

Epigenetic clocks

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

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

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

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

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

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

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)

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

  1. ↑ Gibney, E. R.; Nolan, C. M. (July 2010). "Epigenetics and gene expression" (in en). Heredity 105 (1): 4–13. doi:10.1038/hdy.2010.54. ISSN 1365-2540. PMID 20461105. Bibcode: 2010Hered.105....4G. https://www.nature.com/articles/hdy201054. 
  2. ↑ Moore, Lisa D.; Le, Thuc; Fan, Guoping (January 2013). "DNA Methylation and Its Basic Function" (in en). Neuropsychopharmacology 38 (1): 23–38. doi:10.1038/npp.2012.112. ISSN 1740-634X. PMID 22781841. 
  3. ↑ 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 Khan, A.; Inkster, A. M.; Peñaherrera, M. S.; King, S.; Kildea, S.; Oberlander, T. F.; Olson, D. M.; Vaillancourt, C. et al. (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. PMID 37794499. 
  4. ↑ 4.0 4.1 4.2 4.3 4.4 Horvath, Steve (2013-12-10). "DNA methylation age of human tissues and cell types". Genome Biology 14 (10). doi:10.1186/gb-2013-14-10-r115. ISSN 1474-760X. PMID 24138928.  (Erratum: doi:10.1186/s13059-015-0649-6, PMID 25968125,  Retraction Watch)
  5. ↑ 5.0 5.1 5.2 5.3 Lu, Ake T.; Quach, Austin; Wilson, James G.; Reiner, Alex P.; Aviv, Abraham; Raj, Kenneth; Hou, Lifang; Baccarelli, Andrea A. et al. (2019-01-21). "DNA methylation GrimAge strongly predicts lifespan and healthspan" (in en). Aging 11 (2): 303–327. doi:10.18632/aging.101684. ISSN 1945-4589. 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. 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. 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.). 1238. pp. 51–63. doi:10.1007/978-1-4939-1804-1_3. ISBN 978-1-4939-1803-4. https://pubmed.ncbi.nlm.nih.gov/25421654/. 
  9. ↑ 9.0 9.1 9.2 Bocklandt, Sven; Lin, Wen; Sehl, Mary E.; Sánchez, Francisco J.; Sinsheimer, Janet S.; Horvath, Steve; Vilain, Eric (2011-06-22). "Epigenetic Predictor of Age" (in en). PLOS ONE 6 (6). doi:10.1371/journal.pone.0014821. ISSN 1932-6203. PMID 21731603. Bibcode: 2011PLoSO...614821B. 
  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" (in en). Bioinformatics 30 (10): 1363–1369. doi:10.1093/bioinformatics/btu049. ISSN 1367-4811. PMID 24478339. PMC 4016708. https://academic.oup.com/bioinformatics/article/30/10/1363/267584. 
  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. 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. 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" (in en). Nucleic Acids Research 41 (7). doi:10.1093/nar/gkt090. ISSN 1362-4962. PMID 23476028. PMC 3627582. https://academic.oup.com/nar/article/41/7/e90/1070878. 
  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. 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. 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. 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. 
  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. doi:10.1155/2022/2679050. ISSN 1748-6718. 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" (in en). Epigenetics 15 (11): 1163–1166. doi:10.1080/15592294.2020.1758382. ISSN 1559-2294. PMID 32425094. 
  20. ↑ 20.0 20.1 20.2 Bergsma, Tessa; Rogaeva, Ekaterina (January 2020). "DNA Methylation Clocks and Their Predictive Capacity for Aging Phenotypes and Healthspan" (in en). Neuroscience Insights 15: 263310552094222. doi:10.1177/2633105520942221. ISSN 2633-1055. PMID 32743556. 
  21. ↑ 21.0 21.1 21.2 21.3 21.4 21.5 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" (in en). Nature Reviews Genetics 23 (6): 369–383. doi:10.1038/s41576-022-00465-w. ISSN 1471-0064. PMID 35304597. https://www.nature.com/articles/s41576-022-00465-w. 
  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" (in en). Rejuvenation Research 15 (5): 483–494. doi:10.1089/rej.2012.1324. ISSN 1549-1684. 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. PMID 37046280. 
  24. ↑ Horvath, Steve; Erhart, Wiebke; Brosch, Mario; Ammerpohl, Ole; von Schönfels, Witigo; Ahrens, Markus; Heits, Nils; Bell, Jordana T. et al. (2014-10-28). "Obesity accelerates epigenetic aging of human liver" (in en). Proceedings of the National Academy of Sciences 111 (43): 15538–15543. doi:10.1073/pnas.1412759111. ISSN 0027-8424. PMID 25313081. Bibcode: 2014PNAS..11115538H. 
  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" (in en). Aging 7 (9): 690–700. doi:10.18632/aging.100809. ISSN 1945-4589. 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. et al. (2015-01-01). "Longitudinal changes of telomere length and epigenetic age related to traumatic stress and post-traumatic stress disorder". 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. http://repository.essex.ac.uk/10985/1/Boks_et_al_PNEC.pdf. 
  27. ↑ Bell, Christopher G.; Lowe, Robert; Adams, Peter D.; Baccarelli, Andrea A.; Beck, Stephan; Bell, Jordana T.; Christensen, Brock C.; Gladyshev, Vadim N. et al. (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. PMID 31767039. 
  28. ↑ 28.0 28.1 28.2 Levine, Morgan E.; Lu, Ake T.; Quach, Austin; Chen, Brian H.; Assimes, Themistocles L.; Bandinelli, Stefania; Hou, Lifang; Baccarelli, Andrea A. et al. (2018-04-18). "An epigenetic biomarker of aging for lifespan and healthspan" (in en). Aging 10 (4): 573–591. doi:10.18632/aging.101414. ISSN 1945-4589. PMID 29676998. 
  29. ↑ 29.0 29.1 McCrory, Cathal; Fiorito, Giovanni; Hernandez, Belinda; Polidoro, Silvia; O'Halloran, Aisling M; Hever, Ann; Ni Cheallaigh, Cliona; Lu, Ake T et al. (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. PMID 33211845. 
  30. ↑ Grodstein, Francine; Lemos, Bernardo; Yu, Lei; Klein, Hans-Ulrich; Iatrou, Artemis; Buchman, Aron S.; Shireby, Gemma L.; Mill, Jonathan et al. (2021-09-01). "The association of epigenetic clocks in brain tissue with brain pathologies and common aging phenotypes". Neurobiology of Disease 157. doi:10.1016/j.nbd.2021.105428. ISSN 0969-9961. 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. et al. (August 2020). "An epigenetic clock for human skeletal muscle" (in en). Journal of Cachexia, Sarcopenia and Muscle 11 (4): 887–898. doi:10.1002/jcsm.12556. ISSN 2190-5991. 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. et al. (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. 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. et al. (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. PMID 27717399. 
  34. ↑ Lee, Yunsung; Choufani, Sanaa; Weksberg, Rosanna; Wilson, Samantha L.; Yuan, Victor; Burt, Amber; Marsit, Carmen; Lu, Ake T. et al. (2019-06-24). "Placental epigenetic clocks: estimating gestational age using placental DNA methylation levels" (in en). Aging 11 (12): 4238–4253. doi:10.18632/aging.102049. ISSN 1945-4589. PMID 31235674. 
  35. ↑ Suvakov, Sonja; Ghamrawi, Ranine; Cubro, Hajrunisa; Tu, Haitao; White, Wendy M.; Tobah, Yvonne S. Butler; Milic, Natasa M.; Grande, Joseph P. et al. (August 2021). "Epigenetic and senescence markers indicate an accelerated ageing-like state in women with preeclamptic pregnancies". eBioMedicine 70. doi:10.1016/j.ebiom.2021.103536. ISSN 2352-3964. PMID 34391091. 
  36. ↑ Shrestha, Deepika; Workalemahu, Tsegaselassie; Tekola-Ayele, Fasil (2019-10-03). "Maternal dyslipidemia during early pregnancy and epigenetic ageing of the placenta" (in en). Epigenetics 14 (10): 1030–1039. doi:10.1080/15592294.2019.1629234. ISSN 1559-2294. PMID 31179827. 
  37. ↑ Hannum, Gregory; Guinney, Justin; Zhao, Ling; Zhang, Li; Hughes, Guy; Sadda, SriniVas; Klotzle, Brandy; Bibikova, Marina et al. (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. PMID 23177740. 
  38. ↑ Aanes, Håvard; Bleka, Øyvind; Dahlberg, Pål Skage; Carm, Kristina Totland; Lehtimäki, Terho; Raitakari, Olli; Kähönen, Mika; Hurme, Mikko et al. (2023-02-09). "A new blood based epigenetic age predictor for adolescents and young adults" (in en). Scientific Reports 13 (1): 2303. doi:10.1038/s41598-023-29381-7. ISSN 2045-2322. PMID 36759656. Bibcode: 2023NatSR..13.2303A. 
  39. ↑ 39.0 39.1 Montesanto, Alberto; D'Aquila, Patrizia; Lagani, Vincenzo; Paparazzo, Ersilia; Geracitano, Silvana; Formentini, Laura; Giacconi, Robertina; Cardelli, Maurizio et al. (September 2020). "A New Robust Epigenetic Model for Forensic Age Prediction" (in en). Journal of Forensic Sciences 65 (5): 1424–1431. doi:10.1111/1556-4029.14460. ISSN 0022-1198. PMID 32453457. 
  40. ↑ 40.0 40.1 40.2 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. 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. et al. (September 2022). "A common epigenetic clock from childhood to old age". Forensic Science International: Genetics 60. doi:10.1016/j.fsigen.2022.102743. 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" (in en). Clinical and Experimental Medicine 18 (1): 1–14. doi:10.1007/s10238-017-0467-0. ISSN 1591-9528. PMID 28752221. 
  43. ↑ Zheng, Chunlei; Xu, Rong (2020-05-08). "Predicting cancer origins with a DNA methylation-based deep neural network model" (in en). PLOS ONE 15 (5). doi:10.1371/journal.pone.0226461. ISSN 1932-6203. PMID 32384093. Bibcode: 2020PLoSO..1526461Z. 
  44. ↑ Kang, Shuli; Li, Qingjiao; Chen, Quan; Zhou, Yonggang; Park, Stacy; Lee, Gina; Grimes, Brandon; Krysan, Kostyantyn et al. (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. 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. doi:10.1016/j.eswa.2019.112873. ISSN 0957-4174. 
  46. ↑ Westerman, Kenneth; Fernández-Sanlés, Alba; Patil, Prasad; Sebastiani, Paola; Jacques, Paul; Starr, John M.; J. Deary, Ian; Liu, Qing et al. (2020-04-21). "Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics" (in en). Journal of the American Heart Association 9 (8). doi:10.1161/JAHA.119.015299. ISSN 2047-9980. PMID 32308120. 
  47. ↑ Zhu, Tianyu; Liu, Jacklyn; Beck, Stephan; Pan, Sun; Capper, David; Lechner, Matt; Thirlwell, Chrissie; Breeze, Charles E. et al. (March 2022). "A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution" (in en). Nature Methods 19 (3): 296–306. doi:10.1038/s41592-022-01412-7. ISSN 1548-7105. 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. PMID 22568884. 
  49. ↑ Yuan, Victor; Price, E. Magda; Del Gobbo, Giulia; Mostafavi, Sara; Cox, Brian; Binder, Alexandra M.; Michels, Karin B.; Marsit, Carmen et al. (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. PMID 31399127. 
  50. ↑ Zhang, Fang Fang; Cardarelli, Roberto; Carroll, Joan; Fulda, Kimberly G.; Kaur, Manleen; Gonzalez, Karina; Vishwanatha, Jamboor K.; Santella, Regina M. et al. (May 2011). "Significant differences in global genomic DNA methylation by gender and race/ethnicity in peripheral blood" (in en). Epigenetics 6 (5): 623–629. doi:10.4161/epi.6.5.15335. ISSN 1559-2294. PMID 21739720. 
  51. ↑ Wang, Yucheng; Hannon, Eilis; Grant, Olivia A.; Gorrie-Stone, Tyler J.; Kumari, Meena; Mill, Jonathan; Zhai, Xiaojun; McDonald-Maier, Klaus D. et al. (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. 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" (in en). Scientific Reports 11 (1): 1744. doi:10.1038/s41598-021-81556-2. ISSN 2045-2322. PMID 33462351. Bibcode: 2021NatSR..11.1744T. 
  53. ↑ 53.0 53.1 Hüls, Anke; Czamara, Darina (2020-02-01). "Methodological challenges in constructing DNA methylation risk scores" (in en). Epigenetics 15 (1–2): 1–11. doi:10.1080/15592294.2019.1644879. ISSN 1559-2294. PMID 31318318. 
  54. ↑ Elliott, Hannah R.; Tillin, Therese; McArdle, Wendy L.; Ho, Karen; Duggirala, Aparna; Frayling, Tim M.; Davey Smith, George; Hughes, Alun D. et al. (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. PMID 24485148. 
  55. ↑ Hannon, Eilis; Dempster, Emma; Viana, Joana; Burrage, Joe; Smith, Adam R.; Macdonald, Ruby; St Clair, David; Mustard, Colette et al. (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. PMID 27572077. 



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