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Current Genomics

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ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

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Research Article

Integrative Approaches of DNA Methylation Patterns According to Age, Sex and Longitudinal Changes

Author(s): Jeong-An Gim*

Volume 23, Issue 6, 2022

Published on: 19 December, 2022

Page: [385 - 399] Pages: 15

DOI: 10.2174/1389202924666221207100513

Price: $65

Abstract

Background: In humans, age-related DNA methylation has been studied in blood, tissues, buccal swabs, and fibroblasts, and changes in DNA methylation patterns according to age and sex have been detected. To date, approximately 137,000 samples have been analyzed from 14,000 studies, and the information has been uploaded to the NCBI GEO database.

Methods: A correlation between age and methylation level and longitudinal changes in methylation levels was revealed in both sexes. Here, 20 public datasets derived from whole blood were analyzed using the Illumina BeadChip. Batch effects with respect to the time differences were correlated. The overall change in the pattern was provided as the inverse of the coefficient of variation (COV).

Results: Of the 20 datasets, nine were from a longitudinal study. All data had age and sex as common variables. Comprehensive details of age-, sex-, and longitudinal change-based DNA methylation levels in the whole blood sample were elucidated in this study. ELOVL2 and FHL2 showed the maximum correlation between age and DNA methylation. The methylation patterns of genes related to mental health differed according to age. Age-correlated genes have been associated with malformations (anteverted nostril, craniofacial abnormalities, and depressed nasal bridge) and drug addiction (drug habituation and smoking).

Conclusion: Based on 20 public DNA methylation datasets, methylation levels according to age and longitudinal changes by sex were identified and visualized using an integrated approach. The results highlight the molecular mechanisms underlying the association of sex and biological age with changes in DNA methylation, and the importance of optimal genomic information management.

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[1]
Alisch, R.S.; Barwick, B.G.; Chopra, P.; Myrick, L.K.; Satten, G.A.; Conneely, K.N.; Warren, S.T. Age-associated DNA methylation in pediatric populations. Genome Res., 2012, 22(4), 623-632.
[http://dx.doi.org/10.1101/gr.125187.111] [PMID: 22300631]
[2]
Bergman, Y.; Cedar, H. DNA methylation dynamics in health and disease. Nat. Struct. Mol. Biol., 2013, 20(3), 274-281.
[http://dx.doi.org/10.1038/nsmb.2518] [PMID: 23463312]
[3]
Kołodziej-Wojnar, P.; Borkowska, J.; Wicik, Z.; Domaszewska-Szostek, A.; Połosak, J.; Cąkała-Jakimowicz, M.; Bujanowska, O.; Puzianowska-Kuznicka, M. Alterations in the Genomic Distribution of 5hmC in in vivo Aged Human Skin Fibroblasts. Int. J. Mol. Sci., 2020, 22(1), 78.
[http://dx.doi.org/10.3390/ijms22010078] [PMID: 33374812]
[4]
Lister, R.; Mukamel, E.A.; Nery, J.R.; Urich, M.; Puddifoot, C.A.; Johnson, N.D.; Lucero, J.; Huang, Y.; Dwork, A.J.; Schultz, M.D.; Yu, M.; Tonti-Filippini, J.; Heyn, H.; Hu, S.; Wu, J.C.; Rao, A.; Esteller, M.; He, C.; Haghighi, F.G.; Sejnowski, T.J.; Behrens, M.M.; Ecker, J.R. Global epigenomic reconfiguration during mammalian brain development. Science, 2013, 341(6146), 1237905.
[http://dx.doi.org/10.1126/science.1237905] [PMID: 23828890]
[5]
Pagiatakis, C.; Musolino, E.; Gornati, R.; Bernardini, G.; Papait, R. Epigenetics of aging and disease: a brief overview. Aging Clin. Exp. Res., 2021, 33(4), 737-745.
[http://dx.doi.org/10.1007/s40520-019-01430-0] [PMID: 31811572]
[6]
Saul, D.; Kosinsky, R.L. Epigenetics of aging and aging-associated diseases. Int. J. Mol. Sci., 2021, 22(1), 401.
[http://dx.doi.org/10.3390/ijms22010401] [PMID: 33401659]
[7]
Teschendorff, A.E.; West, J.; Beck, S. Age-associated epigenetic drift: implications, and a case of epigenetic thrift? Hum. Mol. Genet., 2013, 22(R1), R7-R15.
[http://dx.doi.org/10.1093/hmg/ddt375] [PMID: 23918660]
[8]
Breiling, A.; Lyko, F. Epigenetic regulatory functions of DNA modifications: 5-methylcytosine and beyond. Epigenetics Chromatin, 2015, 8(1), 24.
[http://dx.doi.org/10.1186/s13072-015-0016-6] [PMID: 26195987]
[9]
Hüls, A.; Czamara, D. Methodological challenges in constructing DNA methylation risk scores. Epigenetics, 2020, 15(1-2), 1-11.
[http://dx.doi.org/10.1080/15592294.2019.1644879] [PMID: 31318318]
[10]
Pal, S.; Tyler, J.K. Epigenetics and aging. Sci. Adv., 2016, 2(7), e1600584.
[http://dx.doi.org/10.1126/sciadv.1600584] [PMID: 27482540]
[11]
Rauschert, S.; Melton, P.E.; Heiskala, A.; Karhunen, V.; Burdge, G.; Craig, J.M.; Godfrey, K.M.; Lillycrop, K.; Mori, T.A.; Beilin, L.J.; Oddy, W.H.; Pennell, C.; Järvelin, M.R.; Sebert, S.; Huang, R.C. Machine learning-based DNA methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults. Environ. Health Perspect., 2020, 128(9), 097003.
[http://dx.doi.org/10.1289/EHP6076] [PMID: 32930613]
[12]
Zhu, T.; Gao, Y.; Wang, J.; Li, X.; Shang, S.; Wang, Y.; Guo, S.; Zhou, H.; Liu, H.; Sun, D.; Chen, H.; Wang, L.; Ning, S. CancerClock: A DNA methylation age predictor to identify and characterize aging clock in pan-cancer. Front. Bioeng. Biotechnol., 2019, 7, 388.
[http://dx.doi.org/10.3389/fbioe.2019.00388] [PMID: 31867319]
[13]
Bhak, Y.; Jeong, H.; Cho, Y.S.; Jeon, S.; Cho, J.; Gim, J.A.; Jeon, Y.; Blazyte, A.; Park, S.G.; Kim, H.M.; Shin, E.S.; Paik, J.W.; Lee, H.W.; Kang, W.; Kim, A.; Kim, Y.; Kim, B.C.; Ham, B.J.; Bhak, J.; Lee, S. Depression and suicide risk prediction models using blood-derived multi-omics data. Transl. Psychiatry, 2019, 9(1), 262.
[http://dx.doi.org/10.1038/s41398-019-0595-2] [PMID: 31624227]
[14]
Jeremian, R.; Chen, Y.; De Luca, V.; Vincent, J.B.; Kennedy, J.L.; Zai, C.C.; Strauss, J. Investigation of correlations between DNA methylation, suicidal behavior and aging. Bipolar Disord., 2017, 19(1), 32-40.
[http://dx.doi.org/10.1111/bdi.12466] [PMID: 28276657]
[15]
McCartney, D.L.; Stevenson, A.J.; Walker, R.M.; Gibson, J.; Morris, S.W.; Campbell, A.; Murray, A.D.; Whalley, H.C.; Porteous, D.J.; McIntosh, A.M.; Evans, K.L.; Deary, I.J.; Marioni, R.E. Investigating the relationship between DNA methylation age acceleration and risk factors for Alzheimer’s disease. Alzheimers Dement. (Amst.), 2018, 10(1), 429-437.
[http://dx.doi.org/10.1016/j.dadm.2018.05.006] [PMID: 30167451]
[16]
Mehta, D.; Bruenig, D.; Lawford, B.; Harvey, W.; Carrillo-Roa, T.; Morris, C.P.; Jovanovic, T.; Young, R.M.; Binder, E.B.; Voisey, J. Accelerated DNA methylation aging and increased resilience in veterans: The biological cost for soldiering on. Neurobiol. Stress, 2018, 8, 112-119.
[http://dx.doi.org/10.1016/j.ynstr.2018.04.001] [PMID: 29888306]
[17]
Wolf, E.J.; Logue, M.W.; Hayes, J.P.; Sadeh, N.; Schichman, S.A.; Stone, A.; Salat, D.H.; Milberg, W.; McGlinchey, R.; Miller, M.W. Accelerated DNA methylation age: Associations with PTSD and neural integrity. Psychoneuroendocrinology, 2016, 63, 155-162.
[http://dx.doi.org/10.1016/j.psyneuen.2015.09.020] [PMID: 26447678]
[18]
Zhao, W.; Ammous, F.; Ratliff, S.; Liu, J.; Yu, M.; Mosley, T.H.; Kardia, S.L.R.; Smith, J.A. Education and lifestyle factors are associated with DNA methylation clocks in older African Americans. Int. J. Environ. Res. Public Health, 2019, 16(17), 3141.
[http://dx.doi.org/10.3390/ijerph16173141] [PMID: 31466396]
[19]
Hughes, A.; Smart, M.; Gorrie-Stone, T.; Hannon, E.; Mill, J.; Bao, Y.; Burrage, J.; Schalkwyk, L.; Kumari, M. Socioeconomic position and DNA methylation age acceleration across the life course. Am. J. Epidemiol., 2018, 187(11), 2346-2354.
[http://dx.doi.org/10.1093/aje/kwy155] [PMID: 30060108]
[20]
El-Maarri, O.; Becker, T.; Junen, J.; Manzoor, S.S.; Diaz-Lacava, A.; Schwaab, R.; Wienker, T.; Oldenburg, J. Gender specific differences in levels of DNA methylation at selected loci from human total blood: a tendency toward higher methylation levels in males. Hum. Genet., 2007, 122(5), 505-514.
[http://dx.doi.org/10.1007/s00439-007-0430-3] [PMID: 17851693]
[21]
Zhang, F.F.; Cardarelli, R.; Carroll, J.; Fulda, K.G.; Kaur, M.; Gonzalez, K.; Vishwanatha, J.K.; Santella, R.M.; Morabia, A. Significant differences in global genomic DNA methylation by gender and race/ethnicity in peripheral blood. Epigenetics, 2011, 6(5), 623-629.
[http://dx.doi.org/10.4161/epi.6.5.15335] [PMID: 21739720]
[22]
Boks, M.P.; Derks, E.M.; Weisenberger, D.J.; Strengman, E.; Janson, E.; Sommer, I.E.; Kahn, R.S.; Ophoff, R.A. The relationship of DNA methylation with age, gender and genotype in twins and healthy controls. PLoS One, 2009, 4(8), e6767.
[http://dx.doi.org/10.1371/journal.pone.0006767] [PMID: 19774229]
[23]
Johnson, R.K.; Vanderlinden, L.A.; Dong, F.; Carry, P.M.; Seifert, J.; Waugh, K.; Shorrosh, H.; Fingerlin, T.; Frohnert, B.I.; Yang, I.V.; Kechris, K.; Rewers, M.; Norris, J.M. Longitudinal DNA methylation differences precede type 1 diabetes. Sci. Rep., 2020, 10(1), 3721.
[http://dx.doi.org/10.1038/s41598-020-60758-0] [PMID: 32111940]
[24]
Svane, A.; Soerensen, M.; Lund, J.; Tan, Q.; Jylhävä, J.; Wang, Y.; Pedersen, N.; Hägg, S.; Debrabant, B.; Deary, I.; Christensen, K.; Christiansen, L.; Hjelmborg, J. DNA methylation and all-cause mortality in middle-aged and elderly Danish twins. Genes (Basel), 2018, 9(2), 78.
[http://dx.doi.org/10.3390/genes9020078] [PMID: 29419728]
[25]
Coit, P.; Ortiz-Fernandez, L.; Lewis, E.E.; McCune, W.J.; Maksimowicz-McKinnon, K.; Sawalha, A.H. A longitudinal and transancestral analysis of DNA methylation patterns and disease activity in lupus patients. JCI Insight, 2020, 5(22), e143654.
[http://dx.doi.org/10.1172/jci.insight.143654] [PMID: 33108347]
[26]
Grant, C.D.; Jafari, N.; Hou, L.; Li, Y.; Stewart, J.D.; Zhang, G.; Lamichhane, A.; Manson, J.E.; Baccarelli, A.A.; Whitsel, E.A.; Conneely, K.N. A longitudinal study of DNA methylation as a potential mediator of age-related diabetes risk. Geroscience, 2017, 39(5-6), 475-489.
[http://dx.doi.org/10.1007/s11357-017-0001-z] [PMID: 29159506]
[27]
Tharakan, R.; Ubaida-Mohien, C.; Moore, A.Z.; Hernandez, D.; Tanaka, T.; Ferrucci, L. Blood DNA methylation and aging: A cross-sectional analysis and longitudinal validation in the InCHIANTI study. J. Gerontol. A Biol. Sci. Med. Sci., 2020, 75(11), 2051-2055.
[http://dx.doi.org/10.1093/gerona/glaa052] [PMID: 32147700]
[28]
Wang, D.; Liu, X.; Zhou, Y.; Xie, H.; Hong, X.; Tsai, H.J.; Wang, G.; Liu, R.; Wang, X. Individual variation and longitudinal pattern of genome-wide DNA methylation from birth to the first two years of life. Epigenetics, 2012, 7(6), 594-605.
[http://dx.doi.org/10.4161/epi.20117] [PMID: 22522910]
[29]
Wang, Y.; Pedersen, N.L.; Hägg, S. Implementing a method for studying longitudinal DNA methylation variability in association with age. Epigenetics, 2018, 13(8), 866-874.
[http://dx.doi.org/10.1080/15592294.2018.1521222] [PMID: 30251590]
[30]
Kim, Y.; Han, B.G.; Group, K. Cohort profile: the Korean genome and epidemiology study (KoGES) consortium. Int. J. Epidemiol., 2017, 46(2), e20-e20.
[http://dx.doi.org/10.1093/ije/dyv316] [PMID: 27085081]
[31]
Barrett, T.; Wilhite, S.E.; Ledoux, P.; Evangelista, C.; Kim, I.F.; Tomashevsky, M.; Marshall, K.A.; Phillippy, K.H.; Sherman, P.M.; Holko, M.; Yefanov, A.; Lee, H.; Zhang, N.; Robertson, C.L.; Serova, N.; Davis, S.; Soboleva, A. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res., 2013, 41, D991-D995.
[PMID: 23193258]
[32]
Leek, J.T.; Johnson, W.E.; Parker, H.S.; Jaffe, A.E.; Storey, J.D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics, 2012, 28(6), 882-883.
[http://dx.doi.org/10.1093/bioinformatics/bts034] [PMID: 22257669]
[33]
Sehl, M.E.; Carroll, J.E.; Horvath, S.; Bower, J.E. The acute effects of adjuvant radiation and chemotherapy on peripheral blood epigenetic age in early stage breast cancer patients. NPJ Breast Cancer, 2020, 6(1), 23.
[http://dx.doi.org/10.1038/s41523-020-0161-3] [PMID: 32566744]
[34]
Zapatka, M.; Tausch, E.; Öztürk, S.; Yosifov, D.Y.; Seiffert, M.; Zenz, T.; Schneider, C.; Blöhdorn, J.; Döhner, H.; Mertens, D. Clonal evolution in chronic lymphocytic leukemia is scant in relapsed but accelerated in refractory cases after chemo (immune) therapy. Haematologica, 2022, 107(3), 604-614.
[PMID: 33691380]
[35]
Curtis, S.W.; Cobb, D.O.; Kilaru, V.; Terrell, M.L.; Kennedy, E.M.; Marder, M.E.; Barr, D.B.; Marsit, C.J.; Marcus, M.; Conneely, K.N.; Smith, A.K. Exposure to polybrominated biphenyl (PBB) associates with genome-wide DNA methylation differences in peripheral blood. Epigenetics, 2019, 14(1), 52-66.
[http://dx.doi.org/10.1080/15592294.2019.1565590] [PMID: 30676242]
[36]
Kok, D.E.G.; Dhonukshe-Rutten, R.A.M.; Lute, C.; Heil, S.G.; Uitterlinden, A.G.; van der Velde, N.; van Meurs, J.B.J.; van Schoor, N.M.; Hooiveld, G.J.E.J.; de Groot, L.C.P.G.M.; Kampman, E.; Steegenga, W.T. The effects of long-term daily folic acid and vitamin B12 supplementation on genome-wide DNA methylation in elderly subjects. Clin. Epigenetics, 2015, 7(1), 121.
[http://dx.doi.org/10.1186/s13148-015-0154-5] [PMID: 25628764]
[37]
Flanagan, J.M.; Brook, M.N.; Orr, N.; Tomczyk, K.; Coulson, P.; Fletcher, O.; Jones, M.E.; Schoemaker, M.J.; Ashworth, A.; Swerdlow, A.; Brown, R.; Garcia-Closas, M. Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer Epidemiol. Biomarkers Prev., 2015, 24(1), 221-229.
[http://dx.doi.org/10.1158/1055-9965.EPI-14-0767] [PMID: 25371448]
[38]
Johansson, Å.; Enroth, S.; Gyllensten, U. Continuous aging of the human DNA methylome throughout the human lifespan. PLoS One, 2013, 8(6), e67378.
[http://dx.doi.org/10.1371/journal.pone.0067378] [PMID: 23826282]
[39]
Vanderlinden, L.A.; Johnson, R.K.; Carry, P.M.; Dong, F.; DeMeo, D.L.; Yang, I.V.; Norris, J.M.; Kechris, K. An effective processing pipeline for harmonizing DNA methylation data from Illumina’s 450K and EPIC platforms for epidemiological studies. BMC Res. Notes, 2021, 14(1), 352.
[http://dx.doi.org/10.1186/s13104-021-05741-2] [PMID: 34496950]
[40]
Lehne, B.; Drong, A.W.; Loh, M.; Zhang, W.; Scott, W.R.; Tan, S.T.; Afzal, U.; Scott, J.; Jarvelin, M.R.; Elliott, P.; McCarthy, M.I.; Kooner, J.S.; Chambers, J.C. A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies. Genome Biol., 2015, 16(1), 37.
[http://dx.doi.org/10.1186/s13059-015-0600-x] [PMID: 25853392]
[41]
Wahl, S.; Drong, A.; Lehne, B.; Loh, M.; Scott, W.R.; Kunze, S.; Tsai, P.C.; Ried, J.S.; Zhang, W.; Yang, Y.; Tan, S.; Fiorito, G.; Franke, L.; Guarrera, S.; Kasela, S.; Kriebel, J.; Richmond, R.C.; Adamo, M.; Afzal, U.; Ala-Korpela, M.; Albetti, B.; Ammerpohl, O.; Apperley, J.F.; Beekman, M.; Bertazzi, P.A.; Black, S.L.; Blancher, C.; Bonder, M.J.; Brosch, M.; Carstensen-Kirberg, M.; de Craen, A.J.M.; de Lusignan, S.; Dehghan, A.; Elkalaawy, M.; Fischer, K.; Franco, O.H.; Gaunt, T.R.; Hampe, J.; Hashemi, M.; Isaacs, A.; Jenkinson, A.; Jha, S.; Kato, N.; Krogh, V.; Laffan, M.; Meisinger, C.; Meitinger, T.; Mok, Z.Y.; Motta, V.; Ng, H.K.; Nikolakopoulou, Z.; Nteliopoulos, G.; Panico, S.; Pervjakova, N.; Prokisch, H.; Rathmann, W.; Roden, M.; Rota, F.; Rozario, M.A.; Sandling, J.K.; Schafmayer, C.; Schramm, K.; Siebert, R.; Slagboom, P.E.; Soininen, P.; Stolk, L.; Strauch, K.; Tai, E.S.; Tarantini, L.; Thorand, B.; Tigchelaar, E.F.; Tumino, R.; Uitterlinden, A.G.; van Duijn, C.; van Meurs, J.B.J.; Vineis, P.; Wickremasinghe, A.R.; Wijmenga, C.; Yang, T.P.; Yuan, W.; Zhernakova, A.; Batterham, R.L.; Smith, G.D.; Deloukas, P.; Heijmans, B.T.; Herder, C.; Hofman, A.; Lindgren, C.M.; Milani, L.; van der Harst, P.; Peters, A.; Illig, T.; Relton, C.L.; Waldenberger, M.; Järvelin, M.R.; Bollati, V.; Soong, R.; Spector, T.D.; Scott, J.; McCarthy, M.I.; Elliott, P.; Bell, J.T.; Matullo, G.; Gieger, C.; Kooner, J.S.; Grallert, H.; Chambers, J.C. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature, 2017, 541(7635), 81-86.
[http://dx.doi.org/10.1038/nature20784] [PMID: 28002404]
[42]
Kanehisa, M.; Furumichi, M.; Sato, Y.; Ishiguro-Watanabe, M.; Tanabe, M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res., 2021, 49(D1), D545-D551.
[http://dx.doi.org/10.1093/nar/gkaa970] [PMID: 33125081]
[43]
Ulgen, E.; Ozisik, O.; Sezerman, O.U. pathfindR: An R package for comprehensive identification of enriched pathways in omics data through active subnetworks. Front. Genet., 2019, 10, 858.
[http://dx.doi.org/10.3389/fgene.2019.00858] [PMID: 31608109]
[44]
Hannum, G.; Guinney, J.; Zhao, L.; Zhang, L.; Hughes, G.; Sadda, S.; Klotzle, B.; Bibikova, M.; Fan, J.B.; Gao, Y.; Deconde, R.; Chen, M.; Rajapakse, I.; Friend, S.; Ideker, T.; Zhang, K. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell, 2013, 49(2), 359-367.
[http://dx.doi.org/10.1016/j.molcel.2012.10.016] [PMID: 23177740]
[45]
Horvath, S.; Garagnani, P.; Bacalini, M.G.; Pirazzini, C.; Salvioli, S.; Gentilini, D.; Di Blasio, A.M.; Giuliani, C.; Tung, S.; Vinters, H.V.; Franceschi, C. Accelerated epigenetic aging in Down syndrome. Aging Cell, 2015, 14(3), 491-495.
[http://dx.doi.org/10.1111/acel.12325] [PMID: 25678027]
[46]
Levine, M.E.; Lu, A.T.; Chen, B.H.; Hernandez, D.G.; Singleton, A.B.; Ferrucci, L.; Bandinelli, S.; Salfati, E.; Manson, J.E.; Quach, A.; Kusters, C.D.J.; Kuh, D.; Wong, A.; Teschendorff, A.E.; Widschwendter, M.; Ritz, B.R.; Absher, D.; Assimes, T.L.; Horvath, S. Menopause accelerates biological aging. Proc. Natl. Acad. Sci. USA, 2016, 113(33), 9327-9332.
[http://dx.doi.org/10.1073/pnas.1604558113] [PMID: 27457926]
[47]
Chen, R.; Xia, L.; Tu, K.; Duan, M.; Kukurba, K.; Li-Pook-Than, J.; Xie, D.; Snyder, M. Longitudinal personal DNA methylome dynamics in a human with a chronic condition. Nat. Med., 2018, 24(12), 1930-1939.
[http://dx.doi.org/10.1038/s41591-018-0237-x] [PMID: 30397358]
[48]
Chuang, Y.H.; Lu, A.T.; Paul, K.C.; Folle, A.D.; Bronstein, J.M.; Bordelon, Y.; Horvath, S.; Ritz, B. Longitudinal epigenome-wide methylation study of cognitive decline and motor progression in Parkinson’s disease. J. Parkinsons Dis., 2019, 9(2), 389-400.
[http://dx.doi.org/10.3233/JPD-181549] [PMID: 30958317]
[49]
Giuliani, C.; Cilli, E.; Bacalini, M.G.; Pirazzini, C.; Sazzini, M.; Gruppioni, G.; Franceschi, C.; Garagnani, P.; Luiselli, D. Inferring chronological age from DNA methylation patterns of human teeth. Am. J. Phys. Anthropol., 2016, 159(4), 585-595.
[http://dx.doi.org/10.1002/ajpa.22921] [PMID: 26667772]
[50]
Chao, D.L.; Skowronska-Krawczyk, D. ELOVL2: Not just a biomarker of aging. Transl. Med. Aging, 2020, 4, 78-80.
[http://dx.doi.org/10.1016/j.tma.2020.06.004] [PMID: 33043173]
[51]
Habibe, J.J.; Clemente-Olivo, M.P.; de Vries, C.J. How (Epi)genetic regulation of the LIM-domain protein FHL2 impacts multifactorial disease. Cells, 2021, 10(10), 2611.
[http://dx.doi.org/10.3390/cells10102611] [PMID: 34685595]
[52]
Jung, S.E.; Lim, S.M.; Hong, S.R.; Lee, E.H.; Shin, K.J.; Lee, H.Y. DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples. Forensic Sci. Int. Genet., 2019, 38, 1-8.
[http://dx.doi.org/10.1016/j.fsigen.2018.09.010] [PMID: 30300865]
[53]
Hannon, E.; Knox, O.; Sugden, K.; Burrage, J.; Wong, C.C.Y.; Belsky, D.W.; Corcoran, D.L.; Arseneault, L.; Moffitt, T.E.; Caspi, A.; Mill, J. Characterizing genetic and environmental influences on variable DNA methylation using monozygotic and dizygotic twins. PLoS Genet., 2018, 14(8), e1007544.
[http://dx.doi.org/10.1371/journal.pgen.1007544] [PMID: 30091980]
[54]
Price, E.M.; Robinson, W.P. Adjusting for batch effects in DNA methylation microarray data, a lesson learned. Front. Genet., 2018, 9, 83.
[http://dx.doi.org/10.3389/fgene.2018.00083] [PMID: 29616078]
[55]
Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; Mesirov, J.P. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA, 2005, 102(43), 15545-15550.
[http://dx.doi.org/10.1073/pnas.0506580102] [PMID: 16199517]
[56]
Maksimovic, J.; Oshlack, A.; Phipson, B. Gene set enrichment analysis for genome-wide DNA methylation data. Genome Biol., 2021, 22(1), 173.
[http://dx.doi.org/10.1186/s13059-021-02388-x] [PMID: 34103055]
[57]
Kim, S.; Wyckoff, J.; Morris, A.T.; Succop, A.; Avery, A.; Duncan, G.E.; Jazwinski, S.M. DNA methylation associated with healthy aging of elderly twins. Geroscience, 2018, 40(5-6), 469-484.
[http://dx.doi.org/10.1007/s11357-018-0040-0] [PMID: 30136078]
[58]
Dhingra, R.; Kwee, L.C.; Diaz-Sanchez, D.; Devlin, R.B.; Cascio, W.; Hauser, E.R.; Gregory, S.; Shah, S.; Kraus, W.E.; Olden, K.; Ward-Caviness, C.K. Evaluating DNA methylation age on the illumina MethylationEPIC bead chip. PLoS One, 2019, 14(4), e0207834.
[http://dx.doi.org/10.1371/journal.pone.0207834] [PMID: 31002714]
[59]
Bartlett, A.H.; Liang, J.W.; Sandoval-Sierra, J.V.; Fowke, J.H.; Simonsick, E.M.; Johnson, K.C.; Mozhui, K. Longitudinal study of leukocyte DNA methylation and biomarkers for cancer risk in older adults. Biomark. Res., 2019, 7(1), 10.
[http://dx.doi.org/10.1186/s40364-019-0161-3] [PMID: 31149338]
[60]
Moore, S.R.; Humphreys, K.L.; Colich, N.L.; Davis, E.G.; Lin, D.T.S.; MacIsaac, J.L.; Kobor, M.S.; Gotlib, I.H. Distinctions between sex and time in patterns of DNA methylation across puberty. BMC Genomics, 2020, 21(1), 389.
[http://dx.doi.org/10.1186/s12864-020-06789-3] [PMID: 32493224]
[61]
Heyn, H.; Li, N.; Ferreira, H.J.; Moran, S.; Pisano, D.G.; Gomez, A.; Diez, J.; Sanchez-Mut, J.V.; Setien, F.; Carmona, F.J.; Puca, A.A.; Sayols, S.; Pujana, M.A.; Serra-Musach, J.; Iglesias-Platas, I.; Formiga, F.; Fernandez, A.F.; Fraga, M.F.; Heath, S.C.; Valencia, A.; Gut, I.G.; Wang, J.; Esteller, M. Distinct DNA methylomes of newborns and centenarians. Proc. Natl. Acad. Sci. USA, 2012, 109(26), 10522-10527.
[http://dx.doi.org/10.1073/pnas.1120658109] [PMID: 22689993]
[62]
Simo-Riudalbas, L.; Diaz-Lagares, A.; Gatto, S.; Gagliardi, M.; Crujeiras, A.B.; Matarazzo, M.R.; Esteller, M.; Sandoval, J. Genome-wide DNA methylation analysis identifies novel hypomethylated non-pericentromeric genes with potential clinical implications in ICF syndrome. PLoS One, 2015, 10(7), e0132517.
[http://dx.doi.org/10.1371/journal.pone.0132517] [PMID: 26161907]
[63]
Zhang, N.; Zhao, S.; Zhang, S.H.; Chen, J.; Lu, D.; Shen, M.; Li, C. Intra-monozygotic twin pair discordance and longitudinal variation of whole-genome scale DNA methylation in adults. PLoS One, 2015, 10(8), e0135022.
[http://dx.doi.org/10.1371/journal.pone.0135022] [PMID: 26248206]
[64]
Kananen, L.; Marttila, S.; Nevalainen, T.; Jylhävä, J.; Mononen, N.; Kähönen, M.; Raitakari, O.T.; Lehtimäki, T.; Hurme, M. Aging-associated DNA methylation changes in middle-aged individuals: the Young Finns study. BMC Genomics, 2016, 17(1), 103.
[http://dx.doi.org/10.1186/s12864-016-2421-z] [PMID: 26861258]
[65]
Mishra, P.P.; Hänninen, I.; Raitoharju, E.; Marttila, S.; Mishra, B.H.; Mononen, N.; Kähönen, M.; Hurme, M.; Raitakari, O.; Törönen, P.; Holm, L.; Lehtimäki, T. Epigenome-450K-wide methylation signatures of active cigarette smoking: The Young Finns Study. Biosci. Rep., 2020, 40(7), BSR20200596.
[http://dx.doi.org/10.1042/BSR20200596] [PMID: 32583859]
[66]
Paul, K.C.; Binder, A.M.; Horvath, S.; Kusters, C.; Yan, Q.; Rosario, I.D.; Yu, Y.; Bronstein, J.; Ritz, B. Accelerated hematopoietic mitotic aging measured by DNA methylation, blood cell lineage, and Parkinson’s disease. BMC Genomics, 2021, 22(1), 696.
[http://dx.doi.org/10.1186/s12864-021-08009-y] [PMID: 34565328]
[67]
Horvath, S.; Ritz, B.R. Increased epigenetic age and granulocyte counts in the blood of Parkinson’s disease patients. Aging (Albany NY), 2015, 7(12), 1130-1142.
[http://dx.doi.org/10.18632/aging.100859] [PMID: 26655927]
[68]
Chuang, Y.H.; Paul, K.C.; Bronstein, J.M.; Bordelon, Y.; Horvath, S.; Ritz, B. Parkinson’s disease is associated with DNA methylation levels in human blood and saliva. Genome Med., 2017, 9(1), 76.
[http://dx.doi.org/10.1186/s13073-017-0466-5] [PMID: 28851441]
[69]
Horvath, S.; Gurven, M.; Levine, M.E.; Trumble, B.C.; Kaplan, H.; Allayee, H.; Ritz, B.R.; Chen, B.; Lu, A.T.; Rickabaugh, T.M.; Jamieson, B.D.; Sun, D.; Li, S.; Chen, W.; Quintana-Murci, L.; Fagny, M.; Kobor, M.S.; Tsao, P.S.; Reiner, A.P.; Edlefsen, K.L.; Absher, D.; Assimes, T.L. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol., 2016, 17(1), 171.
[http://dx.doi.org/10.1186/s13059-016-1030-0] [PMID: 27511193]
[70]
Chuang, Y.H.; Quach, A.; Absher, D.; Assimes, T.; Horvath, S.; Ritz, B. Coffee consumption is associated with DNA methylation levels of human blood. Eur. J. Hum. Genet., 2017, 25(5), 608-616.
[http://dx.doi.org/10.1038/ejhg.2016.175] [PMID: 28198392]
[71]
Somineni, H.K.; Venkateswaran, S.; Kilaru, V.; Marigorta, U.M.; Mo, A.; Okou, D.T.; Kellermayer, R.; Mondal, K.; Cobb, D.; Walters, T.D. Blood-derived DNA methylation signatures of Crohn’s disease and severity of intestinal inflammation. Gastroenterology, 2019, 156(8), 2254-2265.
[http://dx.doi.org/10.1053/j.gastro.2019.01.270]

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