Skip to main content

Advertisement

SpringerLink
Log in

DNA Methylation Signatures as Biomarkers of Prior Environmental Exposures

  • Genetic Epidemiology (C Amos, Section Editor)
  • Published:
Current Epidemiology Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

This review demonstrates the growing body of evidence connecting DNA methylation to prior exposure. It highlights the potential to use DNA methylation patterns as a feasible, stable, and accurate biomarker of past exposure, opening new opportunities for environmental and gene-environment interaction studies among existing banked samples.

Recent Findings

We present the evidence for association between past exposure, including prenatal exposures, and DNA methylation measured at a later time in the life course. We demonstrate the potential utility of DNA methylation-based biomarkers of past exposure using results from multiple studies of smoking as an example. Multiple studies show the ability to accurately predict prenatal smoking exposure based on DNA methylation measured at birth, in childhood, and even adulthood. Separate sets of DNA methylation loci have been used to predict past personal smoking exposure (postnatal) as well. Further, it appears that these two types of exposures, prenatal and previous personal exposure, can be isolated from each other. There is also a suggestion that quantitative methylation scores may be useful for estimating dose. We highlight the remaining needs for rigor in methylation biomarker development including analytic challenges as well as the need for development across multiple developmental windows, multiple tissue types, and multiple ancestries.

Summary

If fully developed, DNA methylation-based biomarkers can dramatically shift our ability to carry out environmental and genetic-environmental epidemiology using existing biobanks, opening up unprecedented opportunities for environmental health.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Wang K, Gaitsch H, Poon H, Cox NJ, Rzhetsky A. Classification of common human diseases derived from shared genetic and environmental determinants. Nat Genet. 2017;49(9):1319–25.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Samet JM. Tobacco smoking: the leading cause of preventable disease worldwide. Thorac Surg Clin. 2013;23(2):103–12.

    Article  PubMed  Google Scholar 

  3. NTP monograph on health effects of low-level lead. NTP Monogr, 2012(1):xiii, xv-148.

  4. Wang G, Divall S, Radovick S, Paige D, Ning Y, Chen Z, et al. Preterm birth and random plasma insulin levels at birth and in early childhood. JAMA. 2014;311(6):587–96.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Newschaffer CJ, Croen LA, Fallin MD, Hertz-Picciotto I, Nguyen DV, Lee NL, et al. Infant siblings and the investigation of autism risk factors. J Neurodev Disord. 2012;4(1):7.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Oken E, Baccarelli AA, Gold DR, Kleinman KP, Litonjua AA, de Meo D, et al. Cohort profile: project viva. Int J Epidemiol. 2015;44(1):37–48.

    Article  PubMed  Google Scholar 

  7. Jaddoe VW, et al. The generation R study: design and cohort profile. Eur J Epidemiol. 2006;21(6):475–84.

    Article  PubMed  Google Scholar 

  8. Magnus P, Birke C, Vejrup K, Haugan A, Alsaker E, Daltveit AK, et al. Cohort profile update: the Norwegian mother and child cohort study (MoBa). Int J Epidemiol. 2016;45(2):382–8.

    Article  PubMed  Google Scholar 

  9. Boyd A, Golding J, Macleod J, Lawlor DA, Fraser A, Henderson J, et al. Cohort profile: the 'children of the 90s'--the index offspring of the Avon longitudinal study of parents and children. Int J Epidemiol. 2013;42(1):111–27.

    Article  PubMed  Google Scholar 

  10. Claus Henn B, Austin C, Coull BA, Schnaas L, Gennings C, Horton MK, et al. Uncovering neurodevelopmental windows of susceptibility to manganese exposure using dentine microspatial analyses. Environ Res. 2018;161:588–98.

    Article  PubMed  CAS  Google Scholar 

  11. Andra SS, Austin C, Arora M. Tooth matrix analysis for biomonitoring of organic chemical exposure: current status, challenges, and opportunities. Environ Res. 2015;142:387–406.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Cross-Disorder Group of the Psychiatric Genomics, C, et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet. 2013;45(9):984–94.

    Article  CAS  Google Scholar 

  14. Benowitz NL. Biomarkers of environmental tobacco smoke exposure. Environ Health Perspect. 1999;107(Suppl 2):349–55.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Benowitz NL. Cotinine as a biomarker of environmental tobacco smoke exposure. Epidemiol Rev. 1996;18(2):188–204.

    Article  PubMed  CAS  Google Scholar 

  16. Lee DH, Jacobs DR Jr. Methodological issues in human studies of endocrine disrupting chemicals. Rev Endocr Metab Disord. 2015;16(4):289–97.

    Article  PubMed  CAS  Google Scholar 

  17. Johns LE, Cooper GS, Galizia A, Meeker JD. Exposure assessment issues in epidemiology studies of phthalates. Environ Int. 2015;85:27–39.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Christensen JM. Human exposure to toxic metals: factors influencing interpretation of biomonitoring results. Sci Total Environ. 1995;166:89–135.

    Article  PubMed  CAS  Google Scholar 

  19. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011;12(8):529–41.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Bakulski KM, Fallin MD. Epigenetic epidemiology: promises for public health research. Environ Mol Mutagen. 2014;55(3):171–83.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Burris HH, Baccarelli AA. Environmental epigenetics: from novelty to scientific discipline. J Appl Toxicol. 2014;34(2):113–6.

    Article  PubMed  CAS  Google Scholar 

  22. Cortessis VK, Thomas DC, Levine AJ, Breton CV, Mack TM, Siegmund KD, et al. Environmental epigenetics: prospects for studying epigenetic mediation of exposure-response relationships. Hum Genet. 2012;131(10):1565–89.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Suter M, Ma J, Harris AS, Patterson L, Brown KA, Shope C, et al. Maternal tobacco use modestly alters correlated epigenome-wide placental DNA methylation and gene expression. Epigenetics. 2011;6(11):1284–94.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Joubert BR, Håberg SE, Nilsen RM, Wang X, Vollset SE, Murphy SK, et al. 450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy. Environ Health Perspect. 2012;120(10):1425–31.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Markunas CA, Xu Z, Harlid S, Wade PA, Lie RT, Taylor JA, et al. Identification of DNA methylation changes in newborns related to maternal smoking during pregnancy. Environ Health Perspect. 2014;122(10):1147–53.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. • Joubert BR, et al. DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis. Am J Hum Genet. 2016;98(4):680–96 Largest epigenome-wide association study for prenatal smoking exposure to date, consisting of 6,685 samples from 13 studies. Identified thousands of loci showing DNA methylation changes in cord blood, at birth, related to in utero exposure to smoking.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Hannon E, et al. Elevated polygenic burden for autism is associated with differential DNA methylation at birth. Genome Med. 2018;10(1):19.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Novakovic B, Ryan J, Pereira N, Boughton B, Craig JM, Saffery R. Postnatal stability, tissue, and time specific effects of AHRR methylation change in response to maternal smoking in pregnancy. Epigenetics. 2014;9(3):377–86.

    Article  PubMed  CAS  Google Scholar 

  29. Breton CV, Byun HM, Wenten M, Pan F, Yang A, Gilliland FD. Prenatal tobacco smoke exposure affects global and gene-specific DNA methylation. Am J Respir Crit Care Med. 2009;180(5):462–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. • Breton CV, et al. Prenatal tobacco smoke exposure is associated with childhood DNA CpG methylation. PLoS One. 2014;9(6):e99716 Provides evidence that DNA methylation patterns present in child DNA reflect prenatal exposure to smoking.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. •• Ladd-Acosta C, et al. Presence of an epigenetic signature of prenatal cigarette smoke exposure in childhood. Environ Res. 2016;144(Pt A):139–48 Reported prenatal smoking associated methylation patterns, originally detected in an independent birth sample, are also present in childhood. They were the first to report prenatal exposure to smoking can be accurately (AUC=0.87) predicted using DNA methylation patterns in the blood of 5 year old children.

    Article  PubMed  CAS  Google Scholar 

  32. • Richmond RC, et al. Prenatal exposure to maternal smoking and offspring DNA methylation across the lifecourse: findings from the Avon Longitudinal Study of Parents and Children (ALSPAC). Hum Mol Genet. 2015;24(8):2201–17 Repeated biosampling in children, from birth to age 17, enabled examination of long-term persistence of prenatal smoking associated methylation changes in the same individuals over time. Significant differences in methylation were observed at multiple loci even after adjusting for postnatal household and personal exposures.

    Article  PubMed  CAS  Google Scholar 

  33. • Lee KW, et al. Prenatal exposure to maternal cigarette smoking and DNA methylation: epigenome-wide association in a discovery sample of adolescents and replication in an independent cohort at birth through 17 years of age. Environ Health Perspect. 2015;123(2):193–9 Shows DNA methylation changes related to prenatal exposure can be detected in adolescence. Also replicated findings in an independent sample at birth and ages 7 and 17.

    Article  PubMed  CAS  Google Scholar 

  34. Wan ES, Qiu W, Baccarelli A, Carey VJ, Bacherman H, Rennard SI, et al. Cigarette smoking behaviors and time since quitting are associated with differential DNA methylation across the human genome. Hum Mol Genet. 2012;21(13):3073–82.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR, et al. Epigenetic signatures of cigarette smoking. Circ Cardiovasc Genet. 2016;9(5):436–47.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Lee MK, Hong Y, Kim SY, London SJ, Kim WJ. DNA methylation and smoking in Korean adults: epigenome-wide association study. Clin Epigenetics. 2016;8:103.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Sharp GC, Arathimos R, Reese SE, Page CM, Felix J, Küpers LK, et al. Maternal alcohol consumption and offspring DNA methylation: findings from six general population-based birth cohorts. Epigenomics. 2018;10(1):27–42.

    Article  PubMed  CAS  Google Scholar 

  38. Liu C, Marioni RE, Hedman ÅK, Pfeiffer L, Tsai PC, Reynolds LM, et al. A DNA methylation biomarker of alcohol consumption. Mol Psychiatry. 2018;23(2):422–33.

    Article  PubMed  CAS  Google Scholar 

  39. Richmond RC, Sharp GC, Herbert G, Atkinson C, Taylor C, Bhattacharya S, et al. The long-term impact of folic acid in pregnancy on offspring DNA methylation: follow-up of the Aberdeen folic acid supplementation trial (AFAST). Int J Epidemiol. 2018;47:928–37.

    Article  PubMed Central  Google Scholar 

  40. Steegers-Theunissen RP, Obermann-Borst SA, Kremer D, Lindemans J, Siebel C, Steegers EA, et al. Periconceptional maternal folic acid use of 400 microg per day is related to increased methylation of the IGF2 gene in the very young child. PLoS One. 2009;4(11):e7845.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Dominguez-Salas P, Moore SE, Baker MS, Bergen AW, Cox SE, Dyer RA, et al. Maternal nutrition at conception modulates DNA methylation of human metastable epialleles. Nat Commun. 2014;5:3746.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Silver MJ, Kessler NJ, Hennig BJ, Dominguez-Salas P, Laritsky E, Baker MS, et al. Independent genomewide screens identify the tumor suppressor VTRNA2-1 as a human epiallele responsive to periconceptional environment. Genome Biol. 2015;16:118.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Waterland RA, Kellermayer R, Laritsky E, Rayco-Solon P, Harris RA, Travisano M, et al. Season of conception in rural Gambia affects DNA methylation at putative human metastable epialleles. PLoS Genet. 2010;6(12):e1001252.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Pauwels S, Ghosh M, Duca RC, Bekaert B, Freson K, Huybrechts I, et al. Maternal intake of methyl-group donors affects DNA methylation of metabolic genes in infants. Clin Epigenetics. 2017;9:16.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Pauwels S, Ghosh M, Duca RC, Bekaert B, Freson K, Huybrechts I, et al. Dietary and supplemental maternal methyl-group donor intake and cord blood DNA methylation. Epigenetics. 2017;12(1):1–10.

    Article  PubMed  Google Scholar 

  46. van Dijk SJ, Zhou J, Peters TJ, Buckley M, Sutcliffe B, Oytam Y, et al. Effect of prenatal DHA supplementation on the infant epigenome: results from a randomized controlled trial. Clin Epigenetics. 2016;8:114.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Jacobsen SC, Brøns C, Bork-Jensen J, Ribel-Madsen R, Yang B, Lara E, et al. Effects of short-term high-fat overfeeding on genome-wide DNA methylation in the skeletal muscle of healthy young men. Diabetologia. 2012;55(12):3341–9.

    Article  PubMed  CAS  Google Scholar 

  48. Tobi EW, Lumey LH, Talens RP, Kremer D, Putter H, Stein AD, et al. DNA methylation differences after exposure to prenatal famine are common and timing- and sex-specific. Hum Mol Genet. 2009;18(21):4046–53.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Heijmans BT, Tobi EW, Stein AD, Putter H, Blauw GJ, Susser ES, et al. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci U S A. 2008;105(44):17046–9.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Finer S, Iqbal MS, Lowe R, Ogunkolade BW, Pervin S, Mathews C, et al. Is famine exposure during developmental life in rural Bangladesh associated with a metabolic and epigenetic signature in young adulthood? A historical cohort study. BMJ Open. 2016;6(11):e011768.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Parent J, Parade SH, Laumann LE, Ridout KK, Yang BZ, Marsit CJ, et al. Dynamic stress-related epigenetic regulation of the glucocorticoid receptor gene promoter during early development: the role of child maltreatment. Dev Psychopathol. 2017;29(5):1635–48.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Gruzieva O, Xu CJ, Breton CV, Annesi-Maesano I, Antó JM, Auffray C, et al. Epigenome-wide meta-analysis of methylation in children related to prenatal NO2 air pollution exposure. Environ Health Perspect. 2017;125(1):104–10.

    Article  PubMed  CAS  Google Scholar 

  53. Wright RO, Schwartz J, Wright RJ, Bollati V, Tarantini L, Park SK, et al. Biomarkers of lead exposure and DNA methylation within retrotransposons. Environ Health Perspect. 2010;118(6):790–5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Cardenas A, Rifas-Shiman SL, Godderis L, Duca RC, Navas-Acien A, Litonjua AA, et al. Prenatal exposure to mercury: associations with global DNA methylation and Hydroxymethylation in cord blood and in childhood. Environ Health Perspect. 2017;125(8):087022.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Sharp GC, Salas LA, Monnereau C, Allard C, Yousefi P, Everson TM, et al. Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: findings from the pregnancy and childhood epigenetics (PACE) consortium. Hum Mol Genet. 2017;26(20):4067–85.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Kazmi N, Gaunt TR, Relton C, Micali N. Maternal eating disorders affect offspring cord blood DNA methylation: a prospective study. Clin Epigenetics. 2017;9:120.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. •• Richmond RC, et al. DNA methylation as a marker for prenatal smoke exposure in adults. Int J Epidemiol. 2018; Showed methylation scores obtained from DNA collected at age 30, can predict prenatal exposure to smoking with 72% accuracy. Also showed loci associated with postnatal personal smoking are not good predictors of prenatal smoking exposure (AUC=0.57), suggesting methylation patterns differ by exposure window.

  58. Leggett RW. An age-specific kinetic model of lead metabolism in humans. Environ Health Perspect. 1993;101(7):598–616.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Simpkin AJ, Suderman M, Howe LD. Epigenetic clocks for gestational age: statistical and study design considerations. Clin Epigenetics. 2017;9:100.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Knight AK, Craig JM, Theda C, Bækvad-Hansen M, Bybjerg-Grauholm J, Hansen CS, et al. An epigenetic clock for gestational age at birth based on blood methylation data. Genome Biol. 2016;17(1):206.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Bohlin J, Håberg SE, Magnus P, Reese SE, Gjessing HK, Magnus MC, et al. Prediction of gestational age based on genome-wide differentially methylated regions. Genome Biol. 2016;17(1):207.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Alisch RS, Barwick BG, Chopra P, Myrick LK, Satten GA, Conneely KN, et al. Age-associated DNA methylation in pediatric populations. Genome Res. 2012;22(4):623–32.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Lin Q, Weidner CI, Costa IG, Marioni RE, Ferreira MRP, Deary IJ, et al. DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy. Aging (Albany NY). 2016;8(2):394–401.

    Article  CAS  Google Scholar 

  64. Weidner CI, et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014;15(2):R24.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda SV, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359–67.

    Article  PubMed  CAS  Google Scholar 

  67. Garagnani P, Bacalini MG, Pirazzini C, Gori D, Giuliani C, Mari D, et al. Methylation of ELOVL2 gene as a new epigenetic marker of age. Aging Cell. 2012;11(6):1132–4.

    Article  PubMed  CAS  Google Scholar 

  68. Bocklandt S, Lin W, Sehl ME, Sánchez FJ, Sinsheimer JS, Horvath S, et al. Epigenetic predictor of age. PLoS One. 2011;6(6):e14821.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Marioni RE, Shah S, McRae AF, Chen BH, Colicino E, Harris SE, et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 2015;16:25.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Knight AK, Conneely KN, Smith AK. Gestational age predicted by DNA methylation: potential clinical and research utility. Epigenomics. 2017.

  71. Horvath S, Gurven M, Levine ME, Trumble BC, Kaplan H, Allayee H, et al. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 2016;17(1):171.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Horvath S, Erhart W, Brosch M, Ammerpohl O, von Schonfels W, Ahrens M, et al. Obesity accelerates epigenetic aging of human liver. Proc Natl Acad Sci U S A. 2014;111(43):15538–43.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Chen BH, Marioni RE, Colicino E, Peters MJ, Ward-Caviness CK, Tsai PC, et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging (Albany NY). 2016;8(9):1844–65.

    Article  CAS  Google Scholar 

  74. Bossuyt PM, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015;351:h5527.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Phan JH, Kothari S, Wang MD. omniClassifier: a desktop grid computing system for big data prediction modeling. ACM BCB. 2014;2014:514–23.

    PubMed  PubMed Central  Google Scholar 

  76. •• Zhuang J, Widschwendter M, Teschendorff AE. A comparison of feature selection and classification methods in DNA methylation studies using the Illumina Infinium platform. BMC Bioinformatics. 2012;13:59 Developed a methylation score in cord blood at birth that reflects prenatal exposure to sustained smoking, The score was able to predict exposure status in an independent birth sample with 90% accuracy.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Reese SE, Zhao S, Wu MC, Joubert BR, Parr CL, Håberg SE, et al. DNA methylation score as a biomarker in newborns for sustained maternal smoking during pregnancy. Environ Health Perspect. 2017;125(4):760–6.

    Article  PubMed  CAS  Google Scholar 

  78. • Shenker NS, et al. DNA methylation as a long-term biomarker of exposure to tobacco smoke. Epidemiology. 2013;24(5):712–6 First to show generalized linear model, using methylation levels at 4 CpG sites, can accurately predict previous personal smoking history among adults.

    Article  PubMed  Google Scholar 

  79. Xu J, Thakkar S, Gong B, Tong W. The FDA's experience with emerging genomics technologies-past, present, and future. AAPS J. 2016;18(4):814–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Andrews SV, Ellis SE, Bakulski KM, Sheppard B, Croen LA, Hertz-Picciotto I, et al. Cross-tissue integration of genetic and epigenetic data offers insight into autism spectrum disorder. Nat Commun. 2017;8(1):1011.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Smith AK, Kilaru V, Kocak M, Almli LM, Mercer KB, Ressler KJ, et al. Methylation quantitative trait loci (meQTLs) are consistently detected across ancestry, developmental stage, and tissue type. BMC Genomics. 2014;15:145.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Ren JC, Wu YX, Wu Z, Zhang GH, Wang H, Liu H, et al. MTHFR gene polymorphism is associated with DNA Hypomethylation and genetic damage among benzene-exposed Workers in Southeast China. J Occup Environ Med. 2018;60(4):e188–92.

    Article  PubMed  CAS  Google Scholar 

  83. Zhang GH, Lu Y, Ji BQ, Ren JC, Sun P, Ding S, et al. Do mutations in DNMT3A/3B affect global DNA hypomethylation among benzene-exposed workers in Southeast China?: effects of mutations in DNMT3A/3B on global DNA hypomethylation. Environ Mol Mutagen. 2017;58(9):678–87.

    Article  PubMed  CAS  Google Scholar 

  84. Declerck K, Remy S, Wohlfahrt-Veje C, Main KM, van Camp G, Schoeters G, et al. Interaction between prenatal pesticide exposure and a common polymorphism in the PON1 gene on DNA methylation in genes associated with cardio-metabolic disease risk-an exploratory study. Clin Epigenetics. 2017;9:35.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Aarabi M, San Gabriel MC, Chan D, Behan NA, Caron M, Pastinen T, et al. High-dose folic acid supplementation alters the human sperm methylome and is influenced by the MTHFR C677T polymorphism. Hum Mol Genet. 2015;24(22):6301–13.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Sipahi L, Wildman DE, Aiello AE, Koenen KC, Galea S, Abbas A, et al. Longitudinal epigenetic variation of DNA methyltransferase genes is associated with vulnerability to post-traumatic stress disorder. Psychol Med. 2014;44(15):3165–79.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe St, W6509, Baltimore, MD, 21205, USA

    Christine Ladd-Acosta

  2. Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Christine Ladd-Acosta & M. Daniele Fallin

  3. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 North Broadway, HH850, Baltimore, MD, 21205, USA

    M. Daniele Fallin

Authors
  1. Christine Ladd-Acosta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Daniele Fallin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Christine Ladd-Acosta or M. Daniele Fallin.

Ethics declarations

Conflict of Interest

M. Daniele Fallin and Christine Ladd-Acosta each declare no potential conflicts of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Genetic Epidemiology

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ladd-Acosta, C., Fallin, M.D. DNA Methylation Signatures as Biomarkers of Prior Environmental Exposures. Curr Epidemiol Rep 6, 1–13 (2019). https://doi.org/10.1007/s40471-019-0178-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40471-019-0178-z

Keywords

Search

Navigation