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Population DNA methylation studies in the Developmental Origins of Health and Disease (DOHaD) framework

Part of: DOHaD 2017 World Congress

Published online by Cambridge University Press:  13 August 2018

J. F. Felix  and
C. A. M. Cecil
J. F. Felix*
Affiliation:
The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
C. A. M. Cecil
Affiliation:
The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
*
Address for correspondence: J. F. Felix, The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, The Netherlands. E-mail: [email protected]
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Abstract

Epigenetic changes represent a potential mechanism underlying associations of early-life exposures and later life health outcomes. Population-based cohort studies starting in early life are an attractive framework to study the role of such changes. DNA methylation is the most studied epigenetic mechanism in population research. We discuss the application of DNA methylation in early-life population studies, some recent findings, key challenges and recommendations for future research. Studies into DNA methylation within the Developmental Origins of Health and Disease framework generally either explore associations between prenatal exposures and offspring DNA methylation or associations between offspring DNA methylation in early life and later health outcomes. Only a few studies to date have integrated prospective exposure, epigenetic and phenotypic data in order to explicitly test the role of DNA methylation as a potential biological mediator of environmental effects on health outcomes. Population epigenetics is an emerging field which has challenges in terms of methodology and interpretation of the data. Key challenges include tissue specificity, cell type adjustment, issues of power and comparability of findings, genetic influences, and exploring causality and functional consequences. Ongoing studies are working on addressing these issues. Large collaborative efforts of prospective cohorts are emerging, with clear benefits in terms of optimizing power and use of resources, and in advancing methodology. In the future, multidisciplinary approaches, within and beyond longitudinal birth and preconception cohorts will advance this complex, but highly promising, the field of research.

Keywords

cohort studiesDNA methylationepidemiologyepigeneticsmolecular
Type
Review
Information
Journal of Developmental Origins of Health and Disease , Volume 10 , Issue 3 , June 2019 , pp. 306 - 313
Copyright
© Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2018 

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References

1. Barker, DJ. Fetal origins of coronary heart disease. BMJ. 1995; 311, 171–174.CrossRefGoogle ScholarPubMed
2. Gluckman, PD, Hanson, MA, Cooper, C, Thornburg, KL. Effect of in utero and early-life conditions on adult health and disease. N Engl J Med. 2008; 359, 61–73.CrossRefGoogle ScholarPubMed
3. Godfrey, KM, Barker, DJ. Fetal nutrition and adult disease. Am J Clin Nutr. 2000; 71, 1344S–1352S.CrossRefGoogle ScholarPubMed
4. Moller, SE, Ajslev, TA, Andersen, CS, Dalgard, C, Sorensen, TI. Risk of childhood overweight after exposure to tobacco smoking in prenatal and early postnatal life. PLoS One. 2014; 9, e109184.CrossRefGoogle ScholarPubMed
5. Roseboom, TJ, Painter, RC, van Abeelen, AF, Veenendaal, MV, de Rooij, SR. Hungry in the womb: what are the consequences? Lessons from the Dutch famine. Maturitas. 2011; 70, 141–145.CrossRefGoogle ScholarPubMed
6. Yu, Z, Han, S, Zhu, J, et al. Pre-pregnancy body mass index in relation to infant birth weight and offspring overweight/obesity: a systematic review and meta-analysis. PLoS One. 2013; 8, e61627.CrossRefGoogle ScholarPubMed
7. Yamada, L, Chong, S. Epigenetic studies in Developmental Origins of Health and Disease: pitfalls and key considerations for study design and interpretation. J Dev Orig Health Dis. 2017; 8, 30–43.CrossRefGoogle ScholarPubMed
8. Jang, HS, Shin, WJ, Lee, JE, Do, JT. CpG and non-CpG methylation in epigenetic gene regulation and brain function. Genes (Basel). 2017; 8, 148.Google Scholar
9. Ziller, MJ, Gu, H, Muller, F, et al. Charting a dynamic DNA methylation landscape of the human genome. Nature. 2013; 500, 477–481.CrossRefGoogle ScholarPubMed
10. Bestor, TH, Edwards, JR, Boulard, M. Notes on the role of dynamic DNA methylation in mammalian development. Proc Natl Acad Sci U S A. 2015; 112, 6796–6799.CrossRefGoogle ScholarPubMed
11. Gordon, L, Joo, JE, Powell, JE, et al. Neonatal DNA methylation profile in human twins is specified by a complex interplay between intrauterine environmental and genetic factors, subject to tissue-specific influence. Genome Res. 2012; 22, 1395–1406.CrossRefGoogle ScholarPubMed
12. Marsit, CJ. Influence of environmental exposure on human epigenetic regulation. J Exp Biol. 2015; 218, 71–79.CrossRefGoogle ScholarPubMed
13. Kundakovic, M, Jaric, I. The epigenetic link between prenatal adverse environments and neurodevelopmental disorders. Genes (Basel). 2017; 8, 108.Google Scholar
14. Heijmans, BT, Tobi, EW, Stein, AD, et al. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci U S A. 2008; 105, 17046–17049.CrossRefGoogle ScholarPubMed
15. Tobi, EW, Lumey, LH, Talens, RP, et al. DNA methylation differences after exposure to prenatal famine are common and timing- and sex-specific. Hum Mol Genet. 2009; 18, 4046–4053.CrossRefGoogle ScholarPubMed
16. Tobi, EW, Slieker, RC, Stein, AD, et al. Early gestation as the critical time-window for changes in the prenatal environment to affect the adult human blood methylome. Int J Epidemiol. 2015; 44, 1211–1223.CrossRefGoogle ScholarPubMed
17. Martino, D, Loke, YJ, Gordon, L, et al. Longitudinal, genome-scale analysis of DNA methylation in twins from birth to 18 months of age reveals rapid epigenetic change in early life and pair-specific effects of discordance. Genome Biol. 2013; 14, R42.CrossRefGoogle ScholarPubMed
18. Joubert, BR, Felix, JF, Yousefi, P, et al. DNA methylation in newborns and maternal smoking in pregnancy: genome-wide consortium meta-analysis. Am J Hum Genet. 2016; 98, 680–696.CrossRefGoogle ScholarPubMed
19. Joubert, BR, Haberg, SE, Nilsen, RM, et al. 450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy. Environ Health Perspect. 2012; 120, 1425–1431.CrossRefGoogle ScholarPubMed
20. Lee, KW, Richmond, R, Hu, P, 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, 193–199.CrossRefGoogle Scholar
21. Markunas, CA, Xu, Z, Harlid, S, et al. Identification of DNA methylation changes in newborns related to maternal smoking during pregnancy. Environ Health Perspect. 2014; 122, 1147–1153.CrossRefGoogle ScholarPubMed
22. Richmond, RC, Simpkin, AJ, Woodward, G, 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, 2201–2217.CrossRefGoogle Scholar
23. Rzehak, P, Saffery, R, Reischl, E, et al. Maternal smoking during pregnancy and DNA-methylation in children at age 5.5 years: epigenome-wide-analysis in the European Childhood Obesity Project (CHOP)-study. PLoS One. 2016; 11, e0155554.CrossRefGoogle ScholarPubMed
24. Kupers, LK, Xu, X, Jankipersadsing, SA, et al. DNA methylation mediates the effect of maternal smoking during pregnancy on birthweight of the offspring. Int J Epidemiol. 2015; 44, 1224–1237.CrossRefGoogle ScholarPubMed
25. Zeilinger, S, Kuhnel, B, Klopp, N, et al. Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLoS One. 2013; 8, e63812.CrossRefGoogle ScholarPubMed
26. Green, BB, Karagas, MR, Punshon, T, et al. Epigenome-wide assessment of DNA methylation in the placenta and arsenic exposure in the New Hampshire Birth Cohort Study (USA). Environ Health Perspect. 2016; 124, 1253–1260.CrossRefGoogle Scholar
27. Cardenas, A, Rifas-Shiman, SL, Agha, G, et al. Persistent DNA methylation changes associated with prenatal mercury exposure and cognitive performance during childhood. Sci Rep. 2017; 7, 288.CrossRefGoogle ScholarPubMed
28. Gruzieva, O, Xu, CJ, Breton, CV, et al. Epigenome-wide meta-analysis of methylation in children related to prenatal NO2 air pollution exposure. Environ Health Perspect. 2017; 125, 104–110.CrossRefGoogle ScholarPubMed
29. Breton, CV, Gao, L, Yao, J, et al. Particulate matter, the newborn methylome, and cardio-respiratory health outcomes in childhood. Environ Epigenet. 2016; 2, dvw005.CrossRefGoogle Scholar
30. Sharp, GC, Salas, LA, Monnereau, C, 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, 4067–4085.CrossRefGoogle ScholarPubMed
31. Irwin, RE, Pentieva, K, Cassidy, T, et al. The interplay between DNA methylation, folate and neurocognitive development. Epigenomics. 2016; 8, 863–879.CrossRefGoogle ScholarPubMed
32. Joubert, BR, den Dekker, HT, Felix, JF, et al. Maternal plasma folate impacts differential DNA methylation in an epigenome-wide meta-analysis of newborns. Nat Commun. 2016; 7, 10577.CrossRefGoogle Scholar
33. Sharp, GC, Arathimos, R, Reese, SE, et al. Maternal alcohol consumption and offspring DNA methylation: findings from six general population-based birth cohorts. Epigenomics. 2018; 10, 27–42.CrossRefGoogle ScholarPubMed
34. Rijlaarsdam, J, Pappa, I, Walton, E, et al. An epigenome-wide association meta-analysis of prenatal maternal stress in neonates: a model approach for replication. Epigenetics. 2016; 11, 140–149.CrossRefGoogle ScholarPubMed
35. Paquette, AG, Lester, BM, Lesseur, C, et al. Placental epigenetic patterning of glucocorticoid response genes is associated with infant neurodevelopment. Epigenomics. 2015; 7, 767–779.CrossRefGoogle Scholar
36. Barker, ED, Walton, E, Cecil, CAM. Annual research review: DNA methylation as a mediator in the association between risk exposure and child and adolescent psychopathology. J Child Psychol Psychiatry. 2017; 4, 303–322.Google Scholar
37. Cecil, CAM, Walton, E, Jaffee, SR, et al. Neonatal DNA methylation and early-onset conduct problems: a genome-wide, prospective study. Dev Psychopathol. 2018; 30, 383–397.CrossRefGoogle ScholarPubMed
38. van Mil, NH, Steegers-Theunissen, RP, Bouwland-Both, MI, et al. DNA methylation profiles at birth and child ADHD symptoms. J Psychiatr Res. 2014; 49, 51–59.CrossRefGoogle ScholarPubMed
39. Walton, E, Pingault, JB, Cecil, CA, et al. Epigenetic profiling of ADHD symptoms trajectories: a prospective, methylome-wide study. Mol Psychiatry. 2017; 22, 250–256.CrossRefGoogle ScholarPubMed
40. Demontis, D, Walters, RK, Martin, J. et al. Discovery of the first genome-wide significant risk loci for ADHD. bioRxiv. 2017; 145581.Google Scholar
41. Walton, E, Cecil, CAM, Suderman, M, et al. Longitudinal epigenetic predictors of amygdala:hippocampus volume ratio. J Child Psychol Psychiatry. 2017; 58, 1341–1350.CrossRefGoogle ScholarPubMed
42. Agha, G, Houseman, EA, Kelsey, KT, et al. Adiposity is associated with DNA methylation profile in adipose tissue. Int J Epidemiol. 2015; 44, 1277–1287.CrossRefGoogle ScholarPubMed
43. Aslibekyan, S, Demerath, EW, Mendelson, M, et al. Epigenome-wide study identifies novel methylation loci associated with body mass index and waist circumference. Obesity (Silver Spring). 2015; 23, 1493–1501.CrossRefGoogle ScholarPubMed
44. Demerath, EW, Guan, W, Grove, ML, et al. Epigenome-wide association study (EWAS) of BMI, BMI change and waist circumference in African American adults identifies multiple replicated loci. Hum Mol Genet. 2015; 24, 4464–4479.CrossRefGoogle ScholarPubMed
45. Dick, KJ, Nelson, CP, Tsaprouni, L, et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet. 2014; 383, 1990–1998.CrossRefGoogle ScholarPubMed
46. Meeks, KAC, Henneman, P, Venema, A, et al. An epigenome-wide association study in whole blood of measures of adiposity among Ghanaians: the RODAM study. Clin Epigenetics. 2017; 9, 103.CrossRefGoogle ScholarPubMed
47. Wahl, S, Drong, A, Lehne, B, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2017; 541, 81–86.CrossRefGoogle ScholarPubMed
48. Agha, G, Hajj, H, Rifas-Shiman, SL, et al. Birth weight-for-gestational age is associated with DNA methylation at birth and in childhood. Clin Epigenetics. 2016; 8, 118.CrossRefGoogle Scholar
49. Engel, SM, Joubert, BR, Wu, MC, et al. Neonatal genome-wide methylation patterns in relation to birth weight in the Norwegian Mother and Child Cohort. Am J Epidemiol. 2014; 179, 834–842.CrossRefGoogle ScholarPubMed
50. Haworth, KE, Farrell, WE, Emes, RD, et al. Methylation of the FGFR2 gene is associated with high birth weight centile in humans. Epigenomics. 2014; 6, 477–491.CrossRefGoogle ScholarPubMed
51. Pan, H, Lin, X, Wu, Y, et al. HIF3A association with adiposity: the story begins before birth. Epigenomics. 2015; 7, 937–950.CrossRefGoogle ScholarPubMed
52. Simpkin, AJ, Suderman, M, Gaunt, TR, et al. Longitudinal analysis of DNA methylation associated with birth weight and gestational age. Hum Mol Genet. 2015; 24, 3752–3763.CrossRefGoogle ScholarPubMed
53. Godfrey, KM, Sheppard, A, Gluckman, PD, et al. Epigenetic gene promoter methylation at birth is associated with child’s later adiposity. Diabetes. 2011; 60, 1528–1534.CrossRefGoogle ScholarPubMed
54. Lillycrop, K, Murray, R, Cheong, C, et al. ANRIL promoter DNA methylation: a perinatal marker for later adiposity. EBioMedicine. 2017; 19, 60–72.CrossRefGoogle ScholarPubMed
55. van Dijk, SJ, Peters, TJ, Buckley, M, et al. DNA methylation in blood from neonatal screening cards and the association with BMI and insulin sensitivity in early childhood. Int J Obes (Lond). 2018; 42, 28–35.CrossRefGoogle ScholarPubMed
56. Ali, O, Cerjak, D, Kent, JW Jr, et al. Methylation of SOCS3 is inversely associated with metabolic syndrome in an epigenome-wide association study of obesity. Epigenetics. 2016; 11, 699–707.CrossRefGoogle Scholar
57. Rzehak, P, Covic, M, Saffery, R, et al. DNA-methylation and body composition in preschool children: epigenome-wide-analysis in the European Childhood Obesity Project (CHOP)-study. Sci Rep. 2017; 7, 14349.CrossRefGoogle ScholarPubMed
58. Wang, S, Song, J, Yang, Y, et al. HIF3A DNA methylation is associated with childhood obesity and ALT. PLoS One. 2015; 10, e0145944.CrossRefGoogle ScholarPubMed
59. Barres, R, Kirchner, H, Rasmussen, M, et al. Weight loss after gastric bypass surgery in human obesity remodels promoter methylation. Cell Rep. 2013; 3, 1020–1027.CrossRefGoogle ScholarPubMed
60. Benton, MC, Johnstone, A, Eccles, D, et al. An analysis of DNA methylation in human adipose tissue reveals differential modification of obesity genes before and after gastric bypass and weight loss. Genome Biol. 2015; 16, 8.CrossRefGoogle ScholarPubMed
61. Richmond, RC, Sharp, GC, Ward, ME, et al. DNA methylation and BMI: investigating identified methylation sites at HIF3A in a causal framework. Diabetes. 2016; 65, 1231–1244.CrossRefGoogle Scholar
62. Guenard, F, Deshaies, Y, Cianflone, K, et al. Differential methylation in glucoregulatory genes of offspring born before vs. after maternal gastrointestinal bypass surgery. Proc Natl Acad Sci U S A. 2013; 110, 11439–11444.CrossRefGoogle ScholarPubMed
63. Cecil, CA, Walton, E, Smith, RG, et al. DNA methylation and substance-use risk: a prospective, genome-wide study spanning gestation to adolescence. Transl Psychiatry. 2016; 6, e976.CrossRefGoogle Scholar
64. Bouwland-Both, MI, van Mil, NH, Tolhoek, CP, et al. Prenatal parental tobacco smoking, gene specific DNA methylation, and newborns size: the Generation R study. Clin Epigenetics. 2015; 7, 83.CrossRefGoogle ScholarPubMed
65. Jiang, R, Jones, MJ, Chen, E, et al. Discordance of DNA methylation variance between two accessible human tissues. Sci Rep. 2015; 5, 8257.CrossRefGoogle ScholarPubMed
66. Davies, MN, Volta, M, Pidsley, R, et al. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol. 2012; 13, R43.CrossRefGoogle ScholarPubMed
67. Edgar, RD, Jones, MJ, Meaney, MJ, Turecki, G, Kobor, MS. BECon: a tool for interpreting DNA methylation findings from blood in the context of brain. Transl Psychiatry. 2017; 7, e1187.CrossRefGoogle ScholarPubMed
68. Hannon, E, Lunnon, K, Schalkwyk, L, Mill, J. Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes. Epigenetics. 2015; 10, 1024–1032.CrossRefGoogle ScholarPubMed
69. Huang, YT, Chu, S, Loucks, EB, et al. Epigenome-wide profiling of DNA methylation in paired samples of adipose tissue and blood. Epigenetics. 2016; 11, 227–236.CrossRefGoogle ScholarPubMed
70. Stueve, TR, Li, WQ, Shi, J, et al. Epigenome-wide analysis of DNA methylation in lung tissue shows concordance with blood studies and identifies tobacco smoke-inducible enhancers. Hum Mol Genet. 2017; 26, 3014–3027.CrossRefGoogle ScholarPubMed
71. Roadmap Epigenomics Consortium, Kundaje, A, Meuleman, W, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015; 518, 317–330.CrossRefGoogle ScholarPubMed
72. Houseman, EA, Accomando, WP, Koestler, DC, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012; 13, 86.CrossRefGoogle ScholarPubMed
73. Reinius, LE, Acevedo, N, Joerink, M, et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS One. 2012; 7, e41361.CrossRefGoogle ScholarPubMed
74. Bakulski, KM, Feinberg, JI, Andrews, SV, et al. DNA methylation of cord blood cell types: applications for mixed cell birth studies. Epigenetics. 2016; 11, 354–362.CrossRefGoogle ScholarPubMed
75. de Goede, OM, Razzaghian, HR, Price, EM, et al. Nucleated red blood cells impact DNA methylation and expression analyses of cord blood hematopoietic cells. Clin Epigenetics. 2015; 7, 95.CrossRefGoogle ScholarPubMed
76. Gervin, K, Page, CM, Aass, HC, et al. Cell type specific DNA methylation in cord blood: a 450K-reference data set and cell count-based validation of estimated cell type composition. Epigenetics. 2016; 11, 690–698.CrossRefGoogle ScholarPubMed
77. Kaushal, A, Zhang, H, Karmaus, WJJ, et al. Comparison of different cell type correction methods for genome-scale epigenetics studies. BMC Bioinformatics. 2017; 18, 216.CrossRefGoogle ScholarPubMed
78. McGregor, K, Bernatsky, S, Colmegna, I, et al. An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies. Genome Biol. 2016; 17, 84.CrossRefGoogle ScholarPubMed
79. Teschendorff, AE, Zheng, SC. Cell-type deconvolution in epigenome-wide association studies: a review and recommendations. Epigenomics. 2017; 9, 757–768.CrossRefGoogle ScholarPubMed
80. Almouzni, G, Altucci, L, Amati, B, et al. Relationship between genome and epigenome--challenges and requirements for future research. BMC Genomics. 2014; 15, 487.CrossRefGoogle ScholarPubMed
81. Felix, JF, Joubert, BR, Baccarelli, AA, et al. Cohort profile: Pregnancy and Childhood Epigenetics (PACE) consortium. Int J Epidemiol. 2017; 47, 22-23u.Google Scholar
82. Benke, KS, Nivard, MG, Velders, FP, et al. A genome-wide association meta-analysis of preschool internalizing problems. J Am Acad Child Adolesc Psychiatry. 2014; 53, 667–676, e667.CrossRefGoogle ScholarPubMed
83. Bradfield, JP, Taal, HR, Timpson, NJ, et al. A genome-wide association meta-analysis identifies new childhood obesity loci. Nat Genet. 2012; 44, 526–531.Google ScholarPubMed
84. Felix, JF, Bradfield, JP, Monnereau, C, et al. Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index. Hum Mol Genet. 2016; 25, 389–403.CrossRefGoogle ScholarPubMed
85. Horikoshi, M, Beaumont, RN, Day, FR, et al. Genome-wide associations for birth weight and correlations with adult disease. Nature. 2016; 538, 248–252.CrossRefGoogle ScholarPubMed
86. Pappa, I St Pourcain, B, Benke, K, et al. A genome-wide approach to children’s aggressive behavior: the EAGLE consortium. Am J Med Genet B Neuropsychiatr Genet. 2015; 171, 562–572.Google ScholarPubMed
87. Bell, JT, Pai, AA, Pickrell, JK, et al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 2011; 12, R10.CrossRefGoogle ScholarPubMed
88. Gaunt, TR, Shihab, HA, Hemani, G, et al. Systematic identification of genetic influences on methylation across the human life course. Genome Biol. 2016; 17, 61.CrossRefGoogle ScholarPubMed
89. Grundberg, E, Meduri, E, Sandling, JK, et al. Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements. Am J Hum Genet. 2013; 93, 876–890.CrossRefGoogle ScholarPubMed
90. Relton, CL, Davey Smith, G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol. 2012; 41, 161–176.CrossRefGoogle ScholarPubMed
91. Caramaschi, D, Sharp, GC, Nohr, EA, et al. Exploring a causal role of DNA methylation in the relationship between maternal vitamin B12 during pregnancy and child’s IQ at age 8, cognitive performance and educational attainment: a two-step Mendelian randomization study. Hum Mol Genet. 2017; 26, 3001–3013.CrossRefGoogle ScholarPubMed
92. Ladd-Acosta, C, Fallin, MD. The role of epigenetics in genetic and environmental epidemiology. Epigenomics. 2016; 8, 271–283.CrossRefGoogle ScholarPubMed
93. Bohlin, J, Haberg, SE, Magnus, P, et al. Prediction of gestational age based on genome-wide differentially methylated regions. Genome Biol. 2016; 17, 207.CrossRefGoogle ScholarPubMed
94. Knight, AK, Craig, JM, Theda, C, et al. An epigenetic clock for gestational age at birth based on blood methylation data. Genome Biol. 2016; 17, 206.CrossRefGoogle ScholarPubMed
95. Liu, C, Marioni, RE, Hedman, AK, et al. A DNA methylation biomarker of alcohol consumption. Mol Psychiatry. 2016; 23, 422–433.Google ScholarPubMed
96. Reese, SE, Zhao, S, Wu, MC, et al. DNA methylation score as a biomarker in newborns for sustained maternal smoking during pregnancy. Environ Health Perspect. 2017; 125, 760–766.CrossRefGoogle ScholarPubMed
97. Guxens, M, Ballester, F, Espada, M, et al. Cohort profile: the INMA--INfancia y Medio Ambiente--(Environment and Childhood) Project. Int J Epidemiol. 2012; 41, 930–940.CrossRefGoogle Scholar
98. Moore, SE, Fulford, AJ, Darboe, MK, et al. A randomized trial to investigate the effects of pre-natal and infant nutritional supplementation on infant immune development in rural Gambia: the ENID trial: early nutrition and immune development. BMC Pregnancy Childbirth. 2012; 12, 107.CrossRefGoogle ScholarPubMed
99. Inskip, HM, Godfrey, KM, Robinson, SM, et al. Cohort profile: the Southampton Women’s Survey. Int J Epidemiol. 2006; 35, 42–48.CrossRefGoogle ScholarPubMed