Longitudinal data reveal strong genetic and weak non-genetic components of ethnicity-dependent blood DNA methylation levels

References

• Bird A. DNA methylation patterns and epigenetic memory. Genes Dev. 2002;16:6–21.
   Google Scholar
• Smith ZD, Meer A. DNA methylation: roles in mammalian development. Nat Rev Genet. 2013;14(3):204–220.
   Google Scholar
• Baylin SB, Jones PA. Epigenetic determinants of cancer. Cold Spring Harb Perspect Biol. 2016 Sep;8(9):a019505.
   Google Scholar
• Ladd-Acosta C, Hansen KD, Briem E, et al. Common DNA methylation alterations in multiple brain regions in autism. Mol Psychiatry. 2014 Sep;19:862–871.
   Google Scholar
• Azkargorta MA, Urdánoz-CasadoA, Sánchez-Ruiz de GordoaJ, et al. DNA methylation alterations in Alzheimer’s disease. Clin Epigenet. 2019;11:91.
   Google Scholar
• Chan MA, Ciaccio CE, Gigliotti NM, et al. DNA methylation levels associated with race and childhood asthma severity. J Asthma. 2017 Sep;54(8):825–832.
   Google Scholar
• Nicodemus-Johnson J, Myers RA, Sakabe NJ, et al. DNA methylation in lung cells is associated with asthma endotypes and genetic risk. JCI Insight. 2016 Dec;1(20).
   Google Scholar
• 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(1):R10.
   Google Scholar
• Smith AK, Kilaru V, Kocak M, et al. Methylation quantitative trait loci (meQTLs) are consistently detected across ancestry, developmental stage, and tissue type. BMC Genomics. 2014;15(1):145.
   Google Scholar
• Breton CV, Siegmund KD, Joubert BR, et al. Prenatal tobacco smoke exposure is associated with childhood DNA CpG methylation. Plos One. 2014 Jun;9(6):e99716.
   Google Scholar
• 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 Oct;122(10):1147–1153.
   Google Scholar
• Ivorra C, Fraga MF, Bayón GF, et al. DNA methylation patterns in newborns exposed to tobacco in utero. J Transl Med. 2015 Jan;13(1):25.
   Google Scholar
• 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 Apr;24(8):2201–2217.
   Google Scholar
• Joubert BR, Håberg 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 Oct;120(10):1425–1431.
   Google Scholar
• Joubert BR, Felix J, Yousefi P, et al. DNA methylation in newborns and maternal smoking in pregnancy: genome-wide consortium meta-analysis. Am J Hum Genet. 2016;98(4):680–696.
   Google Scholar
• 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 May;11(5):e0155554.
   Google Scholar
• Bocklandt S, Lin W, Sehl ME, et al. Epigenetic predictor of age. Plos One. 2011 Jun;6(6):e14821.
   Google Scholar
• Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):3156.
   Google Scholar
• Horvath S, Erhart W, Brosch M, et al. Obesity accelerates epigenetic aging of human liver. Proc Nat Acad Sci. 2014;111(43):15538–15543.
   Google Scholar
• Johnson AA, Akman K, Calimport SRG, et al. The role of DNA methylation in aging, rejuvenation, and age-related disease. Rejuvenation Res. 2012;15(5):483–494.
   Google Scholar
• Knight AK, Jeffrey MC,Christiane T, et al. An epigenetic clock for gestational age at birth based on blood methylation data. Genome Biol. 2016;17(1):206.
   Google Scholar
• Levine ME, Crimmins EM. Evidence of accelerated aging among African Americans and its implications for mortality. Soc Sci Med. 2014;118:27–32.
   Google Scholar
• Marioni RE, Shah S, McRae AF, et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 2015;16(1):25.
   Google Scholar
• Parets SE, Conneely KN, Kilaru V, et al. Fetal DNA methylation associates with early spontaneous preterm birth and gestational age. Plos One. 2013 Jun;8(6):e67489.
   Google Scholar
• Schroeder JW, Conneely KN, Cubells JF, et al. Neonatal DNA methylation patterns associate with gestational age. Epigenetics. 2011 Dec;6(12):1498–1504.
   Google Scholar
• Davey Smith G, Tilling K, Suderman M, et al. Longitudinal analysis of DNA methylation associated with birth weight and gestational age. Hum Mol Genet. 2015 Apr;24(13):3752–3763.
   Google Scholar
• Wu D, Yang H, Winham SJ, et al. Mediation analysis of alcohol consumption, DNA methylation, and epithelial ovarian cancer. J Hum Genet. 2018;63(3):339–348.
   Google Scholar
• Huang JV, Cardenas A, Colicino E, et al. DNA methylation in blood as a mediator of the association of midchildhood body mass index with cardio-metabolic risk score in early adolescence. Epigenetics. 2018;13(10–11):1072–1087.
   Google Scholar
• Adkins RM, Krushkal J, Tylavsky FA, et al. Racial differences in genespecific DNA methylation levels are present at birth. Birth Defects Res A Clin Mol Teratol. 2011;91(8):728–736.
   Google Scholar
• Mozhui K, Smith AK, Tylavsky FA. Ancestry dependent DNA methylation and influence of maternal nutrition. Plos One. 2015 Mar;10(3):e0118466.
   Google Scholar
• Galanter JM, Gignoux CR, Oh SS, et al. Differential methylation between ethnic sub-groups reflects the effect of genetic ancestry and environmental exposures. eLife. 2017 Jan;6:e20532.
   Google Scholar
• Heyn H, Sebastian M, Irene  HH, et al. DNA methylation contributes to natural human variation. Genome Res. 2013 Sep;23(9):1363–1372.
   Google Scholar
• Moen EL, Zhang X, Mu W, et al. Genome-wide variation of cytosine modifications between European and African populations and the implications for complex traits. Genetics. 2013 Aug;194(4):987.
   Google Scholar
• Rahmani E, Shenhav L, Schweiger R, et al. Genome-wide methylation data mirror ancestry information. Epigenetics Chromatin. 2017;10(1):1.
   Google Scholar
• Nguyen AB, Moser R, Chou WY. Race and health profiles in the United States: an examination of the social gradient through the 2009 CHIS adult survey. Public Health. 2014;128(12):1076–1086.
   Google Scholar
• Salvo D, Frediani JK, Ziegler TR, et al. Food group intake patterns and nutrient intake vary across low-income hispanic and African American preschool children in Atlanta: a cross sectional study. Nutr J. 2012;11(1):62.
   Google Scholar
• Skala K, Chuang R-J, Evans A, et al. Ethnic differences in the home food environment and parental food practices among families of low income Hispanic and African-American preschoolers. J Immigr Minor Health. 2012 Dec;14(6):1014–1022.
   Google Scholar
• Chakraborty J, Zandbergen PA. Children at risk: measuring racial/ethnic disparities in potential exposure to air pollution at school and home. J Epidemiol Community Health. 2007 Dec;61(12):1074–1079.
   Google Scholar
• Gern JE, Visness CM, Gergen PJ, et al. The Urban Environment and Childhood Asthma (URECA) birth cohort study: design, methods, and study population. BMC Pulm Med. 2009;9(1):17.
   Google Scholar
• O’Connor GT, Lynch SV, Bloomberg GR, et al. Early-life home environment and risk of asthma among inner-city children. J Allergy Clin Immunol. 2018;141(4):1468–1475.
   Google Scholar
• Breton CV, Marsit CJ, Faustman E, et al. Small-magnitude effect sizes in epigenetic end points are important in children’s environmental health studies: the children’s environmental health and disease prevention research center’s epigenetics working group. Environ Health Perspect. 2017 Apr;125(4):511–526.
   Google Scholar
• Gaunt TR, Shihab HA, Hemani G, et al. Systematic identification of genetic influences on methylation across the human life course. Genome Biol. 2016;17(1):61.
   Google Scholar
• Fu J, Wolfs MGM, Deelen P, et al. Unraveling the regulatory mechanisms underlying tissue-dependent genetic variation of gene expression. PLoS Genet. 2012 Jan;8(1):e1002431.
   Google Scholar
• Lin D, Chen J, Perrone-Bizzozero N, et al. Characterization of cross-tissue genetic-epigenetic effects and their patterns in schizophrenia. Genome Med. 2018 Feb;10(1):13.
   Google Scholar
• Pérez RF, Santamarina P, Tejedor JR, et al. Longitudinal genome-wide DNA methylation analysis uncovers persistent early-life DNA methylation changes. J Transl Med. 2019 Jan;17(1):15.
   Google Scholar
• Olin A, Henckel E, Chen Y, et al. Stereotypic immune system development in newborn children. Cell. 2018 Aug;174(5):1277–1292.
   Google Scholar
• Mohammadkhah AI, Simpson EB, Patterson SG, et al. Development of the gut microbiome in children, and lifetime implications for obesity and cardiometabolic disease. Children (Basel). 2018 Nov;5(12):160.
   Google Scholar
• Chiu C-Y, Yeh K-W, Lin G, et al. Metabolomics reveals dynamic metabolic changes associated with age in early childhood. Plos One. 2016 Feb;11(2):e0149823.
   Google Scholar
• Martin EM, Fry RC. Environmental influences on the epigenome: exposure- associated DNA methylation in human populations. Annu Rev Public Health. 2018;39(1):309–333.
   Google Scholar
• Hompes T, Izzi B, Gellens E, et al. Investigating the influence of maternal cortisol and emotional state during pregnancy on the DNA methylation status of the glucocorticoid receptor gene (NR3C1) promoter region in cord blood. J Psychiatr Res. 2013;47(7):880–891.
   Google Scholar
• Non AL, Binder AM, Kubzansky LD, et al. Genome-wide DNA methylation in neonates exposed to maternal depression, anxiety, or SSRI medication during pregnancy. Epigenetics. 2014 Jul;9(7):964–972.
   Google Scholar
• Oberlander TF, Weinberg J, Papsdorf M, et al. Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant cortisol stress responses. Epigenetics. 2008 Mar;3(2):97–106.
   Google Scholar
• Mansell T, Vuillermin P, Ponsonby A-L, et al. Maternal mental well-being during pregnancy and glucocorticoid receptor gene promoter methylation in the neonate. Dev Psychopathol. 2016;28(4pt2):1421–1430.
   Google Scholar
• Gonseth S, de Smith AJ, Roy R, et al. Genetic contribution to variation in DNA methylation at maternal smoking- sensitive loci in exposed neonates. Epigenetics. 2016 Sep;11(9):664–673.
   Google Scholar
• Hannon E, Knox O, Sugden K, et al. Characterizing genetic and environmental influences on variable DNA methylation using monozygotic and dizygotic twins. PLoS Genet. 2018 Aug;14(8):e1007544.
   Google Scholar
• Aryee MJ, Jaffe AE, Corrada-Bravo H, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–1369.
   Google Scholar
• Maksimovic J, Gordon L, Oshlack A. SWAN: subset-quantile within array normalization for illumina infinium humanmethylation450 beadchips. Genome Biol. 2012 Jun;13(6):R44.
   Google Scholar
• Du P, Zhang X, Huang -C-C, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 2010;11:587.
   Google Scholar
• Tandon A, Patterson N, Reich D. Ancestry informative marker panels for African Americans based on subsets of commercially available SNP arrays. Genet Epidemiol. 2011 Jan;35(1):80–83.
   Google Scholar
• †. I. H. Consortium. The international HapMap project. Nature. 2003 Dec;426:789–796.
   Google Scholar
• McKennan C, Nicolae D. Accounting for unobserved covariates with varying degrees of estimability in high-dimensional biological data. Biometrika. 2019 Sep;106(4):823–840. 0006–3444.
   Google Scholar
• Storey JD. A direct approach to false discovery rates. J R Stat Soc B. 2001;63(3):479–498.
   Google Scholar
• McKennan C, Nicolae D. Estimating and accounting for unobserved covariates in high-dimensional correlated data. J Am Stat Assoc. 2020 May;1–12.
   Google Scholar
• Flutre T, Wen X, Pritchard J, et al. A statistical framework for joint eQTL analysis in multiple tissues. PLoS Genet. 2013 May;9(5):e1003486.
   Google Scholar
• Stephens M. False discovery rates: a new deal. Biostatistics. 2017;18(2):275–294.
   Google Scholar