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• W2330784827 abstract "Epigenetic modifications, including DNA methylation, represent a potential mechanism for environmental impacts on human disease. Maternal smoking in pregnancy remains an important public health problem that impacts child health in a myriad of ways and has potential lifelong consequences. The mechanisms are largely unknown, but epigenetics most likely plays a role. We formed the Pregnancy And Childhood Epigenetics (PACE) consortium and meta-analyzed, across 13 cohorts (n = 6,685), the association between maternal smoking in pregnancy and newborn blood DNA methylation at over 450,000 CpG sites (CpGs) by using the Illumina 450K BeadChip. Over 6,000 CpGs were differentially methylated in relation to maternal smoking at genome-wide statistical significance (false discovery rate, 5%), including 2,965 CpGs corresponding to 2,017 genes not previously related to smoking and methylation in either newborns or adults. Several genes are relevant to diseases that can be caused by maternal smoking (e.g., orofacial clefts and asthma) or adult smoking (e.g., certain cancers). A number of differentially methylated CpGs were associated with gene expression. We observed enrichment in pathways and processes critical to development. In older children (5 cohorts, n = 3,187), 100% of CpGs gave at least nominal levels of significance, far more than expected by chance (p value < 2.2 × 10−16). Results were robust to different normalization methods used across studies and cell type adjustment. In this large scale meta-analysis of methylation data, we identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure. Epigenetic modifications, including DNA methylation, represent a potential mechanism for environmental impacts on human disease. Maternal smoking in pregnancy remains an important public health problem that impacts child health in a myriad of ways and has potential lifelong consequences. The mechanisms are largely unknown, but epigenetics most likely plays a role. We formed the Pregnancy And Childhood Epigenetics (PACE) consortium and meta-analyzed, across 13 cohorts (n = 6,685), the association between maternal smoking in pregnancy and newborn blood DNA methylation at over 450,000 CpG sites (CpGs) by using the Illumina 450K BeadChip. Over 6,000 CpGs were differentially methylated in relation to maternal smoking at genome-wide statistical significance (false discovery rate, 5%), including 2,965 CpGs corresponding to 2,017 genes not previously related to smoking and methylation in either newborns or adults. Several genes are relevant to diseases that can be caused by maternal smoking (e.g., orofacial clefts and asthma) or adult smoking (e.g., certain cancers). A number of differentially methylated CpGs were associated with gene expression. We observed enrichment in pathways and processes critical to development. In older children (5 cohorts, n = 3,187), 100% of CpGs gave at least nominal levels of significance, far more than expected by chance (p value < 2.2 × 10−16). Results were robust to different normalization methods used across studies and cell type adjustment. In this large scale meta-analysis of methylation data, we identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure. Despite years of advisories regarding health risks to the developing fetus from maternal smoking, many pregnant women still smoke, including 12.3% in the US.1Tong V.T. Dietz P.M. Morrow B. D’Angelo D.V. Farr S.L. Rockhill K.M. England L.J. Centers for Disease Control and Prevention (CDC)Trends in smoking before, during, and after pregnancy--Pregnancy Risk Assessment Monitoring System, United States, 40 sites, 2000-2010.MMWR Surveill. Summ. 2013; 62: 1-19PubMed Google Scholar Maternal smoking during pregnancy is regarded as a cause of low birth weight, reduced pulmonary function (PLF [MIM: 608852]), orofacial clefts (OFC1 [MIM: 119530]), and sudden infant death syndrome (SIDS [MIM: 272120]) in exposed newborns.2US Department of Health and Human ServicesThe health consequences of smoking—50 years of progress: A report of the surgeon general. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, Atlanta, GA2014Google Scholar Other adverse birth outcomes3Moritsugu K.P. The 2006 Report of the Surgeon General: the health consequences of involuntary exposure to tobacco smoke.Am. J. Prev. Med. 2007; 32: 542-543Abstract Full Text Full Text PDF PubMed Scopus (119) Google Scholar have been associated with maternal smoking in pregnancy, along with common health problems in children, including asthma (ASRT [MIM: 600807]), otitis media (OMS [MIM: 166760]), and neurobehavioral disorders.2US Department of Health and Human ServicesThe health consequences of smoking—50 years of progress: A report of the surgeon general. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, Atlanta, GA2014Google Scholar The mechanisms for the adverse health effects of maternal smoking during pregnancy on offspring remain poorly understood.2US Department of Health and Human ServicesThe health consequences of smoking—50 years of progress: A report of the surgeon general. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, Atlanta, GA2014Google Scholar Recently, studies have examined the potential role of epigenetic modifications such as DNA methylation at specific CpG sites (CpGs). These include studies examining genome-wide DNA methylation in newborns in relation to maternal smoking in pregnancy with the Illumina Infinium HumanMethylation27 (27K) BeadChip4Breton C.V. Siegmund K.D. Joubert B.R. Wang X. Qui W. Carey V. Nystad W. Håberg S.E. Ober C. Nicolae D. et al.Asthma BRIDGE consortiumPrenatal tobacco smoke exposure is associated with childhood DNA CpG methylation.PLoS ONE. 2014; 9: e99716Crossref PubMed Scopus (94) Google Scholar, 5Flom J.D. Ferris J.S. Liao Y. Tehranifar P. Richards C.B. Cho Y.H. Gonzalez K. Santella R.M. Terry M.B. Prenatal smoke exposure and genomic DNA methylation in a multiethnic birth cohort. Cancer Epidemiol.Biomarkers Prev. 2011; 20: 2518-2523Crossref PubMed Scopus (76) Google Scholar, 6Suter M. Ma J. Harris A. Patterson L. Brown K.A. Shope C. Showalter L. Abramovici A. Aagaard-Tillery K.M. Maternal tobacco use modestly alters correlated epigenome-wide placental DNA methylation and gene expression.Epigenetics. 2011; 6: 1284-1294Crossref PubMed Scopus (212) Google Scholar or the newer platform with wider coverage, the HumanMethylation450 (450K) BeadChip.7Joubert B.R. Håberg S.E. Nilsen R.M. Wang X. Vollset S.E. Murphy S.K. Huang Z. Hoyo C. Midttun Ø. Cupul-Uicab L.A. et al.450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy.Environ. Health Perspect. 2012; 120: 1425-1431Crossref PubMed Scopus (543) Google Scholar, 8Markunas C.A. Xu Z. Harlid S. Wade P.A. Lie R.T. Taylor J.A. Wilcox A.J. Identification of DNA methylation changes in newborns related to maternal smoking during pregnancy.Environ. Health Perspect. 2014; 122: 1147-1153PubMed Google Scholar, 9Richmond R.C. Simpkin A.J. Woodward G. Gaunt T.R. Lyttleton O. McArdle W.L. Ring S.M. Smith A.D. Timpson N.J. Tilling K. 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: 2201-2217Crossref PubMed Scopus (247) Google Scholar, 10Küpers L.K. Xu X. Jankipersadsing S.A. Vaez A. la Bastide-van Gemert S. Scholtens S. Nolte I.M. Richmond R.C. Relton C.L. Felix J.F. et al.DNA methylation mediates the effect of maternal smoking during pregnancy on birthweight of the offspring.Int. J. Epidemiol. 2015; 44: 1224-1237Crossref PubMed Scopus (133) Google Scholar A number of differentially methylated loci have been identified in offspring in relation to maternal smoking in pregnancy in individual studies (references in the Supplemental Note). One study examined the top CpGs with respect to timing of exposure and found that the signals reflect sustained, rather than short-term, exposure to maternal smoking during pregnancy,11Joubert B.R. Haberg S.E. Bell D.A. Nilsen R.M. Vollset S.E. Midttun O. Ueland P.M. Wu M.C. Nystad W. Peddada S.D. et al.Maternal smoking and DNA methylation in newborns: in utero effect or epigenetic inheritance.Cancer Epidemiol. Biomarkers Prev. 2014; 23: 1007-1017Crossref PubMed Scopus (92) Google Scholar but this has not been evaluated genome-wide. A few studies suggest that some of these methylation signals persist into later childhood and adolescence, but data are limited.9Richmond R.C. Simpkin A.J. Woodward G. Gaunt T.R. Lyttleton O. McArdle W.L. Ring S.M. Smith A.D. Timpson N.J. Tilling K. 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: 2201-2217Crossref PubMed Scopus (247) Google Scholar, 12Lee K.W. Richmond R. Hu P. French L. Shin J. Bourdon C. Reischl E. Waldenberger M. Zeilinger S. Gaunt T. 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: 193-199PubMed Google Scholar The combination of genome-wide data across studies via meta-analysis to generate large sample sizes for the discovery of loci that would not have been identified from individual studies has been very successful in genetics, but this approach has rarely been used with methylation data. To address the impact of maternal smoking during pregnancy on newborns with much greater power, we recruited 13 birth cohort studies with data on maternal smoking during pregnancy and DNA methylation in offspring from the 450K BeadChip into the Pregnancy and Childhood Epigenetics consortium (PACE). We meta-analyzed harmonized cohort-specific associations between maternal smoking during pregnancy and DNA methylation in the offspring. We examined both sustained maternal smoking and any smoking during pregnancy. We also examined persistence of DNA methylation patterns related to maternal smoking in newborns among older children, including adjustment for postnatal secondhand tobacco smoke exposure. For functional follow-up of findings, we evaluated the associations between methylation status in the newly identified CpGs and expression levels of nearby genes and performed pathway and functional network analyses. This study represents a large and comprehensive evaluation of the impact of maternal smoking during pregnancy on DNA methylation in offspring. A total of 13 PACE cohorts participated in the meta-analysis of maternal smoking during pregnancy and 450K DNA methylation in newborns. These studies, listed in alphabetical order, are the Avon Longitudinal Study of Parents and Children (ALSPAC), the Center for Health Assessment of Mothers and Children of Salinas (CHAMACOS), the Children’s Health Study (CHS), the GECKO Drenthe cohort, the Generation R Study, Isle of Wight (IOW), Mechanisms of the Development of Allergy (MeDALL), three independent datasets from the Norwegian Mother and Child Cohort Study (MoBa1, MoBa2, and MoBa3), the Norway Facial Clefts Study (NFCS), the Newborn Epigenetics Study (NEST), and Project Viva. MeDALL represents a pooled analysis of four cohorts with coordinated methylation measurements: Infancia y Medio Ambiente (INMA), Etudes des Déterminants pré et postnatals précoces du développement et de la santé de l’Enfant (EDEN), Children’s Allergy Environment Stockholm Epidemiology study (BAMSE), and Prevention and Incidence of Asthma and Mite Allergy (PIAMA). Two of the MeDALL cohorts contributed to the newborn meta-analysis (INMA and EDEN). There were five studies with data on older children: ALSPAC, Genes-environments and Admixture in Latino Americans (GALA II), the Study to Explore Early Development (SEED), MeDALL (INMA, EDEN, BAMSE, and PIAMA), and an independent methylation dataset from BAMSE subjects. Ethical approval for study protocols was obtained for all participating cohorts. Further information on this as well as the study methods for each cohort are described in detail in the Supplemental Note. For this paper, participating cohorts shared only results files from in-house analyses. No individual data were shared for this paper. Therefore, access to the individual cohort-level data for the purpose of reproducing results would require individual data transfer agreements to be negotiated with and approved by each of the contributing cohorts. Cohorts assessed maternal smoking during pregnancy via questionnaires completed by the mothers. The MoBa study (MoBa1 and MoBa2) also used cotinine measurements from maternal blood samples taken during pregnancy as part of the definition of maternal smoking during pregnancy. More details on the cohort-specific smoking variables are in the Supplemental Note. In a previous publication from the MoBa1 study, significant associations between maternal smoking during pregnancy and DNA methylation in newborns were driven not by transient smoking that ended early in pregnancy but rather by sustained smoking during pregnancy.11Joubert B.R. Haberg S.E. Bell D.A. Nilsen R.M. Vollset S.E. Midttun O. Ueland P.M. Wu M.C. Nystad W. Peddada S.D. et al.Maternal smoking and DNA methylation in newborns: in utero effect or epigenetic inheritance.Cancer Epidemiol. Biomarkers Prev. 2014; 23: 1007-1017Crossref PubMed Scopus (92) Google Scholar Thus, each cohort ran separate models to evaluate both sustained smoking and any smoking during pregnancy. The variable (yes/no) for sustained smoking during pregnancy was designed to capture women who smoked at least one cigarette per day through most of pregnancy. To cleanly contrast the effect of sustained smoking through pregnancy with that of never smoking during pregnancy, we excluded women who reported quitting smoking during pregnancy from the sustained smoking models. The variable (yes/no) for any maternal smoking during pregnancy was designed to capture any amount of smoking during pregnancy, at any time, even if a woman reported quitting. Because we did not exclude women who quit smoking during pregnancy from the models representing any smoking during pregnancy, the total sample sizes are slightly larger than those of the models representing sustained smoking during pregnancy. Genome-wide analyses use large sample statistics. We limited meta-analyses to cohorts with at least 15 subjects in both the exposed and unexposed groups. This excluded four cohorts (CHAMACOS, CHS, IOW, and Project Viva) from the sustained smoking models. However these cohorts did participate in the meta-analysis of any smoking during pregnancy. Each cohort independently conducted laboratory measurements and quality control. The samples for each cohort underwent bisulfite conversion via the EZ-96 DNA Methylation kit (Zymo Research). Samples were processed with the Illumina Infinium HumanMethylation450 (450K) BeadChip (Illumina) at Illumina or in cohort-specific laboratories. Quality control of samples was performed by each cohort and failed samples were excluded on the basis of Illumina’s detection p value, low sample DNA concentration, sample call rate, CpG-specific percentage of missing values, bisulfite conversion efficiency, gender verification with multidimensional scaling plots, and other quality control metrics specific to cohorts. Cohorts could also use validated, published statistical methods for normalizing their methylation data on the untransformed methylation beta values (ranging from 0 to 1). Some cohorts also made independent probe exclusions. More details are provided in the Supplemental Note. For the meta-analysis, additional probe exclusions were made across all cohorts. Specifically, we excluded control probes (n = 65), probes that mapped to the X (n = 11,232) or Y (n = 416) chromosomes, probes with an underlying SNP mapping to the last five nucleotides of the probe sequence (N = 9,168) as previously described,7Joubert B.R. Håberg S.E. Nilsen R.M. Wang X. Vollset S.E. Murphy S.K. Huang Z. Hoyo C. Midttun Ø. Cupul-Uicab L.A. et al.450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy.Environ. Health Perspect. 2012; 120: 1425-1431Crossref PubMed Scopus (543) Google Scholar and CpGs with an implausible (zero) value for the SE (n = 67). This left a total of 464,628 CpGs included in the meta-analysis. Each cohort ran independent statistical analyses according to a common pre-specified analysis plan. Robust linear regression was used in R13R Core TeamR: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria2013http://www.R-project.org/Google Scholar to evaluate the association between maternal smoking during pregnancy and cord blood DNA methylation for each probe while accounting for potential heteroskedasticity and/or influential outliers. Each cohort ran the following covariate-adjusted statistical models: (1) the primary model, which used sustained maternal smoking during pregnancy as the exposure and the normalized betas as the outcome, (2) sustained maternal smoking during pregnancy as the exposure and raw betas (not normalized) as the outcome, (3) any maternal smoking during pregnancy as the exposure and normalized betas as the outcome, (4) any maternal smoking during pregnancy as the exposure and raw betas as the outcome, and (5) sustained maternal smoking during pregnancy as the exposure and normalized betas as the outcome, with additional adjustment for cell type proportion. All models were adjusted for maternal age, maternal education (or a surrogate socioeconomic metric), parity, and technical covariates such as batch or plate. Some cohorts used ComBat14Leek 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: 882-883Crossref PubMed Scopus (2291) Google Scholar to account for batch effects and therefore did not include batch or plate as covariates in the models with normalized betas (see Supplemental Note). Additional correction for study design or sampling factors was done as needed in some cohorts. Because maternal smoking during pregnancy is not related to the child’s sex, it cannot be a confounder and thus was not included in models. We did not adjust for principal components (PCs) because not all cohorts had genome-wide genotype data and cohorts with genotype data had it only for a subset of subjects with methylation data. Furthermore, in one large cohort with PC data, models adjusted for PCs showed little variation in the results (correlation of betas = 0.991; correlation of log(p values) = 0.996) when compared to models without this adjustment, despite a reduction in sample size. The statistical models for cohorts with DNA methylation measured in older children were the same, with the additional adjustment for second-hand tobacco smoke exposure. All cohorts independently estimated cell type proportion by using the reference-based Houseman method15Houseman E.A. Accomando W.P. Koestler D.C. Christensen B.C. Marsit C.J. Nelson H.H. Wiencke J.K. Kelsey K.T. DNA methylation arrays as surrogate measures of cell mixture distribution.BMC Bioinformatics. 2012; 13: 86Crossref PubMed Scopus (1902) Google Scholar in the minfi package16Aryee M.J. Jaffe A.E. Corrada-Bravo H. Ladd-Acosta C. Feinberg A.P. Hansen K.D. Irizarry R.A. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays.Bioinformatics. 2014; 30: 1363-1369Crossref PubMed Scopus (2066) Google Scholar with the Reinius et al. dataset for reference.17Reinius L.E. Acevedo N. Joerink M. Pershagen G. Dahlén S.E. Greco D. Söderhäll C. Scheynius A. Kere J. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility.PLoS ONE. 2012; 7: e41361Crossref PubMed Scopus (708) Google Scholar Cell type correction was applied by inclusion of the six estimated cell type proportions (CD8T, CD4T, NK cells, B cells, monocytes, granulocytes) as covariates in cohort-specific statistical models. We performed inverse variance-weighted fixed-effects meta-analysis with METAL.18Willer C.J. Li Y. Abecasis G.R. METAL: fast and efficient meta-analysis of genomewide association scans.Bioinformatics. 2010; 26: 2190-2191Crossref PubMed Scopus (2615) Google Scholar We accounted for multiple testing by controlling the false discovery rate (FDR) at 5%, implementing the method by Benjamini and Hochberg.19Benjamini Y. Hochberg Y. Controlling for False Discovery Rate: a Practical and Powerful Approach to Multiple Testing.J. R. Stat. Soc. Ser. B. 1995; 57: 289-300Google Scholar This method was applied to all instances of FDR correction described in this paper unless otherwise specified. CpGs with an FDR-corrected p value less than 0.05 were considered statistically significant. CpGs that were statistically significant based on the more stringent Bonferroni correction (uncorrected p value < 1.08 × 10−7 to account for 464,628 tests) were also noted. To determine the robustness of our models and findings, we performed an additional analysis in which we removed the cohorts of non-European ancestry (Table S1). We compared the effect estimates, SEs, and the distribution of the p values for the model to the estimates for our primary model to evaluate the consistency of our findings. The FDR-significant CpGs identified in the primary model from the newborn meta-analyses were followed up with a lookup replication approach in the results from five older children cohorts, and FDR correction was applied to account for the number of CpGs tested. We performed a systematic literature review to determine which CpGs represented findings not previously related to smoking exposure and methylation in the literature. A query of NCBI’s PubMed database was performed with the search terms ((“DNA Methylation”[Mesh] OR methylation) AND (“Smoking”[Mesh] OR smoking)) in order to be broad enough to capture all past studies reporting such results. CpGs with previously reported associations with smoking, both from prenatal exposure or in adults, were considered. This search yielded 789 results when performed on March 1, 2015. All results were then reviewed by title and abstract to determine whether they met inclusion criteria. First, results were limited to those performed in healthy human populations. That is, participants could not exclusively have been drawn from disease cases and studies could not have been performed only in cell lines or animals. Case-control analyses that included healthy controls were accepted as meeting this criterion, and no limitation was applied concerning the tissue used for DNA extraction. Second, studies were required to have performed DNA methylation analysis agnostically on a large scale as opposed to targeted interrogation of candidate CpGs. This was operationalized by including only analyses that examined >1,000 sites simultaneously. The Illumina 450K, 27K, and GoldenGate arrays all met this criterion. Third, the exposure was restricted to tobacco cigarette smoking. Related exposures, such as to other forms of tobacco use or smoke exposure, were not included. Lastly, studies had to have reported their significant results publicly. Studies that failed to report p values or gene annotations were excluded. Review of the existing literature on the effect of smoking on DNA methylation identified 25 publications meeting inclusion criteria. Of these, 16 studies reported results for adult smoking exposure,20Besingi W. Johansson A. Smoke-related DNA methylation changes in the etiology of human disease.Hum. Mol. Genet. 2014; 23: 2290-2297Crossref PubMed Scopus (132) Google Scholar, 21Breitling L.P. Yang R. Korn B. Burwinkel B. Brenner H. Tobacco-smoking-related differential DNA methylation: 27K discovery and replication.Am. J. Hum. 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