Our study of maternal smoking in relation to epigenome-wide DNA methylation in newborns in the MoBa cohort is the largest and most extensive that we know of. In addition to the large sample size, we used a highly reproducible platform that assesses methylation at > 470,000 individual CpGs, providing more comprehensive coverage of the epigenome than other studies of maternal smoking published to date. Further, we assessed maternal smoking with a sensitive assay for cotinine, a well-validated biomarker for tobacco smoke. We observed epigenome-wide statistically significant associations between maternal smoking in pregnancy, assessed by plasma cotinine levels and methylation in cord blood at 26 CpGs mapping to 10 genes in MoBa. In an independent birth cohort from the United States, the NEST study, we found a striking degree of replication for our findings. In the NEST replication population, the direction of differential methylation in relation to maternal smoking was consistent with the direction in relation to maternal plasma cotinine for all of the 26 CpGs that were significant (p < 1.06 × 10–7) in the discovery study. In addition, despite the more modest sample size of the replication set (18 newborns born to smoking mothers and 18 born to nonsmokers), estimates for 21 of 26 CpGs had p-values < 0.05. Five CpGs of the 26 met strict Bonferroni-corrected statistical significance in the replication study (p < 0.0019): two in CYP1A1 and one in AHRR, genes known to be involved in the detoxification of compounds from tobacco smoke via the aryl hydrocarbon receptor (AhR) signaling pathway; and two CpGs in GFI1, a gene that has not previously been implicated in responses to tobacco smoke.
Our most statistically significant finding in both the replication and discovery analyses was lower methylation with higher levels of self-reported or cotinine-based evidence of maternal smoking at cg05575921 in AHRR
. Remarkably, a recent study in adults, using the same 450K platform, showed lower methylation at this same CpG (cg05575921) in smokers compared with nonsmokers at epigenome-wide statistical significance (Monick et al. 2012
). That study observed lower methylation in smokers at this CpG for both lymphoblasts and pulmonary alveolar macrophages (Monick et al. 2012
). The authors also studied the functional implications of this methylation change and found that methylation at AHRR
cg05575921 was associated with AHRR
expression. Thus our data show that a methylation change found in adult smokers and implicated as functionally important in AHRR
, a gene involved in a key pathway of response to tobacco smoke components, is already present at birth in newborns due to maternal smoking in pregnancy.
Our findings for genes in the AhR pathway make sense biologically; the pathway is known to mediate the effects of toxicants such as polycyclic aromatic hydrocarbons (PAH) in tobacco smoke. PAH bind to AhR causing its translocation to the nucleus and the formation of a heterodimer with the AhR nuclear transporter. This complex binds DNA regulatory sequences, termed xenobiotic response elements (XREs), and initiates the expression of CYP1A1
and other genes involved in detoxification of these chemicals (Nguyen and Bradfield 2008
). The AhR repressor (AHRR) acts as a negative regulator of AhR activity, suppressing CYP1A1
transcription (Harper et al. 2006
). In our study, maternal smoking, assessed objectively by cotinine, displayed a dose-dependent association with lower methylation of AHRR
CpGs and higher methylation of CYP1A1
CpGs in cord blood. The contrasting effects of maternal smoking during pregnancy on methylation at CpGs in AHRR
are notable because of the opposing function these genes have in the AhR pathway (Kawajiri and Fujii-Kuriyama 2007
Although the role of the AhR pathway in response to toxicants is well known, there is increasing identification of the importance of this pathway in the regulation of other processes, such as immune function (Lawrence and Sherr 2012
). In addition, AhR has also recently been found to play a crucial role in regulating cigarette smoke extract–induced apoptosis in fibroblasts (lung and embryonic) and lung epithelial cells in culture from the mouse (Rico de Souza et al. 2011
We also replicated our novel findings for GFI1,
which has not previously been implicated in response to tobacco smoke. GFI1
plays an essential role in diverse developmental processes including hematopoiesis and the development of the inner ear and pulmonary neuroendocrine cells (Duan et al. 2005
; Khandanpour et al. 2011
influences numerous cellular events such as proliferation, apoptosis, differentiation, lineage decisions, and oncogenesis (Jafar-Nejad and Bellen 2004
). This gene is part of a complex that enables histone modifications and may also control alternative pre-mRNA splicing (Moroy and Khandanpour 2011
). Given the pivotal involvement of GFI1
in fundamental development processes, a role in diverse effects of maternal smoking on the offspring is biologically plausible.
Although our findings for RUNX1
did not meet strict Bonferroni statistical significance in the replication population (NEST p
> 0.00019), there were four RUNX1
CpGs in the top 100 results in the MoBa discovery population [see Supplemental Material, Table S1
(also known as AML1
) is involved in the development of normal hematopoiesis as well as leukemia (Kumano and Kurokawa 2010
). Of note, RUNX1
, AhR, and GFI1
are all involved in the regulation of hematopoietic stem cells (Boitano et al. 2010
; Khandanpour et al. 2011
; Oshima et al. 2011
), suggesting the possibility that cross-talk between these genes may impact smoking-related health outcomes in the offspring.
Correlation of DNA methylation at CpGs within the same gene may contribute to the finding of genes with multiple significant CpGs in our analyses. However, if CpGs are not truly independent, then using strict Bonferroni correction for multiple testing, which assumes independent tests, is quite conservative. This adds support for the results that surpassed this strict threshold, particularly the five CpGs with corrected statistical significance in both the discovery and replication populations.
Cotinine, the biomarker of smoking, was not measured among pregnant women in the replication (NEST) study, so we used maternal self-report of smoking on questionnaires that was consistent with medical records of smoking. Among our MoBa study participants, 8 of the 136 women (5.9%) with cotinine values consistent with active smoking (≥ 56.8 nmol/L) reported that they did not smoke during pregnancy. A study of U.S. reproductive age women using NHANES (National Health and Nutrition Examination Survey) data indicates that U.S. pregnant women also underreport smoking (Dietz et al. 2011
). Thus, we expect that some of the NEST participants classified as nonsmokers might actually have been smokers. However, this type of misclassification should lead to a bias of estimates toward the null, making our replication estimates more conservative than would be expected if smokers had reported their exposure with 100% accuracy.
The 450K Beadchip offers greatly improved genomic coverage over the earlier 27K platform. The 450K content includes 99% of RefSeq genes with multiple probes per gene, 96% of CpG islands from the UCSC database (http://genome.ucsc.edu/
), CpG island shores, and additional content selected from whole-genome bisulfite sequencing data and input from DNA methylation experts (Bibikova et al. 2011
). None of the 26 CpGs with epigenome-wide significance in our study were present on the 27K platform. A study using the 27K platform observed differences in DNA methylation associated with smoking status in adults at CpG cg03636183 in F2RL3
, a gene associated with cardiovascular disease (Breitling et al. 2011
). Employing a single CpG look-up approach (one CpG evaluated, uncorrected for multiple testing), our data provide support for an association between maternal smoking during pregnancy and cord blood DNA methylation at this CpG (coef = –0.020, se = 0.009, p
= 0.016). Another recent study of 27K methylation and adult smoking identified cg19859270 in GPR15
(Wan et al. 2012
). Our single look-up approach for this CpG did not provide supporting results (coef = –0.010, SE = 0.007, p
Maternal smoking during pregnancy has been associated with CpG-specific differential DNA methylation in placental tissue using the 27K Beadchip (Suter et al. 2011
), but we found no overlap between the top hits from that study and the top hits in our study using the 450K Beadchip to measure methylation in newborn cord blood samples. In addition to the limitations of comparing the 27K and 450K platforms (the top 26 CpGs from our study were not covered on the 27K platform), it is likely that altered methylation in response to tobacco smoke exposure is different in placental tissue and newborn cord blood.
We measured DNA methylation in whole cord blood samples. Because hematopoietic differentiation and methylation status of differentially methylated regions (DMRs) are highly coordinated (Schmidl et al. 2009
), significant shifts in cell type pools in the blood should be accompanied by shifts in methylation at dozens, if not hundreds, of cell type–specific DMRs. If our findings were simply a reflection of maternal smoking influencing shifts in cell types, we would expect to find many differentially methylated genes, which we did not. Instead, only 10 genes had differences in methylation levels related to cotinine in our data.
Notably, the recent paper of Monick et al. (2012)
supports the notion that unmeasured confounding by cell type does not explain the altered methylation status that we observed in relation to smoking. In that paper, smoking-induced alteration of AHRR
CpGs (including our top CpG) was seen in both B lymphoblastoid cells and in an independently collected sample of alveolar macrophage cells collected from bronchial lavage. Thus, Monick et al. identified smoking-induced signals across two distinct cell types, which strengthens our replicated whole blood findings.
Although the above evidence suggests that our results are not confounded by cell type, we directly addressed the potential impact of differential cell counts by measuring epigenome-wide DNA methylation, using the 450K assay, in 21 cord blood samples that had been separated, while fresh, into the two major cell pools, PM cells and MN cells. In these 21 paired samples differences in methylation by cell type were very small. These small differences were statistically significant (p < 0.0019) for 3 of the top 26 CpGs associated with maternal plasma cotinine in MoBa, but these CpGs were not significantly associated with maternal smoking in the NEST population. Furthermore, the magnitude of the difference in median methylation between PM and MN cell types was much smaller than the difference in median methylation between smokers and nonsmokers in both the MoBa and NEST study populations. For the percent difference in median methylation by cell type, the maximum was 3.1% and the mean was 1.0%. In contrast, for the percent difference in median methylation between smokers and non-smokers measured in whole blood, which is a mixture of these two major cell types (PM and MN), the maximum was 13.7% (MoBa) and 15.1% (NEST), and the mean was 5.3% (MoBa) and 5.0% (NEST). For our top CpG AHRR cg05575921, the percent difference in median methylation was 0.31% by cell type compared with 7.52% for smokers compared with nonsmokers in MoBa (7.67% in NEST). Thus, the differences in methylation between these two major cell pools are much smaller than the differences in methylation by smoking that we observed in whole blood, suggesting that confounding by cell type is unlikely to explain our findings of differential methylation related to smoking.
It is possible that differences in methylation may exist in more refined subclassifications of cell types that we did not specifically examine. But again, our top finding for AHRR
cg05575921 due to maternal smoking was also reported in adult smokers in two different cell types—alveolar lung macrophage DNA and lymphoblast DNA (Monick et al. 2012
). These various lines of evidence give strong support for the conclusion that our replicated findings are not explained by effects of maternal smoking on the relative prevalence of white blood cell subtypes that differ with regard to CpG methylation.