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Research August 2017 | Volume 125 | Issue 8

Environ Health Perspect; DOI:10.1289/EHP1246

Exposure to Low Levels of Lead in Utero and Umbilical Cord Blood DNA Methylation in Project Viva: An Epigenome-Wide Association Study

Shaowei Wu,1 Marie-France Hivert,2 Andres Cardenas,2 Jia Zhong,3 Sheryl L. Rifas-Shiman,2 Golareh Agha,3 Elena Colicino,3 Allan C. Just,4 Chitra Amarasiriwardena,4 Xihong Lin,5 Augusto A. Litonjua,6 Dawn L. DeMeo,6 Matthew W. Gillman,7 Robert O. Wright,4 Emily Oken,2 and Andrea A. Baccarelli3
Author Affiliations open

1Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China

2Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA

3Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, USA

4Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA

5Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

6Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA

7Environmental Influences on Child Health Outcomes (ECHO) Office of the Director, National Institutes of Health, Department of Health and Human Services, Rockville, Maryland, USA

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  • Background:
    Early-life exposure to lead is associated with deficits in neurodevelopment and with hematopoietic system toxicity. DNA methylation may be one of the underlying mechanisms for the adverse effects of prenatal lead on the offspring, but epigenome-wide methylation data for low levels of prenatal lead exposure are lacking.
    We investigated the association between prenatal maternal lead exposure and epigenome-wide DNA methylation in umbilical cord blood nucleated cells in Project Viva, a prospective U.S.-based prebirth cohort with relatively low levels of lead exposure.
    Among 268 mother–infant pairs, we measured lead concentrations in red blood cells (RBC) from prenatal maternal blood samples, and using HumanMethylation450 Bead Chips, we measured genome-wide methylation levels at 482,397 CpG loci in umbilical cord blood and retained 394,460 loci after quality control. After adjustment for batch effects, cell types, and covariates, we used robust linear regression models to examine associations of prenatal lead exposure with DNA methylation in cord blood at epigenome-wide significance level [false discovery rate (FDR)<0.05].
    The mean [standard deviation (SD)] maternal RBC lead level was 1.22 (0.63) μg/dL. CpG cg10773601 showed an epigenome-wide significant negative association with prenatal lead exposure (−1.4% per doubling increase in lead exposure; p=2.3×10−7) and was annotated to C-Type Lectin Domain Family 11, Member A (CLEC11A), which functions as a growth factor for primitive hematopoietic progenitor cells. In sex-specific analyses, we identified more CpGs with FDR<0.05 among female infants (n=38) than among male infants (n=2). One CpG (cg24637308), which showed a strong negative association with prenatal lead exposure among female infants (−4.3% per doubling increase in lead exposure; p=1.1×10−06), was annotated to Dynein Heavy Chain Domain 1 gene (DNHD1) which is highly expressed in human brain. Interestingly, there were strong correlations between blood and brain methylation for CpG (cg24637308) based on another independent set of samples with a high proportion of female participants.
    Prenatal low-level lead exposure was associated with newborn DNA methylation, particularly in female infants.
  • Received: 18 October 2016
    Revised: 26 May 2017
    Accepted: 13 June 2017
    Published: 25 August 2017

    Address correspondence to S. Wu, Dept. of Occupational and Environmental Health Sciences, School of Public Health, Peking University, 38 Xueyuan Rd., Haidian District, Beijing, China. Telephone: 86-10-82801522. Email:, or A.A. Baccarelli, Dept. of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th St., ARB 11th Floor 1105E, New York, NY 10032 USA. Telephone: 617-432-0037. Email:

    Supplemental Material is available online (

    The authors declare they have no actual or potential competing financial interests.

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Lead is an environmental pollutant whose widespread use has caused extensive environmental contamination and health effects throughout the world (WHO 2015). Lead affects multiple body systems, and there is no known level of lead exposure that is considered safe (WHO 2015). Lead can pass freely across the placenta (Chuang et al. 2001), and as a result, there is substantial concern about the potential adverse effects of prenatal lead exposure on the developing fetus. Fetal and early-life exposure to lead, even at low levels, has been associated with a range of adverse health outcomes, including preterm delivery and small-for-gestational-age birth (Jelliffe-Pawlowski et al. 2006; Perkins et al. 2014), poor growth in childhood (Afeiche et al. 2011), impaired cognitive and behavioral functions (Boucher et al. 2014; Liu et al. 2014; Virgolini et al. 2008), and hematopoietic system toxicity (Jacob et al. 2000; Serrani et al. 1997). Interestingly, sex differences in lead exposure–related health effects have been reported in previous studies (Afeiche et al. 2011; Bunn et al. 2001; Perkins et al. 2014; Schneider et al. 2013; Virgolini et al. 2008), suggesting sexually dimorphic responses to lead exposure between males and females.

Previous animal, in vitro, and epidemiological studies have suggested that DNA methylation may be one of the mechanisms by which lead exposure exerts its biological effects. For example, lead exposure induced dose- and sex-specific responses in DNA methylation at several specific loci that were responsible for phenotypes including weight and coat color in mice (Faulk et al. 2013). An in vitro study showed that lead exposure reduces the global DNA methylation level by noncompetitive inhibition and alteration of DNA methyltransferase (DNMT) expression (Sanchez et al. 2017), whereas additional animal studies also found that lead exposure affects the expression of maintenance enzyme DNMT1 and the de novo methyltransferase DNMT3a in the brain (Schneider et al. 2012; Schneider et al. 2013). Specifically, an in vitro study demonstrated that lead exposure induced changes in the methylation status of genes involved in neurogenetic signaling pathways in human embryonic stem cells and altered their neuronal differentiation (Senut et al. 2014). These studies provide evidence that lead exposure may affect both global and brain genomic methylation. In addition, a recent epidemiological study found that the epigenome-wide DNA methylation profile in umbilical cord blood was associated with high levels of prenatal lead exposure in 127 mother–infant pairs in lead-endemic regions outside the United States (Engström et al. 2015).

Although existing evidence supports the role of lead exposure in modifying DNA methylation, little is known about potential effects of relatively low levels of lead exposure in utero—common to much of the U.S. population—on DNA methylation. It is also unknown whether variation in DNA methylation associated with prenatal lead exposure may last over time, which may help elucidate the long-term health effects of lead exposure. We aimed to investigate these questions by performing an epigenome-wide association study (EWAS) of low-level maternal lead exposure [<5 μg/dL in red blood cells (RBC)] and DNA methylation profiles in umbilical cord blood samples from 268 mother–infant pairs enrolled in Project Viva, a U.S.-based prebirth cohort, overall and separately among boys and girls. We also attempted to replicate the observed prenatal lead exposure–cord blood DNA methylation associations using data collected during midchildhood for 240 children (7–10 y of age).


Study Subjects

Study subjects were mother–infant pairs in Project Viva, a prospective prebirth cohort with 2,128 women recruited between 1999 and 2002 from Atrius Harvard Vanguard Medical Associates in eastern Massachusetts. Eligibility criteria included English speaking, singleton pregnancy, and <22 wk of gestation. All women provided written informed consent, and the research was approved by the Institutional Review Board at Harvard Pilgrim Health Care.

We collected blood samples from 1,614 women at an average of 27.9 wk of gestation and analyzed blood samples for 952 women for lead, selecting those with children who participated in follow-up visits in midchildhood. We measured genome-wide DNA methylation using the Infinium HumanMethylation450 (HM450K) BeadChip (Illumina, Inc.) for 507 umbilical cord blood samples collected at delivery and 473 child blood samples collected during midchildhood. A total of 485 cord blood samples and 460 midchildhood blood samples passed quality-control (QC) procedures (see Table S1). Among these, 269 samples had data on both maternal RBC lead and DNA methylation in cord blood, and 241 samples had data on both maternal RBC lead and DNA methylation in midchildhood blood samples. We excluded one sample with undetectable maternal RBC lead, leaving 268 cord blood samples and 240 midchildhood blood samples for the data analysis. Participants with cord blood samples and midchildhood blood samples (n=377, with 131 overlapped between delivery and midchildhood) were generally similar in baseline characteristics compared with those who were not included (n=1,751, data not shown). However, women included in this analysis were more likely to be white (76% vs. 65%) and to have a college degree (70% vs. 64%) than those who were excluded.

Sample Collection

All blood samples were collected in vacutainer tubes containing ethylenediaminetetraacetic acid (EDTA) and were put on ice immediately. The tubes were centrifuged at 1,700×g for 10 min at 4°C to separate plasma, nucleated cells (including leukocytes and nucleated RBC in cord blood and leukocytes in maternal blood), and RBC within 24 h after collection. We extracted genomic DNA from the nucleated cells with commercially available PureGene Kits (Fisher, Catalog Nos. A407-4, A416-4; Qiagen, Catalog Nos.158908, 158912, 158924). Sample aliquots were then stored at −80°C until analysis.

Measurement of RBC Lead

Lead concentrations in RBC from prenatal blood samples were measured at the Trace Metals Laboratory at Harvard T.H. Chan School of Public Health in Boston, Massachusetts. RBC samples were weighed and digested for 24 h in 2 mL of concentrated nitric acid and 1 mL of 30% hydrogen peroxide per 1 g of RBC. Samples were then diluted to a volume of 10 mL with deionized water and were measured using a dynamic reaction cell–inductively coupled plasma mass spectrometer (Elan DRC II; PerkinElmer) for lead concentrations. Quality-control measures included analysis of initial and continuous calibration verification standards, 1-ppb lead standard, procedural blanks, QC standard [National Institute of Standards and Technology Standard Reference Material (NIST SRM) 1643d, Trace Elements in Water (NIST SRM955b, Lead in Blood)]. Results were calculated as the mean of five replicate measurements. The limit of detection for this procedure was 0.2 ng/mL in RBC.

Epigenome-Wide DNA Methylation Profiling and Quality Controls

Extracted DNA underwent bisulfite modification using the Zymo EZ DNA Methylation kit (Zymo Research). We randomized samples of bisulfite-modified DNA (1 μg for each sample) across different plates and BeadChips to ensure balance by sex and to reduce the influence of batch effects. Epigenome-wide DNA methylation analysis using the HM450K microarray interrogates 482,397 methylation loci at single-nucleotide resolution. The microarray measurements were processed at Illumina, Inc. We preprocessed data using the minfi package in R software (R Project for Statistical Computing). We excluded samples if they a) were replicates; b) were of low quality with individual call rate <0.98; c) had genotype mismatch; or d) had sex mismatch (see Table S1). We excluded methylation probes if a) they were on either the X or the Y chromosome; b) they were nonCpG probes; c) they had nonsignificant detection p-values (p>0.05) for >1% of the samples; d) they had been identified as cross-hybridizing with other genomic locations (Chen et al. 2013); or e) single nucleotide polymorphism (SNP)-associated probes at either the single base extension or within the target region for SNPs that have a minor allele frequency (MAF) ≥0.05. A total of 394,460 autosomal probes passed the QC. We performed background correction and dye-bias equalization using the normal-exponential out-of-band (noob) correction method (Triche et al. 2013). We performed a probe type normalization using the beta-mixture quantile (BMIQ) method in the minfi package in R (Teschendorff et al. 2013).


We collected maternal demographics including age, race/ethnicity, educational level, parity history, and pregnancy health information including smoking status and prepregnancy weight and height, as well as infant sex using self-administered questionnaires and interviews during pregnancy. We collected information on delivery date from medical records. Prepregnancy body mass index (BMI; kilograms per square meter) was calculated from self-reported prepregnancy weight and height. Women reported their last menstrual period (LMP) at enrollment, and we calculated gestational age by subtracting the LMP date from the delivery date or by ultrasound when available and when it differed from the LMP by more than 10 d (Gillman et al. 2004). To estimate cell-type distribution in blood samples, we used a reference panel of nucleated cells (leukocytes and nucleated RBC) isolated from cord blood (Bakulski et al. 2016) for cord blood samples and an adult leukocyte reference panel for blood samples collected in midchildhood as implemented in minfi (Aryee et al. 2014; Reinius et al. 2012). Leukocytes included CD8+ T cells, CD4+ T cells, natural killer (NK) cells, monocytes, and B cells.

Statistical Analysis

We accounted for batch effects from plates and for other potential sources of technical variability in the noob-adjusted and BMIQ-normalized cord blood methylation data using the ComBat adjustment (Johnson et al. 2007). We visually inspected the effectiveness of the removal of batch effects using principal component analysis before and after technical batch adjustment.

ComBat-adjusted cord blood methylation β-values were log2-transformed to M-values to be more appropriate for differential analysis of DNA methylation (Du et al. 2010). Robust linear regression models were constructed using the MASS package in R, with M-values included as dependent variables and log2-transformed maternal RBC lead levels as the main predictor, with adjustment for other potential confounders. Covariates included child sex, gestational age (weeks; as a continuous variable), maternal prepregnancy BMI (kilograms per square meter; as a continuous variable), age at enrollment (years; as a continuous variable), parity history (nulliparous, nonnulliparous), educational level (college graduate, noncollege graduate), smoking status during pregnancy (never, former, current smoker), race (white, nonwhite), and blood cell type distribution in cord blood.

To account for multiple testing, we only report top loci with a false discovery rate (FDR)-corrected p-value< 0.05, as has commonly been implemented in previous EWAS analyses (Engström et al. 2015; Senut et al. 2014). To facilitate interpretation of the results, we report effect estimates in the main text using the β-value scale for top loci with an FDR< 0.05 based on the robust linear regression. We further stratified the EWAS by child sex, using the same criterion of FDR< 0.05 to determine the top loci. We tested the interaction between child sex and lead by adding a multiplicative term in the model along with their main effect terms for all significant loci meeting the criterion FDR< 0.05.

We attempted to replicate the identified associations for top loci in cord blood by further examining associations of prenatal lead exposure with DNA methylation at the same top CpGs in midchildhood blood samples. For these analyses, we again performed robust linear regression analyses with adjustment for the same covariates [but using Houseman blood cell type algorithms based on an adult blood cell reference panel (Aryee et al. 2014; Reinius et al. 2012)] as well as for child age at blood sample collection. We drew visualized regional DNA comethylation patterns for identified potential target genes using the coMET package in R. To address the potential residual confounding due to smoking, we first performed sensitivity analyses excluding infants with sustained maternal smoking during pregnancy (n=7) according to a definition reported elsewhere (Joubert et al. 2016). We further created a methylation score predicting smoking status by calculating the linear combination of methylation values of 28 CpGs as described elsewhere (Reese et al. 2017), and we adjusted for the score as a quartile-covariate instead of adjusting for the smoking status in the EWAS analyses. Finally, we used the bumphunter package from Bioconductor to perform a bumphunting analysis, as previously described (Jaffe et al. 2012), to detect any differentially methylated regions (DMRs) relative to prenatal maternal lead exposure. Briefly, we fit models with adjustment for the same covariates as in the primary EWAS analyses and ran bumphunter on the β-values using log2-transformed RBC-lead as the exposure of interest. All statistical analyses were performed using R 3.2.3 (R Project for Statistical Computing).


Among the 268 mothers included in the primary analysis, 138 (51.5%) had female infants (Table 1). The mean age of women at enrollment was 31.9 y; 51.1% were nulliparous, 79% were white, and 69.0% were college graduates. The mean maternal RBC blood lead level was 1.22 μg/dL [standard deviation (SD), 0.63; range, 0.29–4.97] and was generally similar between mothers of female (1.24 μg/dL) and male (1.19 μg/dL) infants (Wilcoxon rank test p=0.30, see Table S2). This level approximates to a whole-blood level of 0.50 μg/dL based on the estimate that RBC concentrations are the products of the whole-blood level divided by hematocrit (∼40% in women) (Chuang et al. 2001). Characteristics of the 240 mother–child pairs with child blood samples in midchildhood were largely similar. The mean age of children in midchildhood was 7.9 y (SD, 0.8; range, 7–10).

Table 1. Characteristics of Project Viva mother–infant pairs with umbilical cord blood and midchildhood blood methylation measurement.
Characteristic Birth (n=268) Midchildhood (n=240)
 Prepregnancy BMI, kg/m2: Mean (SD) 24.2 (5.0) 24.5 (4.9)
 Age at enrollment, years: Mean (SD) 31.9 (4.7) 32.1 (5.3)
 Nulliparous: n (%) 131 (48.9) 113 (47.1)
 College graduate: n (%) 185 (69.0) 169 (70.4)
 Smoking status: n (%)
  Never 178 (66.4) 168 (70.0)
  Former 55 (20.5) 47 (19.6)
  During pregnancy 35 (13.1) 25 (10.4)
 Maternal white race/ethnicity: n (%) 212 (79.1) 173 (72.1)
 2nd-trimester Pb, μg/dL: Mean (SD) 1.22 (0.63) 1.20 (0.53)
 Female: n (%) 138 (51.5) 121 (50.4)
 Gestational age at birth, wk: Mean (SD) 39.7 (1.6) 39.5 (1.7)
 Age at blood collection, years: Mean (SD) 7.9 (0.8)

Note: BMI, body mass index; Pb, lead; SD, standard deviation.

Our analysis showed that the p-value distribution of all CpGs was generally similar to the theoretical distribution among all newborns and female infants (in quantile–quantile plots, λ values were 0.9308 and 1.0192, respectively) and was slightly different from the theoretical distribution among male infants (λ=0.7967; see Figure S1). We found that methylation levels at four CpGs demonstrated significant negative associations (FDR< 0.05) with maternal RBC lead among all newborns (Table 2; see also Figure S2A). Among these CpGs, one (cg10773601) was annotated to <1,500 bps from the transcription start site of C-Type Lectin Domain Family 11, Member A (CLEC11A). For each doubling increase in maternal RBC lead levels, there was a 1.4% decrease in the methylation level of cg10773601 in cord blood (p=2.3×10−07). This association appeared to be linear over the lead exposure range (Figure 1A). This effect estimate attenuated to −0.5% in midchildhood blood samples (p=0.12; see Table S3). Effect estimates for the other three loci in cord blood and midchildhood blood samples were generally minimal and were not annotated to any genes.

Figure 1A is a scatter plot with a regression line showing CpG methylation (y-axis) for red blood cells cg10773601 and cg24637308 across log sub 2(RBC-Pb) in micrograms per deciliter (x-axis) in newborns. Figures 1B and 1C are scatter plots showing the same for male infants and female infants, respectively.
Figure 1. Scatterplots for prenatal red blood cell lead levels and cg10773601 (left panel) and cg24637308 (right panel) methylation levels of umbilical cord blood DNA among all newborns (A), male infants (B), and female infants (C).
Table 2. CpGs with DNA methylation levels in umbilical cord blood associated with prenatal maternal red blood cell lead concentrations in analyses for all newborns, male infants, and female infants (selected based on FDR< 0.05 for EWAS).
CpG Chr Position Relation to island Gene Relation to gene Average methylation (%)a All newborns Male infants Female infants p-Value for interaction
Effect estimate (SE)b p-Value Effect estimate (SE)b p-Value Effect estimate (SE)b p-Value
Top loci identified for all newborns
cg02272457 chr6 42427790 Open Sea 99.1 −0.1 (0.02) 1.2×10−6 −0.1 (0.03) 4.1×10−3 −0.1 (0.02) 1.2×10−4 0.88
cg20324491 chr7 149128397 N_Shore 94.2 −0.5 (0.1) 8.3×10–8 −0.4 (0.2) 3.5×10–2 −0.4 (0.1) 7.9×10−5 0.55
cg22112000 chr7 5647182 Island 98.5 −0.1 (0.02) 1.1×10−7 −0.1 (0.03) 5.0×10−3 −0.1 (0.02) 2.3×10−4 0.29
cg10773601 chr19 51226046 N_Shore CLEC11A TSS1500 85.8 −1.4 (0.3) 2.3×10−7 −1.2 (0.4) 2.6×10−3 −1.7 (0.4) 8.0×10−5 0.52
Top loci identified for male infants
cg22512536 chr5 154134507 Island LARP1 Body 1.9 0.1 (0.03) 1.7×10−2 0.1 (0.03) 2.2×10−7 −0.01 (0.04) 7.9×10−1 0.002
cg08964024 chr12 14923326 Island 1.6 0.1 (0.02) 4.1×10−3 0.1 (0.02) 2.2×10−8 0.01 (0.02) 7.8×10−1 0.002
Top loci identified for female infants
cg04295372 chr1 101184332 Open Sea VCAM1 TSS1500 41.9 0.9 (0.3) 2.8×10−3 −0.1 (0.6) 8.5×10−1 1.6 (0.3) 3.9×10−6 0.03
cg09356083 chr1 2430057 N_Shelf PLCH2 Body 85.1 −0.4 (0.1) 1.9×10−6 −0.3 (0.2) 4.4×10−2 −0.6 (0.1) 6.9×10−7 0.12
cg20816789 chr1 27462916 Open Sea SLC9A1 Body 87.0 −0.6 (0.1) 1.7×10−5 −0.5 (0.3) 5.2×10−2 −0.8 (0.2) 3.8×10−7 0.11
cg22446399 chr2 27326580 Open Sea CGREF1 Body 95.9 −0.3 (0.1) 1.5×10−3 −0.1 (0.2) 3.6×10−1 −0.5 (0.1) 4.2×10−6 0.06
cg27403609 chr2 11101403 Island 91.8 −0.5 (0.3) 4.8×10−2 0.1 (0.4) 8.1×10−1 −1.2 (0.3) 3.4×10−4 0.004
cg07248017 chr4 8230689 Open Sea SH3TC1 Body 71.1 −0.6 (0.2) 2.1×10−4 −0.2 (0.3) 3.9×10−1 −1.0 (0.2) 1.2×10−6 0.009
cg06239355 chr5 32714010 Island NPR3 Body 1.7 0.1 (0.02) 1.2×10−4 0.03 (0.04) 4.0×10−1 0.1 (0.02) 1.5×10−7 0.04
cg15601915 chr5 1065775 S_Shore SLC12A7 Body 85.7 −0.6 (0.1) 1.6×10−5 −0.4 (0.2) 6.4×10−2 −0.9 (0.2) 1.6×10−6 0.001
cg17599748 chr6 31589597 S_Shore SNORA38 TSS1500 68.1 −0.7 (0.3) 9.3×10−3 0.4 (0.4) 2.9×10−1 −1.4 (0.3) 1.1×10−6 0.002
cg08673909 chr7 1329592 Island 2.1 −0.1 (0.04) 2.4×10−3 −0.01 (0.1) 8.5×10−1 −0.2 (0.05) 6.3×10−6 0.07
cg10090217 chr7 45151583 S_Shore TBRG4 TSS1500 2.2 −0.05 (0.02) 3.7×10−3 −0.004 (0.03) 9.0×10−1 −0.1 (0.02) 5.0×10−6 0.03
cg16565528 chr8 91010920 N_Shelf 83.1 0.8 (0.3) 8.0×10−3 −0.1 (0.4) 8.4×10−1 1.8 (0.4) 7.9×10−7 0.05
cg03152353 chr9 139417194 Island NOTCH1 Body 56.6 −0.6 (0.2) 1.4×10−3 −0.1 (0.3) 7.8×10−1 −1.1 (0.2) 4.6×10−7 0.02
cg13817920 chr9 96587843 N_Shore 89.0 −0.5 (0.1) 4.3×10−5 −0.5 (0.2) 9.1×10−3 −0.6 (0.1) 4.4×10−5 0.10
cg13791644 chr10 119794366 Open Sea RAB11FIP2 Body 93.2 0.5 (0.2) 2.8×10−2 −0.1 (0.3) 7.9×10−1 1.0 (0.2) 1.3×10−5 0.01
cg18454045 chr10 88391658 N_Shore 69.2 −0.6 (0.2) 8.0×10−3 0.1 (0.5) 7.6×10−1 −1.3 (0.3) 2.8×10−7 0.004
cg11127561 chr11 125462151 N_Shore STT3A TSS1500 1.6 0.1 (0.02) 9.7×10−5 0.03 (0.02) 1.8×10−1 0.1 (0.02) 4.6×10−5 0.005
cg17971003 chr11 107582804 OpenSea SLN TSS200 93.3 0.7 (0.2) 1.5×10−4 0.4 (0.2) 9.2×10−2 1.0 (0.2) 1.7×10−6 0.18
cg24637308 chr11 6592297 Island DNHD1 Body 76.2 −3.3 (0.9) 9.1×10−5 −1.9 (1.4) 1.5×10−1 −4.3 (0.9) 1.1×10−6 0.12
cg05959994 chr13 114253916 Open Sea TFDP1 Body 98.6 −0.1 (0.04) 6.1×10−3 0.1 (0.05) 1.1×10−1 −0.2 (0.04) 1.9×10−8 2.9×10−8
cg11203293 chr13 25777762 Open Sea 60.5 −1.1 (0.5) 1.9×10−2 0.4 (0.7) 5.5×10−1 −2.3 (0.4) 3.4×10−7 0.001
cg04545835 chr14 102675570 Open Sea WDR20 Body 97.1 −0.2 (0.1) 8.3×10−3 0.002 (0.2) 9.9×10−1 −0.4 (0.1) 4.0×10−5 0.004
cg08131309 chr15 40399131 N_Shore BMF TSS1500 23.2 −0.9 (0.3) 3.2×10−4 −0.1 (0.4) 7.1×10−1 −1.6 (0.3) 6.2×10−7 0.002
cg04730825 chr16 16116191 Open Sea ABCC1 Body 58.7 −0.8 (0.3) 9.2×10−3 0.1 (0.4) 8.5×10−1 −1.7 (0.4) 3.8×10−6 0.02
cg26686608 chr16 88705716 N_Shore IL17C Body 41.5 0.5 (0.2) 2.2×10−2 −0.1 (0.4) 8.0×10−1 1.2 (0.3) 1.7×10−6 0.0007
cg00461015 chr17 42295635 N_Shore UBTF 5’UTR 1.9 −0.1 (0.03) 2.6×10−3 −0.02 (0.04) 6.6×10−1 −0.1 (0.03) 1.7×10−5 0.07
cg04571282 chr17 44108753 Open Sea KIAA1267 3’UTR 98.3 −0.1 (0.03) 2.3×10−4 −0.1 (0.1) 3.6×10−1 −0.2 (0.04) 4.8×10−6 0.13
cg11252953 chr17 80358829 N_Shelf C17orf101 Body 7.6 0.4 (0.2) 1.4×10−2 0.2 (0.3) 5.5×10−1 0.8 (0.2) 8.7×10−7 0.13
cg15922057 chr17 25784714 S_Shore 31.8 −0.6 (0.3) 3.4×10−2 0.2 (0.4) 5.3×10−1 −1.2 (0.3) 8.9×10−6 0.001
cg06753949 chr19 15334309 Island 1.9 −0.1 (0.03) 2.3×10−4 −0.1 (0.05) 1.3×10−1 −0.2 (0.04) 7.8×10−6 0.27
cg07780528 chr19 35630334 N_Shelf FXYD1 5’UTR 23.3 −0.5 (0.1) 3.1×10−4 −0.1 (0.2) 5.4×10−1 −0.8 (0.2) 2.0×10−6 0.15
cg11610754 chr19 18722595 Island TMEM59L TSS1500 1.7 0.03 (0.01) 2.7×10−3 −0.01 (0.02) 6.8×10−1 0.1 (0.01) 3.1×10−6 0.03
cg18598117 chr19 941126 Island ARID3A Body 84.6 −0.8 (0.3) 1.4×10−2 0.1 (0.8) 9.0×10−1 −1.5 (0.4) 2.8×10−5 0.02
cg22217660 chr19 50765301 S_Shelf MYH14 Body 92.2 −0.3 (0.1) 2.0×10−2 0.1 (0.3) 6.9×10−1 −0.7 (0.2) 1.2×10−5 0.04
cg03373781 chr20 30582113 N_Shore XKR7 Body 74.8 −0.7 (0.2) 3.8×10−4 −0.1 (0.3) 6.4×10−1 −1.1 (0.2) 2.3×10−7 0.02
cg07341934 chr20 57463711 Island GNAS 3’UTR 60.1 −0.8 (0.3) 1.9×10−3 −0.3 (0.5) 5.5×10−1 −1.5 (0.3) 2.2×10−6 0.002
cg21307155 chr20 3216740 N_Shore SLC4A11 Body 87.5 −0.5 (0.1) 9.3×10−4 0.02 (0.2) 9.1×10−1 −0.8 (0.1) 8.1×10−8 0.008
cg17174023 chr22 50987453 Island KLHDC7B 1st Exon 9.0 −0.6 (0.2) 1.5×10−2 0.2 (0.4) 5.9×10−1 −1.0 (0.3) 3.1×10−5 0.05

Note: Body, gene body; Chr, Chromosome; FDR, false discovery rate; SE, standard error; TSS200, within 200 bps from transcription start site; TSS1500, within 1500 bps from transcription start site; UTR, untranslated region.

aAverage methylation in umbilical cord blood among all newborns.

bThe percent change in DNA methylation level for every doubling increase in prenatal maternal red blood cell lead concentrations. Adjusted for child sex, maternal age, maternal prepregnancy body mass index, maternal race, maternal educational level, maternal smoking status, parity, gestational age, and differential nucleated cell proportions (monocytes, CD8+ T cells, CD4+ T cells, natural killer cells, B cells, and nucleated red blood cells).

We identified somewhat different top loci in sex-specific EWAS analyses, and most of the loci had p values< 0.05 for interaction tests between infant sex and lead exposure (Table 2). Among male infants, methylation levels at only two CpGs in cord blood showed significant but small positive associations with maternal RBC lead at FDR< 0.05 (Table 2; see also Figure S2B). Neither of these two CpGs showed an association with prenatal lead in blood DNA collected in midchildhood (see Table S3). Among female infants, maternal RBC lead levels were negatively associated with methylation levels of 29 CpGs and were positively associated with DNA methylation at 9 CpGs in cord blood at FDR< 0.05 (Table 2; see also Figure S2C). One CpG (cg24637308) showed a strong negative association with prenatal lead exposure (the effect estimate was −4.3% for each doubling of maternal RBC lead, p=1.1×10−06). This association also appeared to be linear over the lead exposure range (Figure 1C) and was consistent in midchildhood, with each doubling of maternal RBC lead associated with 4.1% lower methylation in midchildhood females (p=0.003). The CpG cg24637308 is located in the body of the Dynein Heavy Chain Domain 1 gene (DNHD1) and is annotated to a CpG island). Effect estimates for the other loci among female infants were either minor or were substantially attenuated in midchildhood. Visualization of regional DNA comethylation patterns for top loci showed that most CpGs around the DNHD1 gene had similar changes in the same direction as that of the top CpG cg24637308 in association with lead exposure, although they did not pass the FDR adjustment (Figure 2). Lists of annotated CpGs with uncorrected p<0.05 in EWAS analyses and scatterplots for prenatal RBC lead levels and methylation levels at top loci other than cg10773601 and cg24637308 as shown in Table 2 for all newborns, male infants, and female infants have been included in the Supplemental Material for reference (see Tables S4–S6; see also Figure S3). Sensitivity analyses excluding infants with sustained maternal smoking yielded generally similar results (see Tables S7–S9). Results from analyses with adjustment for the methylation score predicting smoking status were also generally consistent with the original results, and major top CpGs, including cg10773601 and cg24637308, still met FDR< 0.05 (see Tables S10–S12).

Visualization of regional DNA comethylation patterns.
Figure 2. Visualization of regional DNA comethylation patterns for genes CLEC11A (A) and DNHD1 (B) in umbilical cord blood among all newborns. Black dots indicate the identified key loci (cg10773601 and cg24637308), blue dots indicate CpGs in the same direction, and red dots indicate CpGs in the opposite direction associated with lead exposure.

Because DNA methylation is tissue-specific, we were interested in examining the consistency of methylation patterns between our surrogate tissue (blood) and the potential target tissue (e.g., brain). We therefore investigated blood–brain DNA methylation correlations for top loci based on an independent sample dominated by adult females (the female:male ratio was approximately 2:1) (Hannon et al. 2015) and found that blood DNA methylation at the cg24637308 locus (DNHD1) was highly correlated with methylation at the same locus in the prefrontal cortex (r=0.77), the entorhinal cortex (r=0.78), the superior temporal gyrus (r=0.75) and the cerebellum (r=0.63) (Figure 3).

Four scatter plots showing methylation levels in prefrontal cortex (rho equals 0.77; p equals 1.33 times 10 super negative 15), entorhinal cortex (rho equals 0.78; p equals 1.78 times 10 super negative 15), superior temporal gyrus (rho equals 0.75; p equals 8.88 times 10 super negative 15), and cerebellum (rho equals 0.63; p equals 4.95 times 10 super negative 9) (y-axis) across methylation blood (x-axis).
Figure 3. Scatterplots and correlations for cg24637308 (DNHD1 gene) methylation levels of blood DNA and four brain regions: prefrontal cortex (PC, n=74), entorhinal cortex (EC, n=71), superior temporal gyrus (STG, n=75), and cerebellum (CE, n=71), based on an independent data set dominated by females (the female:male ratio was approximately 2:1) from (Hannon et al. 2015).

We also investigated the top loci identified by a previous EWAS analysis on prenatal lead exposure and cord blood DNA methylation (Engström et al. 2015) based on our data set and found that one of the loci (cg05374025) was significantly associated with prenatal lead exposure in our samples (p=0.031; see Table S13).

Bumphunting analysis identified only one small DMR with two CpGs in chromosome 13 (chr13: 50194554–50194643) associated with RBC lead for all newborns [family-wise error rate (FWER)<0.05) and two DMRs in chromosome 6 (chr6: 29648271–29649084) and chromosome 5 (chr5: 135415819–135416613) for male infants (FWER< 0.05; see Table S14). However, these regions did not contain any of the top loci reported in Table 2.


In this study of 268 U.S. mother–infant pairs, we found that prenatal RBC lead levels were associated with differential DNA methylation levels at several CpGs in umbilical cord blood. We also observed notable sex differences: RBC lead levels were associated with DNA methylation in more CpGs in female infants than in male infants. Most of the identified CpGs showed lower methylation levels in association with higher prenatal lead exposure. Estimated whole blood lead levels in our sample (0.50 μg/dL) were slightly below the U.S. national average for women (0.60 μg/dL) (Jones et al. 2010). Our results suggest that even at very low levels, prenatal lead exposure may be significantly associated with altered DNA methylation profiles in newborns.

Lead is a multiple-source pollutant well known for its adverse effects, and the main target organs of lead are the hematopoietic, nervous, and renal systems (Papanikolaou et al. 2005). Interestingly, we found that in utero lead exposure was associated with altered methylation levels at CpGs located within or near important regulatory genes of hematopoietic and nervous functions (i.e., CLEC11A and DNHD1). Lead is well known to interfere with hemoglobin synthesis and to affect erythrocyte morphology and survival (Sun et al. 2012). The gene CLEC11A encodes a secreted sulfated glycoprotein (CLEC11A) that functions as a growth factor for primitive hematopoietic progenitor cells (Mio et al. 1998). CLEC11A stimulates the proliferation and differentiation of hematopoietic precursor cells from various lineages, including RBCs, lymphocytes, granulocytes, and macrophages (Hiraoka et al. 2001).

DNHD1 (DYNC1H1) is a member of the dynein heavy chain family and is highly expressed in the brain. Cytoplasmic dynein complex comprises subunits assembled on the DNHD1 dimer and is particularly important for neurons because it carries essential signals and organelles from distal sites to the cell body (Schiavo et al. 2013). Our results on DNHD1 suggest that methylation changes in this gene may be potential targets of in utero lead exposure. Interestingly, there were strong correlations between blood–brain methylation for cg24637308 (DNHD1) based on another independent set of samples with a high proportion of female participants (Hannon et al. 2015). Furthermore, the significant inverse association among female infants seemed consistent through midchildhood, highlighting the possibility of long-term programming effects of lead exposure. In support of our findings, previous studies have documented a strong link between lead exposure and impaired cognitive and behavioral functions (Boucher et al. 2014; Liu et al. 2014; Virgolini et al. 2008).

Our study found substantial sex differences in lead exposure–associated methylation changes. It is plausible that the slightly higher RBC levels in mothers of female infants than in male infants may be partially responsible for the more significant CpGs found in female infants in the study, although the difference in exposure levels did not reach statistical significance. Similarly, our previous study on prenatal maternal lead and birth outcomes and another epidemiological study on prenatal lead and childhood body weight also did not find appreciable differences in lead exposure levels between the sexes despite the presence of different effect estimates associated with lead exposure in males and females (Afeiche et al. 2011; Perkins et al. 2014). In light of the fact that several toxicological studies also observed appreciable effect differences between the sexes at the same doses of lead exposure (Bunn et al. 2001; Virgolini et al. 2008), it is possible that the variation in the lead-associated effect spectrum over the sexes may be associated with sex-specific biological mechanisms rather than with the lead exposure doses. In addition, it has been shown that sex does influence genomic methylation status (Liu et al. 2010), yet little is known about sex differences in the regulation of the epigenome. Interestingly, a previous animal study found that the effects of lead exposure on DNMTs and DNA-binding proteins in the hippocampus appear to be different in males and females and are further differentiated by the period of development during which exposure occurs (Schneider et al. 2013). The authors hypothesized that an interaction between genes and sex hormones during development, modified by lead exposure, may make specific brain regions such as the hippocampus differentially susceptible to methylation-associated modulation of transcriptional regulation. It is possible that the potential effects of lead exposure on epigenetic mechanisms, as observed in previous studies and in the present study, may contribute to a differential susceptibility between males and females to the adverse health outcomes reported in previous epidemiological and toxicological studies (Afeiche et al. 2011; Bunn et al. 2001; Perkins et al. 2014; Virgolini et al. 2008).

A previous study also investigated the epigenome-wide association of prenatal lead exposure with cord blood DNA methylation profiles in a birth cohort in rural Bangladesh (n=127) with high environmental lead levels, and the authors identified a set of loci with FDR< 0.05, among which some were located around the gene glycoprotein VI (GP6) (Engström et al. 2015). However, only one of these loci (cg05374025) reached statistical significance in our samples. It is notable that prenatal lead exposure levels were much higher in the Bangladesh cohort (mean maternal RBC lead level, 94 μg/kg) than in our U.S. samples (mean maternal RBC lead level, 1.22 μg/dL≈12 μg/kg). In addition, several important participant characteristics differed substantially between the two study populations, including ethnic background and BMI (the median maternal BMI was 20 kg/m2 in the Bangladesh cohort and 23 kg/m2 in our cohort). Therefore, the potential adverse effect spectra and the action patterns of prenatal lead exposure may vary substantially between these two cohorts.

Our study has several strengths. We were able to prospectively investigate the methylation changes in association with prenatal lead exposure among male infants and female infants separately. We collected both cord blood samples and midchildhood blood samples, allowing us to examine the consistency of in utero lead exposure–DNA methylation associations over time. In addition, we were able to control for a number of covariates based on the detailed cohort follow-up information.

Our study also has several limitations. First, the distribution of lead exposure was relatively narrow, and the exposure levels were quite low. However, this range gave us the opportunity to investigate the potential effects of very low-level exposure, which represents common exposure levels in the United States and in other Western countries. Our results may not be generalizable to populations with different exposure ranges, or to lower-income and racial/ethnic minority populations. Second, our study measured maternal lead in RBC instead of in whole blood. Nevertheless, this is likely to be an accurate biomarker because 99% of whole-blood lead is found in RBC; thus, RBC lead is very highly correlated with whole-blood concentrations (Leggett 1993). Third, although lead is known to affect multiple organs, DNA methylation was measured using unfractionated blood collected at delivery and midchildhood—the only available biological sample. Whether the associations reported herein may reflect associations between in utero lead exposure and methylation at target organs is uncertain. However, peripheral blood is the major carrier of lead absorbed into the human body, and peripheral blood cells contact and react directly with the internal forms of lead (Chiesa et al. 2006). Furthermore, there are strong correlations between blood and brain methylation for the CpG cg24637308 on the body of the DNHD1 gene, suggesting that peripheral blood may serve well as a surrogate tissue for the target organ brain for certain key regulatory loci. Fourth, we were unable to investigate the exact biological mechanisms behind the observed methylation changes associated with prenatal lead exposure. Although lead exposure has been found to reduce the global DNA methylation level by noncompetitive inhibition and alteration of DNMT (Sanchez et al. 2017), this would not explain why not all affected genes are in the negative direction. Further studies are needed to investigate this issue in detail. Fifth, we were unable to validate the methylation changes associated with lead exposure using another method, such as gene expression assays or pyrosequencing assays, owing to the unavailability of extra biological samples. Nevertheless, the visualization of regional DNA comethylation patterns for potential target genes reveals generally consistent change patterns for CpGs in these genes, strengthening the validity of the reported results. Finally, we aimed to examine the potential effects of in utero lead exposure and did not include lead exposures after birth and before midchildhood; whether the associations between prenatal lead exposure and methylation in midchildhood may be confounded by other exposures is unknown. However, early pregnancy is a critical time window for epigenetic programming in the fetus, and it is during this time period that adverse in utero exposures are of most interest in relation to DNA methylation programming.


Based on an EWAS of relatively low levels of prenatal lead exposure and DNA methylation in a prospective prebirth cohort, we identified several CpGs of biological relevance for effects of in utero exposure to lead. There were sex-specific differences in the top CpGs associated with prenatal lead exposure, suggesting a potential susceptibility difference in relation to lead exposure between males and females. Further studies are needed to replicate our findings and to investigate their relevance to specific adverse health outcomes. Pending further investigation, our results suggest that prenatal lead exposure, even at very low levels, may modify the DNA methylation profiles in offspring, providing a mechanistic explanation for previously reported adverse postpartum health outcomes of prenatal lead exposure.


We thank the Project Viva participants and staff. We also thank E. Hannon and J. Mill for providing assistance in the analysis of blood–brain DNA methylation correlations.

This work was supported by Nationals Institutes of Health (NIH) grants R37 HD 034568, R01 ES016314, RC1 HD063590, K24 HD069408, R01 NR013945, R01 HL111108 and by the Harvard T.H. Chan School of Public Health/National Institute of Environmental Health Sciences (HSPH-NIEHS) Center for Environmental Health New Investigator Fund (P30ES000002). S.W. is supported by startup funding from the “Young Thousand Talents” Program of China and Peking University Health Science Center (no. BMU20160549).


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