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Association of Arsenic Exposure with Whole Blood DNA Methylation: An Epigenome-Wide Study of Bangladeshi Adults.

Introduction

Arsenic exposure from naturally contaminated drinking water is a global public health concern that affects >200 million people worldwide (Naujokas et al. 2013), and ~56 million in Bangladesh (Flanagan et al. 2012). Prior to remediation efforts, ~95% of the drinking water wells in Bangladesh contained water with inorganic arsenic concentrations above 10 [micro]g/L, the World Health Organization (WHO) recommended maximum level of exposure (Ahsan et al. 2000; Anawar et al. 2002; Van Geen et al. 2002). Inorganic arsenic is classified as a group I carcinogen by the International Agency for Research on Cancer, and long-term exposure is associated with risk for nonmelanoma skin (Karagas et al. 2015), lung (Lamm et al. 2015), and bladder cancer (Gamboa-Loira et al. 2017; Lamm et al. 2015) as well as arsenical skin lesions (a hallmark of chronic arsenic exposure). Chronic environmental arsenic exposure also increases the risk of other chronic diseases, including cardiovascular diseases (Moon et al. 2017; Navas-Acien et al. 2005) and respiratory disease (Sanchez et al. 2018) and overall mortality among Bangladeshi adults (Argos et al. 2010).

Arsenic is not considered to be directly genotoxic, and potential mechanisms of arsenic toxicity include induction of oxidative stress and inflammation, inhibition of DNA repair, and epigenetic dysregulation, including alteration of DNA methylation. DNA methylation is characterized by the addition of a methyl group to a cytosine nucleotide, frequently located within CpG sites in the genome. DNA methylation (and the underlying chromatin state it represents) provides an additional level of transcriptional regulation and has an important role in maintaining genomic stability. Exposure to environmental toxicants, like arsenic, can induce changes in both global and gene-specific DNA methylation (Cortessis et al. 2012; Martin and Fry 2018). Some potential mechanisms by which arsenic could disrupt DNA methylation include interaction with methylation or chromatin maintenance machinery, depletion of cofactors involved in DNA methylation synthesis, interaction with transcription factor binding sites (TFBS), and alteration of the inflammatory and oxidative environment of the cell (Bailey and Fry 2014; Ren et al. 2011). Arsenic-induced alterations of DNA methylation may be an important component in the mechanism of arsenic toxicity and carcinogenesis.

While several studies have examined prenatal arsenic exposure and genome-wide DNA methylation (Cardenas et al. 2015a, 2015b; Green et al. 2016; Kile et al. 2014; Koestler et al. 2013; Rojas et al. 2015), only four studies have examined arsenic exposure and genome-wide DNA methylation in adults (Ameer et al. 2017; Argos et al. 2015; Liu et al. 2014; Seow et al. 2014). Argos et al. identified four urinary arsenic-associated CpGs and three blood arsenic-associated CpGs (Bonferroni threshold of p < [10.sup.-7]) in Bangladeshi adults (n = 400), with a wide range of environmental arsenic exposure and diagnosed with skin lesions (Argos et al. 2015). Liu et al. identified 22 differentially methylated CpGs (p < [10.sup.-4]) in adults (n = 46) from the United States with low arsenic exposure compared with adults in Bangladesh (Liu et al. 2014). Ameer et al. identified six urinary arsenic-associated CpGs [false discovery rate (FDR) < 0.05] among Andean women from Argentina (n = 93) with a wide range of environmental exposure, similar to Bangladesh (Ameer et al. 2017). Seow et al. conducted a pilot study comparing genome-wide DNA methylation between 10 arsenical skin lesions cases and 10 lesion-free controls from Bangladesh, but no significant associations with methylation were observed (Seow et al. 2014). These prior studies differed with respect to study population, sample size, exposure assessment and level, disease status, and statistical approaches, likely contributing to the variability in CpGs identified and patterns of genomewide methylation observed (Argos 2015). Prior studies used the Illumina 450 K array, while the newer Illumina Infinium-MethylationEPIC (EPIC) array measures methylation at approximately 850,000 CpGs, including >90% of the CpGs on the 450 K array as well as substantial coverage of CpGs in enhancer regions and CpG shores (Pidsley et al. 2016). By including these additional sites, the EPIC array enables evaluation of methylation within distal regulatory regions of the genome that may be more susceptible to environmental exposures, like arsenic, since methylation at enhancers is more variable and dynamic and not as well preserved (Jones 2012).

In this study, we assess the association between exposure to arsenic measured in urine (prior to arsenic mitigation efforts) and genome-wide DNA methylation assessed using the EPIC array in 396 adults from the Health Effects of Arsenic Longitudinal Study (HEALS). HEALS is a population-based cohort of arsenic-exposed Bangladeshi adults established to assess the health effects associated with consumption of arsenic-contaminated water (Ahsan et al. 2006a). We validate arsenic-associated CpGs by examining their association with arsenic concentration in drinking water and replicate observed associations in an independent cohort of 400 Bangladeshi adults. We then conduct a meta-analysis of both cohorts to identify additional putative arsenic-associated CpGs.

Methods

Study Population

HEALS was initiated to prospectively investigate the health outcomes associated with chronic arsenic exposure through consumption of groundwater in a sample of Bangladeshi adults with homogenous ethnic and sociocultural characteristics (n = ~ 12,000) and little to no genetic admixture in Araihazar, Bangladesh (Pierce et al. 2012). This study has been described previously (Ahsan et al. 2006a). HEALS participants were recruited between October 2000 and May 2002. Participants were sampled from married couples between ages 18-75 y who resided in study area for at least 5 y. Trained study physicians (blinded to arsenic exposure) conducted in-person interviews, clinical evaluations, and skin lesion assessment, and collected urine and blood samples using structured protocols (Ahsan et al. 2006b). Participants in this epigenome-wide association study (EWAS) (n = 396) were randomly selected from the random subcohort recently included in a genome-wide association study (Pierce et al. 2012).

Data from the Bangladesh Vitamin E and Selenium Trial (BEST) were used to replicate associations observed in HEALS. BEST is a 2 x 2 factorial randomized chemoprevention trial evaluating the effects of vitamin E and selenium dietary supplemental and skin cancer risk among individuals with arsenical skin lesions from rural central Bangladesh (Argos et al. 2013). BEST participants were recruited between August 2006 and August 2009 and were between 25-65 y old, resided in the study area, and had arsenical skin lesions. Arsenic exposure was assessed in urine, and whole blood was collected at baseline for all BEST participants. Genome-wide methylation was previously measured on the 450 K array for 400 BEST participants, and the results were reported by Argos et al. (2015). The study protocols were approved by the Institutional Review Boards of the University of Chicago, Columbia University, and the Bangladesh Medical Research Council. Informed consent was obtained from all participants.

Exposure Assessment

Arsenic was measured in both urine and water for each HEALS participant. At baseline, each participant identified the primary well used as their main source of drinking water. Spot urine samples were obtained from each participant at baseline. Arsenic in urine and water was measured using graphite furnace atomic absorption spectrometry (AAnalyst 600 spectrometer; Perkin Elmer) in a single laboratory (Trace Metal Core Laboratory at Columbia University), and the limit of detection for this method was 2 [micro]g/L and 5 [micro]g/L for urine and water, respectively (Nixon et al. 1991). For arsenic in well water, any samples that were below the limit of detection were reanalyzed using inductively coupled plasma mass spectrometry with a detection limit of 0.1 [micro]g/L (Cheng et al. 2004). Urine creatinine was measured by a colorimetric Sigma Diagnostics Kit (Sigma). Total urinary arsenic was divided by urine creatinine to obtain creatinine-adjusted urine arsenic (pg/g creatinine).

DNA Methylation

DNA was extracted from clotted blood using FlexiGene DNA Kit (Qiagen). We then bisulfite converted 500 ng of DNA using EZ-96 DNA Methylation[TM] Kit (Zymo Research). All samples were then prepared and analyzed in accordance with the manufacturer guidelines and protocol for the Infinium MethylationEPIC array (Illumina). The EPIC array measured methylation at 866,895 CpGs. We removed CpGs with a detection p >0.01 in one or more samples (n = 26,629) or missing methylation in >5% of samples (n = 85). Cross-reactive CpGs were removed (n = 41,920), as well as CpGs annotating to a single-nucleotide polymorphism (SNP) or within a single base pair extension (n = 7,791) (Pidsley et al. 2016). We removed CpGs annotating to the X and Y chromosomes to avoid potential gender bias in methylation patterns as well as non-CpG and SNP probes on the array (n = 19,278). The total CpGs included in the analysis were 771,192.

One sample from HEALS was removed due to mismatched sex. After imputing beta values for missing CpG methylation using k-nearest neighbors method (k = 10) (Troyanskaya et al. 2001), the dataset was normalized using the beta mixture quantile (BMIQ) method. BMIQ is an assumption-free approach to adjust for type I/II probe bias (Teschendorff et al. 2013). We applied the ComBat function in R to adjust for batch effect due to plate (Johnson et al. 2007; Leek et al. 2012). This batch effect was removed after reviewing the principal components analysis of the batch-adjusted data. All processing of DNA methylation array data and analyses were conducted in R 3.4.1.

Association Analyses

All statistical analyses were performed using logit transformed beta values and M-values {[log.sub.2][beta/(1 - beta)]}, unless noted. The range of beta values is 0 (0% methylated) to 1 (100% methylated), while M-values range from -[infinity] to [infinity]. We selected covariates a priori and adjusted for age (continuous variable), sex, smoking status (categorized as never, former, and current smoker based on self-report), and body mass index (BMI) category [categorized as normal (reference), underweight, overweight, obese, and unknown]. We [log.sub.2]-transformed urinary and water arsenic to reduce the influence of outlying values; arsenic exposure was also modeled as an untransformed continuous and an ordinal variable across integer-coded quartiles [first (<113 [micro]g/g), second (114-201 [micro]g/g), third (202-350 ug/g), fourth (>350 [micro]g/g)]. We applied surrogate variable analysis (SVA) to adjust for unknown biologic and technical effects (Leek and Storey 2007; Leek et al. 2012). SVA has been demonstrated to perform stably (McGregor et al. 2016) and optimally identify informative CpGs in genome-wide methylation studies (Kaushal et al. 2017), and it was chosen over a cell type-adjusted approach (Houseman et al. 2012) as our primary analysis approach since it produced a lower genomic inflation factor. We estimated surrogate variables (SVs) using a permutation-based approach (Buja and Eyuboglu 1992). For each CpG, we analyzed the association with arsenic using linear regression adjusting for SVs and covariates in limma in R (Ritchie et al. 2015) and computed the genomic inflation factor ([lambda]) for EWAS (Aulchenko et al. 2007; van Iterson et al. 2017). A Bonferroni threshold was determined at [alpha] = 0.05, our threshold for epigenome-wide testing. The FDR p-values were computed using the Benjamini-Hochberg method (Hochberg and Benjamini 1990). [X.sup.2]-tests were applied to compare the distribution of arsenic-associated CpGs to the remaining CpGs across genomic features. UCSC genome (http://genome.ucsc.edu/), FANTOM5 (Andersson et al. 2014), and ENCODE (Thurman et al. 2012) annotations were provided by Illumina and used to annotate and assign genomic features to CpGs based on human reference genome GRCh37. If CpGs were assigned TSS200 or TSS1500 (at least 200 and 1500 base pairs upstream of transcriptional start site), we annotated these CpGs as being located in a promoter region. DNase I hypersensitive sites (DHS) and TFBS regions were indicated by evidence from ENCODE (Thurman et al. 2012) while enhancer regions were indicated by supporting evidence from FANTOM5 (Andersson et al. 2014). In a sensitivity analysis, we also analyzed our data using a reference-based approach, estimating six cell type proportions (monocytes, B-cells, granulocytes, CD8T, CD4T, and natural killer cells) from a whole blood reference panel (Houseman et al. 2012). For the reference-based approach, we performed linear regression adjusting for estimated cell type proportions and covariates using limma.

Differential Methylation Regional Analysis

We examined associations between urinary arsenic in HEALS and differentially methylated regions (DMRs) using DMRcate (Peters et al. 2015). DMRcate is agnostic to the direction of the association for each CpG and all annotations except those related to the spatial location (i.e., chromosome number and map position). DMRs are then defined by agglomerating CpG locations with an adjusted p-value below a selected threshold (based on FDR) that are at most [lambda] nucleotides from each other. Each DMR is assigned a minimum FDR (minFDR) that is from the lowest adjusted p-value among the CpG locations contained within it. We searched for DMRs using a bandwidth of 1,000 nucleotides (k = 1,000) and scaling factor of 2 for the bandwidth (C = 2) (recommended parameters for 450 K and EPIC arrays). We restricted to DMRs that contained at least two CpGs, and applied a stringent cutoff threshold of FDR < [10.sup.-4]. The genomic region for the top three DMRs are visualized using coMET (Martin et al. 2015).

Gene Set Enrichment Analyses

To evaluate the annotation of urinary arsenic-associated CpGs to pathways and biologic processes from the discovery analysis and the meta-analysis, we conducted gene set enrichment analyses (GSEA) using the gometh function in missMethyl (Phipson et al. 2016). Gometh accounts for the potential bias in GSEA due to number of CpGs per gene by computing prior probabilities (Geeleher et al. 2013) and evaluates enrichment using a hypergeometric test. We tested enrichment among arsenic-associated CpGs among the top 500 arsenic-associated CpGs and arsenic-associated CpGs below FDR of 0.05 within gene sets from the KEGG pathways (https://www.genome.jp/kegg/) (n = 324 pathways) and hallmark gene set collection (n = 54 sets), concise sets curated from multiple founder gene sets and gene expression datasets (Liberzon et al. 2015). Our justification for examining enrichment among the top 500 arsenic-associated CpGs was that it was difficult to examine genomic enrichments among a limited number of arsenic-associated CpGs at an FDR <0.05, and arsenic likely has numerous biologic effects on the epigenome that might not reach epigenome-wide significance. We extracted results with p < 0.05 since our analysis was exploratory and underpowered to detect enrichment at more stringent significance thresholds.

Meta-Analysis

We conducted a meta-analysis of genome-wide methylation and urinary arsenic using data from HEALS (n = 396) and the BEST cohort (n = 400). Using the same procedure applied to the HEALS EPIC array data, we preprocessed, BMIQ normalized, estimated SVs, and analyzed the BEST 450K array data. Among the overlapping CpGs in HEALS and BEST (n = 390,810), the association estimate and p-value for each CpG were obtained from BEST and HEALS and meta-analyzed using the sample size based approach in METAL software (Willer et al. 2010).

Gene Expression

Gene expression data were only available for the BEST participants included in this study and have been previously described (Argos et al. 2015; Gao et al. 2015). Briefly, gene expression was measured using the Illumina HT-12 v4 BeadChip according to manufacturer's protocol. The chip includes 37,231 probes and covers 31,335 genes. The gene expression values were quantile normalized and then [log.sub.2] transformed. COMBAT was used to adjust for batch effect (Johnson et al. 2007). We extracted gene expression probes corresponding to genes that annotated to our arsenic-associated CpGs identified in the meta-analysis for 371 BEST participants with expression data. To assess the associations between gene expression and methylation, we ran Pearson correlations and linear models adjusted for age and sex, and extracted the direction and p-value for each association.

Local Methylation Quantitative Trait Loci (cis-mQTL) Analyses of Arsenic-Associated CpGs

HEALS participants were genotyped on the Illumina Human-CytoSNP-12 v2.1 array with 299,140 markers. We used MaCH software (Li et al. 2010) to conduct genotype imputation using 1,000 genomes reference haplotypes (version 5; 1000G Phase 3). We examined cis associations between SNPs and CpGs within a 1-megabase window (500 kb upstream and downstream of the CpG) using genotype dosages and matrixeQTL software (Shabalin 2012). The model was adjusted for age, sex, and first four methylation principal components (PCs). We summarized the total SNPs tested for each CpG and number that reached FDR of 0.01, and reported the genomic information for the lead SNP (smallest p-value) and distance from CpG.

Replication Analysis for Meta-Analysis Arsenic-Associated CpGs among Andean Women

We attempted replication of the urinary arsenic-associated CpGs identified the meta-analysis (n = 221) using data from an independent population of Andean women from Argentina that have been previously published (Ameer et al. 2017). Methylation was measured on the 450 K array for 93 women. For each CpG, the association between methylation (M-values) and log-transformed urinary arsenic was examined and adjusted for age, coca usage (yes/no), and estimated fractions of granulocyte and natural killer cells. We extracted the direction of association and p-value for 217 of our 221 CpGs with available data from the Ameer et al. (2017) analysis.

Results

Our HEALS subcohort used for the discovery EWAS (n = 396) was randomly drawn from previously genotyped participants in HEALS (also randomly selected). The median age of our subcohort was 36.5 y [interquartile range (IQR): 30.0, 45.0 y] (Table 1), and there were more women (58%) than men (42%). Current smokers comprised 34% of the HEALS subcohort. At baseline, 76% of our participants consumed water from arsenic-contaminated hand-pumped tube wells with estimated arsenic concentrations above the WHO guideline of 10 [micro]g/L, and 50% of our participants consumed water with concentrations above the national Bangladesh standard of 50 [micro]g/L. The median urinary arsenic was 201.5 [micro]g/g creatinine (IQR: 113.5, 350.0 [micro]g/g).

Urinary arsenic-associated DNA Methylation in HEALS

We conducted an EWAS of [log.sub.2]-transformed urinary arsenic (creatinine adjusted) using data on 771,192 CpGs measured on the EPIC array among 396 HEALS participants (Figure 1; Figure S1; Excel Table S1). After adjustment for covariates and SVs (n = 27), 34 CpGs were associated with [log.sub.2]- transformed urinary arsenic at an FDR of 0.05 (p < 2.2 x [10.sup.-6]) (Table 2; Figure S2). From the analysis of beta values (range 0 to 1), the association estimates across these 34 CpGs ranged from 0.016 decrease to a 0.008 increase in methylation per doubling of urinary arsenic. Decreased methylation was associated with increased arsenic exposure for 23 of the 34 associated CpGs (FDR <0.05). Arsenic-associated CpGs (FDR < 0.05) were predominantly located in CpG shores (n = 14) and non-CpG islands (n = 14) (Figure 1B). Seven CpGs that passed Bonferroni-threshold (p < 6.5 x [10.sup.-8]) were located upstream of the ABR (ABR activator of RhoGEF and GTPase) gene on chromosome 17 (cg01912040, cg10003262), in the SEMA4G (semaphorin 4G) gene body on chromosome 10 (cg05962511), in the MAPRE2 (microtubule associated protein RP/EB family member 2) gene body (cg17420142), in the GBAP1 (glucosylceramidase beta pseudogene 1) gene body (cg06466147), in the NSMF gene body (cg09082427), and in the NBR1 (NBR1 autophagy cargo receptor) 5' untranslated region (UTR) (cg04193083).

The results from the [log.sub.2]-transformed urinary arsenic analysis were compared with results from analyses where urinary arsenic was modeled as an untransformed continuous or an ordinal quartile (integer-coded) variable. Across arsenic exposure models, the genomic inflation was similar, and all untransformed continuous and ordinal quartile arsenic-associated CpGs (p < 0.05) overlapped with [log.sub.2]-transformed arsenic-associated CpGs (FDR <0.05; n = 34) (Table S1). Among the 34 arsenic-associated CpGs identified (FDR <0.05), 31 were significantly associated with [log.sub.2]-transformed urinary arsenic (p < 0.05) prior to adjustment for covariates and SVs. Of the 34 arsenic-associated CpGs identified from the SV model, 68% (n = 23) and 97% (n = 33) were also associated with urinary arsenic at FDR of 0.05 and p < 0.05, respectively, in the cell type-adjusted model. When we examined the associations between urinary arsenic and estimated cell type proportions, [log.sub.2]-transformed urinary arsenic was associated with decreased CD4T percentage [[beta] = -0.5 (0.2); p = 0.01] and increased granulocyte percentage [[beta] = 0.8 (0.4); p = 0.05] after adjustment for age, sex, smoking status, and BMI (Table S2), suggesting that urinary arsenic was modestly associated with variation in estimated cell composition.

Among the 34 urinary arsenic-associated CpGs (FDR < 0.05) discovered in HEALS, we attempted replication for the 16 CpGs present on the 450 K array using previously measured 450 K methylation data from 400 BEST participants. Ten CpGs replicated with consistent direction of association in BEST (p < 0.05) (Table 2). Among the urinary arsenic-associated CpGs discovered in HEALS at the p = [10.sup.-5] threshold (n = 67), 26 CpGs were measured in BEST (Figure 2; Excel Table S1). Among these 26 CpGs, 15 (58%) were associated with urinary arsenic (p < 0.05), and 14 (93%) were directionally consistent in BEST.

Water Arsenic-Associated DNA Methylation in HEALS

To examine whether urinary arsenic-associated CpGs were consistent across different exposure assessments of arsenic, we examined arsenic in drinking water and its association with genome-wide methylation. Both water and urinary arsenic were measured in HEALS, and the correlation between the two measures was 0.61 (p < [10.sup.-16]) (Figure S3). Four CpGs passed a Bonferroni threshold (p < 6.5 x [10.sup.-8]) when we assessed associations with [log.sub.2]-transformed water arsenic (Figures S4; Figure S5; Excel Table S2). At an FDR of 0.05, 24 water arsenic-associated CpGs were identified. Among the urinary arsenic-associated CpGs (n = 34, FDR <0.05), all 34 CpGs were associated with water arsenic (i.e., p < 0.05 and consistent direction), and eight were associated at a FDR of 0.05. When we examined all CpG methylation and arsenic associations across the epigenome for both water and urine, the correlation between the -[log.sup.10] (p-values) and direction of association was 0.67 (p < [10.sup.-16]), mirroring the correlation we observed between the two exposures (Figure 3).

Arsenic-Associated Regions in HEALS

To identify regions of DNA methylation associated with urinary arsenic, we searched for DMRs using the DMRcate method. We identified 45 DMRs with minFDR<[10.sup.-4] (Figure 4A; Table S3). Eight CpGs individually associated with urinary arsenic (p < 6.5 x [10.sup.-8]) annotated to six of these DMRs based on region start and end locations (see CpG location in Table 2). The average methylation decreased with increased urinary arsenic in 64.4% (n = 29) of these DMRs. Most DMRs (82.2%; n = 37) contained CpGs annotated to a gene; however, we also identified eight intergenic DMRs. Among the DMRs that annotated to a gene, the DMRs most frequently spanned promoters (n = 19), gene bodies (n =17), and 5'UTRs (n =11). The top three DMRs were a) upstream of ABR on chromosome 17 (minFDR = 6.4 x [10.sup.-26]), overlapping an enhancer region (Figure 4B); b) in the body of SEMA4G on chromosome 10 (minFDR = 6.7 x [10.sup.-20]), overlapping a weak promoter (Figure 4C); and c) a region within the NSMF gene on chromosome 9 (minFDR = 6.7 x [10.sup.-20]), overlapping an enhancer region (Figure 4D). All three regions spanned a DNase cluster region. The mean effect size related to the [log.sub.2]-transformed arsenic-associated change in DNA methylation (beta values) across the DMR was -0:008 for ABR, -0:003 for SEMA4G, and -0.005 for NSMF.

Enrichment of Arsenic-Associated CpGs within Genomic Features and Gene Sets

We examined the enrichment of arsenic-associated CpGs within promoters, enhancers, TFBS, and DHS regions. Arsenic-associated CpGs (FDR <0:05) were enriched in shores and depleted in islands compared with CpGs not associated with arsenic (p = 3.9 x [10.sup.-3]) [see "Discovery (EPIC)" in Figure 5A]. We observed no enrichment/depletion of arsenic-associated CpGs in gene regions, promoters, TFBS, and enhancers. Arsenic-associated CpGs were 29% more likely to be annotated to DHS regions (p = 0.04) [see "Discovery (EPIC)" in Figure 5B].

GSEA was applied to the urinary arsenic-associated CpGs discovered in HEALS. No gene sets were enriched among the genes annotated to the arsenic-associated CpGs at FDR of 0.05 (21 unique genes). We assessed enrichment of hallmark gene sets among the genes that annotated to our top 500 arsenic-associated CpGs (348 unique genes) (see "HEALS" in Table 3) and observed enrichment for genes annotated to the tumor necrosis factor [alpha] (TNF[alpha]) signaling via NF[kappa]B hallmark (seven genes; p = 0.02), cholesterol homeostasis (four genes; p = 0.01), and angiogenesis (three genes; p = 6.4 x[10.sup.-3]). Among KEGG pathways, the hematopoietic cell lineage pathway was significantly enriched (four genes; p = 0.04) (Table S4).

Meta-Analysis of Urinary Arsenic and Genome-Wide DNA Methylation in HEALS and BEST

We conducted a meta-analysis of 390,810 CpGs measured in both HEALS and BEST (see Figure S6 and Figure S7 for full results) to more robustly identify urinary arsenic-associated CpGs. This meta-analysis identified 41 CpGs passing the Bonferroni threshold (p < 1.3 x [10.sup.-7]) (Figure 6; Table 4). At an FDR of 0.05, 221 urinary arsenic-associated CpGs were identified, and all had consistent direction of association in each study (p < 2.8 x [10.sup.-5]) (Excel Table S3). Among the 41 arsenic-associated CpGs (p < 1.3 x [10.sup.-7]), 34 (82.9%) were negatively associated with increased exposure, and this pattern of hypomethylation with increased arsenic persisted among associated CpGs with FDR of 0.05 (n = 170, 76.9%) (Table 4). Our top two arsenic-associated CpGs were located upstream of ABR (cg01912040, cg10003262). The remaining CpGs of the top 10 CpGs annotated to the upstream region of SEMA4G (cg05962511), C19orf66 promoter (cg13480898), EFNA1 (ephrin A1) gene body (cg07207669), SQSTM1 (sequestosome 1) 5'UTR (cg01225779), EML2 (echinoderm 486 microtubule associated protein like 2) gene body (cg06381803), UNKL (unk like zinc finger) TSS200 (cg09183146), MYEOV (myeloma overexpressed) promoter (cg08759026), and SPSB1 (splA/ryanodine receptor domain and SOCS box containing 1) 5'UTR (cg17489312).

Among the BEST participants, gene expression data was available for 26 of the 28 genes that annotated to our urinary arsenic-associated CpGs (p < 1.3 x [10.sup.-7]) (Table S5). At a threshold of p = 0.05, methylation was positively correlated with gene expression for four genes: RNF144A (ring finger protein 144A) (cg19240637; p = 1.9 x [10.sup.-5]), C19orf66 (cg13480898; p = 1.6 x [10.sup.-3]), SEMA5B (semaphorin 5B) (cg02306995; p = 0.035), and NELF (negative elongation factor) (cg04622454; p = 0.031). Inverse correlations between methylation and expression were observed for five CpGs in four genes: EML2 (cg06381803; p = 0.039), FCER2 (Fc fragment of IgE receptor II) (cg12261095; p = 0.010), B3GALT5 (beta-1,3-galactosyltransferase 5) (cg26390598; p = 0.035), and LCN8 (lipocalin 8) (cg14145338; p = 7.0 x [10.sup.-4] and cg13764516; p = 6.4 x [10.sup.-3]). After adjustment for age and sex, six of the nine associations between expression and methylation persisted (p < 0.05). Among the HEALS participants, genotyping information was available for 389 participants, and we examined whether the top arsenic-associated CpGs identified in the meta-analysis (n = 41) were associated with genetic variants (within 1 megabase window) (Table S6). Twenty-five out of the 41 arsenic-associated CpGs had one or more cis-mQTL pairs (FDR <0.01), suggesting that these CpGs and the genetic variants that influence their methylation status could be further explored in gene environment studies.

We attempted replication of the arsenic-associated CpGs identified in this meta-analysis in a group of Andean women from Argentina, who were exposed to arsenic via drinking water, (n = 93) with DNA methylation measured on the 450K array, as previously described by Ameer et al. (2017). Among the arsenic-associated CpGs from the meta-analysis (n = 217), only 16 CpGs were associated with urinary arsenic (p < 0.05) among the Andean women and 13 (81.3%) with consistent direction of association (Excel Table S4). While these results suggest some consistency among the arsenic-associated CpGs identified in our study, there is heterogeneity with respect to study population, sample size, study design, and analysis approach.

Enrichment of Arsenic-Associated CpGs (from Meta-Analysis) in Genomic Features and Gene Sets

Similar to our discovery analysis in HEALS using the EPIC array, we examined enrichment of genomic features among the urinary arsenic-associated CpGs identified in the meta-analysis. Arsenic-associated CpGs identified in the meta-analysis were enriched in shores and non-CpG islands and depleted in islands compared with CpGs not associated with arsenic (p = 7.9 x [10.sup.-8]) (Figure 5A). Arsenic-associated CpGs were depleted in promoters (p = 1.1 x [10.sup.-3]) and enriched in DHS regions (p = 2.2 x [10.sup.-7]) (Figure 5B). Notably, arsenic-associated CpGs were 2.6-fold more likely to be located within enhancers compared with CpGs not associated with arsenic (p = 1.2 x [10.sup.-3]). When we stratified by the direction of association, the genomic enrichment in DHS regions and enhancers and depletion in promoters persisted among negatively arsenic-associated CpGs (see Figure S8).

GSEA was applied to the urinary arsenic-associated CpGs identified in the meta-analysis among the associated CpGs with FDR of 0.05 (153 unique genes) and among the top 500 arsenic-associated CpGs (338 unique genes). Among both sets of arsenic-associated CpGs, we observed enrichment of genes annotating to the PI3K/AKT/mTOR, allograft rejection, reactive oxygen species pathway, inflammatory response, and TNF[alpha] signaling via NFkB hallmarks (p < 0.05) (Table 3). We observed enrichment of 13 KEGG pathways among the arsenic-associated CpGs with FDR of 0.05 (Table S4). Among the top 500 arsenic-associated CpGs, genes in this set annotated to KEGG pathways related to cell adhesion molecules (CAMs) (nine genes; p = 2.9 x [10.sup.-3]), mitogen-activated protein kinase signaling pathway (14 genes; p = 0.013), estrogen signaling pathway (eight genes; p = 0.015), hematopoietic cell lineage (four genes; p = 0.038), cysteine and methionine metabolism (three genes; p = 0.048), and NF[kappa]B signaling pathway (five genes; p = 0.029).

Discussion

This EWAS of arsenic exposure provides evidence that arsenic exposure is associated with DNA methylation levels at specific CpG sites in the leukocytes of Bangladeshi adults. In our discovery analysis, we identified 34 novel CpGs associated with arsenic exposure assessed in urine in HEALS prior to any arsenic mitigation efforts. Sixteen of these novel arsenic-associated CpGs were also present on the 450 K array, and ten replicated in an independent cohort (BEST). For the 34 novel arsenic-associated CpGs observed in HEALS, results for arsenic exposure measured in drinking water were highly consistent with results based on urinary arsenic. Our meta-analysis of HEALS and BEST identified 221 CpGs associated with arsenic exposure assessed in urine in Bangladeshi adults. Arsenic-associated CpGs were more likely to be hypomethylated and were enriched in CpG shores, DHS regions, and enhancers.

Our results are relevant to understanding how arsenic impacts the epigenome and how alteration of the epigenome may be a mechanism involved in arsenic toxicity. Among our top CpGs from both the discovery analysis and meta-analysis, we observed that higher arsenic exposure tends to be associated with hypomethylation, a phenomenon not observed in prior studies (Ameer et al. 2017; Argos et al. 2015; Liu et al. 2014; Seow et al. 2014), potentially due to the increased coverage of the EPIC array of intergenic and non-promoter regulatory genomic regions compared with the 450 K array. While the prior adult EWAS of arsenic exposure identified more CpGs associated with hypermethylation than hypomethylation (Argos et al. 2015; Liu et al. 2014), arsenic exposure was associated with decreased global methylation, assessed using the [3H]-methyl incorporation assay in Bangladeshi adults (Niedzwiecki et al. 2013). In addition, methylation is more variable in CpG shores and may be more susceptible to changes due to environmental exposures such as arsenic (Jones 2012). While the location of arsenic-associated CpG methylation in relation to CpG islands needs to be further studied, arsenic-associated CpGs were enriched in CpG shores in the cord blood from low-exposed infants (Koestler et al. 2013) and in non-CpG island regions in the umbilical artery and placenta from Bangladeshi infants (Cardenas et al. 2015a). A meta-analysis of maternal smoking and genome-wide methylation in cord blood observed similar enrichment in CpG shores and within enhancers and DHS regions and depletion in CpG islands and promoter regions (Joubert et al. 2016).

While the mechanisms for alteration of DNA methylation in blood by arsenic remains to elucidated, two proposed mechanisms involve the influence of arsenic: a) on the expression or function of DNA methyltransferases, potentially resulting in less downstream site-specific methylation; or b) on consumption of methyl groups during arsenic metabolism, resulting in depletion of methyl groups available to DNA methyltransferase, affecting DNA methylation synthesis (Eckstein et al. 2017). In addition, it is possible that specific genes are expressed in response to arsenic exposure, and the induction of these genes may change the local epigenetic state, including DNA methylation. Arsenic-associated methylation in DHS regions and enhancers warrants further exploration in both experimental and genome-wide methylation studies.

In our discovery and replication analysis, two of the top CpGs were located upstream of the ABR gene, encoding the active BCR-related protein. ABR is a regulator of the RHO family of small GTPases, signaling proteins involved in cytosketal dynamics, and a paralog of the BCR (BCR activator of RhoGEF and GTPase) gene. ABR has a GTPase-activating protein domain and may be important in cellular signaling and immune processes (Cho et al. 2007; Chuang et al. 1995; Cunnick et al. 2009). In addition, ABR may have a pivotal role in human embryonic stem cell mitosis, suggesting that ABR may be important for maintaining the genomic integrity and renewal of stem cells (Ohgushi et al. 2017). A study of newborns (n = 38) in the Biomarkers of Exposure to Arsenic (BEAR) pregnancy cohort from Mexico identified two CpGs also negatively associated with maternal water arsenic in the ABR gene body (Rojas et al. 2015). Arsenic increased ABR expression in an in vitro study of arsenic exposure in human epidermal keratinocytes (Perez et al. 2008). No prior methylation or experimental evidence has been identified for the other discovered urinary arsenic-associated CpGs.

Among the arsenic-associated CpGs from our meta-analysis, we observed some consistency with prior EWAS and gene expression studies of arsenic exposure. In the BEAR pregnancy cohort of Mexican infants, arsenic exposure was associated with methylation at CpGs within the following genes that also annotated to CpGs identified in our meta-analysis: TBC1D24 (TBC1 domain family member 24), ERC2, and PRRC2A (proline rich coiled-coil 2A) (Rojas et al. 2015). TBC1D24 is a protein that is hypothesized to be involved in vesicle transport and oxidative stress response. The PRRC2A gene is within the vicinity of the TNF-a and TNF-b encoding regions and may be involved in inflammatory response. In a cohort of Bangladeshi infants, methylation in the first exon of ERC2 was differentially methylated with maternal water arsenic (Kile et al. 2014). Maternal arsenic exposure increased EML2 protein levels in the cord blood of infants from the BEAR cohort (Bailey et al. 2014). In vitro arsenic exposure increased SQSTM1 and UNKL expression and decreased SPSB1 transcript stability in human fibroblasts (Qiu et al. 2015) and increased MCC (mutated in colorectal cancer) expression in human epidermal keratinocytes (Perez et al. 2008). The SQSTM1 encodes a protein that is involved in selective autophagy and cell senescence and is within the TNF-a inflammatory response pathway. In Excel Table S5, we identified arsenic-associated CpGs from the prior literature and reported the replication results from HEALS and our meta-analysis.

There was no overlap between our identified arsenicassociated CpGs and metal-associated CpGs reported in prior studies of in utero exposure to mercury (Cardenas et al. 2015b), lead (Sen et al. 2015), and cadmium (Kippler et al. 2013; Mohanty et al. 2015; Sanders et al. 2014). A meta-analysis of in utero cadmium exposure identified a differentially methylated CpG (cg16768966) within GAS7, encoding growth arrest protein 7, and arsenic was also associated with methylation at this same gene but not the same CpG (Everson et al. 2018). Overall, this lack of overlap between arsenic-associated CpGs and CpGs associated with other metals suggests that arsenic may have a toxicant specific effect on the epigenome.

GSEA enabled us to identify pathways potentially affected by arsenic-associated DNA methylation alterations in whole blood. Our arsenic-associated CpGs annotated to genes in hallmark gene sets related to reactive oxygen species, cancer and aging, and inflammatory response pathways, specifically TNF-a signaling via NFkB. Arsenic primarily undergoes biotransformation via methylation by AS3MT but may also involve glutathione conjugation and other antioxidant and xenobiotic metabolizing enzymes (Jomova et al. 2011). Arsenic exposure also induces expression of numerous proteins involved in NFkB response, TNF-a signaling, and inflammation in exposed infants and children (Bailey et al. 2014; Fry et al. 2007; Smeester et al. 2017) and adults (Dutta et al. 2015). The KEGG pathway for CAMs also has been identified as enriched in a study of Bangladeshi infants (Kile et al. 2014) and low-exposed U.S. adults (Liu et al. 2014). CAMs are universally important in hemostasis, immune response, and development. Among a subset of BEST participants, baseline arsenic exposure was associated with increased circulating CAMs at baseline and an increase in CAMs between baseline and 6-month follow-up (Chen et al. 2007). In a study of Bangladeshi infants, arsenic-associated CpGs were also enriched in KEGG pathways, including hematopoietic cell lineage, calcium signaling pathway, Notch signaling, and mTOR signaling (Kile et al. 2014). While there is heterogeneity in GSEA approaches, the results from our GSEA and prior EWAS suggest some associations between arsenic exposure and DNA methylation may be consistent across different stages of development and exposure levels. Within the GWAS Catalog (https://www.ebi.ac. uk/gwas/), several variants within or near genes that annotated to the arsenic-associated CpGs were associated with a broad range of health traits with potential connections to arsenic exposure. These health traits included many types of cancer, lymphocyte and red blood cell phenotypes and composition, pulmonary function, and blood pressure (Table S7), suggesting that genetic variation and potentially epigenetic variation in these genes may contribute to the risk of arsenic-associated disease and health traits.

A major strength of our study was the large sample size, with ~ 400 individuals in both the discovery and replication datasets, resulting in 796 arsenic-exposed adults for meta-analysis. Utilizing methylation data from BEST, we were able to conduct replication analyses, enabling us to validate discovered associations between urinary arsenic and CpG methylation. Our meta-analysis increased the power to detect putative arsenic-associated CpGs that can be examined in future studies. The EPIC array measures methylation at almost twice the number of CpGs compared with the 450 K array used in prior studies and improves coverage within intergenic regions, enhancers, and distal regulatory elements (Pidsley et al. 2016). In HEALS, water and urinary arsenic were assessed at baseline prior to interventions to remediate exposure; thus, these measures are likely to represent historical exposure status, allowing us to potentially identify CpGs associated with long-term exposure, as opposed to more acute responses to exposure. The exposure range in both HEALS and BEST participants was wide, enabling us to evaluate a broad spectrum of environmental arsenic exposure and its effect on genome-wide methylation.

Several limitations must be considered when interpreting the results of our study. While urinary arsenic is the most common biomarker of recent arsenic exposure, sampling and interindividual factors, such as age, sex, and metabolism, can contribute to variation in urinary arsenic measurements (Marchiset-Ferlay et al. 2012). Unmeasured confounding, from unknown biologic and technical variation, and measurement error can also potentially bias our results. DNA methylation was measured in whole blood, and the associations we observed in blood may not be present in other tissues. The EPIC array measures methylation at ~ 3% of CpGs in the genome (Stevens et al. 2013) and is biased toward gene regions, and DMR analysis preferentially identifies DMRs in gene regions since intergenic CpGs are more sparsely distributed on the array (Peters et al. 2015). We currently cannot replicate results for CpGs specific to the EPIC array. The association between gene expression and methylation (measured on EPIC) cannot be assessed in HEALS since RNA is not available. Genetic background can influence DNA methylation, and the arsenic-CpG associations observed in this work may not be present in other arsenic-exposed populations due to genetic differences (Bell et al. 2011; McRae et al. 2014). Even among our significant arsenic-associated CpGs, the effect sizes of the associations between arsenic and DNA methylation at CpGs are small, and it is unknown whether these small effects have functional consequences.

Future studies using the EPIC array (or more comprehensive measurement technologies) will be necessary to confirm the novel CpGs we identified and the enrichment of arsenic-associated CpGs among CpG shores and enhancers. Other factors, such as individual genetic susceptibility to arsenic exposure and timing and duration of arsenic exposure, need to be explored in studies of arsenic-associated methylation. While the EPIC array enables us to study the effect of arsenic on genic regions, we still do not know how arsenic affects global methylation, and bisulfite genomic sequencing approaches are needed to understand the effect of arsenic on the global epigenome, especially in intergenic regions. The relationship between arsenic-related variation in DNA methylation and local histone and chromatin features require further study, and these features likely have important implications for regulation of arsenic response genes as well as arsenic toxicity and disease risk. Finally, arsenic-associated CpGs identified here and elsewhere should be explored as potential biomarkers of arsenic exposure, susceptibility, and toxicity. Because some of the arsenic-associated CpGs identified in our analysis annotate to important inflammatory, oxidative response, and other cell regulation pathways, alterations in DNA methylation may be important biologic responses and/or markers of arsenic exposure that may better inform our understanding of and predict arsenic-associated health effects and disease.

Conclusions

Among Bangladeshi adults chronically exposed to arsenic via drinking contaminated water from tube wells, we identified novel and reproducible arsenic-associated DNA methylation alterations in blood. Our meta-analysis that combines our results with a prior study identified additional CpGs showing a putative association with arsenic exposure. Arsenic-associated CpGs annotated to genes involved in TNF[alpha] signaling via NFkB, CAMs, inflammatory processes, and important signaling pathways in cancer and aging. The implications of these arsenic-associated CpGs for exposure and risk assessment and potential toxicity prevention should be further investigated.

https://doi.org/10.1289/EHP3849

Acknowledgments

We acknowledge fellowship support for Dr. Demanelis provided by the National Institute on Aging (NIA) Specialized Demography and Economics of Aging Training Program (2T32AG000243) at the University of Chicago. Research support for this project is provided by active and past National Institutes of Health (NIH) grants (R01ES020506, R35ES028379, P42ES010349, R01CA102484, R01CA107431, P30CA014599, P30ES027792). We would like to acknowledge the HEALS and BEST study participants and research staff for their contributions to these cohorts and Dr. M. Kibriya for his contributions to generating the DNA methylation data utilized in this analysis.

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Kathryn Demanelis, [1] Maria Argos, [2] Lin Tong, [1] Justin Shinkle, [1] Mekala Sabarinathan, [1] Muhammad Rakibuz-Zaman, [3] Golam Sarwar, [3] Hasan Shahriar, [3] Tariqul Islam, [3] Mahfuzar Rahman, [3,4] Mohammad Yunus, [5] Joseph H. Graziano, [6] Karin Broberg, [7] Karin Engstrom, [8] Farzana Jasmine, [1] Habibul Ahsan, [1,9,10,11] and Brandon L. Pierce [1,9,10]

[1] Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA

[2] Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, USA

[3] UChicago Research Bangladesh, Mohakhali, Dhaka, Bangladesh

[4] Research and Evaluation Division, BRAC, Dhaka, Bangladesh

[5] International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh

[6] Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, USA

[7] Unit of Metals and Health, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden

[8] Division of Occupational and Environmental Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden

[9] Department of Human Genetics, University of Chicago, Chicago, Illinois, USA

[10] University of Chicago Comprehensive Cancer Center, University of Chicago, Chicago, Illinois, USA

[11] Department of Medicine, University of Chicago, Chicago, Illinois, USA

Address correspondence to Dr. Brandon L. Pierce, Department of Public Health Sciences, University of Chicago, 5841 S. Maryland Ave., MC 2000, Chicago, IL 60637. Telephone: 773-702-1917. E-mail: brandonpierce@ uchicago.edu

Supplemental Material is available online (https://doi.org/10.1289/EHP3849).

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

Received 2 May 2018; Revised 23 April 2019; Accepted 23 April 2019; Published 28 May 2019.

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Caption: Figure 1. Genome-wide associations between urinary arsenic concentration and CpG site-specific methylation in the Health Effects of Arsenic Longitudinal Study (HEALS). Using the EPIC (850K) array, the association between [log.sub.2]-transformed urinary arsenic (creatinine adjusted) and methylation was evaluated at 771,192 CpG sites across 396 individuals from HEALS. (A) Manhattan plot of the chromosomal location and p-value for each CpG-arsenic association. (B) Volcano plot presenting association estimate and p-value for each CpG-arsenic association. Colors correspond to CpG relationship to island. In (A) and (B), solid and dashed lines designate the Bonferroni threshold (p = 6.5 x [10.sup.-8]) and false discovery rate (FDR) 0.05 threshold (p = 2.0 x [10.sup.-6]), respectively.

Caption: Figure 2. Associations between urinary arsenic and CpG methylation discovered in the Health Effects of Arsenic Longitudinal Study (HEALS) and tested for replication in the Bangladesh Vitamin E and Selenium Trial (BEST). Using results from BEST, we attempted to validate 26 arsenic-associated CpGs that annotated to 450K array among the 67 CpGs identified using the EPIC array in HEALS (p < [10.sup.-5]). The BEST results consisted of associations between [log.sub.2]-transformed urinary arsenic and methylation evaluated for 390,810 CpGs using data on 400 BEST participants with existing 450K array data. Horizontal dashed line represents -[log.sub.10](p = 0.05), and solid vertical line corresponds to -[log.sub.10] [false discovery rate (FDR) < 0.05]. Direction of association summarizes whether results from the cohorts are both positive (triangle), both negative (square), or inconsistent (directions of association differ) (circle). The corresponding results data are presented in Table 2 and Excel Table S1.

Caption: Figure 3. Comparison between the associations of urinary arsenic and water arsenic with genome-wide methylation in the Health Effects of Arsenic Longitudinal Study (HEALS). The direction of association and -[log.sub.10(p)] for each CpG-arsenic association are plotted for urinary (x-axis) and water (y-axis) (both [log.sub.2] transformed). Solid lines and dashed lines designate -[log.sub.10] (p = 1) and -[log.sub.10] (p = 0.05), respectively. CpGs highlighted in blue diamonds denote [log.sub.2]-transformed urinary arsenic-associated sites [false discovery rate (FDR) < 0.05]. Pearson's correlation between all urinary and water arsenic CpG associations was 0.67 (p < [10.sup.-16]).

Caption: Figure 4. Differentially methylated regions (DMRs) associated with [log.sub.2]-transformed urinary arsenic in the Health Effects of Arsenic Longitudinal Study (HEALS). Using 771,192 CpGs from the EPIC array, DMRs were identified using DMRcate ([lambda] = 1,000 base pairs and C = 2). (A) Manhattan plot of the minimum false discovery rate (minFDR) for the CpG within all regions identified across EPIC array (no significance threshold set) (n = 108,086 regions). Solid and dashed lines designate minFDR of [10.sup.-4] and minFDR of 0.05, respectively. (B-D) coMET plots of top three DMRs associated with urinary arsenic. Genomic annotations to gene, CpG islands, chromatin regulation, and DNase clusters are shown below each plot and are from UCSC CpG Island, UCSC DNase Cluster, and the Broad UCSC ChromatinHMM tracks (http://genome.ucsc.edu/, see coMET software documentation for further information and color coding) (Martin et al. 2015).

Caption: Figure 5. Enrichment of arsenic-associated CpGs among genomic features at false discovery rate (FDR) of 0.05. (A) Locational (with relation to island) distribution of [log.sub.2]-transformed arsenic-associated CpGs (FDR <0.05) from the Health Effects of Arsenic Longitudinal Study (HEALS) discovery analysis on EPIC array (n = 34 CpGs) and meta-analysis of HEALS and the Bangladesh Vitamin E and Selenium Trial (BEST) (n = 221 CpGs) compared with distribution of CpGs on entire EPIC (n = 771,158) and 450K (n = 390,589) array, respectively. Colors correspond to CpG relationship to island. (B) Fold enrichment (FE) is plotted for each genomic feature comparing urinary arsenic-associated CpGs below FDR of 0.05 to the remaining CpGs. CpGs annotating to a promoter region were defined as those annotating to TSS200 or TSS1500. Abbreviations correspond to transcription factor binding site (TFBS) and DNase I hypersensitive site (DHS). P-values were obtained from [X.sup.2]-test comparing distribution of CpGs above and below FDR of 0.05 for each genomic feature [p < 0.05 (*), p < [10.sup.-3] (**), and p < [10.sup.-6] (***)].

Caption: Figure 6. Epigenome-wide meta-analysis of associations between urinary arsenic and DNA methylation in Bangladeshi adults using data from the Health Effects of Arsenic Longitudinal Study (HEALS) and the Bangladesh Vitamin E and Selenium Trial (BEST). Using data from EPIC (HEALS; n = 396) and 450K (BEST; n = 400) arrays, 390,810 CpG sites were meta-analyzed using METAL [summary statistics from covariate- and surrogate variable (SV)-adjusted model were provided as input]. (A) Q-Q plot of the observed distribution of meta-analysis p-values. The red line represents expected distribution of p-values under the null, and [lambda] corresponds to the genomic inflation factor (see "Methods" section). (B) Manhattan plot of the location of each CpG and p-value for each CpG-arsenic association. Solid and dashed lines designate the Bonferroni threshold (p = 1.3 x [10.sup.-7]) and false discovery rate (FDR) 0.05 threshold (p = 2.8 x [10.sup.-5]), respectively.
Table 1. Subcohort descriptive statistics [median (25th, 75th
percentiles) or n (%)] for HEALS (discovery) and BEST (validation).

                                   HEALS cohort
                                   (n =11,224)

Age (years)                        36.0 (29.0,45.0)
Urinary arsenic ([micro]g/g)      199.0 (106.0, 352.0)
Water arsenic ([micro]g/L)         60.0 (12.0,147.0)
Sex
  Male                            4,855 (43.3%)
  Female                          6,369 (56.7%)
Smoking
  Never                           7,204 (64.2%)
  Former                            743 (6.6%)
  Current                         3,271 (29.1%)
  Unknown                             6 (0.1%)
BMI (kg/[m.sup.2])
  Normal (18.5-22.9)              5,067 (45.1%)
  Underweight (< 18.5)            4,421 (39.4%)
  Overweight (23.0-29.9)          1,575 (14.0%)
  Obese ([greater than or            80 (0.7%)
  equal to] 30)
  Unknown                            81 (0.7%)

                                    HEALS subcohort
                                  (discovery; n = 396)

Age (years)                        36.5 (30.0, 45.0)
Urinary arsenic ([micro]g/g)      201.5 (113.5,350.0)
Water arsenic ([micro]g/L)         50.5 (11.0, 124.8)
Sex
  Male                              167 (42.2%)
  Female                            229 (57.8%)
Smoking
  Never                             240 (60.6%)
  Former                             21 (5.3%)
  Current                           135 (34.1%)
  Unknown                             0 (0.0%)
BMI (kg/[m.sup.2])
  Normal (18.5-22.9)                175 (44.2%)
  Underweight (< 18.5)              154 (38.9%)
  Overweight (23.0-29.9)             61 (15.4%)
  Obese ([greater than or             5 (1.3%)
  equal to] 30)
  Unknown                             1 (0.3%)

                                     BEST subcohort
                                  (validation; n = 400)

Age (years)                         44.0 (35.0, 50.3)
Urinary arsenic ([micro]g/g)       137.3 (76.2, 394.4)
Water arsenic ([micro]g/L)            Not measured
Sex
  Male                               212 (53.0%)
  Female                             188 (47.0%)
Smoking
  Never                              251 (62.8%)
  Former                              40 (10.0%)
  Current                            109 (27.3%)
  Unknown                              0 (0.0%)
BMI (kg/[m.sup.2])
  Normal (18.5-22.9)                 176 (44.0%)
  Underweight (< 18.5)               150 (37.5%)
  Overweight (23.0-29.9)              73 (18.3%)
  Obese ([greater than or              1 (0.3%)
  equal to] 30)
  Unknown                              0 (0.0%)

Note: BEST, Bangladesh Vitamin E and Selenium Trial; BMI, body mass
index; HEALS, Health Effects of Arsenic Longitudinal Study.

Table 2. CpGs associated with log2-transformed urinary arsenic (false
discovery rate (FDR) <0.05) discovered in HEALS (EPIC array).

                                                Nearest
Name         Chr   Position     CpG Location      gene      Feature

cg01912040   17      1106553   Shore            ABR        Upstream
cg05962511   10    102730022   Shore            SEMA4G     Body
cgl0003262   17      1106589   Shore            ABR        Upstream
cgl7420142   18     32702783   Non-CpG island   MAPRE2     Body
cg06466147    1    155188982   Non-CpG island   GBAP1      Body
cg09082427    9    140349184   Shore            NSMF       Body
cg04193083   17     41323562   Shore            NBR1       5'UTR
cgl9534475    3    141632139   Non-CpG island   ATP1B3     Body
cgl2608784    9    140349197   Shore            NSMF       Body
cgl4891900   17     76341204   Shelf            SOCS3      Downstream
cgl1308227   17     79202435   Non-CpG island   ENTHD2     3'UTR
cgl3832772    4    186283800   Non-CpG island   SNX25      Body
cgl5108641   10     99263320   Shelf            UBTD1      Body
cg09658504    9    140349188   Shore            NSMF       Body
cg05438461   15     40401720   Shore            BMF        Promoter
cgl0663081   20     36837543   Non-CpG island   KIAA1755   Downstream
cg08759026   11     69061454   Non-CpG island   MYEOV      Promoter
cgl1644394    7    144148615   Non-CpG island   TPK1       Downstream
cg05428706   10    102730130   Shore            SEMA4G     Body
cgl2865207    3    138669373   Shore            FOXF2NB    Body
cg09183146   16      1429863   Island           UNKL       Promoter
cg04622454    9    140349128   Shore            NEFF       Body
cg06381803   19     46119475   Island           EML2       Body
cg02772605    1     28912323   Shelf            SNHG12     Upstream
cgl0283165   19     17375666   Non-CpG island   USHBP1     Promoter
cg04891961   17     27939900   Island           ANKRD13B   Body
cgl2746706    6    169276508   Non-CpG island   SMOC2      Upstream
cg02330195   10     73342047   Non-CpG island   CDH23      Body
cg08077890   18       157838   Shore            USP14      Promoter
cg20433952   17     55607898   Non-CpG island   MSI2       Body
cgl0185759   12     60366859   Non-CpG island   SLC16A7    Downstream
cg05646745   10    135172466   Shore            FUOM       Promoter
cg22345623    9    125050297   Non-CpG island   MRRF       Body
cgl3480898   19     10195914   Shore            C19orf66   Promoter

                             HEALS (discovery, EPIC)

              Distance        Mean       [[beta].sub.sva]
Name         (basepairs)    (SD) (a)           (b)

cg01912040      15,937     0.73 (0.05)        -0.013
cg05962511         732     0.35 (0.05)        -0.011
cgl0003262      15,973     0.34 (0.06)        -0.014
cgl7420142      81,169     0.59 (0.05)        -0.011
cg06466147      19,459     0.62 (0.07)        -0.016
cg09082427       4,602     0.75 (0.05)        -0.007
cg04193083         316     0.04 (0.02)         0.002
cgl9534475      36,669     0.47 (0.04)         0.008
cgl2608784       4,589     0.69 (0.05)        -0.007
cgl4891900      14,954     0.91 (0.01)         0.003
cgl1308227      10,456     0.54 (0.06)        -0.011
cgl3832772      41,913     0.87 (0.03)        -0.005
cgl5108641       4,552     0.35 (0.06)         0.008
cg09658504       4,598     0.47 (0.04)        -0.005
cg05438461         632     0.20 (0.03)         0.005
cgl0663081      51,631     0.47 (0.03)        -0.004
cg08759026         159     0.24 (0.04)        -0.004
cgl1644394         419     0.62 (0.06)         0.008
cg05428706         840     0.79 (0.04)        -0.008
cgl2865207       3,297     0.15 (0.03)         0.004
cg09183146      34,842     0.26 (0.05)        -0.01
cg04622454       4,658     0.63 (0.04)        -0.005
cg06381803      29,300     0.41 (0.07)        -0.012
cg02772605       3,957     0.88 (0.02)         0.003
cgl0283165          61     0.39 (0.05)        -0.006
cg04891961      19,373     0.49 (0.07)        -0.009
cgl2746706     207,834     0.86 (0.03)        -0.005
cg02330195     142,463     0.67 (0.04)         0.005
cg08077890         645     0.77 (0.04)         0.006
cg20433952     273,524     0.51 (0.04)        -0.008
cgl0185759     283,741     0.46 (0.04)        -0.007
cg05646745         937     0.73 (0.04)         0.005
cg22345623      17,154     0.78 (0.05)        -0.007
cgl3480898         892     0.73 (0.05)        -0.005

                   HEALS (discovery, EPIC)

                                     [p.sub.cell
Name            [p.sub.sva]         adjusted] (c)

cg01912040   4.2 x [10.sup.-13]   4.0 x [10.sup.-9]
cg05962511   2.8 x [10.sup.-12]   3.9 x [10.sup.-s]
cgl0003262   2.4 x [10.sup.-11]   6.3 x [10.sup.-9]
cgl7420142   3.1 x [10.sup.-10]   1.7 x [10.sup.-6]
cg06466147   6.2 x [10.sup.-9]    2.0 x [10.sup.-9]
cg09082427   9.9 x [10.sup.-9]    4.3 x [10.sup.-6]
cg04193083   3.3 x [10.sup.-8]    7.0 x [10.sup.-1]
cgl9534475   9.6 x [10.sup.-8]    4.6 x [10.sup.-8]
cgl2608784   1.2 x [10.sup.-7]    2.3 x [10.sup.-5]
cgl4891900   1.3 x [10.sup.-7]    1.8 x [10.sup.-7]
cgl1308227   1.3 x [10.sup.-7]    1.6 x [10.sup.-6]
cgl3832772   1.7 x [10.sup.-7]    1.4 x [10.sup.-2]
cgl5108641   2.5 x [10.sup.-7]    2.8 x [10.sup.-5]
cg09658504   3.0 x [10.sup.-7]    7.4 x [10.sup.-4]
cg05438461   3.5 x [10.sup.-7]    2.4 x [10.sup.-5]
cgl0663081   3.7 x [10.sup.-7]    1.5 x [10.sup.-5]
cg08759026   6.7 x [10.sup.-7]    4.8 x [10.sup.-6]
cgl1644394   7.5 x [10.sup.-7]    5.3 x [10.sup.-4]
cg05428706   8.2 x [10.sup.-7]    1.8 x [10.sup.-5]
cgl2865207   1.0 x [10.sup.-6]    3.4 x [10.sup.-3]
cg09183146   1.2 x [10.sup.-6]    3.4 x [10.sup.-6]
cg04622454   1.3 x [10.sup.-6]    6.1 x [10.sup.-3]
cg06381803   1.4 x [10.sup.-6]    8.8 x [10.sup.-6]
cg02772605   1.4 x [10.sup.-6]    2.7 x [10.sup.-6]
cgl0283165   1.4 x [10.sup.-6]    1.3 x [10.sup.-3]
cg04891961   1.4 x [10.sup.-6]    1.6 x [10.sup.-5]
cgl2746706   1.5 x [10.sup.-6]    8.2 x [10.sup.-3]
cg02330195   1.5 x [10.sup.-6]    3.7 x [10.sup.-3]
cg08077890   1.6 x [10.sup.-6]    1.4 x [10.sup.-3]
cg20433952   1.9 x [10.sup.-6]    4.4 x [10.sup.-3]
cgl0185759   1.9 x [10.sup.-6]    1.3 x [10.sup.-7]
cg05646745   1.9 x [10.sup.-6]    4.0 x [10.sup.-7]
cg22345623   2.0 x [10.sup.-6]    2.3 x [10.sup.-7]
cgl3480898   2.0 x [10.sup.-6]    2.8 x [10.sup.-7]

                       BEST (d) (validation 450 K)

                Mean       [[beta].sub.sva]
Name          (SD) (a)           (b)             [p.sub.sva]

cg01912040   0.72 (0.06)        -0.008        2.8 x [10.sup.-6]
cg05962511   0.34 (0.05)        -0.004        6.6 x [10.sup.-4]
cgl0003262   0.26 (0.05)        -0.007        4.9 x [10.sup.-6]
cgl7420142       --               --                 --
cg06466147       --               --                 --
cg09082427       --               --                 --
cg04193083   0.03 (0.01)         0.000        4.1 x [10.sup.-1]
cgl9534475       --               --                 --
cgl2608784       --               --                 --
cgl4891900   0.93 (0.01)        -0.001        3.2 x [10.sup.-3]
cgl1308227       --               --                 --
cgl3832772   0.94 (0.02)         0.000        6.2 x [10.sup.-1]
cgl5108641       --               --                 --
cg09658504       --               --                 --
cg05438461       --               --                 --
cgl0663081       --               --                 --
cg08759026   0.27 (0.04)        -0.003        7.7 x [10.sup.-5]
cgl1644394       --               --                 --
cg05428706   0.76 (0.04)        -0.005        1.3 x [10.sup.-4]
cgl2865207       --               --                 --
cg09183146   0.24 (0.05)        -0.007        2.4 x [10.sup.-5]
cg04622454   0.57 (0.04)        -0.003        1.1 x [10.sup.-3]
cg06381803   0.40 (0.07)        -0.009        7.9 x [10.sup.-6]
cg02772605   0.89 (0.02)         0.000        9.4 x [10.sup.-1]
cgl0283165       --               --                 --
cg04891961   0.47 (0.08)         0.002        1.8 x [10.sup.-1]
cgl2746706   0.94 (0.01)        -0.001        1.3 x [10.sup.-1]
cg02330195       --               --                 --
cg08077890       --               --                 --
cg20433952   0.52 (0.04)        -0.003        3.4 x [10.sup.-2]
cgl0185759       --               --                 --
cg05646745       --               --                 --
cg22345623       --               --                 --
cgl3480898   0.76 (0.04)        -0.007        3.7 x [10.sup.-8]

Note: Results from the discovery analysis are shown for CpGs associated
with [log.sub.2]-transfonned urinary arsenic (creatinine adjusted)
below false discovery rate (FDR) of 0.05 from models adjusted for age,
sex, BMI category, smoking status, and 27 SVs. The p-values are
obtained from the analyses using methylation expressed as M-values as
the outcome variable, and reported association estimates
([[beta].sub.sva]) are from analysis with methylation expressed as beta
values as the outcome variable (for easier interpretability). Gene
assignments are from UCSC gene annotation provided by Illumina. The
replication results from BEST are also presented for the CpGs measured
on both the EPIC and 450K arrays. Extended results for arsenic-
associated CpGs with p < 0.05 are presented in Excel Table SI. --, no
data; BEST, Bangladesh Vitamin E and Selenium Trial; BMI, body mass
index; Chr, chromosome; HEATS, Health Effects of Arsenic Longitudinal
Study; SD, standard deviation; SV, surrogate variable.

(a) Mean and SD of beta values at each CpG (range: 0 to 1) in HEALS and
BEST.

(b) [[beta].sub.sva] and [p.sub.sva] are association estimate and p-
value from covariate and SV-adjusted analysis model.

(c) [p.sub.cell] adjusted is p-value from the analysis model of
[log.sub.2]-transformed urinary arsenic and adjusted for covariates and
estimated cell type proportions.

(d) Results from the replication analysis in BEST are shown for
[log.sub.2]-transfonned urinary arsenic-CpG associations from models
adjusted for age, sex, BMI category, smoking status, and 24 SVs.

Table 3. Results from gene set enrichment analysis of hallmark gene set
collection (n = 54 sets) among the urinary arsenic-associated CpGs
identified in the HEALS discovery analysis (n = 396) and meta-analysis
of HEALS and BEST (n = 796).

                                          Top 500 arsenic-associated
                                                   CpGs (a)

                                     n    Genes           p
HEALS
  Angiogenesis                       36     3     6.4 x [10.sup.-3]
  Cholesterol homeostasis            74     4     1.2 x [10.sup.-2]
  TNF[alpha] signaling via          200     7     2.1 x [10.sup.-2]
  NF[kappa]B
META-ANALYSIS
  Allograft rejection               200     9     2.2 x [10.sup.-3]
  P[I.sub.3]K/AKT/mTOR              105     6     9.8 x [10.sup.-3]
  Reactive oxygen species            49     3     1.4 x [10.sup.-2]
  pathway
  Inflammatory response             200     6     2.9 x [10.sup.-2]
  TNFa signaling via NF[kappa]B     200     7     4.1 x [10.sup.-2]
  Estrogen response early           200    10     1.1 x [10.sup.-2]
  UV response up-regulated          158     7     1.4 x [10.sup.-2]
  MTORC1 signaling                  200    --            --
  Oxidative phosphorylation         200    --            --
  MYC targets VI                    200    --            --
  Apical junction                   200    --            --

                                    Arsenic-associated CpGs
                                      below FDR < 0.05 (b)

                                    Genes           p
HEALS
  Angiogenesis                       --             --
  Cholesterol homeostasis            --             --
  TNF[alpha] signaling via           --             --
  NF[kappa]B
META-ANALYSIS
  Allograft rejection                 4      1.9 x [10.sup.-2]
  P[I.sub.3]K/AKT/mTOR                3      2.2 x [10.sup.-2]
  Reactive oxygen species             2      9.1 x [10.sup.-3]
  pathway
  Inflammatory response               3      4.4 x [10.sup.-2]
  TNFa signaling via NF[kappa]B       4      3.1 x [10.sup.-2]
  Estrogen response early            --             --
  UV response up-regulated           --             --
  MTORC1 signaling                    4      1.8 x [10.sup.-2]
  Oxidative phosphorylation           3      3.1 x [10.sup.-2]
  MYC targets VI                      3      4.7 x [10.sup.-2]
  Apical junction                     4      4.8 x [10.sup.-2]

Note: Gene annotations for arsenic-associated CpGs are provided as gene
set input for the gometh function. Results are presented for hallmark
gene sets (n = 54 sets) with at least two genes from arsenic-
associated CpG gene set and enrichment (p < 0.05). Background gene set
for 450 K is 19,246 genes, and EPIC is 23,234 genes. The table reports
total number of genes in set (n), genes identified in pathway from CpG
gene set, and p-value from CpG bias-corrected hypergeometric test. --,
no data; BEST, Bangladesh Vitamin E and Selenium Trial; FDR, false
discovery rate; HEALS, Health Effects of Arsenic Longitudinal Study;
TNFa, tumor necrosis factor a.

(a) Top 500 arsenic-associated CpGs, annotated to 348 unique genes in
the HEALS discovery analysis (EPIC array), 338 unique genes in the
meta-analysis (450 K array).

(b) Arsenic-associated CpGs (FDR < 0.05), annotated to 21 unique genes
in the discovery analysis, 153 unique genes in the meta-analysis.

Table 4. CpGs Associated with log2-transformed urinary arsenic (p
< 1.3 x [10.sup.-7]) from meta-analysis of HEALS (n = 396) and
BEST (n = 400).

Name         Chr   Position     CpG location     Nearest gene

cg01912040   17      1106553   Shore             ABR
cg10003262   17      1106589   Shore             ABR
cg05962511   10    102730022   Shore             SEMA4G
cg13480898   19     10195914   Shore             C19orf66
cg07207669    1    155102388   Shore             EFNA1
cg01225779    5    179238472   Shelf             SQSTM1
cg06381803   19     46119475   Island            EML2
cg09183146   16      1429863   Island            UNKL
cg08759026   11     69061454   Non-CpG island    MYEOV
cg17489312    1      9376039   Non-CpG island2   SPSB1
cg00472758   16      2552820   Shelf             TBC1D24
cg05428706   10    102730130   Shore             SEMA4G
cg05425326   16     58439361   Non-CpG island    GINS3
cg26435149    3     55605611   Non-CpG island    ERC2
cg17393635   19     49843565   Island            CD37
cg13223043    1     26492308   Shore             FAM110D
cg03871754   17     79320652   Island            TMEM105
cg19240637    2      7172297   Island            RNF144A
cg07782285   19     13085442   Non-CpG island    DAND5
cg26390598   21     41032396   Non-CpG island    B3GALT5
cg04622454    9    140349128   Shore             NELF
cg22959742   10     13913931   Non-CpG island    FRMD4A
cg00281776    2    209224225   Shore             PTH2R
cg12261095   19      7764345   Non-CpG island    FCER2
cg14718533   10     33355576   Non-CpG island    ITGB1
cg04459545   19     17375685   Non-CpG island    USHBP1
cg14145338    9    139649039   Non-CpG island    LCN8
cg04920032   12     50262986   Non-CpG island    FAIM2
cg18413900   12     58160989   Shore             CYP27B1
cg01757312   13    112720565   Island            SOX1
cg05816193    6     26018127   Shelf             HIST1H1A
cg07367302    1     19967428   Shelf             MINOS-NBL1
cg02306995    3    122635049   Shelf             SEMA5B
cg24318728   17     39649283   Non-CpG island    KRT36
cg04875062    1     17305562   Shore             MFAP2
cg13764516    9    139648911   Non-CpG island    LCN8
cg23050300    1      3281321   Non-CpG island    PRDM16
cg18050715   13     97996992   Shore             MBNL2
cg04826368    6     27130208   Non-CpG island    HIST1H2AH
cg08596618    1     24275885   Non-CpG island    SRSF10
cg27092191   16     31884699   Shore             ZNF267

                              Distance
Name          Feature     (basepairs)   Direction of association

cg01912040   Upstream       15,937      [down arrow][down arrow]
cg10003262   Upstream       15,973      [down arrow][down arrow]
cg05962511   Body             732       [down arrow][down arrow]
cg13480898   Promoter         892       [down arrow][down arrow]
cg07207669   Body            2,039      [down arrow][down arrow]
cg01225779   5'UTR           4,148      [down arrow][down arrow]
cg06381803   Body           29,300      [down arrow][down arrow]
cg09183146   Promoter       34,842      [down arrow][down arrow]
cg08759026   Promoter         159       [down arrow][down arrow]
cg17489312   5'UTR          23,098      [down arrow][down arrow]
cg00472758   3'UTR           4,581      [down arrow][down arrow]
cg05428706   Inside           840       [down arrow][down arrow]
cg05425326   3'UTR          13,063        [up arrow][up arrow]
cg26435149   3'UTR          896,780     [down arrow][down arrow]
cg17393635   Body            4,888      [down arrow][down arrow]
cg13223043   Upstream        3,189      [down arrow][down arrow]
cg03871754   Upstream       16,178      [down arrow][down arrow]
cg19240637   Body           114,774     [down arrow][down arrow]
cg07782285   3'UTR           5,118      [down arrow][down arrow]
cg26390598   5'UTR           3,142        [up arrow][up arrow]
cg04622454   Body            4,658      [down arrow][down arrow]
cg22959742   Body           136,229       [up arrow][up arrow]
cg00281776   Promoter         344       [down arrow][down arrow]
cg12261095   Body            2,687      [down arrow][down arrow]
cg14718533   Upstream       108,283       [up arrow][up arrow]
cg04459545   Promoter         61        [down arrow][down arrow]
cg14145338   Body            3,692      [down arrow][down arrow]
cg04920032   3'UTR          34,774      [down arrow][down arrow]
cg18413900   Promoter         13          [up arrow][up arrow]
cg01757312   Promoter        1,348        [up arrow][up arrow]
cg05816193   Promoter         87        [down arrow][down arrow]
cg07367302   Body           43,957      [down arrow][down arrow]
cg02306995   Body           111,610     [down arrow][down arrow]
cg24318728   Downstream       484       [down arrow][down arrow]
cg04875062   5'UTR           2,519      [down arrow][down arrow]
cg13764516   3'UTR           3,820      [down arrow][down arrow]
cg23050300   Body           295,579     [down arrow][down arrow]
cg18050715   Body           69,106        [up arrow][up arrow]
cg04826368   Upstream       14,867      [down arrow][down arrow]
cg08596618   Downstream     31,068      [down arrow][down arrow]
cg27092191   Promoter         380       [down arrow][down arrow]

Name                 p

cg01912040   3.3 x [10.sup.-17]
cg10003262   1.9 x [10.sup.-15]
cg05962511   2.1 x [10.sup.-13]
cg13480898   4.1 x [10.sup.-13]
cg07207669   3.0 x [10.sup.-12]
cg01225779   1.0 x [10.sup.-11]
cg06381803   4.8 x [10.sup.-11]
cg09183146   1.4 x [10.sup.-10]
cg08759026   2.8 x [10.sup.-10]
cg17489312   2.9 x [10.sup.-10]
cg00472758   5.6 x [10.sup.-10]
cg05428706   5.8 x [10.sup.-10]
cg05425326   6.1 x [10.sup.-10]
cg26435149   7.3 x [10.sup.-10]
cg17393635   1.2 x [10.sup.-9]
cg13223043   2.5 x [10.sup.-9]
cg03871754   3.3 x [10.sup.-9]
cg19240637   3.7 x [10.sup.-9]
cg07782285   7.5 x [10.sup.-9]
cg26390598   1.0 x [10.sup.-8]
cg04622454   1.1 x [10.sup.-8]
cg22959742   1.3 x [10.sup.-8]
cg00281776   1.8 x [10.sup.-8]
cg12261095   2.0 x [10.sup.-8]
cg14718533   2.1 x [10.sup.-8]
cg04459545   3.1 x [10.sup.-8]
cg14145338   3.3 x [10.sup.-8]
cg04920032   3.5 x [10.sup.-8]
cg18413900   3.6 x [10.sup.-8]
cg01757312   3.7 x [10.sup.-8]
cg05816193   4.4 x [10.sup.-8]
cg07367302   4.6 x [10.sup.-8]
cg02306995   5.5 x [10.sup.-8]
cg24318728   5.9 x [10.sup.-8]
cg04875062   6.0 x [10.sup.-8]
cg13764516   6.5 x [10.sup.-8]
cg23050300   8.4 x [10.sup.-8]
cg18050715   8.7 x [10.sup.-8]
cg04826368   1.0 x [10.sup.-7]
cg08596618   1.1 x [10.sup.-7]
cg27092191   1.2 x [10.sup.-7]

Note: Results from meta-analysis are shown CpGs associated with
[log.sub.2]-transformed urinary arsenic (creatinine adjusted) (p < 1.3
x [10.sup.-7]). Meta-analysis was conducted using the METAL software,
and summary statistics from the covariate-and surrogate variable (SV)-
adjusted models for HEALS and BEST were provided as input. Direction of
association indicated by arrows for HEALS and BEST, and downward and
upward arrows correspond to inverse and positive association with
increasing arsenic exposure. UCSC gene annotation provided by Illumina.
BEST, Bangladesh Vitamin E and Selenium Trial; Chr, chromosome; HEALS,
Health Effects of Arsenic Longitudinal Study.
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Title Annotation:Research
Author:Demanelis, Kathryn; Argos, Maria; Tong, Lin; Shinkle, Justin; Sabarinathan, Mekala; Rakibuz-Zaman, M
Publication:Environmental Health Perspectives
Article Type:Clinical report
Geographic Code:1U3IL
Date:May 1, 2019
Words:14907
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