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Aim: To facilitate wide-scale implementation of Illumina Mouse Methylation BeadChip (MMB) technology, array-based measurement of cytosine methylation was compared with the gold-standard assessment of DNA methylation by whole-genome bisulfite sequencing (WGBS). Methods: DNA methylation across two mouse strains (C57B6 and C3H) and both sexes was assessed using the MMB and compared with previously existing deep-coverage WGBS of mice of the same strain and sex. Results & conclusion: The findings demonstrated that 93.3–99.2% of sites had similar measurements of methylation across technologies and that differentially methylated cytosines and regions identified by each technology overlap and enrich for similar biological functions, suggesting that the MMB faithfully recapitulates the findings of WGBS.

Cannabis is widely used worldwide, yet its links to health outcomes are not fully understood. DNA methylation can serve as a mediator to link environmental exposures to health outcomes. We conducted an epigenome-wide association study (EWAS) of peripheral blood-based DNA methylation and lifetime cannabis use (ever vs. never) in a meta-analysis including 9,436 participants (7,795 European and 1,641 African ancestry) from seven cohorts. Accounting for effects of cigarette smoking, our trans-ancestry EWAS meta-analysis revealed four CpG sites significantly associated with lifetime cannabis use at a false discovery rate of 0.05 (p< 5.85 × 10−7): cg22572071 near geneADGRF1, cg15280358 inADAM12, cg00813162 inACTN1, and cg01101459 nearLINC01132. Additionally, our EWAS analysis in participants who never smoked cigarettes identified another epigenome-wide significant CpG site, cg14237301 annotated toAPOBR. We used a leave-one-out approach to evaluate methylation scores constructed as a weighted sum of the significant CpGs. The best model can explain 3.79% of the variance in lifetime cannabis use. These findings unravel the DNA methylation changes associated with lifetime cannabis use that are independent of cigarette smoking and may serve as a starting point for further research on the mechanisms through which cannabis exposure impacts health outcomes.

Background: The development and consequences of hypertension involve multiple biological systems that may include changes in immune profiles. Whether hypertension is related to peripheral immune cell composition has not been examined in large human cohorts. Methods: We estimated circulating proportions of 12 leukocyte subsets from the lymphoid and myeloid lineages by deconvolving cell-type-specific DNA methylation data from 4124 women. Hypertension status at baseline was defined by current use of antihypertensive medication and blood pressure measurements while new incident cases were identified during follow-up via annual health questionnaires. Results: Among hypertension-free women at baseline, higher B cell and lower naïve CD4+ helper T cell proportions were associated with subsequent increased hazard of hypertension incidence (B cells; adjusted HR: 1.17 [95% CI: 1.02–1.35]; P =0.03; naïve CD4+ T cell, adjusted HR: 0.88 [95% CI: 0.78–0.99]; P =0.04). Blood pressure measurements at baseline were similarly positively associated with B cells and inversely associated with naïve CD4+ helper T cells. Compared to normotensive women, women with hypertension had higher circulating proportions of neutrophils (adjusted odds ratio: 1.18 [95% CI: 1.07–1.31]; P =0.001) and lower proportions of CD4+ helper T cells (adjusted odds ratio: 0.90 [95% CI: 0.81–1.00] P =0.05), natural killers (adjusted odds ratio: 0.82 [95% CI: 0.74–0.91]; P P =0.01). Conclusions: These observations suggest that shifts in lymphocyte subsets occur before hypertension development, followed by later changes to neutrophils and additional lymphocytes.

BackgroundThe molecular effects of intermediate and long-term exposure to air pollution and temperature, such as those on extracellular microRNA (ex-miRNA) are not well understood but may have clinical consequences.ObjectivesTo assess the association between exposure to ambient air pollution and temperature and ex-miRNA profiles.MethodsOur study population consisted of 734 participants in the Normative Aging Study (NAS) between 1999 and 2015. We used high-resolution models to estimate four-week, eight-week, twelve-week, six-month, and one-year moving averages of PM2.5, O3, NO2, and ambient temperature based on geo-coded residential addresses. The outcome of interest was the extracellular microRNA (ex-miRNA) profile of each participant over time. We used a longitudinal quantile regression approach to estimate the association between the exposures and each ex-miRNA. Results were corrected for multiple comparisons and ex-miRNAs that were still significantly associated with the exposures were further analyzed using KEGG pathway analysis and Ingenuity Pathway Analysis.ResultsWe found 151 significant associations between levels of PM2.5, O3, NO2, and ambient temperature and 82 unique ex-miRNAs across multiple quantiles. Most of the significant results were associations with intermediate-term exposure to O3, long-term exposure to PM2.5, and both intermediate and long-term exposure to ambient temperature. The exposures were most often associated with the 75th and 90th percentile of the outcomes. Pathway analyses of significant ex-miRNAs revealed their involvement in biological pathways involving cell function and communication as well as clinical diseases such as cardiovascular disease, respiratory disease, and neurological disease.ConclusionOur results show that intermediate and long-term exposure to all our exposures of interest were associated with changes in the ex-miRNA profile of study participants. Further studies on environmental risk factors and ex-miRNAs are warranted.

Epigenetic age acceleration is considered a measure of biological aging based on genome-wide patterns of DNA methylation. Although age acceleration has been associated with the incidence of diseases and death, less is known about how it is related to lifestyle behaviors. Among 2316 women, we evaluate associations between self-reported alcohol consumption and various metrics of epigenetic age acceleration. Recent average alcohol consumption was defined as the mean number of drinks consumed per week within the past year; lifetime average consumption was estimated as the mean number of drinks per year drinking. Whole-blood genome-wide DNA methylation was measured with HumanMethylation450 BeadChips and used to assess 4 epigenetic clocks (Hannum, Horvath, PhenoAge, and GrimAge) and their corresponding metrics of epigenetic age acceleration (Hannum AgeAccel, Horvath AgeAccel, PhenoAgeAccel, and GrimAgeAccel). Although alcohol consumption showed little association with most age acceleration metrics, both lifetime and recent average consumption measures were positively associated with GrimAgeAccel (lifetime, per additional 135 drinks/year: β = 0.30 years, 95% confidence interval [CI]: 0.11, 0.48, p = .002; recent, per additional 5 drinks/week: β = 0.19 years, 95% CI: 0.01, 0.37, p = .04). In a mutually adjusted model, only average lifetime alcohol consumption remained associated with GrimAgeAccel (lifetime, per additional 135 drinks/year: β = 0.27 years, 95% CI: 0.04, 0.50, p = .02; recent, per 5 additional drinks/week: β = 0.05 years, 95% CI: −0.16, 0.26, p = .64). Although alcohol use does not appear to be strongly associated with biological age measured by most epigenetic clocks, lifetime average consumption is associated with higher biological age assessed by the GrimAge epigenetic clock.

Leukocyte DNA methylation (DNAm) at individual sites across the genome can be used to construct measures of biological age. Positive age acceleration—when biological age is older than chronological age—is associated with higher risk of age-associated diseases. Recent adjuvant therapy is reported to increase age acceleration, but the longer-term effects of a breast cancer diagnosis and treatment on age acceleration remain unknown. Here, we use blood samples collected at two timepoints to examine changes in age acceleration over time comparing women who did and did not develop breast cancer. Paired whole blood samples were drawn an average of 8 years apart (range: 5-11 years) in a sample of non-Hispanic White and Black (Hispanic and non-Hispanic) women. DNAm was profiled using Infinium MethylationEPIC BeadChips. Approximately half the women were diagnosed and treated for breast cancer between blood draws (cases; n= 190, baseline mean age= 57) whereas the other half remained breast cancer-free (controls; n= 227, baseline mean age= 56). Longitudinal changes in three age acceleration metrics were compared to determine whether an intervening breast cancer diagnosis and treatment was associated with trajectories in biological aging. On average, the cases were diagnosed with breast cancer 3.5 years after the initial blood draw and 4 years before the second blood draw. Among the cases, 36% were treated with chemotherapy, 65% with radiation therapy, and 70% with hormonal therapies; 45% of the cases received two types of therapy, and 13% received all three. Compared to women who remained cancer-free, women diagnosed and treated for breast cancer had increases in age acceleration over time as measured by PhenoAgeAccel (adjusted standardized mean difference (β)= 0.13, 95% CI: 0.00, 0.26), GrimAgeAccel (β= 0.13, 95% CI: 0.03, 0.24), and DunedinPACE (β= 0.35, 95% CI: 0.23, 0.48). The associations did not vary by timing of diagnosis between the blood draws or race; however, women diagnosed with estrogen receptor (ER) negative tumors appeared to experience faster increases in age acceleration than women diagnosed with ER positive tumors (GrimAgeAccel; ER negative β= 0.27, 95% CI: 0.07, 0.47; ER positive β= 0.10, 95% CI: -0.01, 0.21; P-interaction= 0.14). To investigate the impact of different types of breast cancer therapies, associations were examined using a case-only design. In models that simultaneously included chemotherapy, radiation therapy and hormone therapy, radiation therapy had the strongest associations with accelerated biological aging as measured by PhenoAgeAccel (β= 0.38, 95% CI: 0.18, 0.58), GrimAgeAccel (β= 0.27, 95% CI: 0.09, 0.45), and DunedinPACE (β= 0.25, 95% CI: 0.03, 0.47). We find that years after the initial diagnosis, breast cancer survivors have significantly accelerated biological aging; treatment modalities may differentially influence these rates. Citation Format: Jacob K. Kresovich, Katie M. O'Brien, Zongli Xu, Clarice R. Weinberg, Dale P. Sandler, Jack A. Taylor. Breast cancer diagnosis and treatment associated with acceleration of biological aging over time in a racially diverse cohort of women. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5757.

Environmental metal exposures have been associated with multiple deleterious health endpoints. DNA methylation (DNAm) may provide insight into the mechanisms underlying these relationships. Toenail metals are non-invasive biomarkers, reflecting a medium-term time exposure window.This study examined variation in leukocyte DNAm and toenail arsenic (As), cadmium (Cd), lead (Pb), manganese (Mn), and mercury (Hg) among elderly men in the Normative Aging Study, a longitudinal cohort.We repeatedly collected samples of blood and toenail clippings. We measured DNAm in leukocytes with the Illumina HumanMethylation450 K BeadChip. We first performed median regression to evaluate the effects of each individual toenail metal on DNAm at three levels: individual cytosine-phosphate-guanine (CpG) sites, regions, and pathways. Then, we applied a Bayesian kernel machine regression (BKMR) to assess the joint and individual effects of metal mixtures on DNAm. Significant CpGs were identified using a multiple testing correction based on the independent degrees of freedom approach for correlated outcomes. The approach considers the effective degrees of freedom in the DNAm data using the principal components that explain95% variation of the data.We included 564 subjects (754 visits) between 1999 and 2013. The numbers of significantly differentially methylated CpG sites, regions, and pathways varied by metals. For example, we found six significant pathways for As, three for Cd, and one for Mn. The As-associated pathways were associated with cancer (e.g., skin cancer) and cardiovascular disease, whereas the Cd-associated pathways were related to lung cancer. Metal mixtures were also associated with 47 significant CpG sites, as well as pathways, mainly related to cancer and cardiovascular disease.This study provides an approach to understanding the potential epigenetic mechanisms underlying observed relations between toenail metals and adverse health endpoints.

Although underlying mechanisms of long-term exposure to air pollution and cardiovascular disease remain obscure, effects might partially act through changes in DNA methylation. We examined the associations between long-term ambient fine particulate matter (PM(2.5)) and methylation, considering both a global measure and methylation at several specific inflammation-related loci, in two random sub-cohorts selected from a nationwide prospective study of US women. In one sub-cohort we measured long interspersed nucleotide element (LINE-1); in the other, we measured methylation at three candidates CpG loci related to inflammatory pathways [tumour necrosis factor-alpha (TNF-α) and toll-like receptor-2 (TLR-2)]. Annual average contemporaneous ambient PM(2.5) concentrations were estimated for the current residence. We used both classical least-squares and quantile regression models to estimate the long-term effects. The women in sub-cohorts 1 (n = 491) and 2 (n = 882) had mean ages of 55.8 and 56.7, respectively. Neither modelling approach showed an association between long-term PM(2.5) and LINE-1 methylation or between PM(2.5) and either of the two CpG sites in TLR-2. Using linear regression, there was an estimated change of −6.5% (95% confidence interval CI: −13.34%, 0.35%) in mean methylation of TNF-α per 5 µg/m(3) increase in PM(2.5). Quantile regression showed that the downward shift was mainly in the lower half of the distribution of DNA methylation. Long-term residence in regions with higher ambient PM(2.5) may be associated with increased TNF-α through a reduction in methylation, particularly in the lower tail. Epigenetic markers and quantile regression might provide insight into mechanisms underlying the relationship between air pollution and cardiovascular disease.

Measures derived using blood DNA methylation are increasingly under investigation as indicators of disease and mortality risk. Three existing epigenetic age measures or "epigenetic clocks" appear associated with breast cancer. Two newly-developed epigenetic mortality predictors may be related to all-cancer incidence, but associations with specific cancers have not been examined in large studies. Using HumanMethylation450 BeadChips to measure blood DNA methylation in 2,773 cancer-free women enrolled in the Sister Study, we calculated two epigenetic mortality predictors: 'GrimAgeAccel' and the 'mortality score' (MS). Using Cox proportional hazard models, neither GrimAgeAccel nor the MS were associated with overall breast cancer incidence (GrimAgeAccel hazard ratio [HR]: 1.06, 95% confidence interval [CI]: 0.98-1.14, P=0.17; MS HR: 0.99, 95% CI: 0.92-1.07, P=0.85); however, a weak, positive association was observed for GrimAgeAccel and invasive breast cancer (HR: 1.08, 95% CI: 0.99-1.17, P=0.08). Stratification of invasive cancers by menopause status at diagnoses revealed the association was predominantly observed for postmenopausal breast cancer (HR: 1.10, 95% CI: 1.01, 1.20, P=0.04). Although the MS was unrelated to breast cancer risk, we find evidence that GrimAgeAccel may be weakly associated with invasive breast cancer, particularly for women diagnosed after menopause.

Background Illumina DNA methylation arrays are high-throughput platforms for cost-effective genome-wide profiling of individual CpGs. Experimental and technical factors introduce appreciable measurement variation, some of which can be mitigated by careful “preprocessing” of raw data. Methods Here we describe the ENmix preprocessing pipeline and compare it to a set of seven published alternative pipelines (ChAMP, Illumina, SWAN, Funnorm, Noob, wateRmelon, and RnBeads). We use two large sets of duplicate sample measurements with 450 K and EPIC arrays, along with mixtures of isogenic methylated and unmethylated cell line DNA to compare raw data and that preprocessed via different pipelines. Results Our evaluations show that the ENmix pipeline performs the best with significantly higher correlation and lower absolute difference between duplicate pairs, higher intraclass correlation coefficients (ICC) and smaller deviations from expected methylation level in mixture experiments. In addition to the pipeline function, ENmix software provides an integrated set of functions for reading in raw data files from mouse and human arrays, quality control, data preprocessing, visualization, detection of differentially methylated regions (DMRs), estimation of cell type proportions, and calculation of methylation age clocks. ENmix is computationally efficient, flexible and allows parallel computing. To facilitate further evaluations, we make all datasets and evaluation code publicly available. Conclusion Careful selection of robust data preprocessing methods is critical for DNA methylation array studies. ENmix outperformed other pipelines in our evaluations to minimize experimental variation and to improve data quality and study power.

Background Shift work has been associated with increased risk of age-related morbidity and mortality. Biological age, estimated using DNA methylation (DNAm), may quantify the biological consequences of shift work on the risk of age-related disease. We examined whether prior employment in shift-working occupations was associated with epigenetic age acceleration. Methods In a sample of non-Hispanic White women aged 35–74 (n = 2574), we measured DNAm using the Illumina Infinium Human450 BeadChip and calculated DNAm age using three established epigenetic clocks. Age-acceleration metrics were derived by regressing DNAm age on chronological age and predicting the residuals. Using linear regression, we estimated associations between shift work history and age acceleration. We also conducted an epigenome-wide association study using robust linear-regression models corrected with false discovery rate (FDR) q-values. Results Approximately 7% of women reported any shift work. Higher age acceleration was observed for a 1-year increase in overall [β = 0.11, 95% confidence interval (CI): 0.02–0.21] and night-specific shift work (β = 0.12, 95% CI: 0.03–0.21). The association was strongest for ≥10 years of night shift work (β = 3.16, 95% CI: 1.17–5.15). From the epigenome-wide association study, years of overall and night shift work were associated with DNAm at 66 and 85 CpG sites (FDR Conclusions Shift work was associated with differential CpG site methylation and with differential DNAm patterns, measured by epigenetic age acceleration, consistent with long-term negative health effects.

Background Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. Results Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. Conclusion This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

Women of advanced maternal age account for an increasing proportion of live births in many developed countries across the globe. Offspring of older mothers are at an increased risk for a variety of subsequent health outcomes, including outcomes that do not manifest until childhood or adulthood. The molecular underpinnings of the association between maternal aging and offspring morbidity remain elusive. However, one possible mechanism is that maternal aging produces specific alterations in the offspring’s epigenome in utero, and these epigenetic alterations persist into adulthood. We conducted an epigenome-wide association study (EWAS) of the effect of a mother’s age on blood DNA methylation in 2,740 adult daughters using the Illumina Infinium HumanMethylation450 array. A false discovery rate (FDR) q-value threshold of 0.05 was used to identify differentially methylated CpG sites (dmCpGs). We identified 87 dmCpGs associated with increased maternal age. The majority (84%) of the dmCpGs had lower methylation in daughters of older mothers, with an average methylation difference of 0.6% per 5-year increase in mother’s age. Thirteen genomic regions contained multiple dmCpGs. Most notably, nine dmCpGs were found in the promoter region of the gene LIM homeobox 8 (LHX8), which plays a pivotal role in female fertility. Other dmCpGs were found in genes associated with metabolically active brown fat, carcinogenesis, and neurodevelopmental disorders. We conclude that maternal age is associated with persistent epigenetic changes in daughters at genes that have intriguing links to health.

Background Age is one of the strongest predictors of cancer, chronic disease, and mortality, but biological responses to aging differ among people. Epigenetic DNA modifications have been used to estimate “biological age,” which may be a useful predictor of disease risk. We tested this hypothesis for breast cancer. Methods Using a case-cohort approach, we measured baseline blood DNA methylation of 2764 women enrolled in the Sister Study, 1566 of whom subsequently developed breast cancer after an average of 6 years. Using three previously established methylation-based “clocks” (Hannum, Horvath, and Levine), we defined biological age acceleration for each woman by comparing her estimated biological age with her chronological age. Hazard ratios and 95% confidence intervals for breast cancer risk were estimated using Cox regression models. All statistical tests were two-sided. Results Each of the three clocks showed that biological age acceleration was statistically significantly associated with increased risk of developing breast cancer (5-year age acceleration, Hannum’s clock: hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.00 to 1.21, P = .04; Horvath’s clock: HR = 1.08, 95% CI = 1.00 to 1.17, P = .04; Levine’s clock: HR = 1.15, 95% CI = 1.07 to 1.23, P Conclusions DNA methylation-based measures of biological age may be important predictors of breast cancer risk.

Hormone therapy (HT) is associated with increased risk of breast cancer, strongly dependent on type, duration, and recency of use. HT use could affect cancer risk by changing breast tissue transcriptional programs. We hypothesize that these changes are preceded by changes in DNA methylation. To explore this hypothesis we used histologically normal-appearing breast tissue from the Normal Breast Study (NBS). DNA methylation β-values were obtained using the Illumina HumanMethylation 450 BeadChips for 90 samples including all NBS-participants who used HT within 5 y before surgery. Data were analyzed using the reference-free cell mixture method. Cancer Genome Atlas (TCGA) mRNA-Seq data were used to assess correlation between DNA methylation and gene expression. We identified 527 CpG sites in 403 genes that were associated with ever using HT at genome wide significance (FDR q

Occupational exposure to the arylamines benzidine and β-naphthylamine increase bladder cancer risk up to 100-fold, making them some of the most powerful human carcinogens. We hypothesize that tumors arising in people with occupational exposures have different patterns of gene expression than histologically similar tumors from people without such exposures. In a case-case study, we compare gene expression in 22 formalin-fixed paraffin-embedded (FFPE) bladder tumors from men with high-level occupational exposure to arylamines to that in 26 FFPE bladder tumors from men without such exposure. Gene expression analysis was performed on the NanoString nCounter system using a PanCancer Progression Panel comprised of 740 cancer progression-related genes and a custom panel of 69 arylamine- and bladder cancer-related genes which were chosen from in vitro studies. Although fold differences were small, there was evidence of differential expression by exposure status for 17 genes from the Progression Panel and 4 genes from the custom panel. In total, 10 genes showed dose-response association at a p < 0.01 , of which 4 genes (CD46, NR4A1, BAX, and YWHAZ) passed a false discovery rate (FDR) q value cutoff of 0.05 but were not significant after Bonferroni correction. Overall, we find limited evidence for differentially expressed genes in pathways related to DNA damage signaling and epithelial-to-mesenchymal transition (EMT).

DNA methylation is the most widely studied epigenetic mark in humans and plays an essential role in normal biological processes as well as in disease development. More focus has recently been placed on understanding functional aspects of methylation, prompting the development of methods to investigate the relationship between heterogeneity in methylation patterns and disease risk. However, most of these methods are limited in that they use simplified models that may rely on arbitrarily chosen parameters, they can only detect differentially methylated regions (DMRs) one at a time, or they are computationally intensive. To address these shortcomings, we present a wavelet-based method called ‘Wavelet Screening’ (WS) that can perform an epigenome-wide association study (EWAS) of thousands of individuals on a single CPU in only a matter of hours. By detecting multiple DMRs located near each other, WS identifies more complex patterns that can differentiate between different methylation profiles. We performed an extensive set of simulations to demonstrate the robustness and high power of WS, before applying it to a previously published EWAS dataset of orofacial clefts (OFCs). WS identified 82 associated regions containing several known genes and loci for OFCs, while other findings are novel and warrant replication in other OFCs cohorts.

Epigenetic clocks use DNA methylation to estimate biological age. Whether body composition and physical activity are associated with these clocks is not well understood. Using blood samples collected at enrollment (2003–2009) from 2,758 women in the US nationwide Sister Study, we calculated 6 epigenetic age acceleration metrics using 4 epigenetic clocks (Hannum, Horvath, PhenoAge, GrimAge). Recreational physical activity was self-reported, and adiposity measures were assessed by trained medical examiners (body mass index (BMI), waist-to-hip ratio (WtH), waist circumference). In cross-sectional analyses, all adiposity measures were associated with epigenetic age acceleration. The strongest association was for BMI and PhenoAge, a measure of biological age that correlates with chronic disease (BMI of ≥35.0 vs. 18.5–24.9, β = 3.15 years, 95% confidence interval (CI): 2.41, 3.90; P for trend < 0.001). In a mutual-adjustment model, both were associated with PhenoAge age acceleration (BMI of ≥35.0 vs. 18.5–24.9, β = 2.69 years, 95% CI: 1.90, 3.48; P for trend < 0.001; quartile 4 vs.1 WtH, β = 1.00 years, 95% CI: 0.34, 1.65; P for trend < 0.008). After adjustment, physical activity was associated only with GrimAge (quartile 4 vs. 1, β = −0.42 years, 95% CI: −0.70, −0.14; P for trend = 0.001). Physical activity attenuated the waist circumference associations with PhenoAge and GrimAge. Excess adiposity was associated with epigenetic age acceleration; physical activity might attenuate associations with waist circumference.

Illumina BeadChips are widely utilized in epigenome-wide association studies (EWAS). Several studies have reported that many probes on these arrays have poor reliability. Here, we compare different pre-processing methods to improve intra-class correlation coefficients (ICC). We describe the characteristics of ICC across the genome, within and between studies, and across different array platforms. Using technical duplicates from 128 subjects, we find that with raw data only 22.5% of the CpGs on 450 K array have ‘acceptable’ ICCs (>0.5). Data preprocessing steps, such as background correction and dye bias correction, can reduce technical noise and improve the percentage to 38.5%. Similar to previous studies, we found that ICC is associated with CpG methylation level such that 83% of CpGs with intermediate methylation (0.10.9) have acceptable ICCs. ICC is also correlated with CpG methylation variance; after mutual adjustment for beta-value and variance, only variance remains correlated. Many CpGs with poor ICCs (

Biological ageing estimators derived from DNA methylation (DNAm) data are heritable and correlate with morbidity and mortality. Leveraging DNAm and SNP data from >41,000 individuals, we identify 137 genome-wide significant loci (113 novel) from meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We report strong genetic correlations with longevity and lifestyle factors such as smoking, education, and obesity. Significant associations are observed in polygenic risk score analysis and to a lesser extent in Mendelian randomization analyses. This study illuminates the genetic architecture underlying epigenetic ageing and its shared genetic contributions with lifestyle factors and longevity.

Cannabis use is highly prevalent and is associated with adverse and beneficial effects. To better understand the full spectrum of health consequences, biomarkers that accurately classify cannabis use are needed. DNA methylation (DNAm) is an excellent candidate, yet no blood-based epigenome-wide association studies (EWAS) in humans exist. We conducted an EWAS of lifetime cannabis use (ever vs. never) using blood-based DNAm data from a case-cohort study within Sister Study, a prospective cohort of women at risk of developing breast cancer (Discovery N = 1,730 [855 ever users]; Replication N = 853 [392 ever users]). We identified and replicated an association with lifetime cannabis use at cg15973234 (CEMIP): combined p = 3.3 × 10-8 . We found no overlap between published blood-based cis-meQTLs of cg15973234 and reported lifetime cannabis use-associated single nucleotide polymorphism (SNPs; p < .05), suggesting that the observed DNAm difference was driven by cannabis exposure. We also developed a multi-CpG classifier of lifetime cannabis use using penalized regression of top EWAS CpGs. The resulting 50-CpG classifier produced an area under the curve (AUC) = 0.74 (95% CI [0.72, 0.76], p = 2.00 × 10-5 ) in the discovery sample and AUC = 0.54 ([0.51, 0.57], p = 2.87 × 10-2 ) in the replication sample. Our EWAS findings provide evidence that blood-based DNAm is associated with lifetime cannabis use.

Summary ipDMR is an R software tool for identification of differentially methylated regions (DMRs) using auto-correlated P-values for individual CpGs from epigenome-wide association analysis using array or bisulfite sequencing data. It summarizes P-values for adjacent CpGs, identifies association peaks and then extends peaks to find boundaries of DMRs. ipDMR uses BED format files as input and is easy to use. Simulations guided by real data found that ipDMR outperformed current available methods and provided slightly higher true positive rates and much lower false discovery rates. Availability and implementation ipDMR is available at https://bioconductor.org/packages/release/bioc/html/ENmix.html. Supplementary information Supplementary data are available at Bioinformatics online.

Additional file 2 Details of the statistical methods.

Additional file 1 File containing supplementary figures and tables.