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Laparoscopic resection of the pancreas (LRP) has been implemented to a varying degree because it is technically demanding and requires a long learning curve. In the present study we analyze the risk factors for complications and hospital readmissions in a single center study of 105 consecutive LRPs.We conducted a retrospective study using a prospective database. Data were collected on age, gender, BMI, ASA score, type of surgery, histologic type, operative time, hospital stay, postoperative complications, degree of severity and hospital readmission.The cohort included 105 patients, 63 females and 42 males with a median age and BMI of 58 (53-70) and 25.5 (22,2-27.9) respectively. Eighteen (17%) central pancreatectomies, 5 (4.8%) enucleations, 81 (77.6%) distal pancreatectomies and one total pancreatectomy were performed. Fifty-six patients (53.3%) experienced some type of complication, of which 13 (12.3%) were severe (Clavien-DindoIIIb) and 11 (10.5%) patients were readmitted in the first 30 days after surgery. In the univariate analysis, age, male gender, ASA score, central pancreatectomy and operative time were significantly associated with the development of complications (P0.05). In the multivariate analysis, male gender (OR 7.97; 95% CI 1.08-58.88)), severe complications (OR 59.40; 95% CI, 7.69-458.99), and the development of intrabdominal collections (OR 8.97; 95% CI, 1.28-63.02)) were associated with hospital readmission.Age, male gender, ASA score, operative time and central pancreatectomy are associated with a higher incidence of complications. Male gender, severe complications and intraabdominal collections are associated with more hospital readmissions.

The majority of disease-associated variants identified through genome-wide association studies (GWAS) are located outside of protein-coding regions and are overrepresented in sequences that regulate gene expression. Prioritizing candidate regulatory variants and potential biological mechanisms for further functional experiments, such as genome editing, can be challenging, especially in regions with a high number of variants in strong linkage disequilibrium or multiple proximal gene targets. Improved annotation of the regulatory genome can help identify promising variants and target genes for functional genomics experiments. To advance this area, we developed FORGEdb (https://forge2.altiusinstitute.org/files/forgedb.html), a web-based tool that can rapidly integrate data for individual genetic variants, providing information on associated regulatory elements, transcription factor (TF) binding sites and target genes for over 37 million variants. FORGEdb uses annotations derived from data across a wide range of biological samples to delineate the regulatory context for each variant at the cell type level. Multiple data types, such as Combined Annotation Dependent Depletion (CADD) scores, expression quantitative trait loci (eQTLs), activity-by-contact (ABC) interactions, Contextual Analysis of TF Occupancy (CATO) scores, transcription factor (TF) motifs, DNase I hotspots, histone mark ChIP-seq peaks and chromatin states, are included in FORGEdb and these annotations are integrated into a FORGEdb score to guide assessment of functional importance. In summary, FORGEdb provides an expansive and unique resource of genomic annotations and an integrated score that can be used to accelerate the translation of identified genetic loci into biological insight.

Recent work has highlighted a lack of diversity in genomic studies. However, less attention has been given to epigenomics. Here, we show that epigenomic studies are lacking in diversity and propose several solutions to address this problem.

Purpose: To independently assess upper urinary tract Memokath (MMK-051) stent outcomes in a national tertiary referral center. Materials and Methods: Two researchers, completely independent to the ...

Hundreds of epigenome-wide association studies (EWAS) have been performed, successfully identifying replicated epigenomic signals in processes such as aging and smoking. Despite this progress, it remains a major challenge in EWAS to detect both cell type-specific and cell type confounding effects impacting study results. One way to identify these effects is through eFORGE (experimentally derived Functional element Overlap analysis of ReGions from EWAS), a published tool that uses 815 datasets from large-scale mapping studies to detect enriched tissues, cell types, and genomic regions. Here, I show that eFORGE analysis can be extended to EWAS differentially variable positions (DVPs), identifying target cell types and tissues. In addition, I also show that eFORGE tissue-specific enrichment can be detected for sites below EWAS significance threshold. I develop on these and other analysis examples, extending our knowledge of eFORGE cell type- and tissue-specific enrichment results for different EWAS.

Background: The etiology of lung cancer among never-smokers has not been adequately elucidated despite that globally15% of lung cancer cases in men and 53% in women are not smoking-related. Epigenetic modifications, including changes in DNA methylation (DNAm), have been suggested as possible underlying mechanisms. However, only a few prospective epigenome-wide association studies (EWAS) of lung cancer incidence have been conducted, all exclusively focused on DNAm in peripheral blood cells and included a minimal number of never-smokers. We aimed to investigate genome-wide DNAm associations and epigenetic age acceleration with future risk of lung cancer among never-smokers using pre-diagnostic oral rinse samples. Methods: We conducted a case-control study of 80 never-smoking incident lung cancer cases and 83 comparable never-smoking controls nested in two large prospective cohorts: the Shanghai Women’s Health Study and Shanghai Men’s Health Study. DNAm was measured using the Illumina EPIC array. The top 50 differentially methylated positions (DMPs) were identified from a discovery sample and tested for replication in a validation sample using robust linear regression models. We also conducted an EWAS in the pooled sample. We examined functional overlap enrichment across chromatin states and histone mark broadPeaks for the top 1000 DMPs using eFORGE and constructed enrichment biological pathways analyses. Results: Across discovery and pooled EWAS, we identified four DMPs associated with lung cancer at the epigenome-wide significance level of P Conclusions: To our knowledge, this is the first prospective EWAS of lung cancer among never-smokers using oral rinse samples. Our results show that DNAm in pre-diagnostic oral rinse samples can provide new insights into lung cancer etiology and risk factors. Citation Format: Mohammad L. Rahman, Charles E. Breeze, Xiao-Ou Shu, Jason YY Wong, Andres Cardenas, Xuting Wang, Bu-Tian Ji, Wei Hu, Batel Blechter, Qiuyin Cai, H Dean Hosgood, Gong Yang, Jianxin Shi, Jirong Long, Yu-Tang Gao, Douglas Bell, Wei Zheng, Qing Lan, Nathaniel Rothman. Epigenome-wide association study of lung cancer among never-smokers in two prospective cohorts in Shanghai [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 3483.

Identification and characterization of methylation quantitative trait loci (mQTLs) can help elucidate the role of DNA methylation changes as a potential mediator of genetic risk loci. However, mQTLs remain poorly characterized: they have not yet been mapped in the largest ethnic populations, their cell-type specific nature has not been resolved, and the proportion of mQTLs attributed to different molecular mechanisms is unknown. Here we perform the first mQTL-mapping study in a large Han Chinese population, demonstrating that over 80% of mQTLs are shared with those identified in White Caucasians. We further estimate that over 90% of mQTLs are shared between different blood cell-lineages. mQTLs demonstrate a strong enrichment for variants influencing chromatin accessibility. We identify a number of GWAS-linked transcription factor trans-mQTL hotspots associated with eosinophilia, ulcerative colitis and body mass index, and a subset of trans-mQTLs within the NFKB-pathway that may mediate the risk of obesity. In summary, this study significantly expands our understanding of mQTLs and their potential role in mediating disease risk, whilst also contributing the first mQTL-database in an Asian population.

Background While smoking is known to associate with development of multiple diseases, the underlying mechanisms are still poorly understood. Tobacco smoking can modify the chemical integrity of DNA leading to changes in transcriptional activity, partly through an altered epigenetic state. We aimed to investigate the impact of smoking on lung cells collected from bronchoalveolar lavage (BAL). Methods We profiled changes in DNA methylation (5mC) and its oxidised form hydroxymethylation (5hmC) using conventional bisulphite (BS) treatment and oxidative bisulphite treatment with Illumina Infinium MethylationEPIC BeadChip, and examined gene expression by RNA-seq in healthy smokers. Findings We identified 1667 total 5mC + 5hmC, 1756 5mC and 67 5hmC differentially methylated positions (DMPs) between smokers and non-smokers (FDR-adjusted P 0.15). Both 5mC DMPs and to a lesser extent 5mC + 5hmC were predominantly hypomethylated. In contrast, almost all 5hmC DMPs were hypermethylated, supporting the hypothesis that smoking-associated oxidative stress can lead to DNA demethylation, via the established sequential oxidation of which 5hmC is the first step. While we confirmed differential methylation of previously reported smoking-associated 5mC + 5hmC CpGs using former generations of BeadChips in alveolar macrophages, the large majority of identified DMPs, 5mC + 5hmC (1639/1667), 5mC (1738/1756), and 5hmC (67/67), have not been previously reported. Most of these novel smoking-associating sites are specific to the EPIC BeadChip and, interestingly, many of them are associated to FANTOM5 enhancers. Transcriptional changes affecting 633 transcripts were consistent with DNA methylation profiles and converged to alteration of genes involved in migration, signalling and inflammatory response of immune cells. Interpretation Collectively, these findings suggest that tobacco smoke exposure epigenetically modifies BAL cells, possibly involving a continuous active demethylation and subsequent increased activity of inflammatory processes in the lungs. Fund The study was supported by the Swedish Research Council, the Swedish Heart-Lung Foundation, the Stockholm County Council (ALF), the King Gustav's and Queen Victoria's Freemasons' Foundation, Knut and Alice Wallenberg Foundation, Neuro Sweden, and the Swedish MS foundation.

fifty to 70 percent of pancreatic neuroendocrine tumors are diagnosed incidentally. The objective of this study was to compare the phenotype and oncological outcomes of incidental versus symptomatic pancreatic neuroendocrine tumors.a retrospective study was performed, identifying all incidental and symptomatic tumors resected between 2000 and 2019. Baseline characteristics, symptoms, operative variables and pathological stage were all recorded. Patterns of recurrence and overall and disease-free survival were analyzed in both groups.fifty-one incidental and 45 symptomatic pancreatic tumor resections were performed. Symptomatic tumors were more frequent in females (29 vs 17; p = 0.005) and younger patients (median years; 50 vs 58; p = 0.012) and were detected at a more advanced stage (p = 0.027). There were no differences in location and most resections (n = 49; 51 %) were performed laparoscopically. There were no operative mortalities and 17 (17.7 %) severe complications (≥ IIIb on the Clavien-Dindo classification) were recorded with no differences between the two groups. With a median follow-up of 64.4 months (range 13.5-90), overall survival at five and ten years was 89.7 % and 72.8 % for the non-incidental tumors and 80.9 % and 54.6 % for the incidental tumors (p = ns), respectively. Disease-free survival in both groups (excluding M1a) was 71.2 % and 47.5 %, and 93.7 % and 78.1 %, respectively (p = ns).symptomatic tumors are more frequent in females and present at more advanced pathological stages. There were no significant differences in overall and disease-free survival between the two groups. Resection of incidental tumors ≥ 1.5-2 cm seems advisable, although each case should be assessed on an individual basis.

Bulk-tissue DNA methylomes represent an average over many different cell types, hampering our understanding of cell-type-specific contributions to disease development. As single-cell methylomics is not scalable to large cohorts of individuals, cost-effective computational solutions are needed, yet current methods are limited to tissues such as blood. Here we leverage the high-resolution nature of tissue-specific single-cell RNA-sequencing datasets to construct a DNA methylation atlas defined for 13 solid tissue types and 40 cell types. We comprehensively validate this atlas in independent bulk and single-nucleus DNA methylation datasets. We demonstrate that it correctly predicts the cell of origin of diverse cancer types and discovers new prognostic associations in olfactory neuroblastoma and stage 2 melanoma. In brain, the atlas predicts a neuronal origin for schizophrenia, with neuron-specific differential DNA methylation enriched for corresponding genome-wide association study risk loci. In summary, the DNA methylation atlas enables the decomposition of 13 different human tissue types at a high cellular resolution, paving the way for an improved interpretation of epigenetic data.

Hundreds of epigenome-wide association studies (EWAS) have been performed, successfully identifying replicated epigenomic signals in processes such as ageing and smoking. Despite this progress, it remains a major challenge in EWAS to detect both cell type-specific and cell type confounding effects impacting study results. One way to identify these effects is through eFORGE (experimentally derived Functional element Overlap analysis of ReGions from EWAS), a published tool that uses 815 datasets from large-scale mapping studies to detect enriched tissues, cell types and genomic regions. Here, I show that eFORGE analysis can be extended to EWAS differentially variable positions (DVPs), identifying target cell types and tissues. In addition, I also show that eFORGE tissue-specific enrichment can be detected for sites below EWAS significance threshold. I develop on these and other analysis examples, extending our knowledge of eFORGE cell type- and tissue-specific enrichment results for different EWAS.

Maximum lifespan of a species is the oldest that individuals can survive, reflecting the genetic limit of longevity in an ideal environment. Here we report methylation-based models that accurately predict maximum lifespan (r=0.89), gestational time (r=0.96), and age at sexual maturity (r=0.87), using cytosine methylation patterns collected from over 12,000 samples derived from 192 mammalian species. Our epigenetic maximum lifespan predictor corroborated the extended lifespan in growth hormone receptor knockout mice and rapamycin treated mice. Across dog breeds, epigenetic maximum lifespan correlates positively with breed lifespan but negatively with breed size. Lifespan-related cytosines are located in transcriptional regulatory regions, such as bivalent chromatin promoters and polycomb-repressed regions, which were hypomethylated in long-lived species. The epigenetic estimators of maximum lifespan and other life history traits will be useful for characterizing understudied species and for identifying interventions that extend lifespan.

SummaryEpigenetics has hitherto been studied and understood largely at the level of individual organisms. Here, we report a multi-faceted investigation of DNA methylation across 11,117 samples from 176 different species. We performed an unbiased clustering of individual cytosines into 55 modules and identified 31 modules related to primary traits including age, species lifespan, sex, adult species weight, tissue type and phylogenetic order. Analysis of the correlation between DNA methylation and species allowed us to construct phyloepigenetic trees for different tissues that parallel the phylogenetic tree. In addition, while some stable cytosines reflect phylogenetic signatures, others relate to age and lifespan, and in many cases responding to anti-aging interventions in mice such as caloric restriction and ablation of growth hormone receptors. Insights uncovered by this investigation have important implications for our understanding of the role of epigenetics in mammalian evolution, aging and lifespan.

The accurate detection of cell-type specific DNA methylation alterations in the context of general epigenome studies is an important task to improve our understanding of epigenomics in disease development. Although a number of statistical algorithms designed to address this problem have emerged, the task remains challenging. Here we show that a recent commentary by Rahmani et al, that aims to address misconceptions and best practices in the field, continues to suffer from critical misconceptions in how statistical algorithms should be compared and evaluated. In addition, we report contradictory results on real EWAS datasets.

Infinium methylation arrays are widely used to robustly measure methylation of DNA in humans. However, such arrays are not available for the vast majority of non-human mammals. Moreover, even if species-specific arrays were available, probe differences between them would confound cross-species comparisons. To address these challenges, we developed the Mammalian Methylation Array, a single custom Infinium array that measures cytosine methylation levels of 36 thousand CpG sites that are well conserved across species within the mammalian class. By design, the probes on the array tolerate cross-species mutations. To design the array, we developed the Conserved Methylation Array Probe Selector (CMAPS) algorithm, which takes as input a multi-species sequence alignment and probe design constraints. A greedy search algorithm was used to identify oligonucleotide sequences (probes) with high coverage across different mammalian species. We annotate the probes on the array with respect to genes in 159 different species and provide details on the sequence context including CpG island status and chromatin states. Our calibration experiments demonstrate the high fidelity of this array in humans, rats, and mice. The mammalian methylation array has several strengths: it applies to all mammalian species even those that have not yet been sequenced, it provides deep coverage of specific cytosines facilitating the development of highly robust epigenetic biomarkers, and it covers highly conserved CpGs which greatly increases the probability that biological insights gained in one species will readily translate to others. The mammalian methylation array is expected to find many applications in preclinical studies, comparative biology, and epigenetic studies of aging and development.

As indicated by C Baraniuk in the BMJ , a fact that has become increasingly clear in the current COVID-19 pandemic is the wide disparity of success in disease control between different countries. For example, South Korea has had a remarkable success in controlling the pandemic by limiting the focal spread of cases, at least in part due to the application of stringent COVID-19 testing and containment strategies, as well as giving its citizens access to rapidly updated COVID-19 records and healthcare information.1–3 Indeed, technologies for focal containment have become such a pressing issue that some countries, such as the USA, are creating specific mission-critical data partnerships for smartphone approaches to tackle the COVID-19 pandemic (such as the partnership with Apple).4 While smartphone data access can be a key tool for the National Health Service (NHS), the view of the public is often missing from related discussions. In a UK-based survey, conducted before the COVID-19 pandemic, we found …

Methylation levels at specific CpG positions in the genome have been used to develop accurate estimators of chronological age in humans, mice, and other species. Although epigenetic clocks are generally species-specific, the principles underpinning them appear to be conserved at least across the mammalian class. This is exemplified by the successful development of epigenetic clocks for mice and several other mammalian species. Here, we describe epigenetic clocks for the rhesus macaque (Macaca mulatta), the most widely used nonhuman primate in biological research. Using a custom methylation array (HorvathMammalMethylChip40), we profiled n=281 tissue samples (blood, skin, adipose, kidney, liver, lung, muscle, and cerebral cortex). From these data, we generated five epigenetic clocks for macaques. These clocks differ with regards to applicability to different tissue types (pan-tissue, blood, skin), species (macaque only or both humans and macaques), and measure of age (chronological age versus relative age). Additionally, the age-based human-macaque clock exhibits a high age correlation (R=0.89) with the vervet monkey (Chlorocebus sabaeus), another Old World species. Four CpGs within the KLF14 promoter were consistently altered with age in four tissues (adipose, blood, cerebral cortex, skin). It is expected that the macaque clocks will reveal an epigenetic aging rate associated with a host of health conditions and thus lend themselves for identifying and validating anti-aging interventions.

Bats hold considerable potential for understanding exceptional longevity because some species can live eight times longer than other mammals of similar size [1]. Estimating their age or longevity is difficult because they show few signs of aging. DNA methylation (DNAm) provides a potential solution given its utility for estimating age [2-4] and lifespan [5-7] in humans. Here, we profile DNAm from wing biopsies of nearly 700 individuals representing 26 bat species and demonstrate that DNAm can predict chronological age accurately. Furthermore, the rate DNAm changes at age-informative sites is negatively related to longevity. To identify longevity-informative sites, we compared DNAm rates between three long-lived and two short-lived species. Hypermethylated age and longevity sites are enriched for histone and chromatin features associated with transcriptional regulation and preferentially located in the promoter regions of helix-turn-helix transcription factors (TFs). Predicted TF binding site motifs and enrichment analyses indicate that age-related methylation change is influenced by developmental processes, while longevity-related DNAm change is associated with innate immunity or tumorigenesis genes, suggesting that bat longevity results, in part, from augmented immune response and cancer suppression.

Early mammalian development is orchestrated by genome-encoded regulatory elements populated by a changing complement of regulatory factors, creating a dynamic chromatin landscape. To define the spatiotemporal organization of regulatory DNA landscapes during mouse development and maturation, we generated nucleotide-resolution DNA accessibility maps from 15 tissues sampled at 9 intervals spanning post-conception day 9.5 through early adult, and integrated these with 41 adult-stage DNase-seq profiles to create a global atlas of mouse regulatory DNA. Collectively, we delineated >1.8 million DNase I hypersensitive sites (DHSs), with the vast majority displaying temporal and tissue-selective patterning. Here we show that tissue regulatory DNA compartments show sharp embryonic-to-fetal transitions characterized by wholesale turnover of DHSs and progressive domination by a diminishing number of transcription factors. We show further that aligning mouse and human fetal development on a regulatory axis exposes disease-associated variation enriched in early intervals lacking human samples. Our results provide an expansive new resource for decoding mammalian developmental regulatory programs.

(1) To determine the prevalence of snoring, breathing pauses during sleep and obstructive sleep apnoea syndrome in the United Kingdom (UK) and determine the relation between these events and obesity and other sociodemographic variables. (2) To compare and integrate this data with published UK population data.A total of 664 women and 575 men aged 18-100 years who formed a representative sample of the non-institutionalised UK population participated in an online interview survey directed by a previously validated computerised system.Overall, 38% of men and 30.4% of women report that they snore at night. Furthermore, 8.7% of men and 5.6% of women state that they stop breathing at night. Comparing our data to published data from the 1990s, this study observes a highly significant increase in the rates of reported breathing pauses during sleep (sleep apnoea) in the UK over the last 20 years (p 0.0001). In addition, we observe a highly significant increase in the prevalence of obesity (BMI30) in the UK population between 1994 and 2015 (p 0.0001). Integration of our data with NHS and public health England data on obesity confirms this increase.Our data demonstrate a significant increase in the rates of reported breathing pauses during sleep (sleep apnoea) and obesity in the UK over the last 20 years. Sociodemographic and behavioural changes have likely contributed to this. Moreover, our data also suggests that sleep disordered breathing (SDB) is widely underdiagnosed in the UK.

AIMS: Epigenetics may mediate the effects of environmental risk factors on disease, including heart disease. Thus, measuring the DNA methylome offers the opportunity to identify novel disease biomarkers and novel insights into disease mechanisms. The DNA methylation landscape of ascending aortic dissection (AD) and bicuspid aortic valve (BAV) with aortic aneurysmal dilatation remain uncharacterized. The present study aimed to explore the genome-wide DNA methylation landscape underpinning these two diseases. METHODS AND RESULTS: We used Illumina 450k DNA methylation beadarrays to analyse 21 ascending aorta samples, including 10 cases with AD, 5 with BAV and 6 healthy controls. We adjusted for intra-sample cellular heterogeneity, providing the first unbiased genome-wide exploration of the DNA methylation landscape underpinning these two diseases. We discover that both diseases are characterized by loss of DNA methylation at non-CpG sites. We validate this non-CpG hypomethylation signature with pyrosequencing. In contrast to non-CpGs, AD and BAV exhibit distinct DNA methylation landscapes at CpG sites, with BAV characterized mainly by hypermethylation of EZH2 targets. In the case of AD, integrative DNA methylation gene expression analysis reveals that AD is characterized by a dedifferentiated smooth muscle cell phenotype. Our integrative analysis further reveals hypomethylation associated overexpression of RARA in AD, a pattern which is also seen in cells exposed to smoke toxins. CONCLUSION: Our data supports a model in which increased cellular proliferation in AD and BAV underpins loss of methylation at non-CpG sites. Our data further supports a model, in which AD is associated with an inflammatory vascular remodeling process, possibly mediated by the epigenome and linked to environmental risk factors such as smoking.

Due to cost and logistical reasons, Epigenome-Wide-Association Studies (EWAS) are normally performed in complex tissues, resulting in average DNA methylation profiles over potentially many different cell-types, which can obscure important cell-type specific associations with disease. Identifying the specific cell-types that are altered is a key hurdle for elucidating causal pathways to disease, and consequently statistical algorithms have recently emerged that aim to address this challenge. Comparisons between these algorithms are of great interest, yet here we find that the main comparative study so far was substantially biased and potentially misleading. By using this study as an example, we highlight some of the key issues that need to be considered to ensure that future assessments between methods are more objective.

Summary It is well recognized that cell-type heterogeneity hampers the interpretation of Epigenome-Wide Association Studies (EWAS). Many tools have emerged to address this issue, including several R/Bioconductor packages that infer cell-type composition. Here we present a web application for cell-type deconvolution, which offers the functionality of our EpiDISH Bioconductor/R package in a user-friendly GUI environment. Users can upload their data to infer cell-type composition and differentially methylated cytosines in individual cell-types (DMCTs) for a range of different tissues. Availability and implementation EpiDISH web server is implemented with Shiny in R, and is freely available at https://www.biosino.org/EpiDISH/.

Summary Epigenome-wide association studies (EWAS) provide an alternative approach for studying human disease through consideration of non-genetic variants such as altered DNA methylation. To advance the complex interpretation of EWAS, we developed eFORGE (http://eforge.cs.ucl.ac.uk/), a new standalone and web-based tool for the analysis and interpretation of EWAS data. eFORGE determines the cell type-specific regulatory component of a set of EWAS-identified differentially methylated positions. This is achieved by detecting enrichment of overlap with DNase I hypersensitive sites across 454 samples (tissues, primary cell types, and cell lines) from the ENCODE, Roadmap Epigenomics, and BLUEPRINT projects. Application of eFORGE to 20 publicly available EWAS datasets identified disease-relevant cell types for several common diseases, a stem cell-like signature in cancer, and demonstrated the ability to detect cell-composition effects for EWAS performed on heterogeneous tissues. Our approach bridges the gap between large-scale epigenomics data and EWAS-derived target selection to yield insight into disease etiology., Graphical Abstract, Highlights • Development of a tool for the analysis and interpretation of EWAS data • Identification of cell type-specific signals in heterogeneous EWAS data • Identification of cell-composition effects in EWAS • Compilation of eFORGE catalog of 20 published EWAS, Breeze et al. develop a tool for the analysis and interpretation of EWAS data. The eFORGE tool identifies cell type-specific, disease-relevant signals in heterogeneous EWAS data and can also identify cell-composition effects. Explore consortium data at the Cell Press IHEC webportal at http://www.cell.com/consortium/IHEC.