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Background Intra-sample cellular heterogeneity presents numerous challenges to the identification of biomarkers in large Epigenome-Wide Association Studies (EWAS). While a number of reference-based deconvolution algorithms have emerged, their potential remains underexplored and a comparative evaluation of these algorithms beyond tissues such as blood is still lacking. Results Here we present a novel framework for reference-based inference, which leverages cell-type specific DNAse Hypersensitive Site (DHS) information from the NIH Epigenomics Roadmap to construct an improved reference DNA methylation database. We show that this leads to a marginal but statistically significant improvement of cell-count estimates in whole blood as well as in mixtures involving epithelial cell-types. Using this framework we compare a widely used state-of-the-art reference-based algorithm (called constrained projection) to two non-constrained approaches including CIBERSORT and a method based on robust partial correlations. We conclude that the widely-used constrained projection technique may not always be optimal. Instead, we find that the method based on robust partial correlations is generally more robust across a range of different tissue types and for realistic noise levels. We call the combined algorithm which uses DHS data and robust partial correlations for inference, EpiDISH ( Epi genetic D issection of I ntra- S ample H eterogeneity). Finally, we demonstrate the added value of EpiDISH in an EWAS of smoking. Conclusions Estimating cell-type fractions and subsequent inference in EWAS may benefit from the use of non-constrained reference-based cell-type deconvolution methods.

INTRODUCTION We investigated blood DNA methylation patterns associated with 15 well‐established cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathophysiology, neuroinflammation, and neurodegeneration. METHODS We assessed DNA methylation in 885 blood samples from the European Medical Information Framework for Alzheimer's Disease (EMIF‐AD) study using the EPIC array. RESULTS We identified Bonferroni‐significant differential methylation associated with CSF YKL‐40 (five loci) and neurofilament light chain (NfL; seven loci) levels, with two of the loci associated with CSF YKL‐40 levels correlating with plasma YKL‐40 levels. A co‐localization analysis showed shared genetic variants underlying YKL‐40 DNA methylation and CSF protein levels, with evidence that DNA methylation mediates the association between genotype and protein levels. Weighted gene correlation network analysis identified two modules of co‐methylated loci correlated with several amyloid measures and enriched in pathways associated with lipoproteins and development. DISCUSSION We conducted the most comprehensive epigenome‐wide association study (EWAS) of AD‐relevant CSF biomarkers to date. Future work should explore the relationship between YKL‐40 genotype, DNA methylation, and protein levels in the brain. Highlights Blood DNA methylation was assessed in the EMIF‐AD MBD study. Epigenome‐wide association studies (EWASs) were performed for 15 Alzheimer's disease (AD)–relevant cerebrospinal fluid (CSF) biomarker measures. Five Bonferroni‐significant loci were associated with YKL‐40 levels and seven with neurofilament light chain (NfL). DNA methylation in YKL‐40 co‐localized with previously reported genetic variation. DNA methylation potentially mediates the effect of single‐nucleotide polymorphisms (SNPs) in YKL‐40 on CSF protein levels.

Background There has been a steady increase in the number of studies aiming to identify DNA methylation differences associated with complex phenotypes. Many of the challenges of epigenetic epidemiology regarding study design and interpretation have been discussed in detail, however there are analytical concerns that are outstanding and require further exploration. In this study we seek to address three analytical issues. First, we quantify the multiple testing burden and propose a standard statistical significance threshold for identifying DNA methylation sites that are associated with an outcome. Second, we establish whether linear regression, the chosen statistical tool for the majority of studies, is appropriate and whether it is biased by the underlying distribution of DNA methylation data. Finally, we assess the sample size required for adequately powered DNA methylation association studies. Results We quantified DNA methylation in the Understanding Society cohort ( n  = 1175), a large population based study, using the Illumina EPIC array to assess the statistical properties of DNA methylation association analyses. By simulating null DNA methylation studies, we generated the distribution of p -values expected by chance and calculated the 5% family-wise error for EPIC array studies to be 9 × 10 − 8 . Next, we tested whether the assumptions of linear regression are violated by DNA methylation data and found that the majority of sites do not satisfy the assumption of normal residuals. Nevertheless, we found no evidence that this bias influences analyses by increasing the likelihood of affected sites to be false positives. Finally, we performed power calculations for EPIC based DNA methylation studies, demonstrating that existing studies with data on ~ 1000 samples are adequately powered to detect small differences at the majority of sites. Conclusion We propose that a significance threshold of P  < 9 × 10 − 8 adequately controls the false positive rate for EPIC array DNA methylation studies. Moreover, our results indicate that linear regression is a valid statistical methodology for DNA methylation studies, despite the fact that the data do not always satisfy the assumptions of this test. These findings have implications for epidemiological-based studies of DNA methylation and provide a framework for the interpretation of findings from current and future studies.

Smoking‐associated epigenetic changes have been linked to lung cancer (LC) risk; however, the role of epigenetic alterations independent of smoking is yet to be fully understood. This study aimed to validate 16 previously reported CpG sites that are independent of smoking yet associated with LC risk within a population‐based prospective cohort. Using the Infinium Methylation EPIC BeadChip kit or the Infinium HumanMethylation450K BeadChip Assay, DNA methylation (DNAm) in whole blood was assessed in four subsets (n = 736, 1027, 997, and 312) of a population‐based cohort from Germany. The DNAm levels of the 16 smoking‐independent CpG sites were analyzed. Hazard ratios (HRs) and their 95% confidence intervals (95% CIs) were calculated to assess associations of DNAm at the 16 CpG sites with LC risk, adjusting for multiple covariates, including smoking habits and a smoking‐associated DNAm score. Over 17 years of follow‐up, a total of 199 LCs were observed. Among the 16 CpGs, cg02211449 showed a negative association with LC risk (HR [95% CI] per SD increase, = 0.70 [0.63–0.78]), while cg11385536 (1.04 [1.01–1.07]), cg09736286 (1.64 [1.10–2.44]), cg19907023 (1.64 [1.01–2.66]), and cg22032485 (1.52 [1.04–2.21]) displayed positive associations with LC risk. Five of the 16 suggested smoking‐independent CpGs could be externally validated as predictors of LC risk. Further research should address their potential contribution to enhanced LC risk stratification. This study validated 16 CpG sites independent of smoking and their association with lung cancer (LC) risk in a population‐based cohort from Germany. Among these, five CpGs were significantly associated with LC risk, suggesting their potential utility in enhancing LC risk prediction beyond smoking‐related factors.

Epigenome‐wide association study (EWAS) has been applied to analyze DNA methylation variation in complex diseases for a decade, and epigenome as a research target has gradually become a hot topic of current studies. The DNA methylation microarrays, next‐generation, and third‐generation sequencing technologies have prepared a high‐quality platform for EWAS. Here, the progress of EWAS research is reviewed, its contributions to clinical applications, and mainly describe the achievements of four typical diseases. Finally, the challenges encountered by EWAS and make bold predictions for its future development are presented. Epigenome‐wide association study (EWAS) is applied to analyze DNA methylation variation in complex diseases for a decade, which is accompanied by advances and challenges. This review discusses the research process of EWAS, its application in biology and clinical translation. It also concludes with an analysis regarding the current limitations of EWAS and a forecast of its future.

Background DNA methylation (DNAm) profiling has emerged as a powerful tool for characterizing the placental methylome. However, previous studies have focused primarily on whole placental tissue, which is a mixture of epigenetically distinct cell populations. Here, we present the first methylome-wide analysis of first trimester ( n  = 9) and term ( n  = 19) human placental samples of four cell populations: trophoblasts, Hofbauer cells, endothelial cells, and stromal cells, using the Illumina EPIC methylation array, which quantifies DNAm at > 850,000 CpGs. Results The most distinct DNAm profiles were those of placental trophoblasts, which are central to many pregnancy-essential functions, and Hofbauer cells, which are a rare fetal-derived macrophage population. Cell-specific DNAm occurs at functionally-relevant genes, including genes associated with placental development and preeclampsia. Known placental-specific methylation marks, such as those associated with genomic imprinting, repetitive element hypomethylation, and placental partially methylated domains, were found to be more pronounced in trophoblasts and often absent in Hofbauer cells. Lastly, we characterize the cell composition and cell-specific DNAm dynamics across gestation. Conclusions Our results provide a comprehensive analysis of DNAm in human placental cell types from first trimester and term pregnancies. This data will serve as a useful DNAm reference for future placental studies, and we provide access to this data via download from GEO (GSE159526), through interactive exploration from the web browser ( https://robinsonlab.shinyapps.io/Placental_Methylome_Browser/ ), and through the R package planet , which allows estimation of cell composition directly from placental DNAm data.

The aetiology and pathophysiology of complex diseases are driven by the interaction between genetic and environmental factors. The variability in risk and outcomes in these diseases are incompletely explained by genetics or environmental risk factors individually. Therefore, researchers are now exploring the epigenome, a biological interface at which genetics and the environment can interact. There is a growing body of evidence supporting the role of epigenetic mechanisms in complex disease pathophysiology. Epigenome-wide association studies (EWASes) investigate the association between a phenotype and epigenetic variants, most commonly DNA methylation. The decreasing cost of measuring epigenome-wide methylation and the increasing accessibility of bioinformatic pipelines have contributed to the rise in EWASes published in recent years. Here, we review the current literature on these EWASes and provide further recommendations and strategies for successfully conducting them. We have constrained our review to studies using methylation data as this is the most studied epigenetic mechanism; microarray-based data as whole-genome bisulphite sequencing remains prohibitively expensive for most laboratories; and blood-based studies due to the non-invasiveness of peripheral blood collection and availability of archived DNA, as well as the accessibility of publicly available blood-cell-based methylation data. Further, we address multiple novel areas of EWAS analysis that have not been covered in previous reviews: (1) longitudinal study designs, (2) the chip analysis methylation pipeline (ChAMP), (3) differentially methylated region (DMR) identification paradigms, (4) methylation quantitative trait loci (methQTL) analysis, (5) methylation age analysis and (6) identifying cell-specific differential methylation from mixed cell data using statistical deconvolution.

Background Schizophrenia is a highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Results We performed a multi-stage epigenome-wide association study, quantifying genome-wide patterns of DNA methylation in a total of 1714 individuals from three independent sample cohorts. We have identified multiple differentially methylated positions and regions consistently associated with schizophrenia across the three cohorts; these effects are independent of important confounders such as smoking. We also show that epigenetic variation at multiple loci across the genome contributes to the polygenic nature of schizophrenia. Finally, we show how DNA methylation quantitative trait loci in combination with Bayesian co-localization analyses can be used to annotate extended genomic regions nominated by studies of schizophrenia, and to identify potential regulatory variation causally involved in disease. Conclusions This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological approach that can be used to inform epigenome-wide association study analyses of other complex traits and diseases. We demonstrate the utility of using a polygenic risk score to identify molecular variation associated with etiological variation, and of using DNA methylation quantitative trait loci to refine the functional and regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.

Cell type heterogeneity presents a challenge to the interpretation of epigenome data, compounded by the difficulty in generating reliable single-cell DNA methylomes for large numbers of cells and samples. We present EPISCORE, a computational algorithm that performs virtual microdissection of bulk tissue DNA methylation data at single cell-type resolution for any solid tissue. EPISCORE applies a probabilistic epigenetic model of gene regulation to a single-cell RNA-seq tissue atlas to generate a tissue-specific DNA methylation reference matrix, allowing quantification of cell-type proportions and cell-type-specific differential methylation signals in bulk tissue data. We validate EPISCORE in multiple epigenome studies and tissue types.