The impact of methodology on the reproducibility and rigor of DNA methylation data.

Epigenetic modifications are crucial for normal development and implicated in disease pathogenesis. While epigenetics continues to be a burgeoning research area in neuroscience, unaddressed issues related to data reproducibility across laboratories remain. Separating meaningful experimental changes from background variability is a challenge in epigenomic studies. Here we show that seemingly minor experimental variations, even under normal baseline conditions, can have a significant impact on epigenome outcome measures and data interpretation. We examined genome-wide DNA methylation and gene expression profiles of hippocampal tissues from wild-type rats housed in three independent laboratories using nearly identical conditions. Reduced-representation bisulfite sequencing and RNA-seq respectively identified 3852 differentially methylated and 1075 differentially expressed genes between laboratories, even in the absence of experimental intervention. Difficult-to-match factors such as animal vendors and a subset of husbandry and tissue extraction procedures produced quantifiable variations between wild-type animals across the three laboratories. Our study demonstrates that seemingly minor experimental variations, even under normal baseline conditions, can have a significant impact on epigenome outcome measures and data interpretation. This is particularly meaningful for neurological studies in animal models, in which baseline parameters between experimental groups are difficult to control. To enhance scientific rigor, we conclude that strict adherence to protocols is necessary for the execution and interpretation of epigenetic studies and that protocol-sensitive epigenetic changes, amongst naive animals, may confound experimental results.

Longitudinal DNA methylation dynamics as a practical indicator in clinical epigenetics.

BACKGROUND: One of the fundamental assumptions of DNA methylation in clinical epigenetics is that DNA methylation status can change over time with or without interplay with environmental and clinical conditions. However, little is known about how DNA methylation status changes over time under ordinary environmental and clinical conditions. In this study, we revisited the high frequency longitudinal DNA methylation data of two Japanese males (24 time-points within three months) and characterized the longitudinal dynamics. RESULTS: The results showed that the majority of CpGs on Illumina HumanMethylation450 BeadChip probe set were longitudinally stable over the time period of three months. Focusing on dynamic and stable CpGs extracted from datasets, dynamic CpGs were more likely to be reported as epigenome-wide association study (EWAS) markers of various traits, especially those of immune- and inflammatory-related traits; meanwhile, the stable CpGs were enriched in metabolism-related genes and were less likely to be EWAS markers, indicating that the stable CpGs are stable both in the short-term within individuals and under various environmental and clinical conditions. CONCLUSIONS: This study indicates that CpGs with different stabilities are involved in different functions and traits, and thus, they are potential indicators that can be applied for clinical epigenetic studies to outline underlying mechanisms.

Detecting methylation quantitative trait loci using a methylation random field method.

DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.

Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM.

Epigenetic changes, such as aberrant DNA methylation, contribute to cancer clonal expansion and disease progression. However, identifying subpopulation-level changes in a heterogeneous sample remains challenging. Thus, we have developed a computational approach, DXM, to deconvolve the methylation profiles of major allelic subpopulations from the bisulfite sequencing data of a heterogeneous sample. DXM does not require prior knowledge of the number of subpopulations or types of cells to expect. We benchmark DXM's performance and demonstrate improvement over existing methods. We further experimentally validate DXM predicted allelic subpopulation-methylation profiles in four Diffuse Large B-Cell Lymphomas (DLBCLs). Lastly, as proof-of-concept, we apply DXM to a cohort of 31 DLBCLs and relate allelic subpopulation methylation profiles to relapse. We thus demonstrate that DXM can robustly find allelic subpopulation methylation profiles that may contribute to disease progression using bisulfite sequencing data of any heterogeneous sample.

Detection of DNA Methylation in Genomic DNA by UHPLC-MS/MS.

DNA methylation serves to mark DNA as either a directed epigenetic signaling modification or in response to DNA lesions. Methods for detecting DNA methylation have become increasingly more specific and sensitive over time. Conventional methods for detecting DNA methylation, ranging from paper chromatography to differential restriction enzyme digestion preference to dot blots, have more recently been supplemented by ultrahigh performance liquid chromatography coupled with mass spectrometry (UHPLC-MS/MS) to accurately quantify specific DNA methylation. Methylated DNA can also be sequenced by either methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) or single-molecule real-time sequencing (SMRTseq) for identifying genomic locations of DNA methylation. Here we describe a protocol for the detection and quantification of epigenetic signaling DNA methylation modifications including, N6-methyladenine (6mA), N4-methylcytosine (4mC) and C5-methylcytosine (5mC) in genomic DNA by triple quadrupole liquid chromatography coupled with tandem mass spectrometry (QQQ-LC-MS/MS). The high sensitivity of the UHPLC-MS/MS methodology and the use of calibration standards of pure nucleosides allow for the accurate quantification of DNA methylation.

Interpretation of omics data analyses.

Omics studies attempt to extract meaningful messages from large-scale and high-dimensional data sets by treating the data sets as a whole. The concept of treating data sets as a whole is important in every step of the data-handling procedures: the pre-processing step of data records, the step of statistical analyses and machine learning, translation of the outputs into human natural perceptions, and acceptance of the messages with uncertainty. In the pre-processing, the method by which to control the data quality and batch effects are discussed. For the main analyses, the approaches are divided into two types and their basic concepts are discussed. The first type is the evaluation of many items individually, followed by interpretation of individual items in the context of multiple testing and combination. The second type is the extraction of fewer important aspects from the whole data records. The outputs of the main analyses are translated into natural languages with techniques, such as annotation and ontology. The other technique for making the outputs perceptible is visualization. At the end of this review, one of the most important issues in the interpretation of omics data analyses is discussed. Omics studies have a large amount of information in their data sets, and every approach reveals only a very restricted aspect of the whole data sets. The understandable messages from these studies have unavoidable uncertainty.

The social construction of the social epigenome and the larger biological context.

Epigenetics researchers in developmental, cell, and molecular biology greatly diverge in their understanding and definitions of epigenetics. In contrast, social epigeneticists, e.g., sociologists, scholars of STS, and behavioural scientists, share a focus and definition of epigenetics that is environmentally caused and trans-generationally inherited. This article demonstrates that this emphasis on the environment and on so-called Lamarckian inheritance, in addition to other factors, reflects an interdisciplinary power struggle with genetics, in which epigenetics appears to grant the social sciences a higher epistemic status. Social scientists' understanding of epigenetics, thus, appears in part to be socially constructed, i.e., the result of extra-scientific factors, such as social processes and the self-interest of the discipline. This article argues that social epigeneticists make far-reaching claims by selecting elements from research labelled epigenetics in biology while ignoring widely confirmed scientific facts in genetics and cell biology, such as the dependence of epigenetic marks on DNA sequence-specific events, or the lack of evidence for the lasting influence of the environment on epigenetic marks or the epigenome. Moreover, they treat as a given crucial questions that are far from resolved, such as what role, if any, DNA methylation plays in the complex biochemical system of regulating gene activity. The article also points out incorrect perceptions and media hypes among biological epigeneticists and calls attention to an apparent bias among scientific journals that prefer papers that promote transgenerational epigenetic inheritance over articles that critique it. The article concludes that while research labelled epigenetics contributes significantly to our knowledge about chromatin and the genome, it does not, as is often claimed, rehabilitate Lamarck or overthrow the fundamental biological principles of gene regulation, which are based on specific regulatory sequences of the genome.

Test-statistic inflation in methylome-wide association studies.

Recent years have seen a surge of methylome-wide association studies (MWAS). We observed that many of these studies suffer from test statistic inflation that is most likely caused by commonly used quality control (QC) pipelines not going far enough to remove technical artefacts. To support this claim, we reanalysed GEO datasets with an improved QC pipeline that reduced test-statistic inflation parameter lambda from the original mean/median of 20.16/15.17 to 3.07/1.14. Furthermore, the mean/median number of methylome-wide significant findings was reduced by 65,688/57,805 loci after more thorough QC. To avoid such false positives we argue for more extensive QC and that reporting the test-statistic inflation parameter lambda become standard for all MWAS allowing readers to better assess the risk of false discoveries.

Ewastools: Infinium Human Methylation BeadChip pipeline for population epigenetics integrated into Galaxy.

BACKGROUND: Infinium Human Methylation BeadChip is an array platform for complex evaluation of DNA methylation at an individual CpG locus in the human genome based on Illumina's bead technology and is one of the most common techniques used in epigenome-wide association studies. Finding associations between epigenetic variation and phenotype is a significant challenge in biomedical research. The newest version, HumanMethylationEPIC, quantifies the DNA methylation level of 850,000 CpG sites, while the previous versions, HumanMethylation450 and HumanMethylation27, measured >450,000 and 27,000 loci, respectively. Although a number of bioinformatics tools have been developed to analyse this assay, they require some programming skills and experience in order to be usable. RESULTS: We have developed a pipeline for the Galaxy platform for those without experience aimed at DNA methylation analysis using the Infinium Human Methylation BeadChip. Our tool is integrated into Galaxy (http://galaxyproject.org), a web-based platform. This allows users to analyse data from the Infinium Human Methylation BeadChip in the easiest possible way. CONCLUSIONS: The pipeline provides a group of integrated analytical methods wrapped into an easy-to-use interface. Our tool is available from the Galaxy ToolShed, GitHub repository, and also as a Docker image. The aim of this project is to make Infinium Human Methylation BeadChip analysis more flexible and accessible to everyone.

CFEA: a cell-free epigenome atlas in human diseases.

Epigenetic alterations, including 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC) and nucleosome positioning (NP), in cell-free DNA (cfDNA) have been widely observed in human diseases, and many available cfDNA-based epigenome-wide profiles exhibit high sensitivity and specificity in disease detection and classification. However, due to the lack of efficient collection, standardized quality control, and analysis procedures, efficiently integrating and reusing these data remain considerable challenges. Here, we introduce CFEA (http://www.bio-data.cn/CFEA), a cell-free epigenome database dedicated to three types of widely adopted epigenetic modifications (5mC, 5hmC and NP) involved in 27 human diseases. We developed bioinformatic pipelines for quality control and standard data processing and an easy-to-use web interface to facilitate the query, visualization and download of these cell-free epigenome data. We also manually curated related biological and clinical information for each profile, allowing users to better browse and compare cfDNA epigenomes at a specific stage (such as early- or metastasis-stage) of cancer development. CFEA provides a comprehensive and timely resource to the scientific community and supports the development of liquid biopsy-based biomarkers for various human diseases.

Methodological challenges in constructing DNA methylation risk scores.

Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring GRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses, and 4) to predict individual risks of disease or treatment success. Most GRS approaches can directly be transferred to methylation data. However, since methylation data is more sensitive to confounding, e.g. by age and tissue, it is more complex to find appropriate external weights. In this review, we will outline the adaption of current GRS approaches to methylation data and highlight occurring challenges.

Battle of epigenetic proportions: comparing Illumina's EPIC methylation microarrays and TruSeq targeted bisulfite sequencing.

DNA methylation microarrays have been the platform of choice for epigenome-wide association studies in epidemiology, but declining costs have rendered targeted bisulphite sequencing a feasible alternative. Nonetheless, the literature for researchers seeking guidance on which platform to choose is sparse. To fill this gap, we conducted a comparison study in which we processed cord blood samples from four newborns in duplicates using both the Illumina HumanMethylationEPIC BeadChip and the Illumina TruSeq Methyl Capture EPIC Kit, and evaluated both platforms in regard to coverage, reproducibility, and identification of differential methylation. We conclude that with current analytic goals microarrays still outperform bisulphite sequencing for precise quantification of DNA methylation.

MBD-seq - realities of a misunderstood method for high-quality methylome-wide association studies.

The majority of methylome-wide association studies (MWAS) have been performed using commercially available array-based technologies such as the Infinium Human Methylation 450K and the Infinium MethylationEPIC arrays (Illumina). While these arrays offer a convenient and relatively robust assessment of the probed sites they only allow interrogation of 2-4% of all CpG sites in the human genome. Methyl-binding domain sequencing (MBD-seq) is an alternative approach for MWAS that provides near-complete coverage of the methylome at similar costs as the array-based technologies. However, despite publication of multiple positive evaluations, the use of MBD-seq for MWAS is often fiercely criticized. Here we discuss key features of the method and debunk misconceptions using empirical data. We conclude that MBD-seq represents an excellent approach for large-scale MWAS and that increased utilization is likely to result in more discoveries, advance biological knowledge, and expedite the clinical translation of methylome-wide research findings.

Points-to-consider on the return of results in epigenetic research.

As epigenetic studies become more common and lead to new insights into health and disease, the return of individual epigenetic results to research participants, in particular in large-scale epigenomic studies, will be of growing importance. Members of the International Human Epigenome Consortium (IHEC) Bioethics Workgroup considered the potential ethical, legal, and social issues (ELSI) involved in returning epigenetic research results and incidental findings in order to produce a set of 'Points-to-consider' (P-t-C) for the epigenetics research community. These P-t-C draw on existing guidance on the return of genetic research results, while also integrating the IHEC Bioethics Workgroup's ELSI research on and discussion of the issues associated with epigenetic data as well as the experience of a return of results pilot study by the Personal Genome Project UK (PGP-UK). Major challenges include how to determine the clinical validity and actionability of epigenetic results, and considerations related to environmental exposures and epigenetic marks, including circumstances warranting the sharing of results with family members and third parties. Interdisciplinary collaboration and good public communication regarding epigenetic risk will be important to advance the return of results framework for epigenetic science.

Integration of DNA methylation patterns and genetic variation in human pediatric tissues help inform EWAS design and interpretation.

BACKGROUND: The widespread use of accessible peripheral tissues for epigenetic analyses has prompted increasing interest in the study of tissue-specific DNA methylation (DNAm) variation in human populations. To date, characterizations of inter-individual DNAm variability and DNAm concordance across tissues have been largely performed in adult tissues and therefore are limited in their relevance to DNAm profiles from pediatric samples. Given that DNAm patterns in early life undergo rapid changes and have been linked to a wide range of health outcomes and environmental exposures, direct investigations of tissue-specific DNAm variation in pediatric samples may help inform the design and interpretation of DNAm analyses from early life cohorts. In this study, we present a systematic comparison of genome-wide DNAm patterns between matched pediatric buccal epithelial cells (BECs) and peripheral blood mononuclear cells (PBMCs), two of the most widely used peripheral tissues in human epigenetic studies. Specifically, we assessed DNAm variability, cross-tissue DNAm concordance and genetic determinants of DNAm across two independent early life cohorts encompassing different ages. RESULTS: BECs had greater inter-individual DNAm variability compared to PBMCs and highly the variable CpGs are more likely to be positively correlated between the matched tissues compared to less variable CpGs. These sites were enriched for CpGs under genetic influence, suggesting that a substantial proportion of DNAm covariation between tissues can be attributed to genetic variation. Finally, we demonstrated the relevance of our findings to human epigenetic studies by categorizing CpGs from published DNAm association studies of pediatric BECs and peripheral blood. CONCLUSIONS: Taken together, our results highlight a number of important considerations and practical implications in the design and interpretation of EWAS analyses performed in pediatric peripheral tissues.

Epigenetics, heritability and longitudinal analysis.

BACKGROUND: Longitudinal data and repeated measurements in epigenome-wide association studies (EWAS) provide a rich resource for understanding epigenetics. We summarize 7 analytical approaches to the GAW20 data sets that addressed challenges and potential applications of phenotypic and epigenetic data. All contributions used the GAW20 real data set and employed either linear mixed effect (LME) models or marginal models through generalized estimating equations (GEE). These contributions were subdivided into 3 categories: (a) quality control (QC) methods for DNA methylation data; (b) heritability estimates pretreatment and posttreatment with fenofibrate; and (c) impact of drug response pretreatment and posttreatment with fenofibrate on DNA methylation and blood lipids. RESULTS: Two contributions addressed QC and identified large statistical differences with pretreatment and posttreatment DNA methylation, possibly a result of batch effects. Two contributions compared epigenome-wide heritability estimates pretreatment and posttreatment, with one employing a Bayesian LME and the other using a variance-component LME. Density curves comparing these studies indicated these heritability estimates were similar. Another contribution used a variance-component LME to depict the proportion of heritability resulting from a genetic and shared environment. By including environmental exposures as random effects, the authors found heritability estimates became more stable but not significantly different. Two contributions investigated treatment response. One estimated drug-associated methylation effects on triglyceride levels as the response, and identified 11 significant cytosine-phosphate-guanine (CpG) sites with or without adjusting for high-density lipoprotein. The second contribution performed weighted gene coexpression network analysis and identified 6 significant modules of at least 30 CpG sites, including 3 modules with topological differences pretreatment and posttreatment. CONCLUSIONS: Four conclusions from this GAW20 working group are: (a) QC measures are an important consideration for EWAS studies that are investigating multiple time points or repeated measurements; (b) application of heritability estimates between time points for individual CpG sites is a useful QC measure for DNA methylation studies; (c) drug intervention demonstrated strong epigenome-wide DNA methylation patterns across the 2 time points; and (d) new statistical methods are required to account for the environmental contributions of DNA methylation across time. These contributions demonstrate numerous opportunities exist for the analysis of longitudinal data in future epigenetic studies.

Cell type-specific DNA methylation in neonatal cord tissue and cord blood: a 850K-reference panel and comparison of cell types.

Accounting for cellular heterogeneity is essential in neonatal epigenome-wide association studies (EWAS) performed on heterogeneous tissues, such as umbilical cord tissue (CT) or cord blood (CB). Using a reference-panel-based statistical approach, the cell type composition of heterogeneous tissues can be estimated by comparison of whole tissue DNA methylation profiles with cell type-specific DNA methylation signatures. Currently, there is no adequate DNA methylation reference panel for CT, and existing CB panels have been generated on lower coverage Infinium HumanMethylation450 arrays. In this study, we generate a reference panel for CT and improve available CB panels by using the higher coverage Infinium MethylationEPIC arrays. We performed DNA methylation profiling of 9 cell types isolated from CT and CB samples from 14 neonates. In addition to these cell types, we profiled DNA methylation of unfractionated CT and CB. Cell type composition of these unfractionated tissue samples, as estimated by our reference panels, was in agreement with that obtained by flow cytometry. Expectedly, DNA methylation profiles from CT and CB were distinct, reflecting their mesenchymal and hematopoietic stem cell origins. Variable CpGs from both unfractionated CT and its isolated cell types were more likely to be located in open seas and intronic regions than those in CB. Cell type specific CpGs in CT were enriched in intercellular matrix pathways, while those from CB were enriched in immune-related pathways. This study provides an open source reference panel for estimation and adjustment of cellular heterogeneity in CT and CB, and broadens the scope of tissue utilization assessed in future neonatal EWAS studies.

Identifying mislabeled and contaminated DNA methylation microarray data: an extended quality control toolset with examples from GEO.

Background: Mislabeled, contaminated or poorly performing samples can threaten power in methylation microarray analyses or even result in spurious associations. We describe a set of quality checks for the popular Illumina 450K and EPIC microarrays to identify problematic samples and demonstrate their application in publicly available datasets. Methods: Quality checks implemented here include 17 control metrics defined by the manufacturer, a sex check to detect mislabeled sex-discordant samples, and both an identity check for fingerprinting sample donors and a measure of sample contamination based on probes querying high-frequency SNPs. These checks were tested on 80 datasets comprising 8327 samples run on the 450K microarray from the GEO repository. Results: Nine hundred forty samples were flagged by at least one control metric and 133 samples from 20 datasets were assigned the wrong sex. In a dataset in which a subset of samples appear contaminated with a single source of DNA, we demonstrate that our measure based on outliers among SNP probes was strongly correlated (> 0.95) with another independent measure of contamination. Conclusions: A more complete examination of samples that may be mislabeled, contaminated, or have poor performance due to technical problems will improve downstream analyses and replication of findings. We demonstrate that quality control problems are prevalent in a public repository of DNA methylation data. We advocate for a more thorough quality control workflow in epigenome-wide association studies and provide a software package to perform the checks described in this work. Reproducible code and supplementary material are available at 10.5281/zenodo.1172730.

Positioning Europe for the EPITRANSCRIPTOMICS challenge.

The genetic alphabet consists of the four letters: C, A, G, and T in DNA and C,A,G, and U in RNA. Triplets of these four letters jointly encode 20 different amino acids out of which proteins of all organisms are built. This system is universal and is found in all kingdoms of life. However, bases in DNA and RNA can be chemically modified. In DNA, around 10 different modifications are known, and those have been studied intensively over the past 20 years. Scientific studies on DNA modifications and proteins that recognize them gave rise to the large field of epigenetic and epigenomic research. The outcome of this intense research field is the discovery that development, ageing, and stem-cell dependent regeneration but also several diseases including cancer are largely controlled by the epigenetic state of cells. Consequently, this research has already led to the first FDA approved drugs that exploit the gained knowledge to combat disease. In recent years, the ~150 modifications found in RNA have come to the focus of intense research. Here we provide a perspective on necessary and expected developments in the fast expanding area of RNA modifications, termed epitranscriptomics.