Epigenome-wide association studies: current knowledge, strategies and recommendations

Abstract

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.

A Mixture of Endocrine Disrupting Chemicals Associated with Lower Birth Weight in Children Induces Adipogenesis and DNA Methylation Changes in Human Mesenchymal Stem Cells

“…In short, regression coefficients of adjacent CpGs in a region are smoothed and plotted in a curve. Potential "peaks" or DMRs are then identified as regions where the collection of these coefficients are higher than expected by chance [118][119][120]. The Bumphunter (champ.DMR) function from the ChAMP package was used for DMR analysis with its default setting for DMR composition of seven or more successive DMPs within a 300-base pair genomic region.…”

Birth history is associated with whole-blood and T-cell methylation patterns in relapse onset multiple sclerosis

“…Our EWAS analysis was informed by the guidelines described in Campagna et al (2021) 14 . The Chip Analysis Methylation Pipeline (ChAMP) Bioconductor package 15 was used for methylation data pre-processing in the R statistical environment.…”

“…differentially methylated positions (DMPs), and genomic region level i.e. differentially methylated regions (DMRs) using the filtered and normalised beta matrix, as previously described 14 . We used the ChAMP function champ.DMP to implement an unadjusted logistic model of methylation level at each probe and parity group.…”

A general framework for penalized mixed-effects multitask learning with applications on DNA methylation surrogate biomarkers creation

“…A total of N = 574 patients in the J = 4 cohorts showcase non-missing values for each and every response variable: they comprise the sample onto which all subsequent analyses will be performed. An epigenome-wide association study (Campagna et al, 2021) was performed as a pre-screening procedure: out of the whole set of CpG sites, 13449 DNA methylation features have been retained for subsequent modeling. Together with sex and age, this amounts to a total of p = 13451 predictors and a 5-dimensional response.…”