Commentary: The seven plagues of epigenetic epidemiology

Abstract

Epigenetics is being increasingly combined with epidemiology to add mechanistic understanding to associations observed between environmental, genetic and stochastic factors and human disease phenotypes. Currently, epigenetic epidemiological studies primarily focus on exploring if and where the epigenome (i.e. the overall epigenetic state of a cell) is influenced by specific environmental exposures like prenatal nutrition,1 sun exposure2 and smoking.3 In this issue of the IJE, Nada Borghol et al.4 report an association between childhood social-economic status (SES) and differential DNA methylation in adulthood. Low SES may integrate diverse and heterogeneous environmental influences, and knowing which epigenetic changes are associated with low SES may provide clues about the biological processes underlying its health consequences. The authors stress that their study is preliminary. This statement is, in fact, to a greater or lesser extent applicable to the entire first wave of studies currently being published that likewise aim to discover associations between epigenetic variation measured on a genome-wide scale and environmental exposures or disease phenotypes. When executing such epigenome-wide association studies (EWASs),5 every epigenetic epidemiologist is struggling with the same biological, technical and methodological issues. It is important to take these into consideration when designing a study and interpreting the results. Let us consider seven of those issues, taking the current study on SES as a starting point. Most epigenetic epidemiological studies focus on DNA methylation for various practical and biological reasons, neglecting other layers of the epigenome-like histone modifications that are also likely to be important in influencing disease phenotypes. Our basic understanding of the methylome (i.e. the whole of DNA methylation marks on the genome) is in its infancy, and we are still learning about the specific localization of the features that, when differentially methylated, regulate gene expression and are thus relevant for epigenetic epidemiologists to study. The current study, like many others, evaluated promoter regions, in this case defined as 1000 bp upstream to 250 bp downstream of transcription start sites. Although these features are often enriched for DNA methylation marks influencing the expression of genes, recent work suggests that other regions of the methylome outside of promoters, including inter-genic CpG island shores6 and intra-genic CpG islands,7 may ultimately be more important for regulating phenotypic variation. For any differentially methylated region identified in EWASs it will be important to demonstrate functionality. Promoter methylation in the current study was integrated with public gene expression data and, as expected, highly expressed genes were more commonly flanked by less methylated promoters and vice versa. A limitation is that this observation is for groups of promoters, whereas information is needed about this relationship for individual promoters. Mining the reference epigenomes and transcriptomes that are being generated for different cell types under the umbrella of initiatives such as the National Institutes of Health (NIH) Epigenomics Roadmap8 and the International Human Epigenome Consortium9 may contribute to such information. Additional in vitro experiments will be required to evaluate the transcriptional effects of differential DNA methylation at a specific locus independent of its genomic context.10 The good news is that recent advances in genomic technology mean that genome-scale studies of DNA methylation across multiple samples are now feasible. In practice, however, one has to compromise between coverage and precision in epidemiological studies, which likely incorporate a large number of samples. A large (and growing) number of methods exist for assessing DNA methylation both genome wide and at specific CpG sites,11 and one problem relates to our inability to compare results across studies that have used different platforms. On the one hand there are methods such as that used in the current study in which the methylated portion of the genome is captured using antibodies against methylated DNA and subsequently quantified using microarrays or next-generation sequencing. These approaches can provide coverage across most of the genome and may be optimally suited to discriminate low from high methylation, but have lower reliability for smaller differences and are biased by factors such as CG density.12,13 On the other hand, there are methods based on the bisulphite conversion of DNA combined with next-generation sequencing that provide higher accuracy and single nucleotide resolution. Although whole-genome bisulphite sequencing is currently unfeasible to use across large epidemiological cohorts, the method can be adapted to target a reduced representation of the genome (approximately 3 million out of approximately 28 million CG dinucleotides in