Epigenetics and epigenomics have taken center stage for many questions in biomedical research in the last decade. They have moved from a scientific discipline restricted to a limited number of expert laboratories to a commonly analyzed molecular level accessible to any laboratory performing molecular biology. Epigenetics has been omnipresent over the last decade and epigenetics and epigenomics have experienced a lot of hype. Epigenetics was regarded as a solution for resolving the missing heritability, coming with the disappointing realization that large-scale genetic studies, particularly genome-wide association studies (GWAS), did not fulfill the initial promise of explaining complex diseases. In fact, with the disappointment in genetics, epigenetics was seen as the solution to everything; with its aura of mystery, it was the one-word solution to every question, leading to the attitude of “if you can not explain it with your genomic data, then it is probably epigenetics”.
Epigenomics has been with the community for the last 10 years and has been providing a publication platform with a well-balanced mixture of novel research results mainly related to human diseases, thought provoking comments and comprehensive reviews, which allowed readers to keep up-to-date in this rapidly moving field.
Epigenetic research has been largely driven by technological progress, notably high-throughput sequencing technologies, which allow us to analyze multiple epigenetic levels at single-nucleotide resolution on the same read-out platform, facilitating data integration.
While the epigenetic components, associated DNA methylation changes and chromatin reorganization in cancer have been known for quite some time, the last decade has demonstrated (in a multitude of studies) that changes are also present in a large variety of complex diseases, including autoimmune, inflammatory, neurodegenerative and metabolic disorders.
In analogy to the analysis of genetic variation in GWAS, the last decade has seen the emergence of epigenome-wide association studies (EWAS), which commonly use BeadArray-based technologies to interrogate associations of between 2 and 3% of the CpGs in the human genome with a phenotype of interest.
Some of the first EWAS published were statistically underpowered and confounded by nonphenotype-related differences such as differences in age or sample composition. Study design and statistical methods have since greatly improved.
For the analysis and detection of potential confounding factors, current EWAS do yield changes that can be reproduced in different cohorts, albeit with some room for improvement.
