Cindy Boer

Epigenomics in bone and cartilage disease | 49 1.2 Integrating different omic levels Interpretation of epigenomic data is complicated by the fact that the function of the ge- nome is not fully known. It has become apparent that gene expression can be regulated by genomic features (enhancers/insulators) far away from the gene[120]. In fact, recent studies in cancer show that variation in methylation in distal enhancers account for a larger part in the regulation of expression, then promoter‐methylation variation[121]. Similarly, den Hollander and colleagues observed that only 10% of the differentially methylated regions in cartilage is associated with differential expression of the nearest gene[45]. Integrating epigenomic data with data from other molecular layers, such as RNA expression and/or protein expression, can help to identify the function of the epig- enomic features. Epigenetics as mechanism for genetic etiology of disease Of all the genetic loci identified by GWAS for complex diseases (such as OA and osteo- porosis), the majority does not affect the protein coding but is instead thought to affect gene expression regulation. Epigenetics may mediate genetic risk, meaning that the methylation state of a specific locus is driven by a nearby genetic variant(s), also called a methylation quantitative trait locus (meQTL). In this way, integration of epigenomic ◄ Figure 2: Mendelian randomization to study causal relationships in epigenomic epidemiology . (A) The relationship be- tween DNA methylation and the studied trait is difficult to interpret because it can be subject to classical epidemiological pit- falls, such as confounders and/or reverse causation. (B) To establish causal path- ways for observed associations between (molecular) markers and clinical outcome, genetic variation can be used as a causal anchor. When examining the causal rela- tion from the DNA methylation marker to the skeletal trait, a genetic variant (or combination of genetic variants) (G1) can be used that are robustly associated with the methylation marker. To study reverse causation, one can examine the causal relationship from skeletal trait to meth- ylation by using a second set of genetic variants (G2) that are robustly associated with the skeletal trait of interest. Statisti- cal methods to perform this analysis have been reviewed elsewhere.[115]