Cindy Boer

50 | Chapter 1.2 and genetic data can be used to elucidate the function and biological pathway underly-ing the genetic association. Large-scale studies in blood have shown that meQTLs are widespread (>30% of all CpGs have a meQTL), but that only 10% of the meQTLs also associate with expression levels of nearby genes[122]. A number of studies have ex-amined the relation between previously identified genetic loci for BMD and/or OA and methylation (and/or gene expression) in the target tissue. These studies identified a number of meQTLs specific for cartilage[123-125], suggesting that methylation medi-ates the association between the genetic locus and disease. Notably, absence of a meQTL in these studies does not mean that methylation does not play a role, since methylation is dynamic and context-dependent and can be dependent on a specific stimulus or de-velopmental stage. Annotating epigenomic features: available information regarding skeletal tissues Recent large-scale efforts have provided new understanding in the function of epigen-etic modifications, and efforts are ongoing to map histone modifications, transcription factor binding, and 3D chromatin structure of multiple cell types and tissues[5, 43, 44]. The data generated by these large-scale efforts are publicly available and contain DNA annotation on multiple levels: histone modification, binding of transcription factors and other DNA binding proteins, methylation, gene expression, DNA accessibility, and chro-matin conformation. Some also contain epigenetic information on cartilage and bone tissues; most notably are the “reference” epigenomes for osteoblasts and chondrocytes generated by the ROADMAP consortium ( Table 3 ). The reference epigenomes construct an annotation of functional elements, ie, enhancers, promoters, etc., of the DNA per cell type,[4] which has already been proven to be a valuable resource for skeletal GWAS and EWAS, to finemap causal SNPs, CpGs, and genes, and also to provide for a hypothesis on the mechanism underlying GWAS/EWAS findings for skeletal outcomes[126, 127]. The interesting SUPT3H‐RUNX2 locus provides a good example on how different levels of molecular data can be combined to build a model for the regulatory mecha-nisms involved in the RUNX2 locus, a master regulator of osteoblast and chondrocyte regulation ( Figure 3 ). This locus encompasses a region of roughly ∼ 700 kb, where multiple genetic association signals have been identified for OA[127]. BMD[128], OPLL (ossification of the posterior longitudinal ligament of the spine), cartilage thickness (measured as the minimum joint space width)[127]. height[129]. and facial morpholo-gy[130]. Interestingly, these GWAS signals are independent from each other[127]. Data from the ROADMAP consortium shows that several of the GWAS signals are located in different (potential) enhancers in cartilage and osteoblast cells. This suggests that the genetic association with various skeletal phenotypes might