Cindy Boer

190 | Chapter 4.1 tion we used pre-computed LD scores for European populations. LD scores for the East Asian populations couldn’t be calculated as LD Score method requires a sample size of >4000 individuals. To avoid bias due to overlapping samples, we calculated the genetic correlation between meta-analysis of Icelandic, Norwegian and USA samples and UK, Dutch, Estonian and Greek samples ( Supplementary Table 5 ). The results of the two analyses were subsequently meta-analysed. For genetic correlations with other traits, we calculated the genetic correlation between a meta-analysis of UK, Dutch, Estonian and Greek samples and the Icelandic GWAS summary statistics for each secondary trait, and also between a meta-analysis of Icelandic, Norwegian and USA samples and UKBB GWAS summary statistics for each secondary trait. The results of the two analyses were subsequently meta-analysed. For the analysis of genetic correlation between the oste- oarthritis subtypes, we split the sample-sets of the meta-analysis in two equally sized groups and performed LD score regression between the two groups for each subtype. Annotation of protein coding variants For coding SNVs we considered only the following moderate to high impact annotations when weighting genes for prioritisation: transcript_ablation, splice_acceptor_variant, splice_donor_variant, stop_gained, frameshift_variant, stop_lost, start_lost, transcript_ amplification, inframe_insertion, inframe_deletion, missense_variant, protein_altering_ variant. Investigation of GWAS meta-analysis signals in the Druggable Genome We examined the druggability status for the 376 prioritized genes as mentioned in Ta- ble 3 , using the druggable gene set as defined by Finan et al., 2017 Science Translational Med. This druggable genome contained 4,479 genes and it was divided in three tiers of druggable gene sets based on their drug development. Tier 1 included efficacy targets of approved small molecules and biotherapeutic drugs as well as clinical-phase drug candidates. Tier 2 contained targets with known bioactive drug-like small molecule binding partners and those with significant sequence similarity to approved drug tar- gets. Tier 3 contained proteins with more distant similarity to approved drug targets, and members of key druggable gene families not already included in tier 1 or 2. This tier was further subdivided to prioritize those genes that were in proximity (±50 kbp) to a GWAS SNP from GWAS catalogue and had an extracellular location (Tier 3A). The remainder of the genes were assigned to Tier 3B.