Cindy Boer

Genetics of Osteoarthritis Consortium GWAS Meta-Analyses | 189 4.1 ficient SNP coverage or missing key information (e.g. allele frequency or effect size) in the test region, we conducted a LD check for the sentinel variant for each pQTL against the 30 strongest SNPs in the region associated with the phenotype as an approximate colocalization analysis. r 2 of 0.8 between the sentinel pQTL variant and any of the 30 strongest SNPs associated with the phenotype was used as evidence for approximate colocalization. Nine protein-osteoarthritis associations showed reliable MR and colocaliza- tion evidence (Bonferroni corrected, p-value<2.77x10 -06 and colocalization probability >70%) for a total of six proteins on seven osteoarthritis phenotypes ( Supplementary Table 9, Methods ). Of the eight protein quantitative trait loci (pQTLs) used as genetic predictors of these six proteins, five were in strong LD with missense variants (r 2 >0.8). As missense variants may cause epitope-binding artefacts, we also evaluated the effect of these eight pQTLs on other molecular traits: DNA methylation (meQTL)[102] and gene expression (whole blood eQTLs from eQTLGen and all tissues eQTLs from GTEx; Six of the eight pQTLs were also cis meQTLs and cis eQTLs in the same region, in which four pQTLs are in LD (r2>0.3) with the top meQTL and eQTL in the region ( Supplemen- tary Table 9 ). Pathway analyses Pathway and gene set enrichment analysis were performed using the Gene2Func func- tion of FUMA (Functional Mapping and Annotation of Genome-Wide Association Stud- ies)[103]. Analysis was performed as described in [103] using the following settings: all known genes and transcripts were included in the background gene set, we included the MHC-region, a minimum of 5 genes needed to overlap with the examined gene sets and the significance threshold was set at FDR<0.05. For the total pathway analysis we used all 205 genes included in Table 3 , Two genes were not recognized ( C1orf40 and ICAL2 ) and thus were therefore not included in this analysis. We also performed 3 ad- ditional stratified pathway analysis on (I) all SNVs associated with only weight bearing joints(85 genes), (II) all SNVs associated with only non-weight-bearing joints(8 genes), and (III) all SNVs associated with both weight bearing and non-weight bearing joints(85 genes). Genetic correlation We estimated the genetic correlation between osteoarthritis traits and secondary traits using the cross-trait LD Score regression method as implemented in LDHub[16, 17] in our meta-analyses and summary statistics from traits in the deCODE and UKBB data- sets. We used results for about 1.1 million well imputed variants, and for LD informa-