Cindy Boer

188 | Chapter 4.1 eQTL Colocalization For cis-eQTL colocalization we used summary statistics of SNPs from 48 human tissues from the GTEx v7[100]. For each signal and each tissue we included genes that con- tained at least 1 eQTL (using a threshold of <5% false discovery rate) in GTEx and that overlapped 100kb either side of our signal. For the colocalization analysis we included all variants in common between the GO meta-analysis and the GTEx cis-eQTL analy- sis with the exception of indels. We used the Bayesian statistical methodology which implements the method of Giambartolomei[101]. This method evaluates whether the GWAS and molecular QTL associations best fit a model in which the associations are due to a single shared variant, summarized by the posterior probability (PP). Evidence for colocalization was assessed using the PP4 indicating that there is an association for both traits and they are driven by the same causal variant. A PP4 > 0.8 was considered evidence for colocalization ( Supplementary Table 7 ). Causal inference analysis pQTL Mendelian randomization and colocalization Two-sample Mendelian randomization (MR) was applied to understand the association between plasma proteins on osteoarthritis. In theMR analysis, 1640 proteinswere treat- ed as the exposure and the 11 osteoarthritis phenotypes as the outcomes. The genetic instruments of the plasma proteins were obtained from Zheng et al., where the condi- tional independent pQTLs were pooled from 5 recent GWAS of plasma proteins[33-37]. The genetic instruments were further split into two groups: 1) cis-acting pQTLs within a 500Kb window from each side of the leading pQTL of the protein were used for the MR analysis; 2) trans-acting pQTLs outside the 500Kb window of the leading pQTL were designated as trans instruments. For the MR analysis, the meta-analysis summary sta- tistics of osteoarthritis including UK Biobank participants were used as outcomes. We selected a p-value threshold of 0.05, corrected for 11 osteoarthritis phenotypes and the number of independent tests, as our threshold for prioritising MR results for follow up colocalization analyses (number of tests= 18.030; P-value< 2.77x10 -6 ) ( Supplementary Table 9 ). For 28 protein-osteoarthritis associations that survived the multiple testing threshold in the MR analysis, we further conducted colocalization[101] analysis to distinguish causal effects from genomic confounding due to linkage disequilibrium. A colocalization probability more than 80% in this analysis would suggest that the two association signals are likely to colocalize within the test region. Colocalization analysis was applied to both cis and trans pQTLs. For protein and phenotype GWAS lacking suf-