8 CHAPTER 8 166 all 123 lead variants to all significant cis-eQTLs across tissues using the FDR cut-off of 5% as provided by the GTEx project.37 Next, we also mapped the variants in high LD (r2 > 0.6) with the lead variants to all significant cis-eQTLs. Finally, we filtered the results to include only the new significant gene-tissue pairs that were not implicated by the lead variants. Results are shown in Supplementary Tables 9 and 10. With FUMA v1.3.6,38 we mapped the 123 lead variants, and the variants in high LD (r2 > 0.6) with the lead variants, to the other eQTL data repositories provided by FUMA except GTEx, i.e., Blood eQTL Browser,39 BIOS QTL browser,40 BRAINEAC,41 MuTHER,42 xQTLServer,43 CommonMind Consortium,44 eQTLGen,45 eQTL Cataloque,46 DICE,47 scRNA eQTLs,48 and PsychENCODE.49 Results are shown in Supplementary Tables 9 and 10. To study whether the lead variants were enriched in any of the 49 tissues from GTEx v8, we fitted a linear regression model where the number of lead variants that are significant cis-eQTLs for a specific tissue was used as the outcome, and the overall number of genes with at least one significant cis-eQTL reported by GTEx for the tissue was the predictor.37 We did a separate regression model for each tissue type by leaving the tissue of interest out from the model, and we used the model fitted on the other tissues for predicting the outcome variable for the tissue type of interest. Finally, we checked in which tissues the true observed number of migraine lead variants was outside of the 95% prediction intervals as given by the function ‘predict.lm(, interval=”prediction”)’ in R software. Details of the procedure are in the Supplementary Note. LD Score regression We estimated both the SNP-heritability (h2 SNP) of migraine and pairwise genetic correlations (rG) between each pair of study collections using LDSC v1.0.0. 50, 51 SNP-heritability and genetic correlations were estimated using European LD scores from the 1000 Genomes Project Phase 3 data for the HapMap3 SNPs, downloaded from https://data.broadinstitute.org/alkesgroup/ LDSCORE/. We reformatted the meta-analysis association statistics to LDSC format with munge-tool that excluded variants that did not match with the HapMap3 SNPs, had strand ambiguity (i.e., A/T or G/C SNPs), MAF < 0.01 or missingness more than two-thirds of the 90th percentile of the total sample size, or resided in long-range LD regions,52 in centromere regions or in the major histocompatibility locus (MHC) of chromosome 6, leaving 1,165,201 SNPs for the LDSC analyses. We used a migraine population prevalence of 16% and a sample proportion of cases of 11.7% = 102,084/(102,084 + 771,257) to turn the LDSC slope into the estimate of h2 SNP on the liability scale. 53 Pairwise genetic correlation results are listed in Supplementary Table 2. We note that in the previous migraine meta-analysis,13 LDSC reported h2 SNP value of 14.6% (13.8–15.5%), which was considerably larger than the value 11.2% (10.8–11.6%) that we report in our analysis. When we ran our LDSC pipeline on the data of Gormley et al.13, we estimated h2 SNP value of 10.6% (10.1–11.1%). Thus, it seems that our liability transformation estimates lower values of heritability than the transformation used by Gormley et al..13
RkJQdWJsaXNoZXIy MTk4NDMw