7 CHAPTER 7 142 sets for 54 tissues from GTEX v8,25 and in biological pathways and functional categories from MsigDB, WikiPathways and the NHGRI GWAS catalog.24 P values < 9.26 x 10-4 (0.05/54 tests) were considered statistically significant.24 We also applied two approaches based on variant-level summary statistics: (1) DEPICT v1.194 analysis26 applied to independent variants with a nominal association to CH (p < 1 x 10-6), and (2) LD-Score Regression applied to specifically expressed genes (LDSC-SEG) v1.0.1.27 applied to the full set of summary statistics from the meta-analysis. Both methods were run with default settings. FDR < 0.05 was considered statistically significant. Drug target identification For genes prioritized by at least one of the five methods, we examined their druggability status using the dataset from Finan et al.28 (Table S6). For detailed structured information about drugs and drug targets we integrated information from the DrugBank online database (https://www. drugbank.com)29 (version 5.1.9, released 2022-01-04). Genetic risk score analysis Genetic risk scores (GRS) were based on summary statistics from the meta-analysis of all European ancestry cohorts except the given cohort to create independent test samples. In three cohorts (Dutch, Swedish cohort 1 and Danish) GRS were calculated with LDpred2,30 which uses the whole discovery dataset without applying a p value threshold. In the German cohort GRS were calculated using PRSice2,31 (Tables S7). Sample-specific GRSs were normalized using the target sample mean and standard deviation. Using linear regression, adjusting for sex and the first 4-6 principal components, we examined the association of GRS in each cohort to case-control status, and among cases to episodic vs. chronic CH, male vs. female patients, age at onset, currently smoking yes vs. no and ever vs. never smoked was examined for each cohort. P values < 0.0024 (0.05/21 tests) were considered statistically significant. Genetic correlation In a hypothesis-free fashion, LDSC (v1.0.1.)14 was used to calculate pairwise genetic correlations between CH and 1,150 phenotypes from published GWAS (Table S8) based on GWAS summary statistics. Applying a stringent Bonferroni correction (0.05/1,150), the significance threshold was set at (p < 4.35 x 10-5). To evaluate differences in the correlation profiles for CH and migraine, the genetic correlation was calculated between migraine (48,975 migraine cases and 540,381 controls from Hautakangas et al.,17 not including 23andMe) and each of the traits that were significantly correlated with CH, while Bonferroni correcting for the number of tests (0.05/84, p < 5.95 x 10-5). Colocalization analysis To test whether CH loci that were in close proximity to previously reported migraine loci share causal variants for both CH and migraine, the Bayesian colocalization procedure implemented in
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