CLUSTER HEADACHE GENOME-WIDE ASSOCIATION STUDY AND META-ANALYSIS IDENTIFIES EIGHT LOCI AND IMPLICATES SMOKING AS CAUSAL RISK FACTOR 143 7 the R package ‘coloc’ (v5.1.0) was used with default settings32 and the migraine dataset described above. Colocalization was tested for the region between the two nearest recombination hotspots (https://bitbucket.org/nygcresearch/ldetect-data/src/master/EUR/). Mendelian randomization analysis To test for a causal effect of smoking on CH, we performed a summary statistics-based two-sample inverse-variance weighted (IVW) Mendelian randomization analysis,33 using as instrumental variables 40 independent variants significantly (p < 5 x 10-8) associated with “Cigarettes smoked per day” in a previous GWAS,34 as an indication for smoking intensity (Table S9). Since the IVW method assumes the absence of horizontal pleiotropy, several sensitivity analyses were employed to exclude pleiotropy. Cochran’s Q tests were used to detect heterogeneity.35 In addition, the MREgger intercept was used to detect directional pleiotropy.35, 36 Both models were fit using robust regression and assuming a t-distribution of the fitted parameters. Analyses were performed using the MendelianRandomization package (version 0.5.1) in R (version 3.6.3). To verify the causality between smoking and CH, we applied a latent causal variable (LCV) model to estimate the genetic causality proportion (GCP).37 Here, a latent variable mediates the genetic correlation, avoiding false positives due to genetic correlations when determining causality. A GCP of 0 is interpreted as no, and GCP of 1 as complete, genetic causality. Results European-ancestry GWAS meta-analysis Seven independent genome-wide significant CH associated (p < 5 × 10-8) risk loci (Table 2, Figure 1 and 2) were identified. Associations were consistent across the ten cohorts (heterogeneity p > 0.10, Tables 2 and S10). Named by their nearest protein-coding gene, four of risk loci were previously reported6, 7 (DUSP10, MERTK, FTCDNL1 and FHL5), while three are novel (WNT2, PLCE1, LRP1). A stepwise conditional analysis using FINEMAP16 revealed that two of the identified loci (MERTK and WNT2) contained additional independent signals, increasing the number of independent association signals to nine (Table S11). Fine-mapping with PICS215 suggested that the lead signal in the LRP1 locus (rs11172113) is most likely the causal variant (posterior probability 65.8%). Five other variants in three other loci had PICS2 posterior probability > 10% for being causal (Table S12). The genomic inflation factor (λ) was 1.086, while the LD score regression intercept was 1.004 (SE 0.007), with a ratio of 0.033 (SE 0.062), indicating that 96.7% of the observed signal is caused by true polygenic heritability rather than confounding factors, such as population stratification. The estimated SNP-based heritability (h2) of CH was 14.5% (SE 1.74%) on the liability scale. One additional genome-wide significant CH locus, in CAPN2, was identified when adding the East Asian cohort in an ancestry-adjusted GWAS meta-analysis (Table 3, Table S13, Figure
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