8 CHAPTER 8 168 We tested whether the effect sizes between MA and MO were equal at a Bonferroni corrected significance threshold of α = 0.05/123 by using a normal approximation and accounting for the correlation in effect size estimators. We note that the amount of information in the data (“statistical power”) is taken automatically into account in this model comparison, which we consider an advantage compared to a comparison of the raw P-values between the subtype analyses that does not automatically account for statistical power. In particular, observing a GWS P-value (P < 5 × 10-8) in one subtype but not in the other subtype is not yet evidence for a subtype-specific locus, because the effect could still be non-zero also for the other subtype but simply lack power to reach the stringent GWS threshold. Finally, we point out that the inference in the model comparison approach is conditional on the particular set of models being included in the comparison as well as on the particular choice of the prior distributions. PheWAS with NHGRI GWAS Catalog and FinnGen R4 We performed phenome-wide association studies (PheWAS) for the 123 lead variants using the NHGRI GWAS Catalog and the FinnGen R4 GWAS summary statistics. In addition, we performed the same lookups for the 123 risk loci including all variants in high LD (r2> 0.6) with the lead variants. With the GWAS Catalog, we first downloaded all the available results (4,314 traits) from the GWAS Catalog webpage (accessed 6.4.2020). Next, we obtained all the associations for the 123 risk loci with all the high LD variants included using P-value thresholds of P < 1 × 10-5, P < 1 × 10-6 and P < 1 × 10-4 (Supplementary Table 13A-C). Because the GWAS Catalog includes results from several different GWAS for the same phenotype or for a very similar phenotype with a different name, we divided the phenotype associations into broader categories. The new categories are listed in Supplementary Table 19. The same approach was used for the PheWAS of FinnGen R4. We first downloaded all the available summary statistics (2,263 endpoints), and next, obtained all the associations for the 123 risk loci using the same three P-value thresholds as with the GWAS Catalog (Supplementary Table 13A-C). We also divided similar endpoints into broader categories that are listed in Supplementary Table 20. We tested the direction of allelic effects between migraine and the following three traits that shared multiple associated variants with migraine: coronary artery disease (CAD),59 diastolic blood pressure,60 and systolic blood pressure51. We first took all migraine lead variants that were available also in the summary statistics of the other trait without any P-value threshold and used a binomial test to test whether the proportion of variants with same direction of effects was 0.5. Next, we used a P-value threshold of 1 × 10-5 for the association with the other trait. Results are in Supplementary Table 13D. LD Score regression applied to specifically expressed genes We used LD Score regression applied to specifically expressed genes (LDSC-SEG)14 to identify tissues and cell types implicated by the migraine GWAS results. LDSC-SEG uses gene expression
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