GENOME-WIDE ANALYSIS OF 102,084 MIGRAINE CASES IDENTIFIES 123 RISK LOCI AND SUBTYPE-SPECIFIC RISK ALLELES 169 8 data and GWAS results from all variants together with an LD reference panel. For our analyses, we used the same QC as for the other LDSC analyses and six different sets of readily constructed annotation-specific LD scores downloaded from https://data.broadinstitute.org/alkesgroup/ LDSCORE/LDSC_SEG_ldscores/: multi-tissue gene expression, multi-tissue chromatin, GTEx brain, Cahoy, Corces ATAC and ImmGen LD Scores. FDR was controlled by the BenjaminiHochberg method. The results are in Supplementary Table 14A-F. There were no significant results with the Cahoy, Corces ATAC and ImmGen data at FDR 5%. Multi-marker Analysis of GenoMic Annotation (MAGMA) We applied MAGMA v1.0961 to identify genes and gene sets associated with the migraine metaanalysis results. First, we mapped the meta-analysis SNPs to 18,985 protein-coding genes based on their physical position in the NCBI 37 build by using default settings of MAGMA. Next, we performed a gene-based analysis using the default SNPwise-mean model and the same UK Biobank LD reference as for the other analyses. We applied a Bonferroni correction (α = 0.05/18,985) to identify significantly associated genes for migraine with the results listed in Supplementary Table 16A. Finally, we used the results from the gene-based analysis to perform a gene-set analysis by using two different gene-set collections from the Molecular Signature Database v.7.062, 63: the curated gene sets containing 5,500 gene sets and the GO gene sets containing 9,988 gene sets.The gene-set analysis was performed using the competitive gene set model and one-sided test that tests whether the genes in the gene-set are more strongly associated with the phenotype compared to the other genes. To correct for multiple testing, we used a Bonferroni correction (α= 0.05/(5,500 + 9,988)). Results are in Supplementary Table 16B,C and in Supplementary Figure 7. DEPICT DEPICT64 is an integrative tool to identify the most likely causal genes at associated loci, and enriched pathways and tissues or cell types in which the genes from the associated loci are highly expressed. As an input, DEPICT takes a set of trait-associated SNPs. First, DEPICT uses coregulation data from 77,840 microarrays to predict biological functions of genes and to construct 14,461 reconstituted gene sets. Next, information of similar predicted gene functions is used to identify and prioritize gene sets that are enriched for genes in the associated loci. For the tissue and cell type enrichment analysis,DEPICT uses a set of 37,427 human gene expression microarrays.We used DEPICT v1.194 and ran the analyses twice for each of the P-value thresholds for clumping, as recommended,64 and using the default settings of 500 permutations for bias adjustment and 50 replications for the FDR estimation and for the P-value calculation. As an input, we used only the autosomal SNPs and the same UK Biobank LD reference data as for the other analyses. First, we ran the analysis using a clumping P-value threshold of 5 × 10-8 that resulted in 165 clumps formed from 7,672 variants (Supplementary Table 15D-F). Second, we used a P-value threshold of 1 × 10-5 leading to 612 clumps formed from 22,480 variants (Supplementary Table 15A-C).
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