GENETIC SUSCEPTIBILITY LOCI IN GENOME-WIDE ASSOCIATION STUDY OF CLUSTER HEADACHE 121 6 Genetic correlation LDSC was also used to calculate genetic correlation between CH and migraine.32 For migraine we used summary statistics from Gormley et al.34 without 23andMe (30,465 migraine cases and 143,147 controls), excluding variants with MAF ≤ 0.01,INFO score ≤ 0.6, large-effect variants or variants in an HLA region. In addition, the 38 genome-wide significant migraine loci were tested for association with CH.34 Using the cor.test function in R, the correlation of the effect size (beta) between migraine and CH (current study) was calculated. Gene-based analysis We performed the MAGMA gene-based association analysis implemented in FUMA, using default settings to identify genes associated with CH.22 This calculates a gene test-statistic (p-value) based on all SNPs located within genes. SNPs were assigned to the genes obtained from Ensembl build 85 (only protein-coding genes). Tissue specificity analyses To further test the relationship between tissue-specific expression and genetic associations to CH, we examined all SNPs and their respective effect on the expression of genes up to 1 Mb away (cis-eQTL), using FUMA quantitative trait locus (eQTL) mapping (https://fuma. gtlab.nl/tutorial#eQTLs); all SNPs were mapped based on each of the tissues in the Genotype Tissue Expression (GTEx) v8 dataset using default setting.22 Additionally, we performed tissue expression analysis based on the MAGMA gene property in FUMA.22 This analysis tests for positive relationships between tissue-specific gene expression in 30 general tissue types and 54 specific tissue types in the GTEx v8 RNA-seq data and gene-based p-values from the gene-based analysis described above. RNA-sequencing of CH patients and controls The genes identified by eQTL mapping with FUMA (see above) were further interrogated using existing RNA sequencing (RNA-seq) data generated from peripheral venous blood samples from 39 CH patients and 20 controls matched for age, sex and smoking habits. Data generation and quality control is described in detail elsewhere.35 In short, RNA was extracted, using the PAXgene Blood miRNA kit, and sequenced using Illumina Hiseq4000. RNA-seq reads were aligned and processed using the in house transcriptome analysis pipeline Gentrap (version 0.3.1). Within this pipeline, sequencing reads were aligned to the human genome reference GRCh38 using TopHat (version 2.0.13) and counted per gene using Htseq (version 0.6.1p1). The data was normalized for between-sample variation and for within-sample variation, using Limma voom transformation. Differential expression analysis was performed in Limma, fitting a linear model correcting for age, gender, current smoking status and leukocyte counts. P-values were adjusted for multiple-testing using Bonferroni correction.
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