6 CHAPTER 6 122 To determine the specificity of differential expression results obtained for CH, we examined the (nominally) significant genes from the CH RNA-seq analysis in RNA-seq data obtained from 26 migraine patients and 20 age- and sex-matched controls. Data generation and quality control have been previously described.36 In short, peripheral venous blood samples were drawn when the migraine patients were migraine-free for at least five days and headache-free for 24 hours. RNA was extracted using PAXgene Blood RNA kit, sequenced (using Illumina Novaseq) and aligned. RNA-seq reads were, after quality control, aligned to the human reference transcriptome, using kallisto (version 0.42.5). Resulting count matrices were corrected for library size and gene length, and normalized using the R package DESeq2. Differential expression was performed using the R package DESeq2 by fitting a generalized linear model, correcting for age. Results Study participants The clinical characteristics of cases and controls of the discovery sample are summarized in Table 1.There was a higher proportion of men (69% vs 44%) and smokers (52% vs 14%) among the cases compared to controls. Most patients had episodic CH (69%). A total of 13% of cases had migraine. Fine mapping with PICS identified two variants with causal probability larger than 0.2, at rs11579212 (PICS probability = 0.40) and rs10184573 (PICS probability = 1.0), respectively. Association analysis Overall association results are shown in the Manhattan plot (Figure 1A) and the QQ plot (Figure 2). In total, four independent loci showed genome-wide significant (p < 5 × 10−8) associations with CH (Figure 1B-E) with a combined explained variance of 7.2%. More specifically, we identified rs11579212 (odds ratio (OR) = 1.51, 95% CI 1.33-1.72 near RP11-815M8.1), rs6541998 (OR = 1.53, 95% CI 1.37-1.74 near MERTK), rs10184573 (OR = 1.43, 95% CI 1.26-1.61 near AC093590.1), and rs2499799 (OR = 0.62, 95% CI 0.54-0.73 near UFL1/FHL5) (Table 2). These lead SNPs had either a call rate or imputation metric close to 100%.Three of the four lead SNPs were present in the replication sample (rs11579212, rs6541998 and rs10184573), while for the SNP on chromosome 6 (rs2499799) we selected a proxy SNP (rs976357, r2 = 1.0, D’ = 1). Lead SNPs of loci rs11579212, (OR = 1.58, 95% CI 1.16-2.15) rs10184573 (OR = 1.74, 95% CI 1.29-2.34) and rs976357 (OR = 0.44, 95% CI 0.30-0.64) replicated after Bonferroni correction (Table 3). The genomic inflation factor (λ) was 1.069 in the discovery analysis, while the linkage disequilibrium score regression (LDSR) intercept was 1.044 (SE 0.0077), indicating moderate inflation due to factors other than polygenic architecture. We estimated the SNP-based heritability (h2) of CH at 30.3% (SE 19.4%) on the observed scale. Assuming a population prevalence of 0.1% for CH this corresponds to a h2 of 11.5% (SE 7.4%) on the liability scale.
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