The genetics and pathophysiology of cluster headache and associated disorders

Cluster headache (CH) is described as one of the most painful conditions known to humans. It effects approximately 60,000 individuals in the UK and carries significant morbidity. It exhibits hereditability evident by reports of familial aggregation and is categorised as a trigeminal autonomic cephalalgia (TAC). Despite this, the exact pathophysiological and genetic drivers of this condition remain elusive. The purpose of this thesis is to examine the clinical and genetic determinants of CH, and thus gain insights into the underlying neurobiological mechanisms. This work consists of two components. In the first section, I conduct clinical observational studies to further delineate the CH phenotype. I address the postulated association between pituitary adenomas and CH and question the utility of dedicated pituitary imagining in this patient group. I also describe the largest series of Post-Traumatic Headache of Cluster Headache (PTH-CH) and demonstrate its distinct features and increased intractability to treatment. Finally, through meta-analysis, I estimate the prevalence of familial CH to be 6.27% and demonstrate an overlap with concurrent short-Lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing (SUNCT) in familial cases. The second section explores the genetics architecture underlying CH. I perform a Genome-Wide Association Study (GWAS) to identify replicable susceptibility loci and conduct a downstream analysis. Subsequent genetic correlation analysis showed an overlap with migraine, depression, bipolar and sleep disturbance implying the possibility of a common genetic driver for these conditions, which frequently present concurrently. I then carry out linkage analysis in CH families and replicate a linked region suggestive of significance on chromosome 2 that also overlaps a genome wide significant locus. Finally, I execute whole exome sequencing and utilise rare variant association tests and segregation analysis to identify causal variants for familial CH