Mining large international genetic datasets to identify new therapeutic targets in giant cell arteritis through innovative genetic methodology

Giant cell arteritis (GCA) is the commonest primary systemic vasculitis, occurring exclusively after 50yrs. It is a preventable cause of blindness and stroke. Visual manifestations occur early in 40% and range from transient diplopia to sudden, permanent visual loss. Irreversible ischaemic complications, including blindness, occur in 19% of UK patients, despite prompt treatment. Ongoing vascular inflammation in the extracranial and large vessels leads to late vascular stenoses and has a 17 fold increased incidence of thoracic aortic aneurysms. Most patients commence glucocorticoid monotherapy and there is a high relapse rate with 50% remaining glucocorticoid dependent 2-3 years later. This is in marked contrast with other systemic inflammatory disorders where early immunosuppressive therapy leads to improved patient outcomes and reduced tissue damage.

We hypothesise that major pathogenic pathways active in GCA can be identified through the analysis of polygenic risk scores and protein quantitative trait loci (pQTL) derived from relevant immunological, vascular or tissue remodelling datasets.

Well-phenotyped GCA cohorts with genome-wide genotypic data, histological data and associated sample collections for proteomic analysis will be used, combined with publicly accessible data of traits related to immune and vascular function and also matrix turnover.
Polygenic risk scores will be generated from relevant immunological, cardiovascular and tissue remodelling clinical phenotypes (e.g. from UK Biobank and publicly available datasets. In conjunction with the SCALLOP Consortium (a collaborative international framework for the discovery of pQTLs and novel biomarkers for Olink proteins) pQTLs will be identified for circulating proteins at unprecedented statistical power (980 plasma proteins in ~ 25,000 participants, many of which are implicated in vascular inflammation).
The derived polygenic risk scores and pQTLs will be analysed to identify the influence on GCA susceptibility and selected clinical phenotypes (PMR/ischaemic complications).
A subset of GCA patients' samples will be analysed for circulating proteins. Specific hypotheses relating protein levels and pQTL to clinical subtype or outcome will be formulated based on the earlier work and tested in this subset. A discovery genome-wide association study of pQTLs in GCA will be conducted, weighted by prior information from the SCALLOP Consortium.

There will also be opportunities for the therapeutic targeting of key pathogenic pathways associated with GCA or selected phenotypic subgroups (ischaemic complications, thoracic aortic aneurysms) using computational biology and pharmacological compound profiling tools.