Genetic biomarkers of clinical manifestations in giant cell arteritis define distinct patient subgroups.

Giant cell arteritis (GCA) is a clinically heterogeneous disease, which complicates both diagnosis and management. This study aimed to identify genetic risk factors associated with GCA clinical manifestations and evaluate their utility for defining clinical phenotypes. Genome-wide genotype data from 3498 patients with GCA and 15,550 controls were analysed to investigate the genetic architecture of GCA manifestations. Logistic regression was used to compare patients with and without each manifestation, as well as each subgroup of patients vs controls. Gene annotation was conducted based on functional information using Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA GWAS). Latent class analysis (LCA) was applied to evaluate the ability of associated variants to classify patients with GCA into genetic subgroups. We identified 7 human leukocyte antigen (HLA) variants specifically associated with different clinical features. Furthermore, 7 non-HLA associations across 6 clinical manifestations were found. Gene prioritisation highlighted biologically relevant candidate genes for GCA pathogenesis, including IL17A (limb claudication) and IL22RA1 (jaw claudication), both involved in the Th17 pathway, and ATP2A2 (extracranial form), encoding a transporter that promotes aortic aneurysms. Notably, LCA grouped GCA clinical predisposition into 4 classes capturing cranial-predominant, mixed, extracranial, and ischaemic/occlusive patterns, the latter representing a high-risk subgroup for severe ischaemic and ocular complications. This first genome-wide association study stratified by GCA-specific manifestations deepens our understanding of the genetic basis underlying GCA clinical heterogeneity. Our findings highlight HLA and non-HLA contributors to specific disease phenotypes and support the potential of genetic profiling to guide early diagnosis and personalised management in GCA.