ANCA-associated vasculitides (AAV) are rare but severe autoimmune diseases, where significant patient mortality is linked to organ failure and side-effects of the broadly immunosuppressive treatment. Little is known about the underlying mechanisms leading up to disease. We and others have previously performed large-scale genetic analyses of AAV and identified a number of disease-associated loci. Yet, a substantial proportion of the heritability and, hence, clues to the underlying molecular mechanisms, is still missing. The overall purpose of this project is to improve treatment and outcome for patients with AAV, through an increased understanding of the pathophysiological mechanisms leading up to disease. The specific aims are to identify genetic variants associated with AAV and their endophenotypes and through functional characterization of the genetic variants propose novel treatment targets. In order to study genetic predisposition to AAV, a large collaboration, comprising 14 different centra, has been established in the Nordic countries. In this project, using a genome-wide approach, approximately 1000 AAV patient samples from Sweden and Norway will be analyzed for disease-associated genetic variants; genotypes of 29400 population controls, included in the Swedish CArdioPulmonary bioimaging Study, will be used for comparison. To replicate results, patients from Finland (Finngen Registry) and from the UK (UK Biobank) will be analyzed in a similar manner, followed by a meta-analysis of all the Nordic and British patients. Next, to increase statistical power, a transcontinental meta-analysis will for the first time be performed, including the Nordic patients and all individuals of two previously performed genome-wide association studies from the US and the UK (in total~10000 individuals), respectively. Identified AAV-associated genetic variants will be computationally analyzed for causality and potential function. Cell type-specific effects of the variants on nearby genes, and, hence, protein products and their signaling pathways will be explored and, based on this information, potential drug targets will be identified and prioritized.