SUPR
ANCA-vasculitis genetics
Dnr:

sens2017543

Type:

SNIC SENS

Principal Investigator:

Johanna Dahlqvist

Affiliation:

Uppsala universitet

Start Date:

2017-12-01

End Date:

2025-01-01

Primary Classification:

30107: Medical Genetics

Webpage:

Allocation

  • Castor /proj at UPPMAX: 12000 GiB
  • Cygnus /proj at UPPMAX: 12000 GiB
  • Castor /proj/nobackup at UPPMAX: 1000 GiB
  • Cygnus /proj/nobackup at UPPMAX: 1000 GiB
  • Bianca at UPPMAX: 2 x 1000 core-h/month

Abstract

The antineutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAV) are rare autoimmune disorders of unknown etiology. They are characterized by necrotizing inflammation of small vessels and practically any organ can be affected, often leading to permanent organ damage. In the present study we aim to investigate the pathogenesis of AAV on a genetic and molecular level. DNA samples and extensive clinical data have been collected from 616 Swedish patients with AAV. Approximately 2000 genes have been selected for sequencing, based on their previous association with autoimmune diseases or involvement in the immune system. Coding sequences, promoter regions and conserved elements upstream and downstream of the genes will be sequenced in all patients using Illumina HiSeq2500. Sequence data will be bioinformatically analyzed, together with data from approximately 1000 Swedish controls, for novel and disease-associated genetic variants and their putative function, degree of conservation and enrichment to specific molecular pathways will be scrutinized. Strong mutation candidates will be statistically analyzed using Burden tests. The effects of the candidate genetic variants will be experimentally investigated with gene- and protein expression analyses and cell signaling assays. Molecules of candidate pathways will be analyzed in terms of biomarkers in patient and control serum and biopsy samples. The pathogenesis of most complex autoimmune diseases, including AAV, is poorly understood. Shedding more light over the disease mechanism of these disorders may lead to more specific treatments of patients, as well as improved tools for diagnostics and prediction of prognosis for the individual patient.