RT Journal Article
SR Electronic
T1 Genomic Risk Score impact on susceptibility to systemic sclerosis
JF Annals of the Rheumatic Diseases
JO Ann Rheum Dis
FD BMJ Publishing Group Ltd and European League Against Rheumatism
SP 118
OP 127
DO 10.1136/annrheumdis-2020-218558
VO 80
IS 1
A1 Lara Bossini-Castillo
A1 Gonzalo Villanueva-Martin
A1 Martin Kerick
A1 Marialbert Acosta-Herrera
A1 Elena López-Isac
A1 Carmen P Simeón
A1 Norberto Ortego-Centeno
A1 Shervin Assassi
A1 International SSc Group
A1 Australian Scleroderma Interest Group (ASIG)
A1 PRECISESADS Clinical Consortium
A1 PRECISESADS Flow Cytometry study group
A1 Nicolas Hunzelmann
A1 Armando Gabrielli
A1 J K de Vries-Bouwstra
A1 Yannick Allanore
A1 Carmen Fonseca
A1 Christopher P Denton
A1 Timothy RDJ Radstake
A1 Marta Eugenia Alarcón-Riquelme
A1 Lorenzo Beretta
A1 Maureen D Mayes
A1 Javier Martin
YR 2021
UL http://ard.bmj.com/content/80/1/118.abstract
AB Objectives Genomic Risk Scores (GRS) successfully demonstrated the ability of genetics to identify those individuals at high risk for complex traits including immune-mediated inflammatory diseases (IMIDs). We aimed to test the performance of GRS in the prediction of risk for systemic sclerosis (SSc) for the first time.Methods Allelic effects were obtained from the largest SSc Genome-Wide Association Study (GWAS) to date (9 095 SSc and 17 584 healthy controls with European ancestry). The best-fitting GRS was identified under the additive model in an independent cohort that comprised 400 patients with SSc and 571 controls. Additionally, GRS for clinical subtypes (limited cutaneous SSc and diffuse cutaneous SSc) and serological subtypes (anti-topoisomerase positive (ATA+) and anti-centromere positive (ACA+)) were generated. We combined the estimated GRS with demographic and immunological parameters in a multivariate generalised linear model.Results The best-fitting SSc GRS included 33 single nucleotide polymorphisms (SNPs) and discriminated between patients with SSc and controls (area under the receiver operating characteristic (ROC) curve (AUC)=0.673). Moreover, the GRS differentiated between SSc and other IMIDs, such as rheumatoid arthritis and Sjögren’s syndrome. Finally, the combination of GRS with age and immune cell counts significantly increased the performance of the model (AUC=0.787). While the SSc GRS was not able to discriminate between ATA+ and ACA+ patients (AUC<0.5), the serological subtype GRS, which was based on the allelic effects observed for the comparison between ACA+ and ATA+ patients, reached an AUC=0.693.Conclusions GRS was successfully implemented in SSc. The model discriminated between patients with SSc and controls or other IMIDs, confirming the potential of GRS to support early and differential diagnosis for SSc.