TY - JOUR T1 - Genomic Risk Score impact on susceptibility to systemic sclerosis JF - Annals of the Rheumatic Diseases JO - Ann Rheum Dis SP - 118 LP - 127 DO - 10.1136/annrheumdis-2020-218558 VL - 80 IS - 1 AU - Lara Bossini-Castillo AU - Gonzalo Villanueva-Martin AU - Martin Kerick AU - Marialbert Acosta-Herrera AU - Elena López-Isac AU - Carmen P Simeón AU - Norberto Ortego-Centeno AU - Shervin Assassi AU - International SSc Group AU - Australian Scleroderma Interest Group (ASIG) AU - PRECISESADS Clinical Consortium AU - PRECISESADS Flow Cytometry study group AU - Nicolas Hunzelmann AU - Armando Gabrielli AU - J K de Vries-Bouwstra AU - Yannick Allanore AU - Carmen Fonseca AU - Christopher P Denton AU - Timothy RDJ Radstake AU - Marta Eugenia Alarcón-Riquelme AU - Lorenzo Beretta AU - Maureen D Mayes AU - Javier Martin Y1 - 2021/01/01 UR - http://ard.bmj.com/content/80/1/118.abstract N2 - 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. ER -