Genomic Risk Score impact on susceptibility to systemic sclerosis. Issue 1 (1st October 2020)

• Record Type: Journal Article
• Authors: Bossini-Castillo, Lara; Villanueva-Martin, Gonzalo; Kerick, Martin; Acosta-Herrera, Marialbert; López-Isac, Elena; Simeón, Carmen P; Ortego-Centeno, Norberto; Assassi, Shervin; Hunzelmann, Nicolas; Gabrielli, Armando; de Vries-Bouwstra, J K; Allanore, Yannick; Fonseca, Carmen; Denton, Christopher P; Radstake, Timothy RDJ; Alarcón-Riquelme, Marta Eugenia; Beretta, Lorenzo; Mayes, Maureen D; Martin, Javier
• Other Names: author non-byline.; Zochling J. author non-byline.; Sahhar J. author non-byline.; Roddy J. author non-byline.; Nash P. author non-byline.; Tymms K. author non-byline.; Rischmueller M. author non-byline.; Lester S. author non-byline.; author non-byline.; Belz Doreen author non-byline.; Ingegnoli Francesca author non-byline.; Gómez Yolanda Jimenez author non-byline.; Pedrera Chary Lopez author non-byline.; Lories Rik author non-byline.; Collantes-Estevez Eduardo author non-byline.; Montanelli Gaia author non-byline.; Piantoni Silvia author non-byline.; Pinto Ignasi Rodriguez author non-byline.; Vasconcelos Carlos author non-byline.; author non-byline.; Jamin Christophe author non-byline.; Marañón Concepción author non-byline.; Lann Lucas Le author non-byline.; Simon Quentin author non-byline.; Rouvière Bénédicte author non-byline.; Varela Nieves author non-byline.; Muchmore Brian author non-byline.; Dufour Aleksandra author non-byline.; Alvarez Montserrat author non-byline.; Cremer Jonathan author non-byline.; Barbarroja Nuria author non-byline.; Gerl Velia author non-byline.; Khodadadi Laleh author non-byline.; Cheng Qingyu author non-byline.; Buttgereit Anne author non-byline.; Groof Aurélie De author non-byline.; Ducreux Julie author non-byline.; Trombetta Elena author non-byline.; Li Tianlu author non-byline.; Alvarez-Errico Damiana author non-byline.; Witte Torsten author non-byline.; Kniesch Katja author non-byline.; Azevedo Nancy author non-byline.; Neves Esmeralda author non-byline.; Azevedo Nancy author non-byline.; Neves Esmeralda author non-byline.; Rao Sambasiva author non-byline.; Jouve Pierre-Emmanuel author non-byline.;
• Is Part Of: Annals of the rheumatic diseases. Volume 80:Issue 1(2021)
• Page Start: 118
• Page End: 127
• Publication Date: 2020-10-01
• Subjects: scleroderma -- systemic -- immune complex diseases -- autoimmune diseases; Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2020-218558
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : 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.