Genomic Risk Score impact on susceptibility to systemic sclerosis
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AutorBossini Castillo, Lara; Villanueva Martin, Gonzalo; Kerick, Martin; Acosta Herrera, Marialbert; López Isac, Elena; Ortego Centeno, Norberto; Alarcón Riquelme, Marta Eugenia; Martin, Javier
Bmj Publishing Group
Bossini-Castillo, L., Villanueva-Martin, G., Kerick, M., Acosta-Herrera, M., López-Isac, E., Simeón, C. P., ... & PRECISESADS Flow Cytometry study group. (2021). Genomic Risk Score impact on susceptibility to systemic sclerosis. Annals of the Rheumatic Diseases, 80(1), 118-127. [doi: 10. 1136/annrheumdis- 2020- 218558]
PatrocinadorSpanish Government RTI2018101332-B-100; Red de Investigación en Inflamación y Enfermedades Reumáticas (RIER) from Instituto de Salud Carlos III RD16/0012/0013; EU/EFPIA/Innovative Medicines Initiative Joint Undertaking PRECISESADS 115565; Spanish Ministry of Science and Innovation through the Juan de la Cierva incorporation program IJC2018-035131-I IJC2018-038026-I; Spanish Ministry of Science and Innovation through the Ayudas para contratos predoctorales para la formación de doctores 2019 program RTI2018-101332-B-I00
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.