RT Journal Article
T1 Genomic Risk Score impact on susceptibility to systemic sclerosis.
A1 Bossini-Castillo, Lara
A1 Villanueva-Martin, Gonzalo
A1 Kerick, Martin
A1 Acosta-Herrera, Marialbert
A1 López-Isac, Elena
A1 Simeón, Carmen P
A1 Ortego-Centeno, Norberto
A1 Assassi, Shervin
A1 International SSc Group, 
A1 Australian Scleroderma Interest Group (ASIG), 
A1 PRECISESADS Clinical Consortium, 
A1 PRECISESADS Flow Cytometry study group, 
A1 Hunzelmann, Nicolas
A1 Gabrielli, Armando
A1 de Vries-Bouwstra, J K
A1 Allanore, Yannick
A1 Fonseca, Carmen
A1 Denton, Christopher P
A1 Radstake, Timothy Rdj
A1 Alarcón-Riquelme, Marta Eugenia
A1 Beretta, Lorenzo
A1 Mayes, Maureen D
A1 Martin, Javier
K1 autoimmune diseases
K1 immune complex diseases
K1 scleroderma
K1 systemic
AB 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. 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. 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 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.
YR 2020
FD 2020-10-01
LK http://hdl.handle.net/10668/16358
UL http://hdl.handle.net/10668/16358
LA en
DS RISalud
RD Apr 7, 2025