Genomic Risk Score impact on susceptibility to systemic sclerosis
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Bossini Castillo, Lara María; Villanueva Martin, Gonzalo; Kerick, Martin; Acosta Herrera, Marialbert; López Isac, Elena; Ortego Centeno, Norberto; Alarcón Riquelme, Marta Eugenia; Martin, JavierEditorial
Bmj Publishing Group
Date
2020-10-01Referencia bibliográfica
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]
Sponsorship
Spanish 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-I00Abstract
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.