Show simple item record

dc.contributor.authorBossini Castillo, Lara
dc.contributor.authorVillanueva Martin, Gonzalo
dc.contributor.authorKerick, Martin
dc.contributor.authorAcosta Herrera, Marialbert
dc.contributor.authorLópez Isac, Elena
dc.contributor.authorOrtego Centeno, Norberto 
dc.contributor.authorAlarcón Riquelme, Marta Eugenia 
dc.contributor.authorMartin, Javier
dc.identifier.citationBossini-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]es_ES
dc.description.abstractObjectives 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.es_ES
dc.description.sponsorshipSpanish Government RTI2018101332-B-100es_ES
dc.description.sponsorshipRed de Investigación en Inflamación y Enfermedades Reumáticas (RIER) from Instituto de Salud Carlos III RD16/0012/0013es_ES
dc.description.sponsorshipEU/EFPIA/Innovative Medicines Initiative Joint Undertaking PRECISESADS 115565es_ES
dc.description.sponsorshipSpanish Ministry of Science and Innovation through the Juan de la Cierva incorporation program IJC2018-035131-I IJC2018-038026-Ies_ES
dc.description.sponsorshipSpanish Ministry of Science and Innovation through the Ayudas para contratos predoctorales para la formación de doctores 2019 program RTI2018-101332-B-I00es_ES
dc.publisherBmj Publishing Groupes_ES
dc.rightsAtribución-NoComercial 3.0 España*
dc.titleGenomic Risk Score impact on susceptibility to systemic sclerosises_ES
dc.identifier.doi10. 1136/annrheumdis- 2020- 218558

Files in this item


This item appears in the following Collection(s)

Show simple item record

Atribución-NoComercial 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial 3.0 España