Literature DB >> 33004331

Genomic Risk Score impact on susceptibility to systemic sclerosis.

Lara Bossini-Castillo1, Gonzalo Villanueva-Martin2, Martin Kerick2, Marialbert Acosta-Herrera2, Elena López-Isac2, Carmen P Simeón3, Norberto Ortego-Centeno4, Shervin Assassi5, Nicolas Hunzelmann6, Armando Gabrielli7, J K de Vries-Bouwstra8, Yannick Allanore9, Carmen Fonseca10, Christopher P Denton10, Timothy Rdj Radstake11, Marta Eugenia Alarcón-Riquelme12, Lorenzo Beretta13, Maureen D Mayes5, Javier Martin14.   

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. © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  autoimmune diseases; immune complex diseases; scleroderma; systemic

Mesh:

Substances:

Year:  2020        PMID: 33004331     DOI: 10.1136/annrheumdis-2020-218558

Source DB:  PubMed          Journal:  Ann Rheum Dis        ISSN: 0003-4967            Impact factor:   19.103


  4 in total

Review 1.  A Summary on the Genetics of Systemic Lupus Erythematosus, Rheumatoid Arthritis, Systemic Sclerosis, and Sjögren's Syndrome.

Authors:  Lourdes Ortíz-Fernández; Javier Martín; Marta E Alarcón-Riquelme
Journal:  Clin Rev Allergy Immunol       Date:  2022-06-24       Impact factor: 8.667

Review 2.  Construction and Application of Polygenic Risk Scores in Autoimmune Diseases.

Authors:  Chachrit Khunsriraksakul; Havell Markus; Nancy J Olsen; Laura Carrel; Bibo Jiang; Dajiang J Liu
Journal:  Front Immunol       Date:  2022-06-27       Impact factor: 8.786

Review 3.  Animal Models of Systemic Sclerosis: Using Nailfold Capillaroscopy as a Potential Tool to Evaluate Microcirculation and Microangiopathy: A Narrative Review.

Authors:  Angélica Mandujano; Melissa Golubov
Journal:  Life (Basel)       Date:  2022-05-08

Review 4.  Approaching Shared Pathophysiology in Immune-Mediated Diseases through Functional Genomics.

Authors:  David González-Serna; Gonzalo Villanueva-Martin; Marialbert Acosta-Herrera; Ana Márquez; Javier Martín
Journal:  Genes (Basel)       Date:  2020-12-09       Impact factor: 4.096

  4 in total

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