Genomic Risk Score impact on susceptibility to systemic sclerosis
© 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..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:80 |
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| Enthalten in: |
Annals of the rheumatic diseases - 80(2021), 1 vom: 01. Jan., Seite 118-127 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bossini-Castillo, Lara [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 08.02.2021 Date Revised 07.12.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1136/annrheumdis-2020-218558 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM315747358 |
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| 100 | 1 | |a Bossini-Castillo, Lara |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Genomic Risk Score impact on susceptibility to systemic sclerosis |
| 264 | 1 | |c 2021 | |
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| 500 | |a Date Revised 07.12.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 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. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a autoimmune diseases | |
| 650 | 4 | |a immune complex diseases | |
| 650 | 4 | |a scleroderma | |
| 650 | 4 | |a systemic | |
| 650 | 7 | |a Antibodies, Antinuclear |2 NLM | |
| 650 | 7 | |a Autoantibodies |2 NLM | |
| 650 | 7 | |a anticentromere antibody |2 NLM | |
| 650 | 7 | |a DNA Topoisomerases |2 NLM | |
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| 700 | 1 | |a Kerick, Martin |e verfasserin |4 aut | |
| 700 | 1 | |a Acosta-Herrera, Marialbert |e verfasserin |4 aut | |
| 700 | 1 | |a López-Isac, Elena |e verfasserin |4 aut | |
| 700 | 1 | |a Simeón, Carmen P |e verfasserin |4 aut | |
| 700 | 1 | |a Ortego-Centeno, Norberto |e verfasserin |4 aut | |
| 700 | 1 | |a Assassi, Shervin |e verfasserin |4 aut | |
| 700 | 0 | |a International SSc Group |e verfasserin |4 aut | |
| 700 | 0 | |a Australian Scleroderma Interest Group (ASIG) |e verfasserin |4 aut | |
| 700 | 0 | |a PRECISESADS Clinical Consortium |e verfasserin |4 aut | |
| 700 | 0 | |a PRECISESADS Flow Cytometry study group |e verfasserin |4 aut | |
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| 700 | 1 | |a Rischmueller, M |e investigator |4 oth | |
| 700 | 1 | |a Lester, S |e investigator |4 oth | |
| 700 | 1 | |a Belz, Doreen |e investigator |4 oth | |
| 700 | 1 | |a Ingegnoli, Francesca |e investigator |4 oth | |
| 700 | 1 | |a Gómez, Yolanda Jimenez |e investigator |4 oth | |
| 700 | 1 | |a Pedrera, Chary Lopez |e investigator |4 oth | |
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| 700 | 1 | |a Cremer, Jonathan |e investigator |4 oth | |
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