Comparative Genetic Analysis of Psoriatic Arthritis and Psoriasis for the Discovery of Genetic Risk Factors and Risk Prediction Modeling

BADBIR Study Group

doi:10.1002/art.42154

Comparative Genetic Analysis of Psoriatic Arthritis and Psoriasis for the Discovery of Genetic Risk Factors and Risk Prediction Modeling

BADBIR Study Group

Research output: Contribution to journal › Article › peer-review

19 Citations (SciVal)

Abstract

Objectives: Psoriatic arthritis (PsA) has a strong genetic component, and the identification of genetic risk factors could help identify the ~30% of psoriasis patients at high risk of developing PsA. Our objectives were to identify genetic risk factors and pathways that differentiate PsA from cutaneous-only psoriasis (PsC) and to evaluate the performance of PsA risk prediction models. Methods: Genome-wide meta-analyses were conducted separately for 5,065 patients with PsA and 21,286 healthy controls and separately for 4,340 patients with PsA and 6,431 patients with PsC. The heritability of PsA was calculated as a single-nucleotide polymorphism (SNP)–based heritability estimate (h ² _SNP) and biologic pathways that differentiate PsA from PsC were identified using Priority Index software. The generalizability of previously published PsA risk prediction pipelines was explored, and a risk prediction model was developed with external validation. Results: We identified a novel genome-wide significant susceptibility locus for the development of PsA on chromosome 22q11 (rs5754467; P = 1.61 × 10 ⁻⁹), and key pathways that differentiate PsA from PsC, including NF-κB signaling (adjusted P = 1.4 × 10 ⁻⁴⁵) and Wnt signaling (adjusted P = 9.5 × 10 ⁻⁵⁸). The heritability of PsA in this cohort was found to be moderate (h ² _SNP = 0.63), which was similar to the heritability of PsC (h ² _SNP = 0.61). We observed modest performance of published classification pipelines (maximum area under the curve 0.61), with similar performance of a risk model derived using the current data. Conclusion: Key biologic pathways associated with the development of PsA were identified, but the investigation of risk classification revealed modest utility in the available data sets, possibly because many of the PsC patients included in the present study were receiving treatments that are also effective in PsA. Future predictive models of PsA should be tested in PsC patients recruited from primary care.

Original language	English
Pages (from-to)	1535-1543
Number of pages	9
Journal	Arthritis & Rheumatology
Volume	74
Issue number	9
Early online date	4 May 2022
DOIs	https://doi.org/10.1002/art.42154
Publication status	Published - 30 Sept 2022

Bibliographical note

Funding Information:
Supported by Versus Arthritis (grants 21173, 21754, and 21755), the NIHR Cambridge Biomedical Research Centre (grant BRC‐1215‐20014), and Cambridge Arthritis Research Endeavour (CARE). Mr. Stadler's work was supported by an MRC Doctoral Training Partnership (DTP) Studentship (grant MR/N013751/1). Dr. Jalali‐najafabadi's work was supported by a Medical Research Council (MRC)/University of Manchester Skills Development Fellowship (grant MR/R016615). Dr. Marzo‐Ortega's work was supported by the Leeds NIHR Biomedical Research Centre (LBRC). Dr. Warren's work was supported by the Manchester NIHR Biomedical Research Centre. Dr. Barton's work was supported by the NIHR.

Funding Information:
We are grateful for the assistance given by The University of Manchester IT Services and for the use of the Computational Shared Facility. We thank all of the patient participants and acknowledge the enthusiastic collaboration of all clinicians and research teams in the UK and the Republic of Ireland who recruited for this study. We gratefully acknowledge the substantial contribution to administration of this project by the following members of the Data Monitoring Committee (DMC) of the BADBIR Study Group: Dr. Robert Chalmers, Dr. Carsten Flohr (Chair), Dr. Karen Watson, and David Prieto-Merino. We also thank the following members of the BADBIR Steering Committee: Oras Alabas, Professor Jonathan Barker, Gabrielle Becher, Anthony Bewley, David Burden, Simon Morrison, Professor Phil Laws (Chair), Mr. Ian Evans, Professor Christopher Griffiths, Shehnaz Ahmed, Dr. Brian Kirby, Elise Kleyn, Ms. Linda Lawson, Teena Mackenzie, Tess McPherson, Dr. Kathleen McElhone, Dr. Ruth Murphy, Professor Anthony Ormerod, Dr. Caroline Owen, Professor Nick Reynolds, Amir Rashid, Professor Catherine Smith, and Dr. Richard Warren. We are grateful to the following members of the BSTOP Steering Committee for their valuable role in the oversight of the study delivery: Professor David Burden (Chair), Professor Catherine Smith, Professor Stefan Siebert, Professor Sara Brown, Helen McAteer, Dr. Julia Schofield, and Dr. Nick Dand. Finally, we acknowledge the enthusiastic collaboration of all the dermatologists and specialist nurses in the UK and the Republic of Ireland who provided the BADBIR and BSTOP data. The principal investigators at the participating sites can be found at the following website: http://www.badbir.org/Clinicians/. Open access funding was enabled and organized by Projekt DEAL.

Publisher Copyright:
© 2022 The Authors. Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.

ASJC Scopus subject areas

Immunology and Allergy
Rheumatology
Immunology

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10.1002/art.42154Licence: CC BY

Cite this

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title = "Comparative Genetic Analysis of Psoriatic Arthritis and Psoriasis for the Discovery of Genetic Risk Factors and Risk Prediction Modeling",

abstract = "Objectives: Psoriatic arthritis (PsA) has a strong genetic component, and the identification of genetic risk factors could help identify the ~30% of psoriasis patients at high risk of developing PsA. Our objectives were to identify genetic risk factors and pathways that differentiate PsA from cutaneous-only psoriasis (PsC) and to evaluate the performance of PsA risk prediction models. Methods: Genome-wide meta-analyses were conducted separately for 5,065 patients with PsA and 21,286 healthy controls and separately for 4,340 patients with PsA and 6,431 patients with PsC. The heritability of PsA was calculated as a single-nucleotide polymorphism (SNP)–based heritability estimate (h 2 SNP) and biologic pathways that differentiate PsA from PsC were identified using Priority Index software. The generalizability of previously published PsA risk prediction pipelines was explored, and a risk prediction model was developed with external validation. Results: We identified a novel genome-wide significant susceptibility locus for the development of PsA on chromosome 22q11 (rs5754467; P = 1.61 × 10 −9), and key pathways that differentiate PsA from PsC, including NF-κB signaling (adjusted P = 1.4 × 10 −45) and Wnt signaling (adjusted P = 9.5 × 10 −58). The heritability of PsA in this cohort was found to be moderate (h 2 SNP = 0.63), which was similar to the heritability of PsC (h 2 SNP = 0.61). We observed modest performance of published classification pipelines (maximum area under the curve 0.61), with similar performance of a risk model derived using the current data. Conclusion: Key biologic pathways associated with the development of PsA were identified, but the investigation of risk classification revealed modest utility in the available data sets, possibly because many of the PsC patients included in the present study were receiving treatments that are also effective in PsA. Future predictive models of PsA should be tested in PsC patients recruited from primary care. ",

author = "{BADBIR Study Group} and Mehreen Soomro and Michael Stadler and Nick Dand and James Bluett and Deepak Jadon and Farideh Jalali-Najafabadi and Michael Duckworth and Pauline Ho and Helena Marzo-Ortega and Helliwell, {Philip S} and Ryan, {Anthony W} and David Kane and Eleanor Korendowych and Simpson, {Michael A} and Jonathan Packham and Ross McManus and Cem Gabay and C{\'e}line Lamacchia and Nissen, {Michael J} and Brown, {Matthew A} and Verstappen, {Suzanne M M} and {Van Staa}, Tjeerd and Barker, {Jonathan N} and Smith, {Catherine H} and Oliver FitzGerald and Neil McHugh and Warren, {Richard B} and John Bowes and Anne Barton",

note = "Funding Information: Supported by Versus Arthritis (grants 21173, 21754, and 21755), the NIHR Cambridge Biomedical Research Centre (grant BRC‐1215‐20014), and Cambridge Arthritis Research Endeavour (CARE). Mr. Stadler's work was supported by an MRC Doctoral Training Partnership (DTP) Studentship (grant MR/N013751/1). Dr. Jalali‐najafabadi's work was supported by a Medical Research Council (MRC)/University of Manchester Skills Development Fellowship (grant MR/R016615). Dr. Marzo‐Ortega's work was supported by the Leeds NIHR Biomedical Research Centre (LBRC). Dr. Warren's work was supported by the Manchester NIHR Biomedical Research Centre. Dr. Barton's work was supported by the NIHR. Funding Information: We are grateful for the assistance given by The University of Manchester IT Services and for the use of the Computational Shared Facility. We thank all of the patient participants and acknowledge the enthusiastic collaboration of all clinicians and research teams in the UK and the Republic of Ireland who recruited for this study. We gratefully acknowledge the substantial contribution to administration of this project by the following members of the Data Monitoring Committee (DMC) of the BADBIR Study Group: Dr. Robert Chalmers, Dr. Carsten Flohr (Chair), Dr. Karen Watson, and David Prieto-Merino. We also thank the following members of the BADBIR Steering Committee: Oras Alabas, Professor Jonathan Barker, Gabrielle Becher, Anthony Bewley, David Burden, Simon Morrison, Professor Phil Laws (Chair), Mr. Ian Evans, Professor Christopher Griffiths, Shehnaz Ahmed, Dr. Brian Kirby, Elise Kleyn, Ms. Linda Lawson, Teena Mackenzie, Tess McPherson, Dr. Kathleen McElhone, Dr. Ruth Murphy, Professor Anthony Ormerod, Dr. Caroline Owen, Professor Nick Reynolds, Amir Rashid, Professor Catherine Smith, and Dr. Richard Warren. We are grateful to the following members of the BSTOP Steering Committee for their valuable role in the oversight of the study delivery: Professor David Burden (Chair), Professor Catherine Smith, Professor Stefan Siebert, Professor Sara Brown, Helen McAteer, Dr. Julia Schofield, and Dr. Nick Dand. Finally, we acknowledge the enthusiastic collaboration of all the dermatologists and specialist nurses in the UK and the Republic of Ireland who provided the BADBIR and BSTOP data. The principal investigators at the participating sites can be found at the following website: http://www.badbir.org/Clinicians/. Open access funding was enabled and organized by Projekt DEAL. Publisher Copyright: {\textcopyright} 2022 The Authors. Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.",

year = "2022",

month = sep,

day = "30",

doi = "10.1002/art.42154",

language = "English",

volume = "74",

pages = "1535--1543",

journal = "Arthritis & Rheumatology",

issn = "2326-5191",

publisher = "John Wiley and Sons Inc.",

number = "9",

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TY - JOUR

T1 - Comparative Genetic Analysis of Psoriatic Arthritis and Psoriasis for the Discovery of Genetic Risk Factors and Risk Prediction Modeling

AU - BADBIR Study Group

AU - Soomro, Mehreen

AU - Stadler, Michael

AU - Dand, Nick

AU - Bluett, James

AU - Jadon, Deepak

AU - Jalali-Najafabadi, Farideh

AU - Duckworth, Michael

AU - Ho, Pauline

AU - Marzo-Ortega, Helena

AU - Helliwell, Philip S

AU - Ryan, Anthony W

AU - Kane, David

AU - Korendowych, Eleanor

AU - Simpson, Michael A

AU - Packham, Jonathan

AU - McManus, Ross

AU - Gabay, Cem

AU - Lamacchia, Céline

AU - Nissen, Michael J

AU - Brown, Matthew A

AU - Verstappen, Suzanne M M

AU - Van Staa, Tjeerd

AU - Barker, Jonathan N

AU - Smith, Catherine H

AU - FitzGerald, Oliver

AU - McHugh, Neil

AU - Warren, Richard B

AU - Bowes, John

AU - Barton, Anne

N1 - Funding Information: Supported by Versus Arthritis (grants 21173, 21754, and 21755), the NIHR Cambridge Biomedical Research Centre (grant BRC‐1215‐20014), and Cambridge Arthritis Research Endeavour (CARE). Mr. Stadler's work was supported by an MRC Doctoral Training Partnership (DTP) Studentship (grant MR/N013751/1). Dr. Jalali‐najafabadi's work was supported by a Medical Research Council (MRC)/University of Manchester Skills Development Fellowship (grant MR/R016615). Dr. Marzo‐Ortega's work was supported by the Leeds NIHR Biomedical Research Centre (LBRC). Dr. Warren's work was supported by the Manchester NIHR Biomedical Research Centre. Dr. Barton's work was supported by the NIHR. Funding Information: We are grateful for the assistance given by The University of Manchester IT Services and for the use of the Computational Shared Facility. We thank all of the patient participants and acknowledge the enthusiastic collaboration of all clinicians and research teams in the UK and the Republic of Ireland who recruited for this study. We gratefully acknowledge the substantial contribution to administration of this project by the following members of the Data Monitoring Committee (DMC) of the BADBIR Study Group: Dr. Robert Chalmers, Dr. Carsten Flohr (Chair), Dr. Karen Watson, and David Prieto-Merino. We also thank the following members of the BADBIR Steering Committee: Oras Alabas, Professor Jonathan Barker, Gabrielle Becher, Anthony Bewley, David Burden, Simon Morrison, Professor Phil Laws (Chair), Mr. Ian Evans, Professor Christopher Griffiths, Shehnaz Ahmed, Dr. Brian Kirby, Elise Kleyn, Ms. Linda Lawson, Teena Mackenzie, Tess McPherson, Dr. Kathleen McElhone, Dr. Ruth Murphy, Professor Anthony Ormerod, Dr. Caroline Owen, Professor Nick Reynolds, Amir Rashid, Professor Catherine Smith, and Dr. Richard Warren. We are grateful to the following members of the BSTOP Steering Committee for their valuable role in the oversight of the study delivery: Professor David Burden (Chair), Professor Catherine Smith, Professor Stefan Siebert, Professor Sara Brown, Helen McAteer, Dr. Julia Schofield, and Dr. Nick Dand. Finally, we acknowledge the enthusiastic collaboration of all the dermatologists and specialist nurses in the UK and the Republic of Ireland who provided the BADBIR and BSTOP data. The principal investigators at the participating sites can be found at the following website: http://www.badbir.org/Clinicians/. Open access funding was enabled and organized by Projekt DEAL. Publisher Copyright: © 2022 The Authors. Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.

PY - 2022/9/30

Y1 - 2022/9/30

N2 - Objectives: Psoriatic arthritis (PsA) has a strong genetic component, and the identification of genetic risk factors could help identify the ~30% of psoriasis patients at high risk of developing PsA. Our objectives were to identify genetic risk factors and pathways that differentiate PsA from cutaneous-only psoriasis (PsC) and to evaluate the performance of PsA risk prediction models. Methods: Genome-wide meta-analyses were conducted separately for 5,065 patients with PsA and 21,286 healthy controls and separately for 4,340 patients with PsA and 6,431 patients with PsC. The heritability of PsA was calculated as a single-nucleotide polymorphism (SNP)–based heritability estimate (h 2 SNP) and biologic pathways that differentiate PsA from PsC were identified using Priority Index software. The generalizability of previously published PsA risk prediction pipelines was explored, and a risk prediction model was developed with external validation. Results: We identified a novel genome-wide significant susceptibility locus for the development of PsA on chromosome 22q11 (rs5754467; P = 1.61 × 10 −9), and key pathways that differentiate PsA from PsC, including NF-κB signaling (adjusted P = 1.4 × 10 −45) and Wnt signaling (adjusted P = 9.5 × 10 −58). The heritability of PsA in this cohort was found to be moderate (h 2 SNP = 0.63), which was similar to the heritability of PsC (h 2 SNP = 0.61). We observed modest performance of published classification pipelines (maximum area under the curve 0.61), with similar performance of a risk model derived using the current data. Conclusion: Key biologic pathways associated with the development of PsA were identified, but the investigation of risk classification revealed modest utility in the available data sets, possibly because many of the PsC patients included in the present study were receiving treatments that are also effective in PsA. Future predictive models of PsA should be tested in PsC patients recruited from primary care.

AB - Objectives: Psoriatic arthritis (PsA) has a strong genetic component, and the identification of genetic risk factors could help identify the ~30% of psoriasis patients at high risk of developing PsA. Our objectives were to identify genetic risk factors and pathways that differentiate PsA from cutaneous-only psoriasis (PsC) and to evaluate the performance of PsA risk prediction models. Methods: Genome-wide meta-analyses were conducted separately for 5,065 patients with PsA and 21,286 healthy controls and separately for 4,340 patients with PsA and 6,431 patients with PsC. The heritability of PsA was calculated as a single-nucleotide polymorphism (SNP)–based heritability estimate (h 2 SNP) and biologic pathways that differentiate PsA from PsC were identified using Priority Index software. The generalizability of previously published PsA risk prediction pipelines was explored, and a risk prediction model was developed with external validation. Results: We identified a novel genome-wide significant susceptibility locus for the development of PsA on chromosome 22q11 (rs5754467; P = 1.61 × 10 −9), and key pathways that differentiate PsA from PsC, including NF-κB signaling (adjusted P = 1.4 × 10 −45) and Wnt signaling (adjusted P = 9.5 × 10 −58). The heritability of PsA in this cohort was found to be moderate (h 2 SNP = 0.63), which was similar to the heritability of PsC (h 2 SNP = 0.61). We observed modest performance of published classification pipelines (maximum area under the curve 0.61), with similar performance of a risk model derived using the current data. Conclusion: Key biologic pathways associated with the development of PsA were identified, but the investigation of risk classification revealed modest utility in the available data sets, possibly because many of the PsC patients included in the present study were receiving treatments that are also effective in PsA. Future predictive models of PsA should be tested in PsC patients recruited from primary care.

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Comparative Genetic Analysis of Psoriatic Arthritis and Psoriasis for the Discovery of Genetic Risk Factors and Risk Prediction Modeling

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