Nature Communications (Oct 2018)
Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients
- Matthew T. Patrick,
- Philip E. Stuart,
- Kalpana Raja,
- Johann E. Gudjonsson,
- Trilokraj Tejasvi,
- Jingjing Yang,
- Vinod Chandran,
- Sayantan Das,
- Kristina Callis-Duffin,
- Eva Ellinghaus,
- Charlotta Enerbäck,
- Tõnu Esko,
- Andre Franke,
- Hyun M. Kang,
- Gerald G. Krueger,
- Henry W. Lim,
- Proton Rahman,
- Cheryl F. Rosen,
- Stephan Weidinger,
- Michael Weichenthal,
- Xiaoquan Wen,
- John J. Voorhees,
- Gonçalo R. Abecasis,
- Dafna D. Gladman,
- Rajan P. Nair,
- James T. Elder,
- Lam C. Tsoi
Affiliations
- Matthew T. Patrick
- Department of Dermatology, University of Michigan Medical School
- Philip E. Stuart
- Department of Dermatology, University of Michigan Medical School
- Kalpana Raja
- Department of Dermatology, University of Michigan Medical School
- Johann E. Gudjonsson
- Department of Dermatology, University of Michigan Medical School
- Trilokraj Tejasvi
- Department of Dermatology, University of Michigan Medical School
- Jingjing Yang
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan
- Vinod Chandran
- Department of Medicine, Division of Rheumatology, University of Toronto
- Sayantan Das
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan
- Kristina Callis-Duffin
- Department of Dermatology, University of Utah
- Eva Ellinghaus
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel
- Charlotta Enerbäck
- Department of Dermatology, Linköping University
- Tõnu Esko
- Estonian Genome Center, University of Tartu
- Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel
- Hyun M. Kang
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan
- Gerald G. Krueger
- Department of Dermatology, University of Utah
- Henry W. Lim
- Department of Dermatology, Henry Ford Hospital
- Proton Rahman
- Memorial University
- Cheryl F. Rosen
- Division of Dermatology, Toronto Western Hospital, University of Toronto
- Stephan Weidinger
- Department of Dermatology, University Medical Center Schleswig-Holstein
- Michael Weichenthal
- Department of Dermatology, University Medical Center Schleswig-Holstein
- Xiaoquan Wen
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan
- John J. Voorhees
- Department of Dermatology, University of Michigan Medical School
- Gonçalo R. Abecasis
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan
- Dafna D. Gladman
- Department of Medicine, Division of Rheumatology, University of Toronto
- Rajan P. Nair
- Department of Dermatology, University of Michigan Medical School
- James T. Elder
- Department of Dermatology, University of Michigan Medical School
- Lam C. Tsoi
- Department of Dermatology, University of Michigan Medical School
- DOI
- https://doi.org/10.1038/s41467-018-06672-6
- Journal volume & issue
-
Vol. 9,
no. 1
pp. 1 – 10
Abstract
Approximately 30% of psoriasis patients develop psoriatic arthritis (PsA) and early diagnosis is crucial for the management of PsA. Here, Patrick et al. develop a computational pipeline involving statistical and machine-learning methods that can assess the risk of progression to PsA based on genetic markers.
