Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

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

ABSTRACT

Psoriatic arthritis (PsA) is a complex chronic musculoskeletal condition that occurs in ~30% of psoriasis patients. Currently, no systematic strategy is available that utilizes the differences in genetic architecture between PsA and cutaneous-only psoriasis (PsC) to assess PsA risk before symptoms appear. Here, we introduce a computational pipeline for predicting PsA among psoriasis patients using data from six cohorts with >7000 genotyped PsA and PsC patients. We identify 9 new loci for psoriasis or its subtypes and achieve 0.82 area under the receiver operator curve in distinguishing PsA vs. PsC when using 200 genetic markers. Among the top 5% of our PsA prediction we achieve >90% precision with 100% specificity and 16% recall for predicting PsA among psoriatic patients, using conditional inference forest or shrinkage discriminant analysis. Combining statistical and machine-learning techniques, we show that the underlying genetic differences between psoriasis subtypes can be used for individualized subtype risk assessment. More... »

PAGES

4178

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41467-018-06672-6

    DOI

    http://dx.doi.org/10.1038/s41467-018-06672-6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1107377866

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30301895


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