Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Patrick, Matthew; Stuart, Philip; Raja, Kalpana; Gudjonsson, Johann; Tejasvi, Trilokraj; Yang, Jingjing; Chandran, Vinod; Das, Sayantan; Callis-Duffin, Kristina; Ellinghaus, Eva; Enerbäck, Charlotta; Esko, Tõnu; Franke, Andre; Kang, Hyun; Krueger, Gerald; Lim, Henry; Rahman, Proton; Rosen, Cheryl; Weidinger, Stephan; Weichenthal, Michael; Wen, Xiaoquan; Voorhees, John; Abecasis, Gonçalo; Gladman, Dafna; Nair, Rajan; Elder, James; Tsoi, Lam
• Is Part Of: Nature communications. Volume 9:Issue 1(2018)
• Page Start: 1
• Page End: 10
• Publication Date: 2018-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-018-06672-6
• Languages: English
• ISSNs: 2041-1723

Abstract

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. 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.