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Patrick, Matthew T; Stuart, Philip E; Raja, Kalpana; Gudjonsson, Johann E; Tejasvi, Trilokraj; Yang, Jingjing; Chandran, Vinod; Das, Sayantan; Callis-Duffin, Kristina; Ellinghaus, Eva; Enerbäck, Charlotta; Esko, Tõnu; Franke, Andre; Kang, Hyun M; Krueger, Gerald G; Lim, Henry W; Rahman, Proton; Rosen, Cheryl F; Weidinger, Stephan; Weichenthal, Michael; Wen, Xiaoquan; Voorhees, John J; Abecasis, Gonçalo R; Gladman, Dafna D; Nair, Rajan P; Elder, James T; Tsoi, Lam C
Nature communications, 10/2018, Volume: 9, Issue: 1Journal Article
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.
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