Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• Nature communications, 2018-10, Vol.9 (1), p.4178-10, Article 4178

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2018

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.