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

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
Year of publication:
2018
Volume:
9
Issue:
1
Issn:
2041-1723
Journal title abbreviated:
NAT COMMUN
Journal title long:
Nature communications
Impact factor:
12.121
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.