Journal Article

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

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

2018

Overview

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.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 45/43 / 631/114/1305 / 631/114/2413 / 631/208/205/2138 / 692/4023/1670/2766/1900 / Arthritis / Arthritis, Psoriatic - genetics / Biomarkers - metabolism / Cohort Studies / Computer applications / Discriminant analysis / Enhancer Elements, Genetic - genetics / Gene Expression Profiling / Genetic Loci / Genetic markers / Humanities and Social Sciences / Humans / Learning algorithms / Machine learning / Meta-Analysis as Topic / multidisciplinary / Patients / Predictions / Psoriasis / Psoriatic arthritis / Risk Assessment / Science / Science (multidisciplinary) / Shrinkage / Skin diseases / Statistical analysis / Statistical inference