Dataset Information

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

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.

SUBMITTER: Patrick MT

PROVIDER: S-EPMC6177414 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Patrick Matthew T MT Stuart Philip E PE Raja Kalpana K Gudjonsson Johann E JE Tejasvi Trilokraj T Yang Jingjing J Chandran Vinod V Das Sayantan S Callis-Duffin Kristina K Ellinghaus Eva E Enerbäck Charlotta C Esko Tõnu T Franke Andre A Kang Hyun M HM Krueger Gerald G GG Lim Henry W HW Rahman Proton P Rosen Cheryl F CF Weidinger Stephan S Weichenthal Michael M Wen Xiaoquan X Voorhees John J JJ Abecasis Gonçalo R GR Gladman Dafna D DD Nair Rajan P RP Elder James T JT Tsoi Lam C LC

Nature communications 20181009 1

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 psorias ...[more]

PMID: 30301895

Similar Datasets

Project description:IntroductionPsoriatic arthritis (PsA) is a chronic, inflammatory arthritis that affects an estimated 30% of patients with psoriasis. PsA is underdiagnosed in primary care and dermatology clinics due to a variety of reasons, including failure of healthcare providers to ask about symptoms, overlap of symptoms and signs with other rheumatologic conditions, and lack of a specific diagnostic test. A delay in PsA diagnosis and treatment, even as short as 6 months, can lead to decreased quality of life, increased joint damage, and worse long-term physical function. In this study, we sought to identify the clinical and genetic factors that help discriminate patients with PsA from those with cutaneous psoriasis only.MethodsWe analyzed a cohort of 974 psoriasis patients at an academic medical center, of whom 175 had confirmed PsA, and performed univariate, multivariate, and predictive modeling to determine factors associated with PsA.ResultsThe univariate analysis revealed significant positive associations of PsA with age, nail involvement, scalp involvement, skin fold involvement, elbow/knee involvement, psoriasis severity, plaque subtype, erythrodermic subtype, hypertension, type 2 diabetes, and coronary artery disease, and a significant negative association of PsA with the human leukocyte antigen (HLA)-C*06:02 allele. In the multivariate analysis, nail involvement, type 2 diabetes, and pustular psoriasis remained significantly associated with PsA, while HLA-C*06:02 positivity remained protective. There was a trend towards an association of PsA with older age, younger age of psoriasis onset, and skin fold involvement, while there was protective trend for smoking. A predictive model including both clinical and genetic factors showed reasonable discriminative ability between psoriasis and PsA, with an area under the curve of 0.87 for a receiver operating characteristic curve.ConclusionThis study identified a number of clinical and genetic features that could help stratify patients who are at higher risk for having PsA and for whom rheumatology referral may be beneficial.

Project description:ObjectivesPsoriatic arthritis (PsA) has a strong genetic component, and the identification of genetic risk factors could help identify the ~30% of psoriasis patients at high risk of developing PsA. Our objectives were to identify genetic risk factors and pathways that differentiate PsA from cutaneous-only psoriasis (PsC) and to evaluate the performance of PsA risk prediction models.MethodsGenome-wide meta-analyses were conducted separately for 5,065 patients with PsA and 21,286 healthy controls and separately for 4,340 patients with PsA and 6,431 patients with PsC. The heritability of PsA was calculated as a single-nucleotide polymorphism (SNP)-based heritability estimate (h2 SNP ) and biologic pathways that differentiate PsA from PsC were identified using Priority Index software. The generalizability of previously published PsA risk prediction pipelines was explored, and a risk prediction model was developed with external validation.ResultsWe identified a novel genome-wide significant susceptibility locus for the development of PsA on chromosome 22q11 (rs5754467; P = 1.61 × 10-9 ), and key pathways that differentiate PsA from PsC, including NF-κB signaling (adjusted P = 1.4 × 10-45 ) and Wnt signaling (adjusted P = 9.5 × 10-58 ). The heritability of PsA in this cohort was found to be moderate (h2 SNP = 0.63), which was similar to the heritability of PsC (h2 SNP = 0.61). We observed modest performance of published classification pipelines (maximum area under the curve 0.61), with similar performance of a risk model derived using the current data.ConclusionKey biologic pathways associated with the development of PsA were identified, but the investigation of risk classification revealed modest utility in the available data sets, possibly because many of the PsC patients included in the present study were receiving treatments that are also effective in PsA. Future predictive models of PsA should be tested in PsC patients recruited from primary care.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets