Comparative Genetic Analysis of Psoriatic Arthritis and Psoriasis for the Discovery of Genetic Risk Factors and Risk Prediction Modeling

Abstract

Objectives: Psoriatic 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.

Methods: Genome-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.

Results: We 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.

Conclusion: Key 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.

Figure 1

Manhattan plots showing the P values of genome‐wide significance from the meta‐analysis of summary statistics obtained from psoriatic arthritis (PsA) patients compared to population controls (top), and PsA patients compared to cutaneous‐only psoriasis (PsC) patients (bottom). The genome‐wide significance threshold was set at P = 5 × 10⁻⁸ and is indicated by the dashed lines. Each dot represents a single‐nucleotide polymorphism (SNP). Red dots indicate the most significant SNPs in both data sets.

Figure 2

Receiver operating characteristic (ROC) curves showing the sensitivity and specificity of the random forest (RF) and the conditional inference forest (CF) machine learning algorithms in discriminating between psoriatic arthritis (PsA) and cutaneous‐only psoriasis (PsC). Both the RF and CF models showed modest performance across the PsA‐Biomarkers of Systemic Treatment Outcomes in Psoriasis (PsA‐BSTOP) study data set (A) and the UK Biobank International Statistical Classification of Diseases and Related Health Problems, Tenth Revision data set (B). The Concordance statistic for each model was <0.6 by external validation.