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Evaluation of a Genetic Risk Score for Diagnosis of Psoriatic Arthritis


J Psoriasis Psoriatic Arthritis. 2020 Apr;5(2):61-67. doi: 10.1177/2475530320910814.Epub 2020 Mar 4.

Mary Patricia Smith 1Karen Ly 1Quinn Thibodeaux 1Kristen Beck 1Eric Yang 1 2Isabelle Sanchez 1 3Joanne Nititham 1Tina Bhutani 1Wilson Liao 1

Author Information

1 Department of Dermatology, University of California, San Francisco, CA, USA.

2 Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA.

3 Department of Dermatology, University of Illinois at Chicago, IL, USA.


Background: Diagnosis of psoriatic arthritis (PsA) can be challenging, resulting in delays that contribute to irreversible joint damage, reduced quality of life, and increased mortality.

Objective: Use genetic markers to develop and evaluate a PsA genetic risk score (GRS) for its ability to discriminate between psoriasis (PsO) only and PsO with PsA among a psoriatic cohort with full genome-wide genotype data.

Methods: Genome-wide single-nucleotide polymorphism genotyping was performed on 724 psoriatic patients. A set of 11 candidate risk genes previously shown to be preferentially associated with PsO or PsA were selected. To evaluate the cumulative effects of these risk loci, a PsA GRS was developed using an unweighted risk allele count (cGRS) and a weighted (wGRS) approach. Additional analyses included only human leukocyte antigen (HLA) risk alleles.

Results: The discriminative power attributable to each GRS was evaluated by calculating the areas under the receiver operator characteristic curve (AUROC). The AUROC for the wGRS is 56.2% versus 54.1% for the cGRS, and the AUROC for the HLA-only wGRS model was 56.9% versus 55.7% for the HLA-only cGRS.

Conclusion: The AUROC of 56.9% for HLA-only wGRS indicates that this approach has the greatest power in discriminating PsA from PsO among these models. Given that an AUROC of 56.9% is quite modest, this study suggests that using a small number of well-validated genetic loci provides limited predictive power for PsA, and that future approaches may benefit from using a larger number of genetic loci.