Using polygenic risk scores to aid diagnosis of patients with early inflammatory arthritis: results from the Norfolk Arthritis Register

Objectives
There is growing evidence that genetic data is of benefit in the rheumatology outpatient setting by aiding early diagnosis. A genetic probability tool (G-PROB) has been developed to aid diagnosis has not yet been tested in a real-world setting.

Our aim was to assess whether G-PROB could aid diagnosis in the rheumatology outpatient setting using data from the Norfolk Arthritis Register (NOAR), a prospective observational cohort of patients presenting with early inflammatory arthritis.

Methods
Genotypes and clinician diagnoses were obtained from patients from NOAR. Six G-probabilities (0-100%) were created for each patient based on known disease-associated odds ratios of published genetic risk variants, each corresponding to one disease of rheumatoid arthritis, systemic lupus erythematosus, psoriatic arthritis, spondyloarthropathy, gout or “other diseases”. Performance of the G-probabilities compared with clinician diagnosis was assessed.
Results
We tested G-PROB on 1,047 patients. Calibration of G-probabilities with clinician diagnosis was high, with regression coefficients of 1·047, where 1·00 is ideal. G-probabilities discriminated clinician diagnosis with pooled areas under the curve (95% CI) of 0·85 (0·84-0·86). G-probabilities <5% corresponded to a negative predictive value of 96·0% where it was possible to suggest >2 unlikely diseases for 94% of patients, and >3 for 53·7% of patients. G-probabilities >50% corresponded to a positive predictive value of 70·4%. In 55·7% of patients, the disease with the highest G-probability corresponded to clinician diagnosis.

Conclusions
G-PROB converts complex genetic information into meaningful, and interpretable conditional probabilities which may be especially helpful at eliminating unlikely diagnoses in the rheumatology outpatient setting.