| Title: |
Using polygenic risk scores to aid diagnosis of patients with early inflammatory arthritis: results from the Norfolk Arthritis Register |
| Authors: |
Hum, Ryan Malcolm; Sharma, Seema; Stadler, Michael; Viatte, Sebastien; Ho, Pauline; Nair, Nisha; Shi, Chenfu; Yap, Chuan Fu; Soomro, Mehreen; Plant, Darren; Humphreys, Jenny; Macgregor, Alexander; Yates, Max; Verstappen, Suzanne; Barton, Anne; Bowes, John |
| Source: |
Hum, R M, Sharma, S, Stadler, M, Viatte, S, Ho, P, Nair, N, Shi, C, Yap, C F, Soomro, M, Plant, D, Humphreys, J, Macgregor, A, Yates, M, Verstappen, S, Barton, A, Bowes, J & NOAR collaborators 2024, 'Using polygenic risk scores to aid diagnosis of patients with early inflammatory arthritis: results from the Norfolk Arthritis Register', Arthritis and Rheumatology. https://doi.org/10.1002/art.42760 |
| Publication Year: |
2024 |
| Collection: |
The University of Manchester: Research Explorer - Publications |
| Subject Terms: |
Genetics; Diagnostics; Rheumatology; Inflammatory; Arthritis |
| Description: |
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 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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| ISSN: |
2326-5191; 2326-5205 |
| Relation: |
info:eu-repo/semantics/altIdentifier/pissn/2326-5191; info:eu-repo/semantics/altIdentifier/eissn/2326-5205 |
| DOI: |
10.1002/art.42760 |
| Availability: |
https://research.manchester.ac.uk/en/publications/8096ea21-658b-43c2-8b7b-7ec286655cfd; https://doi.org/10.1002/art.42760 |
| Rights: |
info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.A08718A6 |
| Database: |
BASE |