Sci Transl Med. 2020 May 27;12(545):eaay1548. doi: 10.1126/scitranslmed.aay1548. | PubMed

Rachel Knevel^1,2,3, Saskia le Cessie⁴, Chikashi C Terao^5,6,7, Kamil Slowikowski^3,8,9,10, Jing Cui¹, Tom W J Huizinga², Karen H Costenbader¹, Katherine P Liao^1,11, Elizabeth W Karlson¹, Soumya Raychaudhuri^{12,3,8,9,10,13}

1. Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
2. Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, Netherlands.
3. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
4. Department of Clinical Epidemiology and Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZA Leiden, Netherlands.
5. Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan.
6. Clinical Research Center, Shizuoka General Hospital, Shizuoka 230-0045, Japan.
7. Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 420-8527, Japan.
8. Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
9. Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
10. Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
11. Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA.
12. Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. soumya@broadinstitute.org.
13. Centre for Genetics and Genomics Versus Arthritis and Centre for Musculoskeletal Research, Manchester M13 9PL, UK.

Abstract

It is challenging to quickly diagnose slowly progressing diseases. To prioritize multiple related diagnoses, we developed G-PROB (Genetic Probability tool) to calculate the probability of different diseases for a patient using genetic risk scores. We tested G-PROB for inflammatory arthritis-causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis, and gout). After validating on simulated data, we tested G-PROB in three cohorts: 1211 patients identified by International Classification of Diseases (ICD) codes within the eMERGE database, 245 patients identified through ICD codes and medical record review within the Partners Biobank, and 243 patients first presenting with unexplained inflammatory arthritis and with final diagnoses by record review within the Partners Biobank. Calibration of G-probabilities with disease status was high, with regression coefficients from 0.90 to 1.08 (1.00 is ideal). G-probabilities discriminated true diagnoses across the three cohorts with pooled areas under the curve (95% CI) of 0.69 (0.67 to 0.71), 0.81 (0.76 to 0.84), and 0.84 (0.81 to 0.86), respectively. For all patients, at least one disease could be ruled out, and in 45% of patients, a likely diagnosis was identified with a 64% positive predictive value. In 35% of cases, the clinician's initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease, which improved to 51% (P < 0.0001) after adding G-probabilities. Converting genotype information before a clinical visit into an interpretable probability value for five different inflammatory arthritides could potentially be used to improve the diagnostic efficiency of rheumatic diseases in clinical practice.

Presented By Rachel Knevel | ORCID iD