Publications by Year: 2020

Knevel R, le Cessie S, Terao CC, Slowikowski K, Cui J, Huizinga TWJ, Costenbader KH, Liao KP, Karlson EW, Raychaudhuri S. Using genetics to prioritize diagnoses for rheumatology outpatients with inflammatory arthritis [Internet]. Science Translational Medicine 2020;12(545) Full TextAbstract
Multiple slowly progressing diseases initially present with inflammatory arthritis, and it can be difficult to clinically differentiate these conditions. Knevel et al. show that genetic data could be used to triage inflammatory arthritis–causing diagnoses at a patient’s first visit, improving the likelihood of a correct initial diagnosis and potentially expediting appropriate treatment. Their genetic diagnostic tool, here optimized for rheumatic disease diagnosis, could, in principle, be calibrated for other phenotypically similar diseases with different underlying genetics.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.
Gutierrez-Arcelus M#, Baglaenko Y#, Arora J, Hannes S, Luo Y, Amariuta T, Teslovich N, Rao DA, Ermann J, Jonsson AH, for Consortium NHLBIT-OPM (TOPM), Navarrete C, Rich SS, Taylor KD, Rotter JI, Gregersen PK, Esko T, Brenner MB, Raychaudhuri S. Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci [Internet]. Nature Genetics 2020;52:247-253. Publisher's Version
Slowikowski K#, Nguyen# HN, Noss EH, Simmons DP, Mizoguchi F, Watts GFM, Gurish MF, Brenner MB*, Raychaudhuri S*. CUX1 and IκBζ (NFKBIZ) mediate the synergistic inflammatory response to TNF and IL-17A in stromal fibroblasts. [Internet]. Proc Natl Acad Sci. 2020;117(10):5532-5541. Publisher's Version