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.

快速诊断缓慢进展的疾病具有挑战性。为了优先考虑多种相关诊断，我们开发了 G-PROB（遗传概率工具），利用遗传风险评分来计算患者患不同疾病的概率。我们针对炎症性关节炎引起的疾病（类风湿性关节炎、系统性红斑狼疮、脊柱关节病、银屑病关节炎和痛风）测试了 G-PROB。对模拟数据进行验证后，我们在三个队列中测试了 G-PROB：通过 eMERGE 数据库中的国际疾病分类 (ICD) 代码识别的 1211 名患者、通过 Partners Biobank 中的 ICD 代码和病历审查识别的 245 名患者以及 243 名患者首先出现无法解释的炎症性关节炎，并通过合作伙伴生物库内的记录审查进行最终诊断。 G 概率与疾病状态的校准很高，回归系数从 0.90 到 1.08（1.00 是理想的）。 G 概率区分了三个队列的真实诊断，曲线下汇总面积 (95% CI) 分别为 0.69（0.67 至 0.71）、0.81（0.76 至 0.84）和 0.84（0.81 至 0.86）。对于所有患者，至少可以排除一种疾病，并且在 45% 的患者中，可能的诊断被确定为 64% 的阳性预测值。在 35% 的病例中，临床医生的初步诊断是错误的。初始临床诊断解释了最终疾病的 39% 差异，在添加 G 概率后，该差异改善至 51% ( P < 0.0001)。在临床就诊之前将基因型信息转换为五种不同炎症性关节炎的可解释概率值可能会用于提高临床实践中风湿性疾病的诊断效率。