Genetic risk factors and clinical manifestations of systemic lupus erythematosus: Large-scale analysis of genetic predisposition and disease subtypes.
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Reid S, Sandling JK, Pucholt P, Sayadi A, Frodlund M, Lerang K, Gunnarsson I, Jönsen A, Syvänen AC, Molberg Ø, Rantapää-Dahlqvist S, Rudin A, Sjöwall C, Svenungsson E, Bengtsson AA, Rönnblom L, Leonard D
J. Intern. Med. 299 (1) 95-108 [2026-01-00; online 2025-11-07]
Systemic lupus erythematosus (SLE) is an autoimmune disease with a heterogenous clinical picture. This study aimed to link genetic SLE predisposition with relevant clinical manifestations. Datasets best corresponding to the 11 American College of Rheumatology 1982 (ACR-82) classification criteria for SLE in a large, public database (FinnGen consortium, >218,000 individuals) were identified. Mendelian randomization analysis was conducted to evaluate the effect of a high genetic SLE predisposition on each manifestation. Next, validation was conducted in a clinical SLE cohort comprising 1487 genotyped Scandinavian patients with detailed clinical data. Based on the public datasets, genetic risk scores (GRSs) for each relevant manifestation were constructed for each patient. Associations between each GRS and the corresponding ACR-82 criterion were evaluated using logistic regression. In the FinnGen biobank, the cumulative effect of the 57 SLE risk SNPs was associated with an increased risk of rosacea, OR 1.09 (1.03-1.16), polyarthropathies, OR 1.10 (1.06-1.14), pleural effusions, OR 1.09 (1.04-1.14), and hemolytic anemia, OR 1.32 (1.10-1.58). In the clinical cohort, 5 of the 11 GRSs generated from the public datasets were associated with their corresponding ACR-82 criterion: arthritis, OR 1.15 (1.02-1.31), renal disorder, OR 1.15 (1.04-1.29), neurologic disorder, OR 1.24 (1.04-1.47), hematologic disorder, OR 1.12 (1.00-1.24), and immunologic disorder, OR 1.37 (1.22-1.56). The findings demonstrate that known SLE risk gene variants play a role in the development of at least half of the ACR-82 criteria for SLE, indicating a future possibility of using genetics to predict a variety of disease sub-phenotypes in SLE.
NGI Uppsala (SNP&SEQ Technology Platform) [Service]
National Genomics Infrastructure [Service]
PubMed 41200769
DOI 10.1111/joim.70040
Crossref 10.1111/joim.70040