Methylation and polygenic risk scores capture different features of Systemic Lupus Erythematosus.
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Vestin H, Oparina N, Eloranta ML, Skoglund E, Giannakou I, Frodlund M, Gunnarsson I, Sjöwall C, Svenungsson E, Rönnblom L, Imgenberg-Kreuz J, Leonard D
Rheumatology (Oxford) 65 (8) - [2026-08-01; online 2026-07-11]
The aetiopathogenesis of SLE encompasses genetic, environmental and epigenetic factors. We investigated associations between an SLE methylation risk score (MRS), HLA-DRB1*03:01, a non-HLA polygenic risk score (PRS), and clinical and immunological phenotypes. DNA methylation in whole blood from patients fulfilling ≥4 ACR-82 criteria and from controls was investigated using the Illumina HM450K array. The discovery cohort included 311 patients and 400 controls, and the replication cohort comprised 175 patients and 187 controls. Seventeen independent, top differentially methylated CpG sites (Δβ of ≥0.1) from case-control comparisons were used to calculate the MRS. Genotyping was performed using the Immunochip, and the PRS included 57 non-HLA SLE single-nucleotide variants. Clinical data were collected from patient charts, and serum IFN-α2 was measured using Simoa. Higher MRS was strongly associated with serum IFN-α2 levels (P = 1.04 × 10-14). In both cohorts, higher MRS was associated with DLE, immunologic involvement, and anti-SSA/SSB/RNP/Sm autoantibodies (all P < 0.05), and with higher disease activity in the discovery cohort (P = 1.50 × 10-4). MRS was also elevated in patients with multiple autoantibodies (P < 1.0 × 10-15) and in HLA-DRB1*03:01 carriers (P < 1.0 × 10-3). In contrast, higher PRS was associated with nephritis, anti-dsDNA positivity, and lower prevalence of anti-SSB antibodies (all P < 0.05). No correlation was observed between the MRS and the PRS (P = 0.35). The MRS defines an IFN-high, HLA-DRB1*03:01-linked SLE subset with multiple autoantibodies, partly distinct from PRS-associated nephritis risk, highlighting potentially divergent pathogenic pathways. These findings underscore the value of integrating genetic and epigenetic data to better understand underlying disease mechanisms in SLE.
NGI Uppsala (SNP&SEQ Technology Platform) [Service]
National Genomics Infrastructure [Service]
PubMed 42434801
DOI 10.1093/rheumatology/keag359
Crossref 10.1093/rheumatology/keag359
pmc: PMC13452054
pii: 8732433