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S4A:5 High genetic risk score is associated with organ damage in systemic lupus erythematosus
  1. S Reid1,
  2. A Alexsson1,
  3. M Frodlund2,
  4. E Svenungsson3,
  5. JK Sandling1,
  6. A Jönsen4,
  7. C Bengtsson5,
  8. I Gunnarsson3,
  9. A Bengtsson4,
  10. S Rantapää-Dahlqvist5,
  11. A-C Syvänen1,
  12. C Sjöwal2,
  13. L Rönnblom1 and
  14. D Leonard1
  1. 1Uppsala University, Dept of Medical Sciences, Science for Life Laboratories, Uppsala, Sweden
  2. 2Linköping University, Dept of Clinical and Experimental Medicine, Linköping, Sweden
  3. 3Karolinska University Hospital, Karolinska Institutet, Dept of Medicine, Stockholm, Sweden
  4. 4Skåne University Hospital, Dept of Rheumatology, Lund, Sweden
  5. 5Umeå University, Dept of Public Health and Clinical Medicine/Rheumatology, Umeå, Sweden


Background Systemic lupus erythematosus (SLE) is a chronic, autoimmune disease with a complex genetic aetiology. The overall effect of hereditary risk on organ damage has so far not been studied. We therefore assessed the relationship between genetic risk and development of organ damage in SLE.

Methods Patients with SLE (Sweden, n=1001) were genotyped using a 200K Immunochip single nucleotide polymorphism (SNP) Array (Illumina). The Immunochip was HLA imputed using HLA*IMP:02. A non-HLA (58 SNPs) and a HLA (5 SNPs) genetic risk score (GRS) was assigned to each patient based on SNPs which in previous studies have shown association (p<5×10–8) with SLE in European populations (Chen et al. 2017). For each SNP, the natural logarithm of the odds ratio (OR) for SLE susceptibility was multiplied by the number of risk alleles in each individual. The sum of all products for each patient was defined as the GRS. Clinical data, including the Systemic Lupus International Collaborating Clinics Damage Index (SLICC-DI), was retrieved from medical records. The relationship between GRS and SLICC-DI was analysed using an ordinal regression model.

Results A higher non-HLA GRS was associated with increased organ damage (OR 1.10 (1.00–1.21), p=4.2×10–2), nephritis (OR 1.26 (1.13–1.41), p=2.8×10–5), anti-dsDNA (OR 1.33 (1.17–1.52), p=1.0×10–5) and a younger age at diagnosis (OR 1.33 (1.14–1.54), p=1.7×10–4).

When analysing the relationship between individual SNPs (n=63) and SLICC-DI, we observed positive associations between SLICC-DI and rs6568431 (ATG5, OR 1.28 (1.08–1.51), p=3.6×10–3) and rs11889341 (STAT4, OR 1.27 (1.07–1.50), p=5.0×10–3). Rs1269852 (TNXB-ATF6B, OR 0.80 (0.66–0.98), p=2.7×10–2) and rs1132200 (TMEM39A, OR 0.72 (0.56–0.91), p=6.7×10–3) were negatively associated with SLICC-DI. Using a Kendall Tau correlation model, a positive correlation between the TNXB-ATF6B risk allele and the HLA DRB1*03:01 haplotype was observed (τ=0.91, p<1.0×10–15).

Conclusion In patients with SLE, a high genetic risk score is linked to increased organ damage and a younger age of disease onset. Further, the ATG5 and STAT4 risk alleles were associated with increased organ damage whereas the TNXB-ATF6B and TMEM39A risk alleles were associated with less organ damage. Consequently, genetic profiling of patients with SLE may provide a tool for predicting severity of the disease.

  • Genetic risk score
  • Organ damage
  • SNP

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