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  • P77 Identification of a cluster of SLE risk loci associated with levels of multiple blood biomarkers in the general population – Implication for SLE sub-phenotypes

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Poster Presentations
P77 Identification of a cluster of SLE risk loci associated with levels of multiple blood biomarkers in the general population – Implication for SLE sub-phenotypes
  1. Nina Oparina1,
  2. Sarah Reid1,
  3. Ahmed Sayadi1,
  4. Maija-Leena Eloranta1,
  5. Martina Frodlund2,
  6. Karoline Lerang3,
  7. Andreas Jönsen4,
  8. Solbritt Rantapää-Dahlqvist5,
  9. Anders A Bengtsson4,
  10. Anna Rudin6,
  11. Øyvind Molberg3,
  12. Christopher Sjöwall2,
  13. Lars Rönnblom1 and
  14. Dag Leonard1
  1. 1Dept. of Medical Sciences, Rheumatology, Uppsala University, Uppsala, Sweden
  2. 2Dept. of Biomedical and Clinical Sciences, Division of Inflammation and Infection/Rheumatology, Linköping University, Linköping, Sweden
  3. 3Dept. of Rheumatology, Oslo University Hospital, Oslo, Norway
  4. 4Dept. of Clinical Sciences, Rheumatology, Lund University, Lund, Sweden
  5. 5Dept. of Public Health and Clinical Medicine/Rheumatology, Umeå University, Umeå, Sweden
  6. 6Dept. of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

Abstract

Objective Genetic contribution is crucial for SLE pathogenesis, but linking risk variants to specific mechanisms in SLE is challenging. We utilize UK Biobank data and a large well defined SLE cohort to investigate associations between SLE risk loci, blood biomarkers and clinical phenotype. Our objective: to study potential combinatorial effects of multiple biomarker associated SLE SNVs in clinical SLE data.

Methods We extracted SLE associated SNVs from published European ancestry GWAS data (with p<5×10–8) and selected 112 SNVs. Associations (p<5×10–8) between these SNVs and blood biomarkers in the UK Biobank were investigated and a 17 SNV cluster associated with 38 biomarkers was identified. Patients with SLE (ACR-97 or SLICC-12, European decent, n=1498) were genotyped using Illumina’s Global Screening Array and clinical data was collected. A weighted polygenic risk score (PRS) including the 17 SNVs identified in the cluster analysis was calculated for each patient and clinical manifestations (ACR-97 criteria, antibodies) of patients with a high (50%) and low (50%) PRS were compared by logistic regression with age and sex covariates.

Results We identified a cluster of 17 SLE risk SNVs associated with multiple blood biomarkers in the general population. The biomarkers associated with the highest number of these SNVs include eosinophil percentage (SNV=16), aspartate aminotransferase (SNV=12), apolipoprotein A (SNV=12) and cystatin C (SNV=11). Patients with high PRS had higher total ACR -97 SLE criteria score (OR 1.35 (1.06–1.69), p=0.014) and higher prevalence of anti-SSA (OR 2.71 (2.16–3.40), p<0.001) and anti-SSB antibodies OR 4.58 (3.39–6.61) p<0.001) compared with other patients. Further, high PRS was associated with lower prevalence of thrombocytopenia (OR 0.63 (0.48–0.84), p=0.001) and anti-Sm antibodies (OR 0.59 (0.39–0.89), p=0.011).

Conclusions We identified a cluster of SLE risk variants associated with multiple blood biomarkers in the UK Biobank. A PRS including these loci associate with an unspecific SLE phenotype including increased number of classification criteria and presence of SSA/SSB antibodies. Further studies are needed to clarify the role of these potential pleiotropic SLE risk loci in lupus pathogenesis.

Acknowledgements Supported by the Swedish Society for Medical Research(S20–0127), the Swedish Rheumatism Association, King Gustaf V’s 80-Year Foundation, the Gustafsson Foundation.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/lupus-2024-el.131

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