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OP0283 Cross-disease meta-analysis in four systemic autoimmune diseases to identify shared genetic etiologies
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  1. M. Acosta-Herrera1,
  2. M. Kerick1,
  3. D. Gonzalez-Serna1,
  4. C. Wijmenga2,
  5. A. Franke3,
  6. L. Padyukov4,
  7. T. Vyse5,6,
  8. M.E. Alarcon-Riquelme7,
  9. M.D. Mayes8,
  10. J. Martin1,
  11. on behalf of The Myositis Genetics Consortium,
  12. Scleroderma Genetics Consortium
  1. 1Institute of Parasitology and Biomedicine Lopez-Neyra, Granada, Spain
  2. 2Department of Genetics, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
  3. 3Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
  4. 4Rheumatology Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
  5. 5Division of Immunology, Infection and Inflammatory Disease
  6. 6Division of Genetics and Molecular Medicine, King’s College London, London, UK
  7. 7Centro de Genómica e Investigación Oncológica (GENYO), Pfizer-Universidad de Granada-Junta de Andalucía, Granada, Spain
  8. 8The University of Texas Health Science Center–Houston, Houston, USA

Abstract

Background Cross-disease genome-wide association studies (GWAS) in autoimmune diseases (AIDs) has become a powerful tool to expose new genetic variants associated with disease susceptibility and to reveal shared biological mechanisms in the pathophysiology of these conditions.

Objectives The goal of our study was to identify shared genetic etiologies by performing a large-scale meta-analysis of four systemic AIDs in individuals from European-descent populations, including rheumatoid arthritis [4595 cases and 3372 controls], systemic lupus erythematosus [3154 cases and 8775 controls], systemic sclerosis [2255 cases and 4407 controls] and myositis [1674 cases and 3150 controls]

Methods PLINK and EIGENSTRAT were utilised for quality control and population stratification adjustments. Genotype imputation was performed using Minimac in the Michigan Imputation Server and the Haplotype Reference Consortium as reference panel.

Results We meta-analysed ~6.5 million single nucleotide polymorphisms (SNPs) (MAF >1%, Rsq >0.3) across the four diseases and were able to identify 27 genome-wide significant independent loci with at least two diseases leading the association. Our new findings include five unreported shared risk loci: NAB1, KPNA4-ARL14, DGQK, LIMK1, and PRR12. The results from the meta-analysis were functionally enriched in transcription factor binding sites, promoter and enhancer histone marks and DNase cleavage hotspots in immune cell lines, as well as in epithelial and epidermal cell lines. This is consistent with the clinical manifestations across diseases related to the immune system and the connective tissue. Interestingly, several associated variants were able to modify the expression of the nearest genes and constitute shared expression quantitative trait loci across diseases.

Conclusions These studies offer the opportunity to uncover new biological pathways, address patient classification based on their molecular taxonomy and provide an opportunity for drug repositioning by targeting shared mechanisms across diseases.

Acknowledgements Partially funded by EU/EFPIA Innovative Medicines Initiative Joint Undertaking PRECISESADS (115565), The Ministry of Economy and Competitiveness (SAF2015–66761 P), Consejería de Innovación, Ciencia y Tecnología, Junta de Andalucía (P12-BIO-1395), and Juan de la Cierva fellowship (FJCI-2015–24028).

Disclosure of Interest None declared

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