Novel risk genes for systemic lupus erythematosus predicted by random forest classificationUppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Rheumatology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Rheumatology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Karolinska Univ Hosp, Karolinska Inst, Dept Med, Rheumatol Unit, Stockholm, Sweden..
Umea Univ, Dept Publ Hlth & Clin Med Rheumatol, Umea, Sweden..
Lund Univ, Skane Univ Hosp, Dept Clin Sci, Rheumatol, Lund, Sweden..
Umea Univ, Dept Publ Hlth & Clin Med Rheumatol, Umea, Sweden..
Linkoping Univ, Dept Clin & Expt Med, AIR Rheumatol, Linkoping, Sweden..
Lund Univ, Skane Univ Hosp, Dept Clin Sci, Rheumatol, Lund, Sweden..
Karolinska Univ Hosp, Karolinska Inst, Dept Med, Rheumatol Unit, Stockholm, Sweden..
Karolinska Univ Hosp, Karolinska Inst, Dept Med, Rheumatol Unit, Stockholm, Sweden..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Rheumatology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Rheumatology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
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2017 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 7, article id 6236Article in journal (Refereed) Published
Abstract [en]
Genome-wide association studies have identified risk loci for SLE, but a large proportion of the genetic contribution to SLE still remains unexplained. To detect novel risk genes, and to predict an individual's SLE risk we designed a random forest classifier using SNP genotype data generated on the "Immunochip" from 1,160 patients with SLE and 2,711 controls. Using gene importance scores defined by the random forest classifier, we identified 15 potential novel risk genes for SLE. Of them 12 are associated with other autoimmune diseases than SLE, whereas three genes (ZNF804A, CDK1, and MANF) have not previously been associated with autoimmunity. Random forest classification also allowed prediction of patients at risk for lupus nephritis with an area under the curve of 0.94. By allele-specific gene expression analysis we detected cis-regulatory SNPs that affect the expression levels of six of the top 40 genes designed by the random forest analysis, indicating a regulatory role for the identified risk variants. The 40 top genes from the prediction were overrepresented for differential expression in B and T cells according to RNA-sequencing of samples from five healthy donors, with more frequent over-expression in B cells compared to T cells.
Place, publisher, year, edition, pages
2017. Vol. 7, article id 6236
National Category
Rheumatology and Autoimmunity
Identifiers
URN: urn:nbn:se:uu:diva-333524DOI: 10.1038/s41598-017-06516-1ISI: 000406260100040PubMedID: 28740209OAI: oai:DiVA.org:uu-333524DiVA, id: diva2:1156922
Funder
Swedish Research Council, 521-2014-2263, 521-2013-28302017-11-142017-11-142022-09-15Bibliographically approved