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Westra, Harm-Jan

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Westra

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Harm-Jan

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Westra, Harm-Jan

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Now showing 1 - 2 of 2
  • Publication

    Pathogenic implications for autoimmune mechanisms derived by comparative eQTL analysis of CD4+ versus CD8+ T cells

    (Public Library of Science, 2017) Kasela, Silva; Kisand, Kai; Tserel, Liina; Kaleviste, Epp; Remm, Anu; Fischer, Krista; Esko, Tõnu; Westra, Harm-Jan; Fairfax, Benjamin P.; Makino, Seiko; Knight, Julian C.; Franke, Lude; Metspalu, Andres; Peterson, Pärt; Milani, Lili

    Inappropriate activation or inadequate regulation of CD4+ and CD8+ T cells may contribute to the initiation and progression of multiple autoimmune and inflammatory diseases. Studies on disease-associated genetic polymorphisms have highlighted the importance of biological context for many regulatory variants, which is particularly relevant in understanding the genetic regulation of the immune system and its cellular phenotypes. Here we show cell type-specific regulation of transcript levels of genes associated with several autoimmune diseases in CD4+ and CD8+ T cells including a trans-acting regulatory locus at chr12q13.2 containing the rs1131017 SNP in the RPS26 gene. Most remarkably, we identify a common missense variant in IL27, associated with type 1 diabetes that results in decreased functional activity of the protein and reduced expression levels of downstream IRF1 and STAT1 in CD4+ T cells only. Altogether, our results indicate that eQTL mapping in purified T cells provides novel functional insights into polymorphisms and pathways associated with autoimmune diseases.

  • Publication

    reGenotyper: Detecting mislabeled samples in genetic data

    (Public Library of Science, 2017) Zych, Konrad; Snoek, Basten L.; Elvin, Mark; Rodriguez, Miriam; Van der Velde, K. Joeri; Arends, Danny; Westra, Harm-Jan; Swertz, Morris A.; Poulin, Gino; Kammenga, Jan E.; Breitling, Rainer; Jansen, Ritsert C.; Li, Yang

    In high-throughput molecular profiling studies, genotype labels can be wrongly assigned at various experimental steps; the resulting mislabeled samples seriously reduce the power to detect the genetic basis of phenotypic variation. We have developed an approach to detect potential mislabeling, recover the “ideal” genotype and identify “best-matched” labels for mislabeled samples. On average, we identified 4% of samples as mislabeled in eight published datasets, highlighting the necessity of applying a “data cleaning” step before standard data analysis.