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Definition of arthritis candidate risk genes by combining rat linkage-mapping results with human case control association data
  1. Liselotte Bäckdahl (liselotte.backdahl{at}
  1. Karolinska Institutet, Sweden
    1. Jian Ping Guo
    1. Karolinska Institutet, Sweden
      1. Maja Jagodic
      1. Karolinska Institutet, Sweden
        1. Kristina Becanovic
        1. Karolinska Institutet, Sweden
          1. Bo Ding
          1. Karolinska Institutet, Sweden
            1. Tomas Olsson
            1. Karolinska Institutet, Sweden
              1. Johnny C. Lorentzen (johnny.lorentzen{at}
              1. Karolinska Institutet, Sweden


                Objective: To define genomic regions that link to rat arthritis, and to determine the potential association with rheumatoid arthritis (RA) of the corresponding human genomic regions.

                Methods: Advanced intercross lines (AIL) between arthritis susceptible DA rats and arthritis resistant PVG.1AV1 rats were injected with differently arthritogenic oils to achieve an experimental situation with substantial phenotypic variation in the rat study population. Genotyping of microsatellite markers was performed over genomic regions with documented impact on arthritis, located on rat chromosomes 4, 10, and 12. Linkage between genotypes and phenotypes were determined by R/QTL. Potential association with RA of single nucleotide polymorphisms (SNPs) in homologous human chromosome regions was evaluated from public WTCCC data derived from 2000 cases and 3000 controls.

                Results: High frequency of arthritis (57 %) was recorded in 422 rats injected with pristane. Maximum linkage to pristane-induced arthritis occurred less than 130 kb from the known genetic arthritis determinants Ncf1 and APLEC, demonstrating remarkable mapping precision. Five novel quantitative trait loci were mapped on rat chromosomes 4 and 10, with narrow confidence intervals. Some exerted sex-biased effects and some were linked to chronic arthritis. Human homologous genomic regions contain loci where multiple nearby SNPs associate nominally with RA, e.g. at the genes encoding protein kinase C alpha and interleukin-17 receptor alpha.

                Conclusions: High-resolution mapping in AIL populations defines limited sets of candidate risk genes, some of which appear also to associate with RA and thus may give clues to evolutionarily conserved pathways that lead to arthritis.

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