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Increased Autoimmune Diabetes in pIgR-Deficient NOD Mice Is Due to a "Hitchhiking" Interval that Refines the Genetic Effect of Idd5.4
Authors:Kim R Simpfendorfer  Richard A Strugnell  Thomas C Brodnicki  Odilia L C Wijburg
Institution:1. The Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia.; 2. The Australian Bacterial Pathogenesis Program, The University of Melbourne, Parkville, Victoria, Australia.; 3. Immunology & Diabetes Unit, St Vincent’s Institute of Medical Research, Fitzroy, Victoria, Australia.; University of Cincinnati College of Medicine, UNITED STATES,
Abstract:Selective breeding to introduce a gene mutation from one mouse strain onto the genetic background of another strain invariably produces “hitchhiking” (i.e. flanking) genomic intervals, which may independently affect a disease trait of interest. To investigate a role for the polymeric Ig receptor in autoimmune diabetes, a congenic nonobese diabetic (NOD) mouse strain was generated that harbors a Pigr null allele derived from C57BL/6 (B6) mice. These pIgR-deficient NOD mice exhibited increased serum IgA along with an increased diabetes incidence. However, the Pigr null allele was encompassed by a relatively large “hitchhiking” genomic interval that was derived from B6 mice and overlaps Idd5.4, a susceptibility locus for autoimmune diabetes. Additional congenic NOD mouse strains, harboring smaller B6-derived intervals, confirmed Idd5.4 independently of the other three known susceptibility loci on chromosome 1, and further localized Idd5.4 to an interval proximal to Pigr. Moreover, these congenic NOD mice showed that B6 mice harbor a more diabetogenic allele than NOD mice for this locus. The smallest B6-derived interval encompassing the Pigr null allele may, however, confer a small degree of protection against diabetes, but this protection appears to be dependent on the absence of the diabetogenic B6 allele for Idd5.4. This study provides another example of the potential hidden effects of “hitchhiking" genomic intervals and how such intervals can be used to localize disease susceptibility loci.
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