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Human Molecular Genetics Advance Access originally published online on December 1, 2005
Human Molecular Genetics 2006 15(1):113-128; doi:10.1093/hmg/ddi433
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© The Author 2005. Published by Oxford University Press. All rights reserved.
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact: journals.permissions@oxfordjournals.org

Multidimensional genome scans identify the combinations of genetic loci linked to diabetes-related phenotypes in mice

Katsuhiko Togawa1,2,*, Maki Moritani1, Hiroshi Yaguchi3 and Mitsuo Itakura1

1Division of Genetic Information, Institute for Genome Research, The University of Tokushima, Tokushima 770–8503, Japan, 2First Institute of New Drug Discovery, Otsuka Pharmaceutical, Inc., Tokushima 771–0192, Japan and 3Division of Pharmacology, Drug Safety and Metabolism, Otsuka Pharmaceutical Factory, Inc., Tokushima 770-8601, Japan

* To whom correspondence should be addressed at: Division of Genetic Information, Institute for Genome Research, The University of Tokushima, 3-18-15 Kuramoto-cho, Tokushima 770-8503, Japan. Tel: +81 886339454; Fax: +81 886339455; Email: togawa{at}genome.tokushima-u.ac.jp

Received September 15, 2005; Accepted November 20, 2005

Most quantitative trait loci (QTL) studies have focused on detecting the genetic effects of individual QTLs. This study thoroughly dissected the genetic components of type 2 diabetic mice, including a search for epistatic interactions and multi-locus additive effects that result in variation in diabetes-related phenotypes. F2 population was generated from BKS.Cg-Leprdb+/+m and DBA/2 intercross and separated into six subpopulations by sex and the db-dependent diabetes severity. Single-locus and pairwise genome scans first identified the QTLs in these F2 subpopulations, and next covariate-dependent scans confirmed their sex-, db- and sex-by-db-specific effects in the combined populations. Single-locus genome scans detected four QTLs (QBIS1, QBIS2, QBIS3 and QBIS4) that presented their genetic effects beyond sex, but most QTLs showed their effects specifically in limited conditions. This highly conditional feature of the QTLs was accentuated in the pairwise analysis. The pairwise genome scans uncovered a total of 27 significantly interacting or additively acting pairs of loci, showing a better fit to explain the total phenotypic variation of the traits. These significant pairs affected the traits under constantly varying combinations of loci in a time series or in both sexes. In addition, pairwise analysis indicated the appropriate genetic background in constructing congenic strains to obtain the maximum power in the replication of phenotypes. Our study showed high degree of complexity in the genetics of type 2 diabetes in mice, and it suggested that a comprehensive understanding of the multi-locus effects was essential to disentangle the complex genetics of diabetes and obesity in humans.


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