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Mammalian Genome

, 17:927 | Cite as

Diabetic modifier QTLs identified in F2 intercrosses between Akita and A/J mice

  • Shigeru Takeshita
  • Maki Moritani
  • Kiyoshi Kunika
  • Hiroshi Inoue
  • Mitsuo Itakura
Article

Abstract

To identify novel genetic modifiers of type 2 diabetes (T2D), we performed quantitative trait loci (QTL) analysis on F2 progeny of hypoinsulinemic diabetic Akita mice, heterozygous for the Ins2 gene Cys96Tyr mutation, and nondiabetic A/J mice. We generated 625 heterozygous (F2-Hetero) and 338 wild-type (F2-Wild) mice with regard to the Ins2 mutation in F2 intercross progeny. We measured quantitative traits, including plasma glucose and insulin concentrations during the intraperitoneal glucose tolerance test (IPGTT), and body weight (BW). We observed three significant QTLs in hypoinsulinemic hyperglycemic male F2-Hetero mice, designated Dbm1, Dbm3, and Dbm4 on Chromosomes 6, 14, and 15, respectively. They showed linkage to plasma glucose concentrations, with significant maximum logarithm of odds (LOD) scores of 4.12, 4.17, and 6.17, respectively, all exceeding threshold values by permutation tests. In normoinsulinemic normoglycemic male F2-Wild mice, Dbm1 on Chromosome 6 showed linkage to both plasma insulin concentrations and BW, and Dbm2 on Chromosome 11 showed linkage to plasma glucose concentrations only, with LOD scores of 4.52 and 6.32, and 5.78, respectively. Based on these results, we concluded that Dbm1, Dbm2, Dbm3, and Dbm4 represent four major modifier QTLs specifically affecting T2D-related traits and that these diabetic modifier QTLs are conditional on the heterozygous Ins2 gene mutation and sex to exert their modifier functions. Identification of the genes responsible for these QTLs would provide new drug development targets for human T2D.

Keywords

Quantitative Trait Locus Quantitative Trait Locus Analysis Plasma Glucose Concentration Plasma Insulin Concentration Fast Plasma Glucose Concentration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This study was supported by grants from the Japan Society for the Promotion of Science (Grant for Genome Research of the Research for the Future Program), and Cooperative Link of Unique Science and Technology for Economy Revitalization (CLUSTER). The authors thank Katsuhiko Togawa for helpful discussions on the QTL approach.

Supplementary material

supp.pdf (507 kb)

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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Shigeru Takeshita
    • 1
    • 2
  • Maki Moritani
    • 2
  • Kiyoshi Kunika
    • 2
  • Hiroshi Inoue
    • 2
  • Mitsuo Itakura
    • 2
    • 3
  1. 1.Department of Diabetes, Pharmacology Research LaboratoriesAstellas Pharma Inc.IbarakiJapan
  2. 2.Division of Genetic Information, Institute for Genome ResearchThe University of TokushimaTokushimaJapan
  3. 3.Division of Genetic Information, Institute for Genome ResearchThe University of TokushimaTokushimaJapan

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