Mammalian Genome

, Volume 21, Issue 3–4, pp 115–129 | Cite as

Genetic resistance to diet-induced obesity in chromosome substitution strains of mice

  • Lindsay C. Burrage
  • Annie E. Baskin-Hill
  • David S. Sinasac
  • Jonathan B. Singer
  • Colleen M. Croniger
  • Andrew Kirby
  • E. J. Kulbokas
  • Mark J. Daly
  • Eric S. Lander
  • Karl W. Broman
  • Joseph H. Nadeau


Discovery of genes that confer resistance to diseases such as diet-induced obesity could have tremendous therapeutic impact. We previously demonstrated that the C57BL/6J-ChrA/J/NaJ panel of chromosome substitution strains (CSSs) is a unique model for studying resistance to diet-induced obesity. In the present study, three replicate CSS surveys showed remarkable consistency, with 13 A/J-derived chromosomes reproducibly conferring resistance to high-fat-diet-induced obesity. Twenty CSS intercrosses, one derived from each of the 19 autosomes and chromosome X, were used to determine the number and location of quantitative trait loci (QTLs) on individual chromosomes and localized six QTLs. However, analyses of mean body weight in intercross progeny versus C57BL/6J provided strong evidence that many QTLs discovered in the CSS surveys eluded detection in these CSS intercrosses. Studies of the temporal effects of these QTLs suggest that obesity resistance was dynamic, with QTLs acting at different ages or after different durations of diet exposure. Thus, these studies provide insight into the genetic architecture of complex traits such as resistance to diet-induced obesity in the C57BL/6J-ChrA/J/NaJ CSSs. Because some of the QTLs detected in the CSS intercrosses were not detected using a traditional C57BL/6J × A/J intercross, our results demonstrate that surveys of CSSs and congenic strains derived from them are useful complementary tools for analyzing complex traits.


Parental Strain Inbred Strain Diet Exposure Congenic Strain Resistance QTLs 
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.



We thank Dr. Gary Churchill for thoughtful discussions about this work, and Drs. Mark Adams, Richard Hanson, Matthew Warman, and Arthur Zinn for their critical comments of this article. We thank Nicole Nadeau and Christine Jimenez for their assistance. This work was funded by National Institutes of Health (T32 GM07250-30) (LCB), National Center for Research Resources (RR12305), National Cancer Institute (U54CA116867), and the Charles B. Wang Foundation. DSS was supported by a Canadian Diabetes Association Fellowship.

Supplementary material

335_2010_9247_MOESM1_ESM.doc (456 kb)
Supplementary material 1 (DOC 456 kb)


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Lindsay C. Burrage
    • 1
    • 7
  • Annie E. Baskin-Hill
    • 1
  • David S. Sinasac
    • 1
    • 8
  • Jonathan B. Singer
    • 2
    • 9
  • Colleen M. Croniger
    • 3
  • Andrew Kirby
    • 4
  • E. J. Kulbokas
    • 4
  • Mark J. Daly
    • 4
  • Eric S. Lander
    • 5
  • Karl W. Broman
    • 6
  • Joseph H. Nadeau
    • 1
  1. 1.Department of GeneticsCase Western Reserve University School of MedicineClevelandUSA
  2. 2.Broad Institute of MIT and Harvard UniversityCambridgeUSA
  3. 3.Department of NutritionCase Western Reserve University School of MedicineClevelandUSA
  4. 4.Center for Human Genetics Research, MGH Simches Research CenterBostonUSA
  5. 5.Department of Systems BiologyHarvard Medical SchoolBostonUSA
  6. 6.Department of Biostatistics and Medical InformaticsUniversity of WisconsinMadisonUSA
  7. 7.Department of PediatricsUniversity Hospitals Case Medical CenterClevelandUSA
  8. 8.Biochemical Genetics LaboratoryAlberta Children’s HospitalCalgaryCanada
  9. 9.Clinical PharmacogeneticsNovartis Institutes for Biomedical ResearchCambridgeUSA

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