Genetic Analysis of Rodent Obesity and Diabetes

Sally Chiu, Janis S. Fisler, Craig H Warden

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

This chapter demonstrates that there are many expressed non-protein coding regions in the mammalian genome. The core power of positional genetics approaches is that one can identify the causative factor whether it is an expressed gene, microRNA, antisense, or other unknown mechanisms. New phenotyping resources may also facilitate gene discovery programs. The Mouse Phenome Database includes extensive phenotyping information on dozens of strains. One possibility is that this phenotyping can be combined with high-density mouse haplotyping to find genes for many complex traits. Using genetic approaches leads to unpredictable results; only a small fraction may identify genes that can be targeted by weight loss drugs. Although each obesity gene may have its own interesting story, not all genes have human orthologs that cause obesity; and not all human obesity genes provide safe and effective drug targets. It is not always clear that weight gain or loss in mice can be predicted from measurements of food intake and energy expenditure, possibly because the assays used are not accurate enough to capture the small imbalances of intake and expenditure that cause fat accumulation or because the assays are used for cross-sectional studies at one time point, but the obesity-causing energy imbalance occurs at another time. Settling these questions will allow for a more precise understanding of the mechanisms by which alleles cause fat accumulation.

Original languageEnglish (US)
Title of host publicationThe Mouse in Biomedical Research
PublisherElsevier Inc.
Pages617-636
Number of pages20
Volume3
ISBN (Print)9780123694546
DOIs
StatePublished - 2007

ASJC Scopus subject areas

  • veterinary(all)

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    Chiu, S., Fisler, J. S., & Warden, C. H. (2007). Genetic Analysis of Rodent Obesity and Diabetes. In The Mouse in Biomedical Research (Vol. 3, pp. 617-636). Elsevier Inc.. https://doi.org/10.1016/B978-012369454-6/50073-X