| Literature DB >> 16799083 |
Yingying Guo1, Paul Weller, John Allard, Jonathan Usuka, Mohammad Masjedizadeh, Shao-Yong Wu, Bill Fitch, Douglas Clark, J David Clark, Steve Shafer, Jianmei Wang, Guochun Liao, Gary Peltz.
Abstract
Analysis of mouse genetic models of human disease-associated traits has provided important insight into the pathogenesis of human disease. As one example, analysis of a murine genetic model of osteoporosis demonstrated that genetic variation within the 15-lipoxygenase (Alox15) gene affected peak bone mass, and that treatment with inhibitors of this enzyme improved bone mass and quality in rodent models. However, the method that has been used to analyze mouse genetic models is very time consuming, inefficient, and costly. To overcome these limitations, a computational method for analysis of mouse genetic models was developed that markedly accelerates the pace of genetic discovery. It was used to identify a genetic factor affecting the rate of metabolism of warfarin, an anticoagulant that is commonly used to treat clotting disorders. Computational analysis of a murine genetic model of narcotic drug withdrawal suggested a potential new approach for treatment of narcotic drug addiction. Thus, the results derived from computational mouse genetic analysis can suggest new treatment strategies, and can provide new information about currently available medicines.Entities:
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Year: 2006 PMID： 16799083 PMCID： PMC2658704 DOI： 10.1513/pats.200601-014AW
Source DB: PubMed Journal: Proc Am Thorac Soc ISSN： 1546-3222