Nature Genetics 37, 1224 - 1233 (2005)
Published online: 2 October 2005; | doi:10.1038/ng1619
Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traitsMargarete Mehrabian1, Hooman Allayee2, Jirina Stockton1, Pek Yee Lum3, Thomas A Drake4, Lawrence W Castellani1, Michael Suh1, Christopher Armour3, Stephen Edwards3, John Lamb3, Aldons J Lusis1, 5, 6, 7
& Eric E Schadt31
Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
90095-1679, USA. 2
Department of Preventive Medicine and Institute for Genetic Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
90089-9075, USA. 3
Rosetta Inpharmatics, 401 Terry Ave. North, Seattle, Washington
98109, USA. 4
Departments of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
90095-1679, USA. 5
Microbiology, Immunology and Molecular Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
90095-1679, USA. 6
Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
90095-1679, USA. 7
Molecular Biology Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
90095-1679, USA.
Correspondence should be addressed to Eric E Schadt eric_schadt@merck.com Forward genetic approaches to identify genes involved in complex traits such as common human diseases have met with limited success. Fine mapping of linkage regions and validation of positional candidates are time-consuming and not always successful. Here we detail a hybrid procedure to map loci involved in complex traits that leverages the strengths of forward and reverse genetic approaches. By integrating genotypic and expression data in a segregating mouse population, we show how clusters of expression quantitative trait loci linking to regions of the genome accurately reflect the underlying perturbation to the transcriptional network induced by DNA variations in genes that control the complex traits. By matching patterns of gene expression in a segregating population with expression responses induced by single-gene perturbation experiments, we show how genes controlling clusters of expression and clinical quantitative trait loci can be mapped directly. We demonstrate the utility of this approach by identifying 5-lipoxygenase as underlying previously identified quantitative trait loci in an F2 cross between strains C57BL/6J and DBA/2J and showing that it has pleiotropic effects on body fat, lipid levels and bone density.
MORE ARTICLES LIKE THIS These links to content published by NPG are automatically generated.
|
