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Article
Nature Genetics  37, 710 - 717 (2005)
Published online: 19 June 2005; | doi:10.1038/ng1589

An integrative genomics approach to infer causal associations between gene expression and disease

Eric E Schadt1, John Lamb1, Xia Yang2, Jun Zhu1, Steve Edwards1, Debraj GuhaThakurta1, Solveig K Sieberts1, Stephanie Monks3, Marc Reitman4, Chunsheng Zhang1, Pek Yee Lum1, Amy Leonardson1, Rolf Thieringer5, Joseph M Metzger6, Liming Yang6, John Castle1, Haoyuan Zhu1, Shera F Kash7, Thomas A Drake8, Alan Sachs1 & Aldons J Lusis2

1  Rosetta Inpharmatics, Seattle, Washington 98109, USA.

2  Departments of Microbiology, Molecular Genetics, and Immunology; Medicine; and Human Genetics, University of California Los Angeles, Los Angeles, California 90095, USA.

3  Department of Statistics, Oklahoma State University, Stillwater, Oklahoma 74078, USA.

4  Department of Metabolic Disorders, Merck Research Laboratories, Rahway, New Jersey 07065, USA.

5  Department of Cardiovascular Disease, Merck Research Laboratories, Rahway, New Jersey 07065, USA.

6  Department of Pharmacology, Merck Research Laboratories, Rahway, New Jersey 07065, USA.

7  Deltagen, Inc., San Carlos, California 94070, USA.

8  Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California 90095, USA.

Correspondence should be addressed to Eric E Schadt eric_schadt@merck.com
A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene−perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.

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Nature Genetics
ISSN: 1061-4036
EISSN: 1546-1718
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