Article

Nature 452, 429-435 (27 March 2008) | doi:10.1038/nature06757; Received 5 October 2007; Accepted 28 January 2008; Published online 16 March 2008

Variations in DNA elucidate molecular networks that cause disease

Yanqing Chen1,7, Jun Zhu1,7, Pek Yee Lum1, Xia Yang1, Shirly Pinto2, Douglas J. MacNeil2, Chunsheng Zhang1, John Lamb1, Stephen Edwards1, Solveig K. Sieberts1, Amy Leonardson1, Lawrence W. Castellini3, Susanna Wang3, Marie-France Champy6, Bin Zhang1, Valur Emilsson1, Sudheer Doss3, Anatole Ghazalpour3, Steve Horvath4, Thomas A. Drake5, Aldons J. Lusis3,4 & Eric E. Schadt1

  1. Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA
  2. Department of Metabolic Disorders, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, USA
  3. Department of Microbiology, Molecular Genetics, and Immunology,
  4. Department of Human Genetics, and,
  5. Department of Pathology and Laboratory Medicine, UCLA, 650 Young Drive South, Los Angeles, California 90095, USA
  6. Institut de Genetique et de Biologie Moleculaire et Cellulaire, CNRS/INSERM/ULP, 67404 Illkirch, France
  7. These authors contributed equally to this work.

Correspondence to: Eric E. Schadt1 Correspondence and requests for materials should be addressed to E.E.S. (Email: eric_schadt@merck.com).

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Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.

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