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Variations in DNA elucidate molecular networks that cause disease

Nature volume 452, pages 429–435 (2008)Cite this article

Abstract

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 β (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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Figure 1: The distal half of chromosome 1 strongly influences metabolic and gene expression traits.
Figure 2: Genetic loci perturb molecular phenotypes that in turn lead to variations in disease-associated traits.
Figure 3: Genes in the MEM network validated as having a causal relationship with obesity traits.

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Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

The liver and adipose microarray data for the B × H cross have been deposited into the GEO database under accession numbers GSE2814 and GSE3086, respectively. Expression data associated with the diet-induced obesity, Zfp90 transgenic, Alox5-/- and roziglitazone-treated animals have been uploaded to the GEO database under accession numbers GSE7028, GSE7029, GSE7026 and GSE7027, respectively.

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Acknowledgements

This work was supported in part by grants from the NIH/NIDDK and NIH/NHLBI to A.J.L. and T.A.D.

Author Contributions S.P., D.J.M. and M.-F.C. constructed and characterized the Ppm1l knockout mouse. X.Y., L.W.C., S.W., S.D., A.G., T.A.D. and A.J.L. constructed and characterized the B × H cross, the Lpl knockout mouse and the Lactb transgenic mouse. S.H., A.G., S.D. and B.Z. assisted in the co-expression network analyses. S.E. and A.J.L. performed bioinformatic analyses. All authors discussed the results and commented on the manuscript. S.K.S. and C.Z. aided in the data analysis. P.Y.L. and J.L. aided in the study design and interpretation of the experimental results. Y.C., J.Z. and E.E.S. designed the study, developed methods, analysed the data and wrote the paper.

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Author notes

  1. Yanqing Chen and Jun Zhu: These authors contributed equally to this work.

Authors and Affiliations

  1. Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA,

    Yanqing Chen, Jun Zhu, Pek Yee Lum, Xia Yang, Chunsheng Zhang, John Lamb, Stephen Edwards, Solveig K. Sieberts, Amy Leonardson, Bin Zhang, Valur Emilsson & Eric E. Schadt

  2. Department of Metabolic Disorders, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, USA,

    Shirly Pinto & Douglas J. MacNeil

  3. Department of Microbiology, Molecular Genetics, and Immunology,

    Lawrence W. Castellini, Susanna Wang, Sudheer Doss, Anatole Ghazalpour & Aldons J. Lusis

  4. Department of Human Genetics, and,

    Steve Horvath & Aldons J. Lusis

  5. Department of Pathology and Laboratory Medicine, UCLA, 650 Young Drive South, Los Angeles, California 90095, USA,

    Thomas A. Drake

  6. Institut de Genetique et de Biologie Moleculaire et Cellulaire, CNRS/INSERM/ULP, 67404 Illkirch, France,

    Marie-France Champy

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Correspondence to Eric E. Schadt.

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Competing interests

The following authors own stocks or stock options of Merck & Co. Inc.: Y.C., J.Z., P.Y.L., X.Y., S.P., D.J.M., C.Z., J.L., S.E., S.K.S., A.L., B.Z., V.E. and E.E.S.

Supplementary information

Supplementary Information

The file contains Supplementary Discussion, Supplementary Notes, Supplementary Figures 1-11 and Legends and Supplementary Tables 1-2 The Supplementary Discussion and Supplementary Figures 7-9 provide details on inferring causal relationships for traits under feedback control. In addition, the Supplementary Discussion and Supplementary Figures 1 and 2, additional information on Tnfsf4 and Apoa2 expression in the BXH cross. Supplementary Figures 3-5 provide the coexpression networks, enrichments, and characteristics that are critical to the results in the main text. Supplementary Figure 6 provides details on the oral glucose tolerance test carried out on the Ppm1l knockout mice. Supplementary Figure 10 provides details on the Ppm1l knockout construct. Supplementary Figure 11 provides details on the selection of the p-value threshold used to construct the liver and adipose coexpression networks (PDF 1554 kb)

Supplementary Table 3

The file contains Supplementary Table 3 which lists genes contained in the macrophage-enriched network described in the main text. (PDF 103 kb)

Supplementary Table 4

The file contains Supplementary Table 4. The file includes series of indicators for each gene in the macrophage enriched network listed in Supplementary Table 3 that indicate whether a given gene in this network was annotated as belonging to an inflammatory pathway; expressed in macrophage-related tissues; a member of the diet-induced obesity, Zfp90, Alox5, or Pparg perturbation gene expression signatures; or supported as causal for any of the metabolic traits. (PDF 99 kb)

Supplementary Table 5

The file contains Supplementary Table 5 For each gene in the macrophage-enriched metabolic network that was supported as causal for at least one metabolic trait, this table provides the number of metabolic traits a given gene was supported as causal for. (PDF 71 kb)

Supplementary Table 6

The file contains Supplementary Table 6 which includes the list of genes comprising each module in the liver coexpression network described in the text and depicted in Supplementary Figure 3A. (PDF 128 kb)

Supplementary Table 7

The file contains Supplementary Table 7 which lists genes and annotations represented on the microarray used to profile the BXH cross tissues. (PDF 9553 kb)

Supplementary Methods

This file includes Supplementary Methods. This file was uploaded on 2nd April 2008. (PDF 165 kb)

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Chen, Y., Zhu, J., Lum, P. et al. Variations in DNA elucidate molecular networks that cause disease. Nature 452, 429–435 (2008). https://doi.org/10.1038/nature06757

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