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Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks

Nature Genetics volume 41, pages 415–423 (2009)Cite this article

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Abstract

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.

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Figure 1: Adiposity (fat/muscle ratio) or body weight growth curves in the mouse models.
Figure 2: Disruption of metabolic pathways involved in fat pad mass in mouse models of the candidate genes.
Figure 3: A portion of the core subnetwork, derived from the liver transcriptional subnetworks representative of gene expression signatures of the mouse models of the candidate genes.

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  • 22 March 2009

    NOTE: In the version of this article initially published online, there was an error in one of the genotypes in Table 5, a missing genotype in the third-to-last paragraph on page 7, and two minor typographical errors in the right column on page 6. These errors have been corrected for the print, PDF and HTML versions of this article.

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Acknowledgements

The authors thank R. Davis, P. Wen, M. Rosales, X. Wu, K. Ranola and X. Xia for helping with the tissue collection. We would also like to thank L. Ingram-Drake and S. Charugundla for technical support and O. Mirochnitchenko and I. Goldberg for providing mouse models. The study was funded by US National Institutes of Health grants DK072206, HL28481 and HL30568.

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Authors and Affiliations

  1. Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, USA

    Xia Yang, Joshua L Deignan, Hongxiu Qi, Lawrence W Castellani, Charles R Farber, Margarete Mehrabian & Aldons J Lusis

  2. Rosetta Inpharmatics, LLC, a wholly owned subsidiary of Merck & Co. Inc., Seattle, Washington, USA

    Jun Zhu, Judy Zhong, Robert R Kleinhanz, Kai Wang, Tao Xie, Michael Coon, Chunsheng Zhang, Bin Zhang, John R Lamb, Pek Y Lum & Eric E Schadt

  3. Department of Metabolic Disorders, Merck & Co. Inc., Rahway, New Jersey, USA

    Su Qian, Sheena Mumick & Douglas J MacNeil

  4. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California, USA

    Gevork Torosyan, Sana Majid, Brie Falkard, Daria Estrada-Smith, Susanna S Wang, Atila van Nas, Anatole Ghazalpour, Katrina M Dipple & Aldons J Lusis

  5. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, USA

    Jenny Karlsson & Thomas A Drake

  6. Department of Clinical Pharmacology, Merck & Co. Inc., Rahway, New Jersey, USA

    Marc L Reitman

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Contributions

T.A.D., A.J.L., E.E.S., P.Y.L., X.Y., K.M.D., M.L.R. and D.J.M. designed the study. X.Y., H.Q., G.T., S. Majid, B.F., S.Q., L.W.C., D.E.-S., S. Mumick, S.S.W., A.v.N., A.G., M.M. and C.R.F. performed the experiments. X.Y., J.L.D., J. Zhu, G.T., J.K., K.W, J.R.L., T.X., M.C., J. Zhong, C.Z. and B.Z. participated in data analysis. X.Y., J.L.D., J. Zhu, S.Q. and J. Zhong wrote the manuscript, with advice and editing from T.A.D., A.J.L. and E.E.S. R.R.K. coordinated the study. T.A.D., the corresponding author, certifies that all authors have agreed to all content in the manuscript, including the data as presented.

Corresponding author

Correspondence to Thomas A Drake.

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Competing interests

X.Y., J. Zhu, S.Q., J. Zhong, R.R.K., S. Mumick, T.X., M.C., C.Z., B.Z., D.J.M., M.L.R., J.L.L., P.Y.L. and E.E.S. are all employees of Merck & Co. Inc.

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Supplementary Text and Figures

Supplementary Figures 1–4, Supplementary Tables 1–14 and Supplementary Methods (PDF 703 kb)

Supplementary Figure 5

(Separate scaleable vector graphic file; open with web browser enabled with svg viewer.) The core subnetwork derived from the liver transcriptional subnetworks representative of gene expression signatures of the mouse models of the candidate genes. The liver transcriptional network is the union of Bayesian networks constructed from three crosses derived from B6, C3H and CAST. This core subnetwork consists of key regulators for fatty acid and lipid metabolism, including Insig1 and Insig2 (in red), and is enriched for genes involved in related GO biological processes. (ZIP 172 kb)

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Yang, X., Deignan, J., Qi, H. et al. Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks. Nat Genet 41, 415–423 (2009). https://doi.org/10.1038/ng.325

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