The human disease network

Kwang Goh

Michael Cusick

David Valle

Barton Childs

Marc Vidal

Albert Barabási

*Center for Complex Network Research and Department of Physics, University of Notre Dame, Notre Dame, IN 46556;

^{Center for Cancer Systems Biology (CCSB) and}

^{Department of Cancer Biology, Dana–Farber Cancer Institute, 44 Binney Street, Boston, MA 02115;}

^{Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115;}

^{Department of Physics, Korea University, Seoul 136-713, Korea; and}

^{Department of Pediatrics and the McKusick–Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205}

**To whom correspondence may be addressed. E-mail: ude.dn@bla or ude.dravrah.icfd@ladiv_cram

Edited by H. Eugene Stanley, Boston University, Boston, MA, and approved April 3, 2007

Author contributions: D.V., B.C., M.V., and A.-L.B. designed research; K.-I.G. and M.E.C. performed research; K.-I.G. and M.E.C. analyzed data; and K.-I.G., M.E.C., D.V., M.V., and A.-L.B. wrote the paper.

Edited by H. Eugene Stanley, Boston University, Boston, MA, and approved April 3, 2007

Received 2007 Feb 14

Abstract

A network of disorders and disease genes linked by known disorder–gene associations offers a platform to explore in a single graph-theoretic framework all known phenotype and disease gene associations, indicating the common genetic origin of many diseases. Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, supporting the existence of distinct disease-specific functional modules. We find that essential human genes are likely to encode hub proteins and are expressed widely in most tissues. This suggests that disease genes also would play a central role in the human interactome. In contrast, we find that the vast majority of disease genes are nonessential and show no tendency to encode hub proteins, and their expression pattern indicates that they are localized in the functional periphery of the network. A selection-based model explains the observed difference between essential and disease genes and also suggests that diseases caused by somatic mutations should not be peripheral, a prediction we confirm for cancer genes.

Keywords: biological networks, complex networks, human genetics, systems biology, diseasome

Abstract

Decades-long efforts to map human disease loci, at first genetically and later physically (1), followed by recent positional cloning of many disease genes (2) and genome-wide association studies (3), have generated an impressive list of disorder–gene association pairs (4, 5). In addition, recent efforts to map the protein–protein interactions in humans (6, 7), together with efforts to curate an extensive map of human metabolism (8) and regulatory networks offer increasingly detailed maps of the relationships between different disease genes. Most of the successful studies building on these new approaches have focused, however, on a single disease, using network-based tools to gain a better understanding of the relationship between the genes implicated in a selected disorder (9).

Here we take a conceptually different approach, exploring whether human genetic disorders and the corresponding disease genes might be related to each other at a higher level of cellular and organismal organization. Support for the validity of this approach is provided by examples of genetic disorders that arise from mutations in more than a single gene (locus heterogeneity). For example, Zellweger syndrome is caused by mutations in any of at least 11 genes, all associated with peroxisome biogenesis (10). Similarly, there are many examples of different mutations in the same gene (allelic heterogeneity) giving rise to phenotypes currently classified as different disorders. For example, mutations in TP53 have been linked to 11 clinically distinguishable cancer-related disorders (11). Given the highly interlinked internal organization of the cell (12 –17), it should be possible to improve the single gene–single disorder approach by developing a conceptual framework to link systematically all genetic disorders (the human “disease phenome”) with the complete list of disease genes (the “disease genome”), resulting in a global view of the “diseasome,” the combined set of all known disorder/disease gene associations.

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Acknowledgments

We thank Victor McKusick, Ada Hamosh, Joanna Amberger, and the rest of the OMIM team for their hard work and dedication and Tom Deisboeck, Zoltán Oltvai, Joanna Amberger, Todd Golub, Gerardo Jimenez-Sanchez and the members of the M.V. laboratory and the Center for Cancer Systems Biology, especially David E. Hill, for useful discussions. K.-I.G. and A.-L.B. were supported by National Institutes of Health (NIH) Grants IH U01 A1070499-01 and U56 {"type":"entrez-nucleotide","attrs":{"text":"CA113004","term_id":"34966311","term_text":"CA113004"}}CA113004 and National Science Foundation Grant ITR DMR-0926737 IIS-0513650. This work was supported by the Dana–Farber Cancer Institute (DFCI) Strategic Initiative (M.V.) and grants from the W. M. Keck Foundation (to M.V.) and the NIH/National Human Genome Research Institute and NIH/National Institute of General Medical Sciences (to M.V.).

Acknowledgments

Abbreviations

DGN	disease gene network
HDN	human disease network
GO	Gene Ontology
OMIM	Online Mendelian Inheritance in Man
PCC	Pearson correlation coefficient.

Abbreviations

Footnotes

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at www.pnas.org/cgi/content/full/0701361104/DC1.

Footnotes

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