Shadows of complexity: what biological networks reveal about epistasis and pleiotropy

Authors

  • Anna L. Tyler,

    1. Computational Genetics Laboratory, Department of Genetics, Dartmouth Medical School, Lebanon, NH, USA
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  • Folkert W. Asselbergs,

    1. Department of Cardiology, University Medical Center Groningen, Groningen, The Netherlands
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  • Scott M. Williams,

    1. Center for Human Genetics Research, Department of Medicine, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN, USA
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  • Jason H. Moore Ph.D.

    Corresponding author
    1. Computational Genetics Laboratory, Department of Genetics, Dartmouth Medical School, Lebanon, NH, USA
    2. Department of Community and Family Medicine, Dartmouth Medical School, Lebanon, NH, USA
    3. Department of Computer Science, University of Vermont, Burlington, Vermont, USA
    4. Department of Computer Science, University of New Hampshire, Durham, NH, USA
    5. Translational Genomics Research Institute, Phoenix, AZ, USA
    • 706 Rubin Building, HB 7937, One Medical Center Drive, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA 03756.
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Abstract

Pleiotropy, in which one mutation causes multiple phenotypes, has traditionally been seen as a deviation from the conventional observation in which one gene affects one phenotype. Epistasis, or gene–gene interaction, has also been treated as an exception to the Mendelian one gene–one phenotype paradigm. This simplified perspective belies the pervasive complexity of biology and hinders progress toward a deeper understanding of biological systems. We assert that epistasis and pleiotropy are not isolated occurrences, but ubiquitous and inherent properties of biomolecular networks. These phenomena should not be treated as exceptions, but rather as fundamental components of genetic analyses. A systems level understanding of epistasis and pleiotropy is, therefore, critical to furthering our understanding of human genetics and its contribution to common human disease. Finally, graph theory offers an intuitive and powerful set of tools with which to study the network bases of these important genetic phenomena.

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