Ecogeographic genetic epidemiology

Authors

  • Chantel D. Sloan,

    1. Computational Genetics Laboratory, Department of Genetics, Dartmouth Medical School, Lebanon, New Hampshire
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  • Eric J. Duell,

    1. Department of Community and Family Medicine, Dartmouth Medical School, Lebanon, New Hampshire
    2. Norris-Cotton Cancer Center, Dartmouth Medical School, Lebanon, New Hampshire
    3. Lifestyle, Environment and Cancer Group, International Agency for Research on Cancer, Cedex, France
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  • Xun Shi,

    1. Department of Geography, Dartmouth College, Hanover, New Hampshire
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  • Rebecca Irwin,

    1. Department of Biological Sciences, Dartmouth College, Hanover, New Hampshire
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  • Angeline S. Andrew,

    1. Department of Community and Family Medicine, Dartmouth Medical School, Lebanon, New Hampshire
    2. Norris-Cotton Cancer Center, Dartmouth Medical School, Lebanon, New Hampshire
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  • Scott M. Williams,

    1. Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee
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  • Jason H. Moore

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

Complex diseases such as cancer and heart disease result from interactions between an individual's genetics and environment, i.e. their human ecology. Rates of complex diseases have consistently demonstrated geographic patterns of incidence, or spatial “clusters” of increased incidence relative to the general population. Likewise, genetic subpopulations and environmental influences are not evenly distributed across space. Merging appropriate methods from genetic epidemiology, ecology and geography will provide a more complete understanding of the spatial interactions between genetics and environment that result in spatial patterning of disease rates. Geographic information systems (GIS), which are tools designed specifically for dealing with geographic data and performing spatial analyses to determine their relationship, are key to this kind of data integration. Here the authors introduce a new interdisciplinary paradigm, ecogeographic genetic epidemiology, which uses GIS and spatial statistical analyses to layer genetic subpopulation and environmental data with disease rates and thereby discern the complex gene-environment interactions which result in spatial patterns of incidence. Genet. Epidemiol. 2009. © 2008 Wiley-Liss, Inc.

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