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Ecogeographic genetic epidemiology

Year: 2008
Type: review
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.
DOI: 10.1002/gepi.20386
Authors: Chantel D. Sloan, Eric J. Duell, Xun Shi, Rebecca Irwin, Angeline S. Andrew, Scott M. Williams, Jason H. Moore
Institutions: Dartmouth College, Centre international de recherche sur le cancer, Dartmouth Cancer Center, Vanderbilt University, University of Vermont, Translational Genomics Research Institute, University of New Hampshire
Language: English
Subfield: Genetics
Open Access status: green
Awards: G1318433929, G1697600358, G2299545052, G4643106208, Machine Learning Prediction of Cancer Susceptibility, Bioinformatics Strategies: Biodefense Vaccine Research, Cellular and Molecular Mechanisms of Lung Disease
OpenAlex ID: W2088097174
FWCI: 0.125
Cites: 84
Cited by: 17
Related to: 10