Ecological genomics and epidemiology



The huge amount of genomic data now becoming available offers both opportunities and challenges for epidemiologists.In this “preview“ of likely developments as the field of ecological genomics evolves and merges with epidemiology,we discuss how epidemiology can use new information about genetic sequences and gene expression to form predictions about epidemic features and outcomes and for understanding host resistance and pathogen evolution.DNA sequencing is now complete for some hosts and several pathogens. Microarrays make it possible to measure gene expression simultaneously for thousands of genes.These tools will contribute to plant disease epidemiology by providing information about which resistance or pathogenicity genes are present in individuals and populations,what genes other than those directly involved in resistance and virulence are important in epidemics,the role of the phenotypic status of hosts and pathogens,and the role of the status of the environmental metagenome.Conversely,models of group dynamics supplied by population biology and ecology may be used to interpret gene expression within individual organisms and in populations of organisms.Genomic tools have great potential for improving understanding of resistance gene evolution and the durability of resistance.For example,DNA sequence analysis can be used to evaluate whether an arms race model of co-evolution is supported.Finally,new genomic tools will make it possible to consider the landscape ecology of epidemics in terms of host resistance both as determined by genotype and as expressed in host phenotypes in response to the biotic and abiotic environment.Host phenotype mixtures can be modeled and evaluated,with epidemiological pre- dictions based on phenotypic characteristics such as physiological age and status in terms of induced systemic resistance or systemic acquired resistance.


Host Individual Host Genotype Abiotic Environment Local Gene Expression Epidemic Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer 2006

Authors and Affiliations

  1. 1.Department of Plant PathologyKansas State UniversityManhattanUSA
  2. 2.Department of Bioagricultural Sciences and Pest ManagementColorado State UniversityFt.CollinsUSA

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