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Accounting for Dependence in Similarity Data from DNA Fingerprinting

Author

Listed:
  • Hepworth Graham

    (The University of Melbourne)

  • Gordon Ian R

    (The University of Melbourne)

  • McCullough Michael J

    (The University of Melbourne)

Abstract

Differentiating strains of a pathogen is often central to investigating its epidemiological aspects. The genetic similarity of a group of strains can be assessed by calculating a matrix of dissimilarities from their DNA fingerprinting profiles. The mean dissimilarity for each strain across other strains within the group is then used as an observation in a statistical analysis. These observations are not independent of each other, and so standard analysis techniques such as the t-test are inappropriate, because they underestimate the variance of the group means, and hence overstate the statistical significance of any differences. By examining the correlation between elements of the dissimilarity matrix, it is shown that the variance is underestimated by a factor of between about 2 and 4. Permutation tests are proposed as a way of addressing the problem of dependence, and are applied to a study of fluconazole resistance in Candida albicans.

Suggested Citation

  • Hepworth Graham & Gordon Ian R & McCullough Michael J, 2007. "Accounting for Dependence in Similarity Data from DNA Fingerprinting," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-15, January.
    • Handle: RePEc:bpj:sagmbi:v:6:y:2007:i:1:n:1
    DOI: 10.2202/1544-6115.1212
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