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Chapter 4 : Analysis of Similarity and Relatedness in Molecular Epidemiology

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Abstract:

The science of classifying organisms must be distinguished from the science of molecular epidemiology, but the principles used in systematics are essential to molecular epidemiologic analyses of pathogens. These include, but are not limited to, (i) analytical methods used to group strains, (ii) validity or optimization criteria used to define strain relatedness, (iii) methods of inferring evolutionary relationships of genes of interest, (iv) assumptions made about genetic mutational and recombination events in an organism that determine its population structure, (v) appropriateness and choice of molecular techniques used to classify strains, and (vi) computer-assisted techniques to compare data generated from strain-typing methods to assess relatedness. This chapter discusses these issues, with the proviso that it is not meant to present a comprehensive review of systematics.

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
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Figures

Image of Figure 4.1
Figure 4.1

Hypothetical gel electrophoretic fingerprint patterns of two strains (OTU A and OTU B), from which different similarity coefficients can be calculated.

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
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Image of Figure 4.2
Figure 4.2

Dendrograms derived from the same set of strains subjected to IS-RFLP analysis. Similarity analysis of the generated electrophoretic band patterns was performed by the comparison of (A) Dice coefficient indices, which take into account band positions only, and (B) Pearson product-moment correlation coefficient indices, which take into account band positions as well as differences in width of bands. (GelCompar analysis figures provided courtesy of Lucilaine Ferrazoli, São Paulo, Brazil.)

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
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Image of Figure 4.3
Figure 4.3

A schematic depiction of electrophoretic fingerprint patterns of OTUs A through E.

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
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Figure 4.4

(A) Phenogram based on UPGMA analysis of OTUs A through E in Fig. 4.3. Branch lengths are based on distance indices. (B) Phenogram based on nearest-neighbor cluster analysis of OTUs A through E in Fig. 4.3. Branch lengths are based on similarity indices.

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
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Image of Figure 4.5a
Figure 4.5a

(A) Alignment of hypothetical DNA sequences, depicted in the matrix table and dendrogram as percent similarity between OTUs A through G. The nucleotide changes compared with OTU-A sequence are indicated in bold type. One resampled set with its corresponding tree is shown as an example of bootstrapping. Note the small change in topology of the dendrogram (bottom).

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
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Image of Figure 4.5b
Figure 4.5b

(B) Original and resampled sets, with the number of times each column is resampled indicated above each nucleotide position.

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
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References

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Tables

Generic image for table
Table 4.1a

Computer programs and software packages useful for molecular epidemiologic studies

Service sites to which sequence data can be submitted for online analysis.

Programs used to perform sequence analysis, alignments, and phylogenetic and cluster analyses.

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
Generic image for table
Table 4.1b

Computer programs and software packages useful for molecular epidemiologic studies

Service sites to which sequence data can be submitted for online analysis.

Programs used to perform sequence analysis, alignments, and phylogenetic and cluster analyses.

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
Generic image for table
Table 4.2

Jaccard similarity coefficient matrix, based on gel electrophoresis fingerprint patterns of OTUs A through E shown in Fig. 4.3

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
Generic image for table
Table 4.3

UPGMA analysis based on distance indices derived from analysis of gel electrophoresis fingerprint patterns of OTUs A through E shown in Fig. 4.3

OTU could be a single strain or a cluster of strains

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
Generic image for table
Table 4.4

OTUs C and D, joined to form a new cluster, C-D

OTU could be a single strain or a cluster of strains

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
Generic image for table
Table 4.5

OTUs A and B, joined to form a new cluster, A-B

OTU could be a single strain or a cluster of strains. C-D and A-B can next be joined to make one large cluster, A-B-C-D, which can be joined to E

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4
Generic image for table
Table 4.6

Typical requirements in a laboratory for database analysis software packages used to conduct a molecular epidemiology project

Apart from these software packages, all others listed are available free of charge online. The vendors and sources of these packages are indicated in Table 4.1

Citation: Riley L. 2004. Analysis of Similarity and Relatedness in Molecular Epidemiology, p 91-124. In Molecular Epidemiology of Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/9781555817688.ch4

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