A Statistical Framework for Microbial Source Attribution [electronic resource] : Measuring Uncertainty in Host Transmission Events Inferred from Genetic Data (Part 2 of a 2 Part Report).

Published:: Washington, D.C. : United States. Dept. of Energy, 2009.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description:: PDF-file: 33 pages; size: 1.3 Mbytes
Additional Creators:: Lawrence Berkeley National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information

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Summary:

This report explores the question of whether meaningful conclusions can be drawn regarding the transmission relationship between two microbial samples on the basis of differences observed between the two sample's respective genomes. Unlike similar forensic applications using human DNA, the rapid rate of microbial genome evolution combined with the dynamics of infectious disease require a shift in thinking on what it means for two samples to 'match' in support of a forensic hypothesis. Previous outbreaks for SARS-CoV, FMDV and HIV were examined to investigate the question of how microbial sequence data can be used to draw inferences that link two infected individuals by direct transmission. The results are counter intuitive with respect to human DNA forensic applications in that some genetic change rather than exact matching improve confidence in inferring direct transmission links, however, too much genetic change poses challenges, which can weaken confidence in inferred links. High rates of infection coupled with relatively weak selective pressure observed in the SARS-CoV and FMDV data lead to fairly low confidence for direct transmission links. Confidence values for forensic hypotheses increased when testing for the possibility that samples are separated by at most a few intermediate hosts. Moreover, the observed outbreak conditions support the potential to provide high confidence values for hypothesis that exclude direct transmission links. Transmission inferences are based on the total number of observed or inferred genetic changes separating two sequences rather than uniquely weighing the importance of any one genetic mismatch. Thus, inferences are surprisingly robust in the presence of sequencing errors provided the error rates are randomly distributed across all samples in the reference outbreak database and the novel sequence samples in question. When the number of observed nucleotide mutations are limited due to characteristics of the outbreak or the availability of only partial rather than whole genome sequencing, indel information was shown to have the potential to improve performance but only for select outbreak conditions. In examined HIV transmission cases, extended evolution proved to be the limiting factor in assigning high confidence to transmission links, however, the potential to correct for extended evolution not associated with transmission events is demonstrated. Outbreak specific conditions such as selective pressure (in the form of varying mutation rate), are shown to impact the strength of inference made and a Monte Carlo simulation tool is introduced, which is used to provide upper and lower bounds on the confidence values associated with a forensic hypothesis.

Report Numbers:

E 1.99:llnl-tr-420850
llnl-tr-420850

Subject(s):

Basic Biological Sciences

Other Subject(s):

Note:

Published through SciTech Connect.
11/16/2009.
"llnl-tr-420850"
Allen, J; Velsko, S.

Funding Information:

W-7405-ENG-48

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