Reconstructing disease outbreaks from genetic data: a graph approach

Abstract

Epidemiology and public health planning will increasingly rely on the analysis of genetic sequence data. In particular, genetic data coupled with dates and locations of sampled isolates can be used to reconstruct the spatiotemporal dynamics of pathogens during outbreaks. Thus far, phylogenetic methods have been used to tackle this issue. Although these approaches have proved useful for informing on the spread of pathogens, they do not aim at directly reconstructing the underlying transmission tree. Instead, phylogenetic models infer most recent common ancestors between pairs of isolates, which can be inadequate for densely sampled recent outbreaks, where the sample includes ancestral and descendent isolates. In this paper, we introduce a novel method based on a graph approach to reconstruct transmission trees directly from genetic data. Using simulated data, we show that our approach can efficiently reconstruct genealogies of isolates in situations where classical phylogenetic approaches fail to do so. We then illustrate our method by analyzing data from the early stages of the swine-origin A/H1N1 influenza pandemic. Using 433 isolates sequenced at both the hemagglutinin and neuraminidase genes, we reconstruct the likely history of the worldwide spread of this new influenza strain. The presented methodology opens new perspectives for the analysis of genetic data in the context of disease outbreaks.

Molecular Evolution Analysis of the Human Immunodeficiency Virus Type 1 Envelope in Simian/Human Immunodeficiency Virus-Infected Macaques: Implications for Challenge Dose Selection

“…The transmitted variants were identified and enumerated using SeqTrack (20). To test for the nonuniform probability of transmission of particular variants, we fitted a mixture of two binomial distributions to the number of times (out of 7) a variant was transmitted, and we compared the goodness of fit using Akaike's information criterion (where lower scores indicate a better fit).…”

Impacts of climate change on plant diseases—opinions and trends

“…Progress in our understanding of how both groups of plant pathogens will respond to climate change will be facilitated by the application of emerging genetic techniques (Pritchard 2011). Genetic analyses will be instrumental in devising strategies to cope with an increased pressure from established and new diseases as a result of better suitability of the climatic conditions and/or more intense and farreaching trade (Bawa and Dayanandan 1998;Archie et al 2008;Jombart et al 2011). For example, there is genetic evidence that some ash trees (Fraxinus excelsior) in Denmark are resistant against the emerging fungal pathogen Chalara fraxinea (McKinney et al 2011;Kjaer et al 2012), which is now reported to cause ash dieback throughout Europe, from Poland to France and from Sweden to Switzerland (Bengtsson et al 2012;Gross et al 2012).…”

From types to trees: Reconstructing the spatial spread of Staphylococcus aureus based on DNA variation

“…The genealogical relationships among isolates from a single outbreak or epidemic can be reconstructed on the basis of serially sampled DNA sequences, if these are sufficiently variable (Jombart et al, 2011). On this basis, and by 10 considering sampling dates and locations, the time course and pathways of transmission and spatial spread can then be determined and visualized (Jombart et al, 2011, Lemey et al, 2009). …”

“…This short-term evolutionary rate was virtually identical in two different clones of MRSA and much faster than previously acknowledged. Importantly, such a measurable accumulation of DNA variation over epidemiologically relevant 5 timescales can be exploited to accurately infer the spatial and temporal dynamics of pathogen spread (Cottam et al, 2008, Hue et al, 2005, Jombart et al, 2011, Lemey et al, 2009. The genealogical relationships among isolates from a single outbreak or epidemic can be reconstructed on the basis of serially sampled DNA sequences, if these are sufficiently variable (Jombart et al, 2011).…”

“…Importantly, such a measurable accumulation of DNA variation over epidemiologically relevant 5 timescales can be exploited to accurately infer the spatial and temporal dynamics of pathogen spread (Cottam et al, 2008, Hue et al, 2005, Jombart et al, 2011, Lemey et al, 2009. The genealogical relationships among isolates from a single outbreak or epidemic can be reconstructed on the basis of serially sampled DNA sequences, if these are sufficiently variable (Jombart et al, 2011). On this basis, and by 10 considering sampling dates and locations, the time course and pathways of transmission and spatial spread can then be determined and visualized (Jombart et al, 2011, Lemey et al, 2009).…”