Detecting pathogen transmission from genetic sequence data

Alexander Beams

Simon Fraser University

"Detecting pathogen transmission from genetic sequence data"

The accrual of nucleotide substitutions in pathogen genomes accompanies their transmission through host populations. Because lineages with higher fitness tend to transmit rapidly to new hosts before incurring very many substitutions, large numbers of related sequences are usually interpreted as evidence of transmission success. Quantities like the local branching index (LBI) aim to identify successful lineages in this way by scoring sequences according to the number of close relatives captured in the dataset. While statistics like LBI are easily calculated from a given phylogenetic tree (or a distribution of trees), observation errors related to sampling bias and censoring may introduce spurious signals of transmission success. To disentangle these effects, we use stochastic compartmental models to simulate outbreaks and generate distributions of phylogenies under a variety of testing programs (such as surveillance of symptomatic cases, or cross-sectional prevalence studies). By characterizing the types of phylogenies expected under these situations, we can work towards a clearer understanding of the types of signals that are likely to be detected with sequence data.

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