Speaker
Description
Bayesian phylogeographic inference is a powerful tool in molecular epidemiological studies, enabling the reconstruction of the dispersal history of rapidly evolving pathogens. BEAST, a Bayesian phylogenetic inference software package, provides a discrete trait analysis (DTA) that integrates geographic information as discrete characters and infers transition events among discrete sampling locations. The DTA model can be coupled to a model averaging procedure to elucidate the subset of epidemiological links that appropriately explain the diffusion process, which provides a Bayes factor (BF) test to identify significant migration links. The BF support for a particular link is the ratio between the a posteriori and the a priori expection that this migration link helps explain the migration history. In its current setup, however, the a priori expectation only depends on the number of trait states but does not account for the relative abundance of the involved trait states. This can bias inference in the presence of uneven sampling, and appropriately identifying relevant patterns is crucial for reliable hypothesis testing.
To mitigate potential artifacts from sampling bias, Vrancken and colleagues recently introduced and applied an adjusted Bayes factor (BFadj) which incorporates information on the relative abundance of samples by location.
In this study we (i) optimize the set up of the tip-state DTA and (ii) formally evaluate the performance of the BFadj metric, specifically determining to what extent it can identify false positives from the standard approach. In other words, we seek to assess how well the BFadj identifies transition events or inferred root location where their high associated BFs may result from sampling bias. Our results indicate that the BFadj leads to the identification of fewer false positive transitions events but also fewer true positives, hence it appears to more efficiently classify transitions events as non-signiificant at the expense of being more conservative.
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