Identification of hidden population structure in time-scaled phylogenies

Population structure influences genealogical patterns, however data
pertaining to how populations are structured are often unavailable or not
directly observable. Inference of population structure is highly important
in molecular epidemiology where pathogen phylogenetics is increasingly
used to infer transmission patterns and detect outbreaks. Discrepancies
between observed and idealised genealogies, such as those generated by the
coalescent process, can be quantified, and where significant differences
occur, may reveal the action of natural selection, host population
structure, or other demographic and epidemiological heterogeneities. We
have developed a fast non-parametric statistical test for detection of
cryptic population structure in time-scaled phylogenetic trees. The test
is based on contrasting estimated phylogenies with the theoretically
expected phylodynamic ordering of common ancestors in two clades within a
coalescent framework. These statistical tests have also motivated the
development of algorithms which can be used to quickly screen a
phylogenetic tree for clades which are likely to share a distinct
demographic or epidemiological history. Epidemiological applications
include identification of outbreaks in vulnerable host populations or
rapid expansion of genotypes with a fitness advantage. To demonstrate the
utility of these methods for outbreak detection, we applied the new
methods to large phylogenies reconstructed from thousands of HIV-1 partial
pol sequences. This revealed the presence of clades which had grown
rapidly in the recent past, and was significantly concentrated in young
men, suggesting recent and rapid transmission in that group. Furthermore,
to demonstrate the utility of these methods for the study of antimicrobial
resistance, we applied the new methods to a large phylogeny reconstructed
from whole genome Neisseria gonorrhoeae sequences. We find that population
structure detected using these methods closely overlaps with the
appearance and expansion of mutations conferring antimicrobial resistance.