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Identification of hidden population structure in time-scaled phylogenies
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Volz, Erik M., Wiuf, Carsten, Grad, Yonatan H., Frost, Simon D. W., Dennis, Ann M. and Didelot, Xavier (2020) Identification of hidden population structure in time-scaled phylogenies. Systematic Biology, 69 (5). pp. 884-896. doi:10.1093/sysbio/syaa009
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WRAP-identification-hidden-population-structure-time-scaled-phylogenie-Didelot-2020.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1870Kb) | Preview |
Official URL: https://doi.org/10.1093/sysbio/syaa009
Abstract
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.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | H Social Sciences > HA Statistics H Social Sciences > HB Economic Theory Q Science > QH Natural history |
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| Divisions: | Faculty of Science > Life Sciences (2010- ) | |||||||||||||||
| SWORD Depositor: | Library Publications Router | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Population , Population forecasting , Population -- Statistics , Phylogeny | |||||||||||||||
| Journal or Publication Title: | Systematic Biology | |||||||||||||||
| Publisher: | Oxford University Press | |||||||||||||||
| ISSN: | 1063-5157 | |||||||||||||||
| Official Date: | September 2020 | |||||||||||||||
| Dates: |
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| Volume: | 69 | |||||||||||||||
| Number: | 5 | |||||||||||||||
| Page Range: | pp. 884-896 | |||||||||||||||
| DOI: | 10.1093/sysbio/syaa009 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Access rights to Published version: | Open Access | |||||||||||||||
| RIOXX Funder/Project Grant: |
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