Journal article icon

Journal article

Using phylogenetics to infer HIV-1 transmission direction between known transmission pairs

Abstract:
Inferring the transmission direction between linked individuals living with HIV provides unparalleled power to understand the epidemiology that determines transmission. Phylogenetic ancestral-state reconstruction approaches infer the transmission direction by identifying the individual in whom the most recent common ancestor of the virus populations originated. While these methods vary in accuracy, it is unclear why. To evaluate the performance of phylogenetic ancestral-state reconstruction to determine the transmission direction of HIV-1 infection, we inferred the transmission direction for 112 transmission pairs where transmission direction and detailed additional information were available. We then fit a statistical model to evaluate the extent to which epidemiological, sampling, genetic, and phylogenetic factors influenced the outcome of the inference. Finally, we repeated the analysis under real-life conditions with only routinely available data. We found that whether ancestral-state reconstruction correctly infers the transmission direction depends principally on the phylogeny's topology. For example, under real-life conditions, the probability of identifying the correct transmission direction increases from 32%—when a monophyletic–monophyletic or paraphyletic–polyphyletic tree topology is observed and when the tip closest to the root does not agree with the state at the root—to 93% when a paraphyletic–monophyletic topology is observed and when the tip closest to the root agrees with the root state. Our results suggest that documenting larger differences in relative intrahost diversity increases our confidence in the transmission direction inference of linked pairs for population-level studies of HIV. These findings provide a practical starting point to determine our confidence in transmission direction inference from ancestral-state reconstruction.
Publication status:
Published
Peer review status:
Peer reviewed

Actions

Access Document

Publisher copy:
10.1073/pnas.2210604119

Authors

More by this author
Role:
Author
ORCID:
0000-0001-9928-3968
More by this author
Role:
Author
ORCID:
0000-0002-8580-6905
More by this author
Role:
Author
ORCID:
0000-0001-6598-1784
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Big Data Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Biology
Research group:
Big Data Institute, NDM
Oxford college:
Brasenose College
Role:
Author
ORCID:
0000-0002-7089-7680


Publisher:
National Academy of Sciences
Journal:
Proceedings of the National Academy of Sciences More from this journal
Volume:
119
Issue:
38
Article number:
e2210604119
Place of publication:
United States
Publication date:
2022-09-14
Acceptance date:
2022-07-25
DOI:
EISSN:
1091-6490
ISSN:
0027-8424
Pmid:
36103580

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP