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Title: Phylogenetic patterns recover known HIV epidemiological relationships and reveal common transmission of multiple variants

Journal Article · · Nature Microbiology

The growth of human immunodeficiency virus (HIV) sequence databases resulting from drug resistance testing has motivated efforts using phylogenetic methods to assess how HIV spreads. Such inference is potentially both powerful and useful for tracking the epidemiology of HIV and the allocation of resources to prevention campaigns. We recently used simulation and a small number of illustrative cases to show that certain phylogenetic patterns are associated with different types of epidemiological linkage. Our original approach was later generalized for large next-generation sequencing datasets and implemented as a free computational pipeline. Previous work has claimed that direction and directness of transmission could not be established from phylogeny because one could not be sure that there were no intervening or missing links involved. We address this issue by investigating phylogenetic patterns from 272 previously identified HIV transmission chains with 955 transmission pairs representing diverse geography, risk groups, subtypes, and genomic regions. These HIV transmissions had known linkage based on epidemiological information such as partner studies, mother-to-child transmission, pairs identified by contact tracing, and criminal cases. Here, we show that the resulting phylogeny inferred from real HIV genetic sequences indeed reveals distinct patterns associated with direct transmission contra transmissions from a common source. Thus, our results establish how to interpret phylogenetic trees based on HIV sequences when tracking who-infected-whom, when and how genetic information can be used for improved tracking of HIV spread. We also investigate limitations that stem from limited sampling and genetic time-trends in the donor and recipient HIV populations.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE Laboratory Directed Research and Development (LDRD) Program
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1479964
Report Number(s):
LA-UR-18-28043
Journal Information:
Nature Microbiology, Vol. 3, Issue 9; ISSN 2058-5276
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 31 works
Citation information provided by
Web of Science

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Cited By (6)

HIV Care Prioritization Using Phylogenetic Branch Length journal January 2021
Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases journal May 2019
FAVITES: simultaneous simulation of transmission networks, phylogenetic trees and sequences journal November 2018
Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases journal May 2019
Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort journal January 2020
Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis journal March 2019

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