Mapping the drivers of within-host pathogen evolution using massive data sets
File(s)
Author(s)
Type
Journal Article
Abstract
Differences among hosts, resulting from genetic variation in the immune system or heterogeneity in drug treatment, can impact within-host pathogen evolution. Genetic association studies can potentially identify such interactions. However, extensive and correlated genetic population structure in hosts and pathogens presents a substantial risk of confounding analyses. Moreover, the multiple testing burden of interaction scanning can potentially limit power. We present a Bayesian approach for detecting host influences on pathogen evolution that exploits vast existing data sets of pathogen diversity to improve power and control for stratification. The approach models key processes, including recombination and selection, and identifies regions of the pathogen genome affected by host factors. Our simulations and empirical analysis of drug-induced selection on the HIV-1 genome show that the method recovers known associations and has superior precision-recall characteristics compared to other approaches. We build a high-resolution map of HLA-induced selection in the HIV-1 genome, identifying novel epitope-allele combinations.
Date Issued
2019-07-09
Date Acceptance
2019-05-20
ISSN
2041-1723
Publisher
Nature Research (part of Springer Nature)
Start Page
1
End Page
14
Journal / Book Title
Nature Communications
Volume
10
Copyright Statement
© The Author(s) 2019. This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit http://creativecommons.org/
licenses/by/4.0/.
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit http://creativecommons.org/
licenses/by/4.0/.
Sponsor
Imperial College Healthcare NHS Trust- BRC Funding
Medical Research Council (MRC)
Imperial College Healthcare NHS Trust- BRC Funding
Identifier
https://www.nature.com/articles/s41467-019-10724-w
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000474506700003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
RDA02 79560
MR/L00528X/1
RDA02
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
HIV-1 DRUG-RESISTANCE
T-CELL RESPONSES
HLA CLASS-I
IMMUNE-RESPONSES
VIRUS
MUTATIONS
SELECTION
ESCAPE
SUSCEPTIBILITY
POLYMORPHISMS
Anti-HIV Agents
Bayes Theorem
Datasets as Topic
Epitopes
Evolution, Molecular
Genome, Viral
HIV Infections
HIV-1
HLA Antigens
Host-Pathogen Interactions
Humans
Models, Genetic
Recombination, Genetic
Selection, Genetic
Humans
HIV-1
HIV Infections
HLA Antigens
Epitopes
Anti-HIV Agents
Bayes Theorem
Evolution, Molecular
Recombination, Genetic
Genome, Viral
Models, Genetic
Host-Pathogen Interactions
Selection, Genetic
Datasets as Topic
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
HIV-1 DRUG-RESISTANCE
T-CELL RESPONSES
HLA CLASS-I
IMMUNE-RESPONSES
VIRUS
MUTATIONS
SELECTION
ESCAPE
SUSCEPTIBILITY
POLYMORPHISMS
Publication Status
Published
Article Number
ARTN 3017
Date Publish Online
2019-07-09