Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication

DOI

10.1038/s41467-019-10724-w

Type

Journal article

Journal

Nat Commun

Publication Date

09/07/2019

Volume

10

Keywords

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