Mapping the drivers of within-host pathogen evolution using massive data sets

Duncan S. Palmer (), Isaac Turner, Sarah Fidler, John Frater, Dominique Goedhals, Philip Goulder, Kuan-Hsiang Gary Huang, Annette Oxenius, Rodney Phillips, Roger Shapiro, Cloete van Vuuren, Angela R. McLean and Gil McVean
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Duncan S. Palmer: University of Oxford
Isaac Turner: University of Oxford
Sarah Fidler: Wright Fleming Institute, Imperial College
John Frater: The Oxford Martin School
Dominique Goedhals: University of KwaZulu-Natal
Philip Goulder: University of the Free State, and 3 Military Hospital
Kuan-Hsiang Gary Huang: University of Oxford, Peter Medawar Building for Pathogen Research
Annette Oxenius: Swiss Federal Institute of Technology Zurich
Rodney Phillips: The Oxford Martin School
Roger Shapiro: Botswana Harvard AIDS Institute Partnership
Cloete van Vuuren: University of KwaZulu-Natal
Angela R. McLean: The Oxford Martin School
Gil McVean: University of Oxford

Nature Communications, 2019, vol. 10, issue 1, 1-14

Abstract: 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: 2019
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DOI: 10.1038/s41467-019-10724-w

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