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

View ORCID ProfileDuncan S. Palmer, View ORCID ProfileIsaac Turner, View ORCID ProfileSarah Fidler, View ORCID ProfileJohn Frater, Philip Goulder, Dominique Goedhals, View ORCID ProfileKuan-Hsiang Gary Huang, View ORCID ProfileAnnette Oxenius, Rodney Phillips, Roger Shapiro, Cloete van Vuuren, View ORCID ProfileAngela R. McLean, View ORCID ProfileGil McVean
doi: https://doi.org/10.1101/155242
Duncan S. Palmer
Department of Statistics, University of Oxford, 24-29 St Giles’, Oxford OX1 3LB, UK. Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK. Institute for Emerging Infections, The Oxford Martin School, Oxford OX1 3BD, UK.
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Isaac Turner
Department of Statistics, University of Oxford, 24-29 St Giles’, Oxford OX1 3LB, UK. Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK.
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Sarah Fidler
Division of Medicine, Wright Fleming Institute, Imperial College, London W2 1PG, UK.
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John Frater
Institute for Emerging Infections, The Oxford Martin School, Oxford OX1 3BD, UK. Nuffield Department of Clinical Medicine, University of Oxford, Peter Medawar Building for Pathogen Research, Oxford OX1 3SY, UK. Oxford NIHR Biomedical Research Centre, Oxford OX3 7LE, UK.
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Philip Goulder
Department of Paediatrics, University of Oxford, Peter Medawar Building for Pathogen Research, Oxford OX1 3SY, UK. HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, South Africa.
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Dominique Goedhals
Department of Medical Microbiology and Virology, University of the Free State, and National Heath Laboratory Service, Bloemfontein, South Africa.
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Kuan-Hsiang Gary Huang
Nuffield Department of Clinical Medicine, University of Oxford, Peter Medawar Building for Pathogen Research, Oxford OX1 3SY, UK. Einstein Medical Center Philadelphia, 5501 Old York Road, PA 19141, USA.
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Annette Oxenius
Institute of Microbiology, Swiss Federal Institute of Technology Zurich, 8093 Zurich, Switzerland.
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Rodney Phillips
Institute for Emerging Infections, The Oxford Martin School, Oxford OX1 3BD, UK. Nuffield Department of Clinical Medicine, University of Oxford, Peter Medawar Building for Pathogen Research, Oxford OX1 3SY, UK. Oxford NIHR Biomedical Research Centre, Oxford OX3 7LE, UK.
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Roger Shapiro
Botswana Harvard AIDS Institute Partnership, Gaborone BO 320, Botswana. Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, MA 02215, USA.
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Cloete van Vuuren
Department of Medical Microbiology and Virology, University of the Free State, and National Heath Laboratory Service, Bloemfontein, South Africa.
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Angela R. McLean
Institute for Emerging Infections, The Oxford Martin School, Oxford OX1 3BD, UK. Zoology Department, South Parks Road, University of Oxford, Oxford OX1 3PS, UK.
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Gil McVean
Department of Statistics, University of Oxford, 24-29 St Giles’, Oxford OX1 3LB, UK. Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK. Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Oxford OX3 7LF, UK.
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Abstract

Differences among hosts, resulting from genetic variation in the immune system or heterogeneity in drug treatment, can impact within-host pathogen evolution. Identifying such interactions can potentially be achieved through genetic association studies. 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. To address these problems, we have developed a Bayesian approach for detecting host influences on pathogen evolution that makes use of 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. Using simulations and empirical analysis of drug-induced selection on the HIV-1 genome we demonstrate the power of the method to recover known associations and show greatly improved 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.

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Posted June 25, 2017.
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Mapping the drivers of within-host pathogen evolution using massive data sets
Duncan S. Palmer, Isaac Turner, Sarah Fidler, John Frater, Philip Goulder, Dominique Goedhals, Kuan-Hsiang Gary Huang, Annette Oxenius, Rodney Phillips, Roger Shapiro, Cloete van Vuuren, Angela R. McLean, Gil McVean
bioRxiv 155242; doi: https://doi.org/10.1101/155242
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Mapping the drivers of within-host pathogen evolution using massive data sets
Duncan S. Palmer, Isaac Turner, Sarah Fidler, John Frater, Philip Goulder, Dominique Goedhals, Kuan-Hsiang Gary Huang, Annette Oxenius, Rodney Phillips, Roger Shapiro, Cloete van Vuuren, Angela R. McLean, Gil McVean
bioRxiv 155242; doi: https://doi.org/10.1101/155242

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