1. Mapping the drivers of within-host pathogen evolution using massive data sets
- Author
-
Angela R. McLean, Sarah Fidler, Kuan-Hsiang Gary Huang, Gil McVean, Philip J. R. Goulder, John Frater, Duncan S. Palmer, Cloete van Vuuren, Rodney E. Phillips, Dominique Goedhals, Roger L. Shapiro, Annette Oxenius, Isaac Turner, Imperial College Healthcare NHS Trust- BRC Funding, and Medical Research Council (MRC)
- Subjects
HLA CLASS-I ,SELECTION ,0301 basic medicine ,Antigen processing and presentation ,Population genetics ,Datasets as Topic ,General Physics and Astronomy ,HIV Infections ,02 engineering and technology ,SUSCEPTIBILITY ,Genome ,Epitopes ,Bayes' theorem ,0302 clinical medicine ,HLA Antigens ,lcsh:Science ,Pathogen ,Genetics ,Recombination, Genetic ,0303 health sciences ,Multidisciplinary ,IMMUNE-RESPONSES ,021001 nanoscience & nanotechnology ,3. Good health ,Multidisciplinary Sciences ,030220 oncology & carcinogenesis ,Host-Pathogen Interactions ,Infectious diseases ,Science & Technology - Other Topics ,VIRUS ,0210 nano-technology ,Host (network) ,Anti-HIV Agents ,Science ,Bayesian probability ,Genome, Viral ,Computational biology ,Biology ,ESCAPE ,Article ,General Biochemistry, Genetics and Molecular Biology ,Evolution, Molecular ,03 medical and health sciences ,Genetic variation ,Humans ,Selection, Genetic ,POLYMORPHISMS ,Selection (genetic algorithm) ,030304 developmental biology ,Genetic association ,Science & Technology ,T-CELL RESPONSES ,Models, Genetic ,MUTATIONS ,HIV-1 DRUG-RESISTANCE ,Bayes Theorem ,General Chemistry ,030104 developmental biology ,Multiple comparisons problem ,HIV-1 ,lcsh:Q - 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., Nature Communications, 10 (1), ISSN:2041-1723
- Published
- 2019
- Full Text
- View/download PDF