PT  - JOURNAL ARTICLE
AU  - Duncan S. Palmer
AU  - Isaac Turner
AU  - Sarah Fidler
AU  - John Frater
AU  - Philip Goulder
AU  - Dominique Goedhals
AU  - Kuan-Hsiang Gary Huang
AU  - Annette Oxenius
AU  - Rodney Phillips
AU  - Roger Shapiro
AU  - Cloete van Vuuren
AU  - Angela R. McLean
AU  - Gil McVean
TI  - Mapping the drivers of within-host pathogen evolution using massive data sets
AID  - 10.1101/155242
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 155242
4099  - http://biorxiv.org/content/early/2017/06/25/155242.short
4100  - http://biorxiv.org/content/early/2017/06/25/155242.full
AB  - 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.