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

Duncan S Palmer1,2,3, Isaac Turner4,5, Sarah Fidler6

  • 1Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK. duncan.stuart.palmer@gmail.com.

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|July 11, 2019
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Summary
This summary is machine-generated.

Host genetic variation influences pathogen evolution. Our new Bayesian method accurately detects these host-pathogen interactions, improving analysis of pathogen genomes like HIV-1.

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Area of Science:

  • Evolutionary biology
  • Genetics
  • Computational biology

Background:

  • Host genetic variation and drug treatments can drive pathogen evolution within a host.
  • Identifying host influences on pathogen evolution is challenging due to confounding genetic structures and multiple testing.
  • Existing genetic association studies may lack the power to detect subtle host-pathogen interactions.

Purpose of the Study:

  • To develop a robust Bayesian approach for detecting host genetic influences on pathogen evolution.
  • To improve the power and precision of identifying host-pathogen interactions, particularly in large pathogen genomic datasets.
  • To control for confounding factors like population structure in host and pathogen genomes.

Main Methods:

  • A novel Bayesian statistical framework was developed to model pathogen evolutionary processes (recombination, selection).
  • The method leverages large pathogen diversity datasets to enhance statistical power and account for population stratification.
  • Simulations and empirical analysis of drug-induced selection in HIV-1 were used for validation.

Main Results:

  • The Bayesian approach successfully identified known host-pathogen associations in simulations and empirical data.
  • The method demonstrated superior precision-recall performance compared to existing approaches for detecting host influences.
  • A high-resolution map of human leukocyte antigen (HLA)-induced selection on the HIV-1 genome was generated, revealing novel epitope-allele associations.

Conclusions:

  • The developed Bayesian method provides a powerful and accurate tool for dissecting host influences on pathogen evolution.
  • This approach can effectively identify specific regions of pathogen genomes affected by host factors, such as immune selection.
  • The findings offer new insights into host-pathogen co-evolution and can guide the development of targeted therapeutic strategies.