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Inference of Host-Pathogen Interaction Matrices from Genome-Wide Polymorphism Data.

Hanna Märkle1,2, Sona John1, Lukas Metzger1

  • 1Population Genetics, Department of Life Science Systems, School of Life Sciences, Technical University of Munich, Freising 85354Germany.

Molecular Biology and Evolution
|August 22, 2024
PubMed
Summary

Understanding host-pathogen coevolution requires mapping genotype × genotype interactions. This study introduces novel indices and methods to infer infection matrices, identifying a new human gene for hepatitis C virus resistance.

Keywords:
G×G interactionshost–pathogen coevolutionlinkage disequilibriumpopulation genomicssingle nucleotide polymorphism

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Area of Science:

  • Evolutionary biology
  • Genetics
  • Infectious disease dynamics

Background:

  • Host-pathogen coevolution involves reciprocal genetic changes influencing infection outcomes.
  • Genome-wide association studies identify interacting genes but not specific genotype-by-genotype resistance profiles.
  • Understanding these infection matrices is crucial for agriculture and medicine.

Purpose of the Study:

  • To derive novel indices for characterizing host-pathogen infection matrices.
  • To develop a computational framework for inferring genotype × genotype (G×G) interactions and infection matrices from genomic data.
  • To identify specific host resistance genes and pathogen adaptations in human hepatitis C virus (HCV) infection.

Main Methods:

  • Derivation of four novel indices based on host-pathogen interaction theories.
  • Application of approximate Bayesian computation to pinpoint G×G interaction loci.
  • Analysis of single nucleotide polymorphism data from infected and uninfected European humans and their HCV strains.

Main Results:

  • Identification of a new human candidate gene conferring resistance to HCV.
  • Discovery of novel HCV mutations that correspond to human genes.
  • Inference of a gene-for-gene infection matrix for significant human-HCV G×G associations.

Conclusions:

  • The developed model-based inference framework integrates theoretical G×G interaction models with genomic data.
  • This approach enables the understanding of evolutionary G×G interactions driving HCV adaptation in human populations.
  • The findings pave the way for future research into host-pathogen evolutionary dynamics.