Analyses of paired host-virus genomic data to understand disease heterogeneity of viral infections

Genome-wide association studies (GWAS) aim to identify the genetic basis of phenotypic traits using the variation that exists within natural populations. Uniquely for infectious diseases, the inter-individual heterogeneity in disease phenotype is linked to both host and pathogen genetic variation. Traditionally, genetic studies of infectious diseases have sought to explain between-individual variation in disease phenotypes by assessing genetic factors separately in humans or pathogens, under the assumption that these factors are independent. Although reasonable for some variants, there is strong theoretical and empirical evidence that genetic interactions between host and viruses play a major role in viral disease aetiology.

In this project you will integrate host and viral genomic data from the same patients to better understand viral pathogenesis and between-individual heterogeneity in disease outcomes. By analysis of paired host-virus genomic data from well-characterised cohorts you will gain novel insights on (a) host polymorphisms linked with viral sequence variation, (b) virus sites under strong host genetic selective pressures, (c) host and virus genetic factors independently contributing to disease phenotypes and (d) host-virus genetic interactions contributing to disease phenotypes. The findings have the potential to: (I) revolutionize our understanding of host-virus interactions and human biology; (II) aid in development of more effective vaccines, drug targets and immunotherapies; and (III) permit better use of therapies through patient stratification. In the age of “Big Data” and “Personalised Medicine”, analysis of paired host-pathogen genomic data will become increasingly important to uncover the mechanisms driving pathogen adaptations and heterogeneity of infection outcomes.