Unbiased forward genetics and systems biology approaches to understanding how gene-environment interactions work to predict susceptibility and outcomes of infections

Novartis Found Symp. 2008:293:156-65; discussion 165-7, 181-3. doi: 10.1002/9780470696781.ch12.


Like most human diseases, infectious diseases are effected by complex genetic traits and multiple, interactive environmental and inherent host factors. By linking specific genotypes to disease susceptibility phenotypes we can identify the genetic basis for inter-individual differences in disease susceptibility as well as gain insight into how gene-environment interactions influence infection outcomes. Our research has focused on delineating interactive pathways and molecular events modulating host resistance or susceptibility to specific pathogens. Our model system has been that of Group A Streptococcus infections that can manifest in starkly different ways and cause distinct diseases in genetically distinct individuals. We have extended our work to other pathogens, including those with a potential of causing major, global biological threats. In as much as it is quite difficult to conduct certain infectious disease studies in humans, there has been a critical need for small animal models for infectious diseases. Appreciating the limitations of existing models, we developed several novel and complementary mouse models that are ideal for use in systems genetics studies of complex diseases. These models not only allow biological validation of known genetic associations, but importantly they afford an unbiased tool for discovering novel genes and pathways contributing to disease outcomes, under different environments.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Review

MeSH terms

  • Bias
  • Disease Susceptibility / diagnosis*
  • Environment*
  • Genes / physiology*
  • Genetics, Medical / methods*
  • Humans
  • Infections / diagnosis*
  • Infections / epidemiology
  • Infections / etiology
  • Models, Biological
  • Prognosis
  • Systems Biology / methods*