Network Modeling of Crohn's Disease Incidence

PLoS One. 2016 Jun 16;11(6):e0156138. doi: 10.1371/journal.pone.0156138. eCollection 2016.

Abstract

Background: Numerous genetic and environmental risk factors play a role in human complex genetic disorders (CGD). However, their complex interplay remains to be modelled and explained in terms of disease mechanisms.

Methods and findings: Crohn's Disease (CD) was modeled as a modular network of patho-physiological functions, each summarizing multiple gene-gene and gene-environment interactions. The disease resulted from one or few specific combinations of module functional states. Network aging dynamics was able to reproduce age-specific CD incidence curves as well as their variations over the past century in Western countries. Within the model, we translated the odds ratios (OR) associated to at-risk alleles in terms of disease propensities of the functional modules. Finally, the model was successfully applied to other CGD including ulcerative colitis, ankylosing spondylitis, multiple sclerosis and schizophrenia.

Conclusion: Modeling disease incidence may help to understand disease causative chains, to delineate the potential of personalized medicine, and to monitor epidemiological changes in CGD.

MeSH terms

  • Adult
  • Alleles
  • Colitis, Ulcerative / diagnosis
  • Colitis, Ulcerative / genetics*
  • Colitis, Ulcerative / pathology
  • Computer Simulation
  • Crohn Disease / diagnosis
  • Crohn Disease / genetics*
  • Crohn Disease / pathology
  • Epistasis, Genetic
  • Female
  • Gene Regulatory Networks*
  • Gene-Environment Interaction
  • Humans
  • Incidence
  • Male
  • Markov Chains
  • Models, Genetic*
  • Multiple Sclerosis / diagnosis
  • Multiple Sclerosis / genetics*
  • Multiple Sclerosis / pathology
  • Odds Ratio
  • Risk Factors
  • Schizophrenia / diagnosis
  • Schizophrenia / genetics*
  • Schizophrenia / pathology
  • Spondylitis, Ankylosing / diagnosis
  • Spondylitis, Ankylosing / genetics*
  • Spondylitis, Ankylosing / pathology

Grants and funding

This work was supported by ANR, Investissements d’Avenir programme ANR-11-IDEX-0005-02 Sorbonne-Paris-Cité Laboratoire d’excellence INFLAMEX, CNRS, INSERM, Université Paris Diderot-Sorbonne Paris-Cité, Université Pierre et Marie Curie, Université Paris 13 and Association François Aupetit. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.