Multi-omics analysis of inflammatory bowel disease

Hu Huang, Pajau Vangay, Christopher E. McKinlay, Dan Knights

Research output: Contribution to journalReview articlepeer-review

37 Scopus citations


Crohn's disease and ulcerative colitis, known together as inflammatory bowel disease (IBD), are severe autoimmune disorders now causing gut inflammation and ulceration, among other symptoms, in up to 1 in 250 people worldwide. Incidence and prevalence of IBD have been increasing dramatically over the past several decades, although the causes for this increase are still unknown. IBD has both a complex genotype and a complex phenotype, and although it has received substantial attention from the medical research community over recent years, much of the etiology remains unexplained. Genome-wide association studies have identified a rich genetic signature of disease risk in patients with IBD, consisting of at least 163 genetic loci. Many of these loci contain genes directly involved in microbial handling, indicating that the genetic architecture of the disease has been driven by host-microbe interactions. In addition, systematic shifts in gut microbiome structure (enterotype) and function have been observed in patients with IBD. Furthermore, both the host genotype and enterotype are associated with aspects of the disease phenotype, including location of the disease. This provides strong evidence of interactions between host genotype and enterotype; however, there is a lack of published multi-omics data from IBD patients, and a lack of bioinformatics tools for modeling such systems. In this article we discuss, from a computational biologist's point of view, the potential benefits of and the challenges involved in designing and analyzing such multi-omics studies of IBD.

Original languageEnglish (US)
Pages (from-to)62-68
Number of pages7
JournalImmunology Letters
Issue number2
StatePublished - Dec 1 2014

Bibliographical note

Publisher Copyright:
© 2014 Elsevier B.V.


  • Bioinformatics
  • Inflammatory bowel diseases
  • Machine learning
  • Metagenomics
  • Microbiome
  • Multi-omics


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