Multiomics to elucidate inflammatory bowel disease risk factors and pathways

Multiomics advance our understanding of disease progression and can facilitate drug discovery. In this Perspective, current knowledge and applications of omics platforms in inflammatory bowel disease diagnosis and in identifying risk factors are discussed. Inflammatory bowel disease (IBD) is an immune-mediated disease of the intestinal tract, with complex pathophysiology involving genetic, environmental, microbiome, immunological and potentially other factors. Epidemiological data have provided important insights into risk factors associated with IBD, but are limited by confounding, biases and data quality, especially when pertaining to risk factors in early life. Multiomics platforms provide granular high-throughput data on numerous variables simultaneously and can be leveraged to characterize molecular pathways and risk factors for chronic diseases, such as IBD. Herein, we describe omics platforms that can advance our understanding of IBD risk factors and pathways, and available omics data on IBD and other relevant diseases. We highlight knowledge gaps and emphasize the importance of birth, at-risk and pre-diagnostic cohorts, and neonatal blood spots in omics analyses in IBD. Finally, we discuss network analysis, a powerful bioinformatics tool to assemble high-throughput data and derive clinical relevance.

Automated summary

This article discusses the current knowledge and applications of omics platforms in the diagnosis of inflammatory bowel disease and identification of risk factors, emphasizing the granular high-throughput data provided by multiomics platforms for characterizing molecular pathways and risk factors in chronic diseases like IBD. It also highlights the knowledge gaps in IBD risk factors and pathways, and stresses the importance of birth, at-risk, and pre-diagnostic cohorts, as well as neonatal blood spots in omics analyses. Moreover, the article touches upon the use of network analysis as a powerful bioinformatics tool to extract clinical relevance from high-throughput data.