Pathway analyses and understanding disease associations

Abstract

High throughput technologies have been applied to investigate the underlying mechanisms of complex diseases, identify disease-associations and help to improve treatment. However it is challenging to derive biological insight from conventional single gene based analysis of "omics" data from high throughput experiments due to sample and patient heterogeneity. To address these challenges, many novel pathway and network based approaches were developed to integrate various "omics" data, such as gene expression, copy number alteration, Genome Wide Association Studies, and interaction data. This review will cover recent methodological developments in pathway analysis for the detection of dysregulated interactions and disease-associated subnetworks, prioritization of candidate disease genes, and disease classifications. For each application, we will also discuss the associated challenges and potential future directions.

Keywords: Genome Wide Association Studies (GWAS); Pathway analysis; disease association; disease classification; dysregulated interaction; gene prioritization.

Figure 1

Summary of pathway analysis and understanding disease associations. Pathway analysis can be used to detect dysregulated interactions and disease-associated pathways, prioritize candidate disease genes, and classify diseases.