Candidate gene discovery and prioritization in rare diseases

Abstract

A rare or orphan disorder is any disease that affects a small percentage of the population. Most genes and pathways underlying these disorders remain unknown. High-throughput techniques are frequently applied to detect disease candidate genes. The speed and affordability of sequencing following recent technological advances while advantageous are accompanied by the problem of data deluge. Furthermore, experimental validation of disease candidate genes is both time-consuming and expensive. Therefore, several computational approaches have been developed to identify the most promising candidates for follow-up studies. Based on the guilt by association principle, most of these approaches use prior knowledge about a disease of interest to discover and rank novel candidate genes. In this chapter, a brief overview of some of the in silico strategies for candidate gene prioritization is provided. To demonstrate their utility in rare disease research, a Web-based computational suite of tools that use integrated heterogeneous data sources for ranking disease candidate genes is used to demonstrate how to run typical queries using this system.