Candidate gene discovery and prioritization in rare diseases

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Rare or orphan disorders affect a small population, with underlying genes and pathways often unknown.
• High-throughput sequencing advances generate vast data, creating a "data deluge" challenge for identifying disease candidate genes.
• Experimental validation of candidate genes is costly and time-consuming, necessitating computational approaches.

Purpose of the Study:

• To provide an overview of in silico strategies for candidate gene prioritization in rare disease research.
• To demonstrate the utility of a web-based computational suite for ranking disease candidate genes using integrated heterogeneous data sources.

Main Methods:

• Review of in silico strategies for candidate gene prioritization based on the guilt-by-association principle.
• Utilizing a web-based computational suite integrating diverse data sources for ranking candidate genes.
• Demonstration of typical query execution within the web tool for rare disease research.

Main Results:

• Computational approaches, particularly those using prior disease knowledge, aid in discovering and ranking novel candidate genes.
• The presented web-based tool effectively integrates heterogeneous data for candidate gene prioritization.
• The system demonstrates practical application in identifying promising candidates for rare disease research.

Conclusions:

• In silico candidate gene prioritization is crucial for efficient rare disease research, mitigating experimental costs and time.
• The described web-based tool offers a valuable resource for researchers by leveraging integrated data to rank candidate genes.
• This approach facilitates the identification of potential disease genes, advancing our understanding of rare disorders.