VarSAn: associating pathways with a set of genomic variants using network analysis

Area of Science:

• Genomics
• Bioinformatics
• Systems Biology

Background:

• Interpreting genomic variants linked to diseases is crucial for understanding disease mechanisms.
• Existing methods often struggle with complex variant sets and non-coding variants.

Purpose of the Study:

• To introduce VarSAn, a novel tool for identifying disease-relevant pathways from sets of genomic variants.
• To evaluate VarSAn's performance against established pathway analysis techniques.

Main Methods:

• VarSAn employs a network analysis algorithm with Random Walk with Restarts to rank pathway relevance.
• The tool differentiates between coding and non-coding variants and accounts for pathway impact.
• A novel benchmarking strategy was used to quantify VarSAn's advantages.

Main Results:

• VarSAn successfully identified relevant pathways for cancer, other diseases, and drug response variations.
• Pathway rankings from VarSAn were found to be complementary to standard gene enrichment tests.
• The tool identified key pathways, such as VEGFA-VEGFR2, associated with Hypoplastic Left Heart Syndrome.

Conclusions:

• VarSAn provides a robust and complementary approach to pathway analysis for genomic variant interpretation.
• The tool enhances the discovery of disease-associated pathways, including those related to rare congenital heart defects.