Oligogenic resource for variant analysis (ORVAL)

Oligogenic resource for variant analysis (ORVAL) predicts and prioritizes candidate pathogenic variant combinations and generates gene-pair networks to support investigation of oligogenic disease mechanisms.

Key Features:

• Combinatorial Variant Pathogenicity Prediction: Employs machine learning techniques to predict the pathogenic potential of variant combinations in oligogenic contexts.
• Network Generation: Generates networks of candidate pathogenic variant combinations in gene pairs to represent genetic interactions underlying oligogenic conditions.
• Pathogenic Gene and Protein Interaction Networks: Produces gene and protein interaction networks that map candidate pathogenic relationships among genes and proteins.
• Ranking of Pathogenic Gene Pairs: Ranks gene pairs by their likelihood of contributing to pathogenicity to support candidate prioritization.
• Visual Mappings (cellular location and pathway information): Maps cellular location and pathway annotations onto candidate variant combinations and gene pairs.
• Integration with External Annotations: Incorporates external annotations to enrich the evidence supporting candidate pathogenic interactions.

Scientific Applications:

• Oligogenic disease research: Enables discovery and analysis of variant combinations that contribute to oligogenic diseases rather than single-gene Mendelian disorders.
• Non-Mendelian inheritance studies: Facilitates investigation of genetic bases for diseases that do not conform to classical Mendelian inheritance by analyzing combinatorial variant effects.
• Candidate prioritization for diagnostics and therapeutics: Supports prioritization of gene-pair candidates for downstream diagnostic validation and potential therapeutic targeting.

Methodology:

Applies machine learning methods to predict pathogenicity of variant combinations, generates gene-pair and gene/protein interaction networks, maps cellular location and pathway annotations, and integrates external annotations.