Perch: Enhanced Genetic Variant Prioritization
ID U-6647
Category Digital Health
Subcategory Bioinformatics
Researchers
Brief Summary
Software that integrates Bayesian analyses for enhanced prediction of genetic variants’ pathogenicity.
Problem Statement
There are multiple tools available to identify, score, and annotate genetic variants within a genome. However, the majority of these tools are restricted to high-penetrance genes for Mendelian diseases and can only analyze certain pedigree structures.
Technology Description
A University of Utah researcher has developed a framework for prioritizing genetic disease variants. This framework, Polymorphism Evaluation, Ranking, and Classification for Heritable traits (PERCH), predicts the pathogenicity of genetic variants better than competing methods. PERCH uses BayesDel, BayesSeg, BayesHLR, and BayesGBA to prioritize variants or gene sets. PERCH measures the biological relevance of each gene to the disease of interest, searching for disease susceptibility genes through whole-exome, whole-genome, or gene-panel sequencing data.
Stage of Development
Application Design
Benefit
- Provides a more accurate disease variant pathogenicity score than competing tools.
- Can be used for disease gene discovery research and new rare variant association.
- Can be implemented in IARC guidelines or can be integrated into ACMG guidelines.
- Automates analysis by calculating every possible variant in the human exome.
- Enables a variety of study designs including case-control samples, extended pedigrees, nuclear pedigrees, or admixtures of the above.
Publications
Feng, B. (2017). PERCH: A Unified Framework for Disease Gene Prioritization. Human Mutation, 38(3), 243-251. doi: 10.1002/humu.23158
Contact Info
Jonathan Tyler
801-587-0515
jonathan.tyler@utah.edu