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BridGE-SGA: A novel computational platform to discover genetic interactions underlying human disease

Status: 
Active
Competition: 
2017 Bioinformatics and Computational Biology Competition
Sector: 
Health
Genome Centre(s):
Ontario Genomics
Project Leader(s):
Charles Boone (University of Toronto), Chad L. Myers (University of Minnesota)
Project Description: 

The ability to sequence the entire human genome at increasingly lower cost has led to a fundamental change in biomedical research. But there is a gap between the amount of data available and our ability to understand and interpret that data. Addressing this gap is essential to realize the promise of precision medicine.

Dr. Charles Boone and Dr. Brenda Andrews of the Donnelly Centre for Cellular and Biomolecular Research at the University of Toronto, and Dr. Chad Myers of the University of Minnesota, have worked together to discover that a significant part of our inability to interpret genomic data likely stems from the reality that disease generally arises from complex genetic interactions. While all humans essentially have the same set of genes, most have around five million unique genetic variants. The effect of any one variant depends on its interactions with other variants. So we need to understand not just the millions of genetic differences that affect gene function, but also how all those genes interact with each other. Current computational methods and technologies lack the statistical power to do so.

Drs. Boone, Andrews, Myers have developed the first complete genetic interaction map for any organism, and have built a computational method, BridGE, to discover genetic interactions. The team is now working to develop an innovative computational platform for genome sequencing data, BridGE-SGA, to enable the discovery of disease-associated genetic interactions from large-scale human genotype data. Their goal is to discover genetic interactions for a variety of diseases. Identifying and understanding these key genetic interactions will improve our ability to interpret data from whole genome sequencing and identify novel gene targets for drug discovery and development.