Mining Big Data to Treat and Prevent Genetic Disease
Using genetic information from large groups of people to identify the root causes of disease holds much promise for targeted treatments and personalized healthcare. But growth in the quantity and quality of “big data” in genetics and disease is challenging our ability to manage and use this information to help people.
Dr. Lisa Strug, Canada Research Chair in Genome Data Sciences, is trying to solve this problem by developing state-of-the art statistical tools that can analyze and integrate large-scale genomic data to identify the basis of disease.
Specifically, she and her research team are building on her successful analytical program, which is unravelling the complex genetic basis of cystic fibrosis. By carrying out and integrating whole-genome sequencing from new technologies that can represent Canadians with cystic fibrosis, they are creating a comprehensive catalogue of individual-level genetic differences. This will enable Strug and her team to determine how different genetic regions affect disease severity and response to treatment.
Strug’s research will also provide a roadmap for how to make cost-effective, high-quality genomic models for solving the genetic causes of many other diseases. It will also inform their treatment targets and cures.
By making her methods and software freely available in open-source libraries, Strug is also helping to advance other research discoveries to help people worldwide.