Using Genomics Data to Understand Complex Disorders
Common diseases, like cancer or major depressive disorder, are caused by many genetic and environmental factors. On top of that, these factors act upon intricately organized biological systems that have diverse components and interactions. As a result, designing effective treatments for these diseases requires untangling these risk factors to better understand their cascading effects.
Dr. Sara Mostafavi, Canada Research Chair in Computational Biology, aims to develop computational models that capture the complexity of these biological systems and separate the factors that cause disease from those that are simply associated with it.
Mostafavi and her research team are developing models that statistically integrate diverse types of biological data. Using high-throughput sequencing (a fast way to sequence and analyze large genomes), they are measuring the different parts of biological systems at various resolutions, such as at the genome, epigenome and transcriptome levels.
Capturing several views of biological systems allows Mostafavi and her team to develop precise models that link the genomic sequence of biological systems to cellular phenotypes that govern cell and tissue behaviour. It also helps them disentangle the various factors that underlie a given disease. Computational analysis of these data can then help determine the contributions of the different genetic and environmental factors and their impact at the molecular, cellular and organismal levels.
Ultimately, Mostafavi and her team aim to discover the role genetics and the environment play in the development of complex diseases. In particular, they hope their research will help identify clinically reliable genetic risk factors for psychiatric disorders.