Profile IntroductionMy lab focuses on developing and applying methods from machine learning and statistics to unravel the genetic basis of complex traits and diseases such as Alzheimer’s and cancer. Some examples of our research include ways to integrate heterogeneous data (genetic, epigenetic, transcriptional) to model how transcriptional regulatory networks change over time and with disease progression, predict which combinations of drugs are optimal for targeting specific cell types and cancer types, and predict how variation in genetics and environmental exposures interact to change our risk of developing complex diseases.
|2012||PhD||Computer Science||University of Toronto|
|2006||MSc||Biochemistry||University of Toronto|
|2004||BMath||Computer Science||University of Waterloo|
Gene network inference and dynamics
The repertoire of genes and regulatory elements that control their expression is typically fixed in all of the different cells of an individual human. However, these genes and regulatory elements are expressed and interact with each other in a highly dynamic fashion, which depends on factors such as cell or tissue type, age, environmental stimuli and disease status. At any given time or context, the expression of, and interactions between, genes and regulatory elements in the genome can be represented as a network (graph), where nodes are genes and elements, and edges are interactions between these entities.
We are developing methods to address several challenges:
- Inferring gene and chromatin networks from epigenomics and transcriptomics profiles
- Identifying topology changes and differentially active pathways during cellular differentiation and cancer
- Aligning networks between species that contain ambiguous mappings between nodes in each network
Genetics of complex diseases
One of the central goals of human genetics is to determine how variation at the DNA sequence level can impact variation at the organism level (e.g. complex traits, disease onset). Two directions we are currently pursuing are:
- Prediction of disease incidence from genetic sequence
- Prediction of the mechanism of action of genetic variation on phenotype
Much of the DNA sequence variation (both germline and somatic) tied to complex diseases is located in non-coding regions of the genome, which until recently has been poorly understood. We leverage data from projects such as ENCODE, the Roadmap Epigenomics Project and GTEx to build models relating the non-coding and coding regions of the genome in order to better understand the mechanism of action of genetic variation and how they impact molecular and cellular function.
Genomics-based personalized medicine
A central goal of our work is to develop and bring genomics based computational tools to the clinic, through collaborations at the UC Davis Comprehensive Cancer Center for example. We are undertaking several collaborative projects, including:
- Predicting cancer patient response to therapy and outcome
- Predicting combinations of compounds that are more effective than expected based on their individual efficacies
Our projects focus first on testing predictions against cell lines using high throughput assays, with future followup studies on animal models and ultimately clinical trials.
While studying the genetics of complex diseases is a focal point of the lab, we are also interested in the complementary question of, to what extent (and through what mechanisms) does the environment impact both organism-level phenotypes and intermediary phenotypes such as epigenetics and transcription. Using data from both unrelated individuals and twin studies, we are interested in novel methods for identifying genetic loci that exhibit non-additive interactions with known and unknown environmental factors, and understanding what pathways these loci target and interact with other loci involved in complex diseases.
Department and Center Affiliations
CBS Grad Group Affiliations
Specialties / Focus
- Integrated Genetics and Genomics
- Human Genetics and Genomics
- Computational Biology