I am a genomic scientist with broad experience across different technologies and disease areas. My PhD at the Wellcome Sanger Institute focused on using iPSC-derived cells as models for complex diseases, as well as statistical modelling to predict causal genetic variants influencing gene expression in human tissues. Prior to this I was the lead bioinformatician for the Canada-wide rare disease sequencing projects FORGE and Care4Rare, where we identified causal gene disruptions for over 50 rare diseases. As a postdoctoral fellow at EMBL-EBI, I now work to fine-map causal genetic variants for Alzheimer's and Parkinson's diseases, using gene expression, epigenomic annotations, and iPSC-derived cell models. Having previously studied computer science and worked as a software engineer, I maintain an interest in machine learning and follow developments in the field of relevance to genomics and health.
European Bioinformatics Institute | EMBL-EBI
October 2017 -
As a postdoctoral fellow at EBI, I work as part of a collaboration between academic and industrial partners to use genomics to identify potential therapeutic targets for Alzheimer's and Parkinson's diseases. Specifically I integrate genomic and epigenomic datasets to identify causal genetic variants and genes leading to neurodegenerative disease risk. For variants that I identify, members of the team use CRISPR-Cas9 genome editing to investigate the causal effects of these variants on gene expression or other molecular phenotypes.
Ph.D. in Genomics
Wellcome Sanger Institute
October 2013 -
Thesis: "Using molecular QTLs to identify cell types and causal variants for complex traits" Among projects during my PhD, I analyzed RNA-sequencing from 123 cell lines differentiated from iPSCs to sensory neurons, and identified genetic variants influencing gene expression in this cell type. I discovered factors in the iPSCs which led to differential gene expression in the derived neurons, and determined how the variability in expression among the neurons impacted on power to detect genetic effects of common gene regulatory variants. I identified gene regulatory variants that were also associated with complex traits from prior genome-wide association studies. This work was published in Nature Genetics. In addition to this, I developed a machine-learning model that uses summary statistics from gene expression studies, along with genetic and epigenetic annotations, to predict the location of causal gene regulatory variants. I then applied these cell type-specific genome-wide regulatory scores to determine strongly associated cell types for a number of complex diseases.