Jessica Dennis

Assistant Professor

Research Classification

Research Interests

Administrative health data
Complex Trait Genetics
Electronic health records
genetic epidemiology
Genetics of Neurological and Psychiatric Diseases
Machine Learning
Mental Health and Psychopathology in Children and Youth
Precision Health
statistical genetics

Relevant Thesis-Based Degree Programs

Affiliations to Research Centres, Institutes & Clusters

Research Options

I am interested in and conduct interdisciplinary research.


Master's students
Doctoral students
Postdoctoral Fellows
Any time / year round

The last decade has seen an unprecedented explosion of data. In medicine, data are increasingly being generated and linked across electronic health records, administrative databases, and biobanked samples. These resources hold tremendous promise for improving human health and achieving precision medicine, which will only be realized by thoughtful study designs and innovative analyses.

My lab uses novel computational methods grounded in genetic epidemiology and statistical genetics to capitalize on today’s big data resources. We aim to understand how genetic and epigenetic differences between people contribute to variation in disease susceptibility, response to treatment, and recovery. A primary goal of our research is to reduce the suffering associated with psychiatric disorders, many of which first manifest in childhood and adolescence. We conduct studies in large population datasets, with a major interest in electronic health records and biobanks, and we work at the intersection of genetics, epidemiology, statistics, bioinformatics, and computer science.

I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research.
I support experiential learning experiences, such as internships and work placements, for my graduate students and Postdocs.
I am open to hosting Visiting International Research Students (non-degree, up to 12 months).
I am interested in hiring Co-op students for research placements.

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Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Finding the functional consequences of genetic risk loci on gene expression and DNA methylation by integrating contextual information (2023)

The majority of genetic loci that influence complex traits are non-coding. Roughly half of these loci affect gene expression in one or more tissues, despite making up less than 10% of common genetic variants. However, it is increasingly unlikely that genetic effects on gene expression will be sufficient to assign a function to all non-coding risk loci. This thesis splits the problem of assigning new functions to complex trait loci into two steps.First, I aim to increase the number of non-coding loci with a known function without requiring new molecular datasets. In my thesis, I explore context-dependent genetic regulation of molecular traits, where the context that affects this process is either inferred from data or a common phenotypic measure like sex. I then advocate for associating genetic variation with molecular data other than gene expression, primarily on DNA methylation.Second, I outline how to tie these novel molecular functions to genetic risk for complex traits. Importantly, this requires methods and workflows that summarize genetic effects at individual loci across interpretable functional units (genes) and genome-wide genetic risk for complex traits.In my scholarship chapters, I first show that environments inferred from global gene expression can correlate with various phenotypic and environmental variables. These inferred contexts are replicated across samples and can subsequently be used to identify novel context-specific genetic regulation of gene expression. I then show that novel context-specific genetic regulation can be approached in DNA methylation using sex, measured in virtually all genetic and molecular datasets.My later chapters demonstrate how to summarize genetic effects to learn which traits are particularly relevant to these novel regulatory relationships. I start with effects and individual loci and then explore methods to interpret the gene-level influence of genetic effects on DNA methylation. Then I demonstrate how cumulative, genome-wide risk for complex traits can provide new insight into the biological functions underlying complex traits beyond the effects I observe at individual loci.Overall this thesis shows that we require various approaches to discover disease-relevant molecular functions of non-coding genetic loci.

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Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Investigating sex differences in the polygenic risk of major depressive disorder and shared associations with cardiovascular disease (2022)

Major depressive disorder (MDD) is a leading cause of morbidity and disability worldwide, with approximately twice as many women reported to have a lifetime occurrence of MDD than men. MDD is a polygenic trait, wherein hundreds to thousands of common genetic variants with small effect sizes contribute to risk of disease. This study investigated sex differences in the risk factor comorbidity and genetic architecture of MDD in over 16,000 people aged 45-85 from the Canadian Longitudinal Study on Aging (CLSA), with 21% of females (n=1,741) and 12% of males (n=1,055) coded with MDD. Polygenic risk scores (PRS) for individuals were made using sex-stratified and non-sex-specific (“both-sexes”) UK Biobank genome-wide association study summary statistics data. The female sex-specific PRS had higher associations with MDD in females in the top decile of PRS risk (OR = 1.68 (1.32-2.14), p = 2.8E-05) than the male-specific PRS in males (OR = 1.43 (1.07-1.93), p = 0.017) and the both-sexes PRS applied to both sexes (OR = 1.51 (1.25-1.83), p = 2.5E-05). Odds of MDD for the sex-specific PRSs, socioeconomic, lifestyle and clinical risk factors were assessed using a multivariable logistic regression model for each sex. Sex-specific risk factor associations with odds of MDD were found in females (hypothyroidism (OR = 1.42 (1.25-1.63), p = 1.74E-07), not being partnered (OR = 1.34 (1.17-1.52), p = 1.26E-05), having diabetes (OR = 1.30 (1.11-1.52), p = 1.03E-03), higher sex-specific autosomal PRS (OR = 1.10 (1.04-1.16), p = 6.15E-04) and a history of ischemic heart disease (OR = 1.52 (1.14-2.01), p = 3.39E-03)) and males (high blood pressure, OR = 1.35 (1.04-1.47), p = 4.55E-05). Significant differences were observed in the proportion of variables that contributed to the most to each model, evaluated by relative pseudo-R2 values. Age contributed the most to the model for both sexes (73% for females, 57% for males), wherein younger age was associated with higher odds of MDD. The results of this thesis underscore the relevance for sex-disaggregating analyses of complex traits, like MDD, and the incorporation of clinical variables into models of MDD, in applications such as early detection and primary prevention.

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