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.
Diagnostic Algorithms to Study Post-Concussion Syndrome Using Electronic Health Records: Validating a Method to Capture an Important Patient Population.
Journal of neurotrauma
Dennis J and Yengo-Kahn AM and Kirby P and Solomon GS and Cox NJ and Zuckerman SL
Phenome-wide Investigation of Health Outcomes Associated with Genetic Predisposition to Loneliness
Abdellaoui A and Sanchez-Roige S and Sealock J and Treur JL and Dennis J and Fontanillas P and Elson S and Nivard M and Fung Ip H and der Zee Mv and Baselmans B and Hottenga JJ and The 23andme Research Team
Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four healthcare systems
Zheutlin AB and Dennis J and Linnér RK and Moscati A and Restrepo N and Straub P and Ruderfer D and Castro VM and Chen C and Ge T and Huckins LM and Charney A and Lester Kirchner H and Smoller JW
Genetic determinants of tissue factor pathway inhibitor plasma levels
Thrombosis and Haemostasis
Jessica Dennis and Irfahan Kassam and Pierre-Emmanuel Morange and David-Alexandre Trégouët and France Gagnon
Blood triglyceride levels are associated with DNA methylation at the serine metabolism gene PHGDH.
Truong V and Huang S and Dennis J and Lemire M and Zwingerman N and Aïssi D and Kassam I and Perret C and Wells P and Morange PE and Wilson M and Trégouët DA and Gagnon F
Beyond the market? New agrarianism and cooperative farmland access in North America
Journal of Rural Studies
Hannah Wittman and Jessica Dennis and Heather Pritchard
Leveraging cell type specific regulatory regions to detect SNPs associated with tissue factor pathway inhibitor plasma levels.
Dennis J and Medina-Rivera A and Truong V and Antounians L and Zwingerman N and Carrasco G and Strug L and Wells P and Trégouët DA and Morange PE and Wilson MD and Gagnon F
Single nucleotide polymorphisms in an intergenic chromosome 2q region associated with tissue factor pathway inhibitor plasma levels and venous thromboembolism
Journal of Thrombosis and Haemostasis
J. Dennis and V. Truong and D. Aïssi and A. Medina-Rivera and S. Blankenberg and M. Germain and M. Lemire and L. Antounians and M. Civelek and R. Schnabel and P. Wells and M. D. Wilson and P.-E. Morange and D.-A. Trégouët and F. Gagnon
Bicycling crashes on streetcar (tram) or train tracks: mixed methods to identify prevention measures.
BMC public health
Teschke K and Dennis J and Reynolds CC and Winters M and Harris MA
Bicycling injury hospitalisation rates in Canadian jurisdictions: analyses examining associations with helmet legislation and mode share.
Teschke K and Koehoorn M and Shen H and Dennis J
Thrombin generation potential and whole-blood DNA methylation.
Rocañín-Arjó A and Dennis J and Suchon P and Aïssi D and Truong V and Trégouët DA and Gagnon F and Morange PE
Genome-wide investigation of DNA methylation marks associated with FV Leiden mutation.
Aïssi D and Dennis J and Ladouceur M and Truong V and Zwingerman N and Rocanin-Arjo A and Germain M and Paton TA and Morange PE and Gagnon F and Trégouët DA
RFC1 80G>A is a genetic determinant of methotrexate efficacy in rheumatoid arthritis: a human genome epidemiologic review and meta-analysis of observational studies.
Arthritis & rheumatology (Hoboken, N.J.)
Kung TN and Dennis J and Ma Y and Xie G and Bykerk V and Pope J and Thorne C and Keystone E and Siminovitch KA and Gagnon F
Challenges of population-based colorectal cancer screening and the importance of time-trend analysis when evaluating system change.
Zarychanski R and Dennis J and Singh H
Helmet legislation and admissions to hospital for cycling related head injuries in Canadian provinces and territories: interrupted time series analysis.
Dennis J and Ramsay T and Turgeon AF and Zarychanski R
The endothelial protein C receptor (PROCR) Ser219Gly variant and risk of common thrombotic disorders: a HuGE review and meta-analysis of evidence from observational studies.
Dennis J and Johnson CY and Adediran AS and de Andrade M and Heit JA and Morange PE and Trégouët DA and Gagnon F
Breast cancer risk in relation to alcohol consumption and BRCA gene mutations--a case-only study of gene-environment interaction.
The breast journal
Dennis J and Krewski D and Côté FS and Fafard E and Little J and Ghadirian P
Bias in the case-only design applied to studies of gene-environment and gene-gene interaction: a systematic review and meta-analysis.
International journal of epidemiology
Dennis J and Hawken S and Krewski D and Birkett N and Gheorghe M and Frei J and McKeown-Eyssen G and Little J
The effects of provincial bicycle helmet legislation on helmet use and bicycle ridership in Canada.
Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention
Dennis J and Potter B and Ramsay T and Zarychanski R
Alcohol consumption and the risk of breast cancer among BRCA1 and BRCA2 mutation carriers.
Breast (Edinburgh, Scotland)
Dennis J and Ghadirian P and Little J and Lubinski J and Gronwald J and Kim-Sing C and Foulkes W and Moller P and Lynch HT and Neuhausen SL and Domchek S and Armel S and Isaacs C and Hereditary Breast Cancer Clinical Study Group
Genetic risk for major depressive disorder and loneliness in gender-specific associations with coronary artery disease: supplementary
Jessica Dennis and Julia Sealock and Rebecca T Levinson and Eric Farber-Eger and Jacob Franco and Sarah Fong and Peter Straub and Donald Hucks and MacRae F Linton and Wen-Liang Song and Pierre Fontanillas and Sarah L Elson and Douglas Ruderfer and Abdel Abdellaoui and Sandra Sanchez-Roige and Abraham A Palmer and Dorret I Boomsma and Nancy Cox and Guanhua Chen and Jonathan D Mosley and Quinn S Wells and Lea Davis
Genetics of Brain-Related Disorders
Brain-related disorders such as depression and Alzheimer’s disease account for more years of life lost to disability and death than either cancer or cardiovascular disease. Three quarters of psychiatric disorders manifest in childhood and adolescence, and brain-related disorders affect not only the person with the disorder, but also their family and support systems as well. Our research is directed towards reducing the suffering associated with psychiatric and neurological disorders by understanding genetic influences on these traits.
We have studied genetic risk factors for loneliness, depression, and schizophrenia in large consortia, including the PsycheMERGE consortium. PsycheMERGE is a growing network of clinical sites with electronic health records linked to genomic data, which we leverage for psychiatric genetics research.
We also have ongoing projects on the role of genetics in traumatic brain injury recovery. Concussions are sustained by one in 150 Canadians each year. In the short term, people with concussions may struggle to resume pre-injury activities months after the injury. In the long term, concussions have been linked to dementia and Alzheimer’s disease. We are looking for genetic risk profiles that help us identify people at risk of poor recovery soon after their injury, so that we can provide treatment before these bad outcomes arise.
Harnessing “Big Data” to Advance Precision Health
Data collected in routine clinical care are a huge source of potentially valuable data. These data include electronic health records (EHRs) and other health administrative records, such as information on prescriptions filled and laboratory test results. The Dennis lab analyzes these data using advanced computational approaches in order to advance precision health. We apply machine learning techniques to clinical data so that we can identify data-driven patient groupings. These groupings often reveal disease patterns that we didn’t know existed. In this way, we’re moving beyond a one-size-fits all disease classification system, towards one that’s more tailored to each person’s individual data.
Clinical data linked to other large genomic and environmental datasets are also leading to advances in precision health. We are applying statistical genetics methods for the prediction and early detection of brain-related disorders in the US-based PsycheMERGE consortium. In Canada, we are moving towards similar approaches. BC Children’s Hospital has prioritized digital health research, which aims to harness data and technologies to improve health outcomes. The Dennis lab is on the forefront of these digital health innovations. We will play a key role in developing data resources combining clinical, genomic, and environmental data, to advance patient-centered, precision health in Canada.
Genomic Data Integration
New technologies are allowing us to query the human body like never before. We can now measure genetic variation across the whole genome, and relate it to gene expression and epigenetic marks in multiple cells and tissues. These measurements have revealed that the interactions between molecules in our bodies are hugely complex and dynamic, changing over time and in response to different environments.
Studying the relationships between different genomic molecules (such as genes expressed and epigenetic marks) will help us understand the basis of health and disease. The Dennis lab uses computational methods that integrate different datasets to learn about disease processes. For example, collaborators studying gene regulation recently discovered that some genomic regions were more versatile than we previously thought, specifically, that some gene promoters could double as gene enhancers. We are now are exploring how mutations in these genomic regions impact human health and disease susceptibility by integrating data on gene regulation with data on human health in large biobank datasets.Research Group Members
Daniil Belikau, INSPIRE Student
Graham Boucher, Data Analyst:Data Analyst
Dr. Jessica Dennis is working to harness big data to enable personalized medicine. She’s using large population health datasets to discover how a person’s genes and environment can affect their likelihood of developing disease, their response to treatment and ultimately, their recovery.