Genetic and Molecular Epidemiology Laboratory
  • Serum and genetic samples:

    300,000+

  • Genetic variants per DNA sample:

    10,000,000

  • Per sample on proteomics platform:

    1,000+ proteins

  • Data processed with computer & bioinformatics:

    200+ terabytes

Biomarker program

Cardio-metabolic diseases such as heart attack, stroke and diabetes are leading causes of death in Canada. Our project aims to identify new blood biomarkers of cardio-metabolic diseases that directly contribute to disease risk. We will do so by integrating information from a comprehensive blood biomarker screen with genetic markers. This will enable us to determine if biomarkers are part of the mechanisms of disease, or merely bystanders. This distinction is important as blood biomarkers that directly or indirectly contribute to disease are more likely to provide unbiased estimates of disease risk and help tailor therapeutic strategies to match individual risk profiles. These blood biomarkers will be used to identify at-risk individuals and help develop new therapeutic interventions.

Stroke genetics

In many individuals who develop stroke, particularly young people, risk factors such as high blood pressure, diabetes, smoking and abnormal heart rhythm are not at play, so stroke is thought to develop through genetic factors as well as one or more known risk factors. In fact, stroke occurs commonly in some families; better understanding the genetic basis of stroke will help provide answers to important clinical questions

Lipoprotein(a) genetics

Increased fats and cholesterol in the blood, which can lead to narrowing and blockage of the blood vessels to the heart, are carried in packages called lipoproteins; one type, called lipoprotein(a) [Lp(a)], is associated with increased risk of heart and blood vessel disease. People of differing genetic ancestry have varying amount and sizes of Lp(a) which makes it difficult to measure in the blood. Smaller size Lp(a) packages lead to larger number of the Lp(a) packages in the blood. Genetic analysis of a large number of people from many different ethnic groups will help us fully understand Lp(a) and its value in helping predict the risk of developing heart and blood vessel disease.

Pharmacogenetics of antithrombotic drugs

Broadly categorized as antiplatelet and anticoagulation agents, antithrombotic drugs prevent and treat venous thromboembolism, acute coronary syndromes and stroke. Not all patients benefit equally from drug treatment, with some individuals showing evidence of aspirin resistance while others carry genetic variants that modify the platelet inhibitory effect of clopidogrel. Likewise, wide variations in the level of active drug have been noted with the anticoagulation agents dabigatran and apixaban. Such variations result in underdosing some patients (i.e. thrombotic events) and overdosing others (i.e. bleeding events). PHRI aims to identify the pharmacogenetic determinants of key antithrombotic drugs and evaluate their clinical relevance.

Novel statistical genetics methods

Finding gene-gene and gene-environment interactions is a major challenge in genetic epidemiology, yet genetic interactions are thought to be critically important to the genetic architecture of complex traits. Gui Paré’s research developed a statistical method to identify gene-gene and gene-environment interactions, and a novel approach to test regional genetic associations, which was extended to estimate polygenic regional variance from summary association statistics. Such studies laid the foundation for the use of machine-learning methods in determining polygenic risk scores. The use of Mendelian randomization (MR) was pioneered to show that diabetes is causally involved in coronary heart disease, that bile acid sequestrants reduce the risk of heart disease, and used MR to show that HER2 is a causal mediator of the protective effect of ACE inhibitors on kidney function.

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