MAY 09, 2019 09:00 AM PDT

Polygenic Risk Scores: PheWas and GWAS and Utility of Biobank Information

SPONSORED BY: Illumina
Speakers
  • Associate Professor, Director, Genetic and Molecular Epidemiology Laboratory, McMaster Genome Institute
    Biography
      Guillaume Pare is an Associate Professor and University Scholar, Dept. of Pathology & Molecular Medicine at McMaster University and Director of the Genetic and Molecular Epidemiology Laboratory. He was recently inducted as a member into the Royal Society of Canada's College of New Scholars, Artists and Scientists. In 2018, AHA selected his publication (Theriault et al., Circ Genom Precis Med 11:e001849, 2018) as one of the 'Top 10 Advances in Cardiovascular Research in 2018'. Dr. Pare currently holds a Canada Research Chair in Genetic & Molecular Epidemiology, as well as a CISCO Professorship. His clinical interests are centered on lipoprotein disorders, obesity and cardiovascular disease prevention. His corresponding research interests are in cardiovascular genetics, biomarker development and pharmacogenomics. These interests have led to expertise in bioinformatics, high-throughput biology and genetic epidemiology. Since joining McMaster University in 2009, Dr. Pare has published over 190 original contributions in peer-reviewed journals, all related to his research program in cardiovascular disease and genetics. These include first or last authored articles in the NEJM, Lancet (comment), Circulation, EHJ, Stroke, JACC, PLoS Genetics, Circulation Genetics and Scientific Reports. Collectively, he has published 207 peer-reviewed manuscripts, which have been cited over 21,000 times.

    Abstract:

    The advent of precision medicine largely depends on the creation of precise and accurate predictive tools. While most late-onset diseases are moderately to highly heritable, using genetic information to make individual predictions about risk of disease has proven to be challenging. Recent technological and methodological advances have established the importance of the polygenic model of inheritance for complex diseases, whereby dozens of thousands of functional variants might contribute to disease risk. Genome-wide association studies have identified thousands of variants associated with disease, yet each individual variant tends to only have a modest effect on risk. We and others have proposed using polygenic risk scores combining information from hundreds to millions of variants to improve genetic risk prediction and identify individuals at very high risk of disease.  We illustrate this approach by studying coronary artery disease (CAD), a leading cause of mortality worldwide. A genetic cause is identified in less than 5% of patients with early onset disease, mainly Mendelian mutations leading to Familial Hypercholesterolemia. Our data show that belonging to the extreme tail of polygenic risk is associated with a greater risk of CAD than carrying a Mendelian mutation, both in terms of risk and prevalence. Indeed, more individuals are at high risk of CAD because of polygenic risk, and such individuals have higher risk than carriers of routinely tested Familial Hypercholesterolemia mutations. Similar findings have been reported for other common diseases such as breast and prostate cancer, among others. These findings support clinical use of polygenic risk scores for common complex disease in the near future.

    Learning Objectives: 

    1. Understand the polygenic nature of complex traits
    2. Understand principles of polygenic risk scores
    3. Clinical applications of polygenic risk scores


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