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ASSISTANT PROFESSOR Genetics and Genomic Sciences


Education B.Sc., University of British Columbia M.Sc., University of British Columbia Ph.D., McGill University Post-doc, Massachusetts General Hospital/Broad Institute/Harvard Medical School Instructor, Harvard Medical School Biography Dr. Ron Do, Ph.D., is an Assistant Professor in the Department of Genetics and Genomic Sciences. He is appointed as a member of the Charles Bronfman Institute for Personalized Medicine, the Center for Statistical Genetics, the Icahn Institute for Genomics and Multiscale Biology, and the Zena and Michael A. Weiner Cardiovascular Institute. Prior to joining Mount Sinai, Dr. Do was a Postdoctoral Fellow (2010 to 2013) and Instructor in Medicine (2013 to 2015) at the Center for Human Genetic Research at Massachusetts General Hospital and Harvard Medical School, and a research affiliate at the Broad Institute of MIT and Harvard. Dr. Do is a human geneticist interested in understanding the genetic and biological bases of cardiovascular disease. He has pursued this interest by applying methods from human genetics, genetic epidemiology, statistical genetics, population genetics and computing to large-scale human genotyping and sequencing datasets. His research has focused on assessing the role of rare variants on myocardial infarction, inferring causal effects of risk factors for complex disease, and implementing a population genetics framework to measure differences in the efficiency of natural selection between human populations. Awards 2010 - 2012 Banting Fellowship Canadian Institutes of Health Research Research Do Lab Description The Do Lab's primary research aim is to discover the genetic and biological bases of cardiovascular disease. To this end, the Lab has focused on the following research areas. 1) Implementing computational approaches to analyze exome sequencing data with a view to discovering novel genes that confer risk for plasma lipids and myocardial infarction (MI). We led a study that analyzed exome sequencing data in families with Mendelian lipid disorders. In this project, we discovered compound heterozygote nonsense mutations in the ANGPTL3 gene as a cause of a novel Mendelian lipid disorder called Familial Combined Hypolipidemia (PMID: 20942659). We expanded upon this work to the study of a large-scale rare variant association study using exome sequencing in 10,000 cases and controls for MI.In this study, we identified a burden of rare mutations in the APOA5 and LDLR genes conferring risk for MI (PMID: 25487149). From this work, we have contributed insights and perspectives to the literature on how best to design and conduct rare variant association studies (PMID: 22983955). 2) Developing and implementing statistical approaches to analyze the causal effects of risk factors for complex disease. We have developed and implemented a method to isolate causal influences amongst a set of correlated risk factors for disease. We applied this approach to investigate the causal influence of plasma lipids on coronary artery disease (CAD). We discovered, using a model accounting for effects on low-density lipoprotein cholesterol and/or high-density lipoprotein cholesterol, that the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk, providing evidence that triglyceride-rich lipoproteins may be causally related to CAD (PMID: 24097064). 3) Developing and implementing a population genetics framework to measure the efficiency of natural selection in human populations with a view to understanding the genetic architecture of complex disease. We have developed a population genetics framework to assess the effect of a population bottleneck on the deleterious load in ancient and modern human populations. We observed no evidence of a difference in efficiency of natural selection between modern human populations. This work has improved our understanding of the role of specific population genetic forces in human history and their impact on the genetic architecture of complex disease (PMID: 25581429). A complete list of publications from the Do lab can be found here.


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