MAY 08, 2019 10:30 AM PDT

Using Networks to Understand Cancer Risk

C.E. CREDITS: CEU | P.A.C.E. CE | Florida CE
Speakers
  • Professor of Computational Biology and Bioinformatics, Chair of the Department of Biostatistics, Harvard University, Dana-Farber Cancer Institute
    Biography
      John Quackenbush is Professor of Computational Biology and Bioinformatics and Chair of the Department of Biostatistics at the Harvard TH Chan School of Public Health and Professor of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute. John's PhD was in Theoretical Physics, in 1992 he received a fellowship from the National Institutes of Health to work on the Human Genome Project, which led him from the Salk Institute to Stanford University to The Institute for Genomic Research (TIGR) before moving to Harvard in 2005. He currently directs the Computational Biology and Quantitative Genetics MS program and is PI of the BD2K Training Grant at HSPH. John's research uses massive data from DNA sequencing and other assays to model functional networks in human cells. By comparing networks between groups of individuals, he has found new drug targets, explored chemotherapy resistance, and investigated differences between the sexes. He has received numerous awards for his work, including recognition in 2013 as a White House Open Science Champion of Change. He is also the co-founder of Genospace, a precision medicine software company that was purchased by the Hospital Corporation of America in 2017.

    Abstract:

    One of the central tenants of biology is that our genetics—our genotype—influences the physical characteristics we manifest—our phenotype. But with more than 25,000 human genes and more than 6,000,000 common genetic variants mapped in our genome, finding associations between our genotype and phenotype is an ongoing challenge. Indeed, genome-wide association studies have found thousands of small effect size genetic variants that are associated with phenotypic traits and disease. The simplest explanation is that these genetic variants work synergistically to help define phenotype and to regulate processes that are responsible for phenotypic state transitions. We will use gene expression and genetic data to explore gene regulatory networks, to study phenotypic state transitions, and to analyze the connections between genotype, gene expression, and phenotyope, and to explore how cancer-risk SNPs exert an influence on the disease risk that extends beyond one locus.

    Learning Objectives: 

    1. Genes and genetic variants work together in complex networks that are associated with individual phenotypes and change as phenotype evolves and changes.
    2. Differences in network structure can help us to better understand the drivers of health and disease.
    3. Networks and their structures can help us understand how small-effect genetic variants can work collectively to influence disease risk.
     


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