MAY 08, 2019 10:30 AM PDT

Using Networks to Understand Cancer Risk

C.E. Credits: CEU
Speaker
  • 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.
     


    Show Resources
    You May Also Like
    MAY 11, 2021 10:00 AM PDT
    Add to Calendar Select one of the following: iCal Google Calendar Outlook Calendar Yahoo Calendar
    C.E. CREDITS
    MAY 11, 2021 10:00 AM PDT
    Add to Calendar Select one of the following: iCal Google Calendar Outlook Calendar Yahoo Calendar
    Date: May 11, 2021 Time: 10:00zm PDT Your samples are some of the most valuable assets in the laboratory. After spending countless hours on extraction and preparation, your conclusions could...
    JUN 09, 2021 7:00 AM PDT
    Add to Calendar Select one of the following: iCal Google Calendar Outlook Calendar Yahoo Calendar
    C.E. CREDITS
    JUN 09, 2021 7:00 AM PDT
    Add to Calendar Select one of the following: iCal Google Calendar Outlook Calendar Yahoo Calendar
    Date: June 9, 2021 Time: 09 June 2021, 7am PDT, 10am EDT, 4pm CEST cells with dramatic implications on the validity of past cell culture related research. The fact that at least 509 cell lin...
    DEC 02, 2020 8:00 AM PST
    C.E. CREDITS
    DEC 02, 2020 8:00 AM PST
    DATE: December 2nd, 2020 TIME: 08:00am PDT, 11:00pm EDT Bioreactors and shakers are used to cultivate microorganisms, plant, insect, and mammalian cells in different volumes. Upscaling of pr...
    NOV 16, 2020 8:00 AM PST
    C.E. CREDITS
    NOV 16, 2020 8:00 AM PST
    Date: November 16, 2020 Time: 8:00am (PST), 11:00am (EST) CRISPR screening has become the prime discovery tool in modern biomedical research and drug discovery. At the same time, most screen...
    MAR 16, 2021 10:00 AM PDT
    C.E. CREDITS
    MAR 16, 2021 10:00 AM PDT
    Date: March 16, 2021 Time: 10:00am (PST) Scientific progress and breakthroughs today are often too expensive for most institutions to acquire. Each year, the National Institutes of Health (N...
    NOV 10, 2020 7:00 AM PST
    C.E. CREDITS
    NOV 10, 2020 7:00 AM PST
    DATE: November 10, 2020 TIME: 7:00am PDT, 10:00am EDT Automation can provide tremendous benefits such as increased pipetting precision and accuracy, productivity, and throughput. Numerous wo...
    MAY 08, 2019 10:30 AM PDT

    Using Networks to Understand Cancer Risk

    C.E. Credits: CEU

    Specialty

    Gene Sequencing

    Molecular Biology

    Molecular Diagnostics

    Cancer Diagnostics

    Cancer Research

    Genetics

    Cancer

    Gene Expression

    Genomics

    Cell

    Dna Sequencing

    Infectious Disease

    Medicine

    Bioinformatics

    Clinical Diagnostics

    Geography

    North America44%

    Asia44%

    Europe11%

    Registration Source

    Website Visitors100%

    Job Title

    Student33%

    Medical Doctor/Specialist17%

    Facility/Department Manager17%

    Educator/Faculty17%

    Genetic Counselor17%

    Organization

    Academic Institution33%

    Clinical Laboratory22%

    Biotech Company22%

    Medical School11%

    General Laboratory11%


    Show Resources
    Loading Comments...
    Show Resources