Biostatistics In-Person Seminar: Dr. Quackenbush on Why Networks Matter: Embracing Biological Complexity
When & Where
April 4
12:00 PM - 1:15 PM
RAS W 102B ( View in Google Map)
Contact
- Scott Dyson
- 713-500-9538
- scott.b.dyson@uth.tmc.edu
Event Description
Date: April 4th, 2023
12:00PM – 1:00PM (CST)
Location: RAS W-102B, WebEx password: VNmPPvMU729, for link see event website
Presenter:
John Quackenbush, Ph.D
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 at the Dana-Farber Cancer Institute. John’s PhD was in Theoretical Physics but a fellowship to work on the Human Genome Project led him through the Salk Institute, Stanford University, and The Institute for Genomic Research (TIGR), before joining Harvard in 2005. His published work has more than 89,000 citations and among his honors are recognition in 2013 as a White House Open Science Champion of Change and election in 2022 to the National Academy of Medicine. In 2012 he founded Genospace, a precision medicine software company that was sold to Hospital Corporation of America in 2017.
Abstract:
One of the central tenets 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 genes and genetic variants work together in complex regulatory networks that help define phenotypes and mediate phenotypic transitions. We have found that the networks, and their structure, provide unique insight into how genetic elements interact with each other and the structure of the network has predictive power for identifying critical processes in health and disease and for identifying potential therapeutic targets. I will touch on multiple examples illustrating the importance of network models, drawing on my work in cancer, in chronic obstructive pulmonary disease, and in the analysis of data from thirty-eight tissues provided by the Genotype-Tissue Expression (GTEx) project. We will use these to explore the development and progression of disease and new ways to identify therapeutics.
Event Site Link
https://uthealth.webex.com/uthealth/j.php?MTID=m019181fa14e32ccd3460202b98f2ebb0
Date: April 4th, 2023
12:00PM – 1:00PM (CST)
Location: RAS W-102B, WebEx password: VNmPPvMU729, for link see event website
Presenter:
John Quackenbush, Ph.D
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 at the Dana-Farber Cancer Institute. John’s PhD was in Theoretical Physics but a fellowship to work on the Human Genome Project led him through the Salk Institute, Stanford University, and The Institute for Genomic Research (TIGR), before joining Harvard in 2005. His published work has more than 89,000 citations and among his honors are recognition in 2013 as a White House Open Science Champion of Change and election in 2022 to the National Academy of Medicine. In 2012 he founded Genospace, a precision medicine software company that was sold to Hospital Corporation of America in 2017.
Abstract:
One of the central tenets 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 genes and genetic variants work together in complex regulatory networks that help define phenotypes and mediate phenotypic transitions. We have found that the networks, and their structure, provide unique insight into how genetic elements interact with each other and the structure of the network has predictive power for identifying critical processes in health and disease and for identifying potential therapeutic targets. I will touch on multiple examples illustrating the importance of network models, drawing on my work in cancer, in chronic obstructive pulmonary disease, and in the analysis of data from thirty-eight tissues provided by the Genotype-Tissue Expression (GTEx) project. We will use these to explore the development and progression of disease and new ways to identify therapeutics.
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