Prof. John Quackenbush
Dana-Farber Cancer Institute and
Harvard T.H. Chan School of Public Health, USA
John Quackenbush is Professor of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute and Professor of Computational Biology and Bioinformatics at the Harvard TH Chan School of Public Health. 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 the The Institute for Genomic Research (TIGR) before moving to Harvard in 2005. 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 made pioneering discoveries about how the genetic variants work together to determine our traits. John has published more than 280 papers; his work has been cited more than 63,000 times. 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.
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 analyse the connections between genotype, gene expression, and phenotyope. 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 SNPs likely to be associated with phenotype through genome wide association studies. I will show multiple examples, 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.