MAY 10, 2017 09:00 AM PDT
Keynote Presentation: Using Networks to Understand the Genotype-Phenotype Connection
Presented at the Genetics and Genomics 2017 Virtual Event
CONTINUING EDUCATION (CME/CE/CEU) CREDITS: CEU | P.A.C.E. CE | Florida CE
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Speakers:
  • Professor of Computational Biology and Bioinformatics, Department of Biostatistics, Harvard University, Dana-Farber Cancer Institute
    Biography
      John Quackenbush received his PhD in theoretical physics from UCLA in 1990. Following a physics postdoc, he received a Special Emphasis Research Career Award from the National Center for Human Genome Research to work on the Human Genome Project, spending two years at the Salk Institute and two years at Stanford University working in genomics and computational biology. In 1997 he moved to The Institute for Genomic Research (TIGR), pioneering expression analysis. He joined the Dana-Farber Cancer Institute and the Harvard School of Public Health in 2005, and works reconstruction of gene networks that drive the development of diseases. In 2012 he and Mick Correll co-Founded GenoSpace, a company that develops software tools to enable precision medicine applications.
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    Abstract:

    Genome Wide Association Studies (GWAS) and expression quantitative trait locus (eQTL) analyses have identified genetic associations with a wide range of human phenotypes. However, many of these variants have weak effects and understanding their combined effect remains a challenge. One hypothesis is that multiple SNPs interact in complex networks to influence functional processes that ultimately lead to complex phenotypes, including disease states. Here we present CONDOR, a method that represents both cis- and trans-acting SNPs and the genes with which they are associated as a bipartite graph and then uses the modular structure of that graph to place SNPs into a functional context. In applying CONDOR to eQTLs in chronic obstructive pulmonary disease (COPD), we found the global network “hub” SNPs were devoid of disease associations through GWAS. However, the network was organized into 52 communities of SNPs and genes, many of which were enriched for genes in specific functional classes. We identified local hubs within each community (“core SNPs”) and these were enriched for GWAS SNPs for COPD and many other diseases. These results speak to our intuition: rather than single SNPs influencing single genes, we see groups of SNPs associated with the expression of families of functionally related genes and that disease SNPs are associated with the perturbation of those functions. These methods are not limited in their application to COPD and can be used in the analysis of a wide variety of disease processes and other phenotypic traits


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