MAY 10, 2017 9:00 AM PDT

Keynote Presentation: Using Networks to Understand the Genotype-Phenotype Connection

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

    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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