MAY 11, 2017 9:00 AM PDT

Keynote Presentation - Using network models to understand common complex disease predisposition and progression

Speaker
  • Director of Computational Sciences and Informatics program for Complex Adaptive Systems and Professor in the School of Life Sciences, Arizona State University
    Biography
      Dr. Ken Buetow is a human genetics and genomics researcher who leverages computational tools to understand complex traits such as cancer, liver disease, and obesity. Dr. Buetow currently serves as Director of Computational Sciences and Informatics program for Complex Adaptive Systems at Arizona State University (CAS@ASU) and is a professor in the School of Life Sciences in ASU's College of Liberal Arts and Sciences. CAS@ASU applies systems approaches that leverage ASU's interdisciplinary research strengths to address complex global challenges. The Computational Sciences and Informatics program is developing and applying information technology to collect, connect, and enhance trans-disciplinary knowledge both within ASU and across the broader knowledge-generating ecosystems. CAS@ASU is creating a Next Generation Cyber Capability to address the challenges and opportunities afforded by "Big Data" and the emergence of 4th Paradigm Data Science. This capability brings state-of-the-art computational approaches to CAS@ASU's transdisciplinary, use-inspired research efforts Dr. Buetow previously served as the Director of the Center for Biomedical Informatics and Information Technology within the National Institutes of Health's National Cancer Institute (NCI). In that capacity he initiated and oversaw the NCI's efforts to connect the global cancer community through community-developed, standards-based, interoperable informatics capabilities that enable secure exchange and use of biomedical data. Buetow designed and built one of the largest biomedical computing efforts in the world. He was responsible for coordinating biomedical informatics and information technology at the NCI. The NCI center he led focused on speeding scientific discovery and facilitated translational research by coordinating, developing and deploying biomedical informatics systems, infrastructure, tools and data in support of NCI research initiatives.

    Abstract

    The study of inherited genomic variation through genome wide association studies (GWAS) promised to provide key biologic insight into common diseases of public health significance such as obesity, type II diabetes (T2D), and cancer. While many large studies of these traits have been conducted, the results have been disappointing – identifying loci of small influence which are difficult to replicate across studies. This difficulty, in part, is due to the heterogeneity of underlying trait evolutionary history and complexity of genetics underlying the trait. Analysis using biologic networks embraces this complexity. Using novel methods that examine variation integrated via networks we find that we can identify common pathways across independent data sets that have markedly higher influence. More provocatively, we find that many of these susceptibility pathways are shared across the complex traits obesity, T2D, and liver cancer. This latter observation suggests that it may be possible to both identify individuals at differential risk of developing disease and better understand why an individual’s disease progresses down specific paths.


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