Genetics and Genomics 2017 Starts Tomorrow and Features John Quakenbush, Marcel Dinger and Kenneth Buetow

With a consistently increased understanding of the genome and its integration with human biology comes the opportunity to apply this knowledge to medicine. Clinical genomics, gene therapy, and precision medicine have been able to take information and technological advances from current research and develop treatments targeted for specific disease types and to unique individual needs. The Genetics and Genomics 2017 virtual conference focuses on this application of basic research to new advances in medicine. LabRoots is thrilled to present three Keynote Speakers that are on the forefront of this exciting era in genomics. All presentations are eligible for Continuing Education Credits with Florida CE and P.A.C.E. CE, with some speakers also certified for CEU. On May 10, 2017 at 9:00 AM PDT, Dr. John Quackenbush, PhD will give his Keynote Presentation: Using Networks to Understand the Genotype-Phenotype Connection. Dr. Quackenbush is a Professor of Computational Biology and Bioinformatics in the Department of Biostatistics at Harvard University and the Dana-Farber Cancer Institute. He has a background in theoretical physics and moved into genetics to work on the Human Genome Project, and later on pioneering expression analysis at The Institute for Genomic Research (TIGR). He currently works on the 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. 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. CONDOR is a new 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. CONDOR can be used in the analysis of a wide variety of disease processes and other phenotypic traits. This talk presents data from the application of CONDOR to chronic obstructive pulmonary disease (COPD). Results highlight 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. On May 11, 2017 at 7:30 AM PDT, Dr. Marcel Dinger, MSc (hons), PhD will give a Keynote Presentation titled “Genomics in the clinic: A revolution for healthcare and medical research.” Marcel is the Head of Clinical Genomics at the Garvan Institute of Medical Research and Conjoint Associate Professor at the University of New South Wales. Dr. Dinger originally studied the role of long noncoding RNAs in mammalian development and disease. He was recruited to the Garvan Institute in 2012. Genetic testing currently plays a relatively niche role in healthcare, with testing typically limited to single genes and targeting a relatively narrow range of diseases, including pediatric disorders, familial cancer and somatic cancer. With exponential decreases in the cost of large-scale DNA sequencing technology, the potential application for genetic testing of other diseases has greatly expanded. In addition to serving as a highly effective approach for diagnosing diseases that can be caused by large numbers of different genes, whole genome sequencing has potential for reanalysis in different contexts. This potential supports a new testing paradigm, where genomic sequencing is undertaken once in an individual’s lifetime and analyzed throughout their lifetime to guide clinical decision-making and optimize health management. Dr. Dinger will discuss the implementation, clinical accreditation and performance of whole genome sequencing in the routine diagnosis of genetic and rare diseases, and discuss the challenges and opportunities for using genomic information to inform whole of life healthcare. On May 11, 2017 at 9:00 AM PDT, Dr. Kenneth Buetow, PhD, FACMI will give a Keynote Presentation on “Using network models to understand common complex disease predisposition and progression.” Dr. Buetow is the Director of Computational Sciences and Informatics program for Complex Adaptive Systems and Professor in the School of Life Sciences at Arizona State University. He is a human genetics and genomics researcher who leverages computational tools to understand complex traits such as cancer, liver disease, and obesity. 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), overseeing 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. 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. Visit the Genetics and Genomics virtual conference site to see all speakers.