MAY 09, 2018 12:00 PM PDT
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Using Genome Wide Sequencing to Identify a Gene-Gene Interaction
Presented at the Genetics and Genomics 2018 Virtual Event
CONTINUING EDUCATION (CME/CE/CEU) CREDITS: P.A.C.E. CE | Florida CE
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Speakers:
  • Associate Director for Population Sciences, Professor of Community and Family Medicine at the Geisel School of Medicine, Norris Cotton Cancer Center, Dartmouth College
    Biography
      Dr. Amos moved in September, 2012 to the Geisel School of Medicine, where he is leading the Center for Genomic Medicine and serving as the Associate Cancer Center Director for Population Studies. Dr. Amos is leading studies to identify genetic risk factors for lung cancer and melanoma risk using genome-wide association and sequencing approaches.  Dr. Amos leads a U19 grant entitled Transdisciplinary Research in Cancer of the Lung (TRICL) to identify genetic factors for lung cancer and interactions with smoking, study these factors in cell biological and animal models and to perform epidemiological studies of gene and environmental contributions to lung cancer risk. This grant includes 16 subcontracts and integrates work from an international consortium. Dr. Amos has served as the leader of the biostatistics core for the Genetic Epidemiology of Lung Cancer Consortium (GELCC) since its inception.  With the move to Dartmouth he has replicated the computing environment that manages studies for GELCC and TRICL.   Dr. Amos also provides direction for a grant that characterizes and sequences nicotinic receptors and uses fMRI to investigate effects of nicotinic receptor variants on responses to smoking cues. Dr. Amos has also worked extensively in the genetic epidemiology of colon cancer. He developed a Peutz-Jeghers syndrome registry while at M.D. Anderson Cancer Center. He has also studied genetic risk factors for sporadic colon cancer and hereditary nonpolyposis colon cancer.  Dr. Amos has developed novel statistical approaches for gene-environment interaction analysis and for the identification of genes influencing complex diseases using either association based approaches or genetic linkage analysis.
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    Abstract:

    In this presentation I will describe results from a family study designed to identify the genetic cause for familial clustering of several early onset cancers.   Data from next generation sequencing combined with expression studies were integrated along with the familial cooccurrence of cancers to identify a genomic region of interest and then to further identify a genetic factor influencing cancer risk.  This factor also interacted with a second factor.  The presentation describes integration of family studies, genomic analysis and expression studies for gene discovery and characterization.  


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