AUG 21, 2014 09:45 AM PDT
Pathway based analyses for genetic susceptibility to cancer and autoimmune disease
Presented at the Genetics and Genomics Virtual Event
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  • Associate Director for Population Sciences, Professor of Community and Family Medicine at the Geisel School of Medicine, Norris Cotton Cancer Center, Dartmouth College
      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.

    In this presentation I describe pathway based analyses of genotyping data to identify pathways related to the development of complex diseases, with a focus on lung cancer and selected autoimmune diseases. The goal of this research has been to identify sets of genes that influence disease risk using extensive data that have been developed by collaborative studies. These studies involve research groups from multiple locations across the world, which raises issues about joint analysis of the data. Rather, we adopt an approach in which analyses are performed by center and then merged, to perform first pass meta-analyses. Additionally, we apply novel approaches to organizing the data into pathways while allowing for correlations among markers to reduce discovery of false positive findings. Results from applications to lung cancer and selected autoimmune diseases will be described.

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