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DTSTAMP:20260408T103836Z
DESCRIPTION:Hierarchical Bayes Modeling Of Mediation Through High-Dimension
 al - Omics Data\n	Friday\, 19 May 2017\n	Purvis Hall\, 1020 Pine Ave. West\,
  Room 25 at 9:00 am\n\nALL ARE WELCOME\n\nDuncan Thomas\, PhD\, Professor 
 of Biostatistics – Dept. of Preventive Medicine and Verna R. Richter Chair
  in Cancer Research\, University of Southern California Keck School of Med
 icine\n\nAbstract: Various high-dimensional epigenetic\, transcriptomic\, 
 proteomic\, metabolomic\, and other – omic data have become available to p
 rovide insight into the mediation of genetic and environmental influences 
 on disease risk through the internal environment. For example\, the “expos
 ome” concept has been implemented using mass spectrometry metabolomic meas
 urements to capture a broad spectrum of internal metabolites of exogenous 
 exposures\, but statistical methods for analyzing these and other - omic d
 ata are in their infancy. The “Meeting-in-the-Middle” principle aims to id
 entify the subset of metabolites that are related to both exposure and dis
 ease. Here\, we introduce a novel hierarchical Bayes framework for impleme
 nting this idea through simultaneous variable selection on exposure-metabo
 lite and metabolite-disease associations\, while incorporating external in
 formation such as the pathways in which the different metabolites are thou
 ght to act. The approach is validated by simulation and applied to data on
  hepatocellular carcinoma of the liver in relation to a panel of 125 metab
 olites and 7 established risk factors from a nested case-control study wit
 hin the EPIC cohort. 15 of the metabolites yielded Bayes factors for media
 tion greater that 20 (“strong” evidence)\, the majority of these with mult
 iple exposures. To explore this phenomenon further\, we expanded the hiera
 rchical model to include the pathways through which these metabolites act 
 as prior covariates. The strongest associations with exposures were found 
 for the class of lysophosphatidylcholines and the strongest with disease f
 or biogenic amines and acylcarnitines. These approaches could be extended 
 to study mediation through multiple types of – omic data.\n	Genetic Epidemi
 ology 2016\;40 (11): 619.Biography: Dr. Thomas is Professor of Biostatisti
 cs in the Department of Preventive Medicine\, and Verna R. Richter Chair i
 n Cancer Research at the University of Southern California\, Keck School o
 f Medicine. He received his Ph.D. from McGill University in 1976\, where h
 e continued as a faculty member until his recruitment to USC in 1984. Ther
 e he served as the Head of the Biostatistics Division until 2013 and co-di
 rected the Southern California Environmental Health Sciences Center and th
 e Cancer Epidemiology Program in the USC/Norris Comprehensive Cancer Cente
 r. His primary research interest has been in the development of statistica
 l methods for environmental and genetic epidemiology\, with numerous colla
 borations in both areas. On the environmental side\, he has been particula
 rly active in radiation carcinogenesis and air pollution health effects re
 search\, notably as one of the senior investigators on the Southern Califo
 rnia Children’s Health Study and the Women’s Environmental Cancer and Radi
 ation Exposure (WECARE) study and as a member of President Clinton’s Advis
 ory Committee on Human Radiation Experiments. On the genetic side\, he is 
 a coinvestigator in the NCI’s Colon Cancer Family Registry\, the Genetic A
 nalysis Workshop\, the ENDGAME consortium to develop methods for genome-wi
 de association studies\, and past President of the International Genetic E
 pidemiology Society.\n	Dr. Thomas has numerous publications\, including the
  textbooks Statistical Methods in Genetic Epidemiology (Oxford University 
 Press\, 2004) and Statistical Methods in Environmental Epidemiology (Oxfor
 d University Press\, 2009). He currently directs a program project grant o
 n “Statistical methods for integrative genomics in cancer.”\n	 \n
DTSTART:20170519T130000Z
DTEND:20170519T140000Z
LOCATION:Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue des Pins 
 Ouest
SUMMARY:SPECIAL SEMINAR: BIOSTATISTICS
URL:https://www.mcgill.ca/channels/event/special-seminar-biostatistics-2679
 57
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