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UID:20260207T201830EST-2328fvGIEI@132.216.98.100
DTSTAMP:20260208T011830Z
DESCRIPTION:Hierarchical Bayes Modeling Of Mediation Through High-Dimension
 al --Omics Data\n\n \n\nDr. Thomas is Professor of Biostatistics in the De
 partment of Preventive Medicine\, and Verna R. Richter Chair in Cancer Res
 earch at the University of Southern California\, Keck School of Medicine. 
 He received his Ph.D. from ���������Թ� in 1976\, where he continued 
 as a faculty member until his recruitment to USC in 1984. There he served 
 as the Head of the Biostatistics Division until 2013 and co-directed the S
 outhern California Environmental Health Sciences Center and the Cancer Epi
 demiology Program in the USC/Norris Comprehensive Cancer Center. His prima
 ry research interest has been in the development of statistical methods fo
 r environmental and genetic epidemiology\, with numerous collaborations in
  both areas. On the environmental side\, he has been particularly active i
 n radiation carcinogenesis and air pollution health effects research\, not
 ably as one of the senior investigators on the Southern California Childre
 n’s Health Study and the Women’s Environmental Cancer and Radiation Exposu
 re (WECARE) study and as a member of President Clinton’s Advisory Committe
 e on Human Radiation Experiments. On the genetic side\, he is a coinvestig
 ator in the NCI’s Colon Cancer Family Registry\, the Genetic Analysis Work
 shop\, the ENDGAME consortium to develop methods for genome-wide associati
 on studies\, and past President of the International Genetic Epidemiology 
 Society. Dr. Thomas has numerous publications\, including the textbooks St
 atistical Methods in Genetic Epidemiology (Oxford University Press\, 2004)
  and Statistical Methods in Environmental Epidemiology (Oxford University 
 Press\, 2009). He currently directs a program project grant on “Statistica
 l methods for integrative genomics in cancer.”\n\n\nVarious high-dimension
 al epigenetic\, transcriptomic\, proteomic\, metabolomic\, and other – omi
 c data have become available to provide insight into the mediation of gene
 tic and environmental influences on disease risk through the internal envi
 ronment. For example\, the “exposome” concept has been implemented using m
 ass spectrometry metabolomic measurements to capture a broad spectrum of i
 nternal metabolites of exogenous exposures\, but statistical methods for a
 nalyzing these and other - omic data are in their infancy. The “Meeting-in
 -the-Middle” principle aims to identify the subset of metabolites that are
  related to both exposure and disease. Here\, we introduce a novel hierarc
 hical Bayes framework for implementing this idea through simultaneous vari
 able selection on exposure-metabolite and metabolite-disease associations\
 , while incorporating external information such as the pathways in which t
 he different metabolites are thought 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 metabolites and 7 established risk factors fro
 m a nested case-control study within the EPIC cohort. 15 of the metabolite
 s yielded Bayes factors for mediation greater that 20 (“strong” evidence)\
 , the majority of these with multiple exposures. To explore this phenomeno
 n further\, we expanded the hierarchical model to include the pathways thr
 ough which these metabolites act as prior covariates. The strongest associ
 ations with exposures were found for the class of lysophosphatidylcholines
  and the strongest with disease for biogenic amines and acylcarnitines. Th
 ese approaches could be extended to study mediation through multiple types
  of – omic data. Genetic Epidemiology 2016\;40 (11): 619.\n
DTSTART:20170519T130000Z
DTEND:20170519T140000Z
LOCATION:Room 25\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Duncan Thomas\, PhD\, University of Southern California
URL:/mathstat/channels/event/duncan-thomas-phd-univers
 ity-southern-california-268088
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