BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250916T095156EDT-9901dE1zgU@132.216.98.100 DTSTAMP:20250916T135156Z 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.\n \n Biography: Dr. Thomas is Professor of Biostat istics in the Department of Preventive Medicine\, and Verna R. Richter Cha ir in Cancer Research at the University of Southern California\, Keck Scho ol of Medicine. He received his Ph.D. from 9I in 1976\, whe re 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 c o-directed the Southern California Environmental Health Sciences Center an d the Cancer Epidemiology Program in the USC/Norris Comprehensive Cancer C enter. His primary research interest has been in the development of statis tical methods for environmental and genetic epidemiology\, with numerous c ollaborations in both areas. On the environmental side\, he has been parti cularly active in radiation carcinogenesis and air pollution health effect s research\, notably as one of the senior investigators on the Southern Ca lifornia Children’s Health Study and the Women’s Environmental Cancer and Radiation Exposure (WECARE) study and as a member of President Clinton’s A dvisory Committee on Human Radiation Experiments. On the genetic side\, he is a coinvestigator in the NCI’s Colon Cancer Family Registry\, the Genet ic Analysis Workshop\, the ENDGAME consortium to develop methods for genom e-wide association studies\, and past President of the International Genet ic Epidemiology Society.\n Dr. Thomas has numerous publications\, including the textbooks Statistical Methods in Genetic Epidemiology (Oxford Univers ity Press\, 2004) and Statistical Methods in Environmental Epidemiology (O xford University Press\, 2009). He currently directs a program project gra nt on “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:/epi-biostat-occh/channels/event/special-seminar-b iostatistics-267957 END:VEVENT END:VCALENDAR