This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 189
Type: Invited
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #306478
Title: Joint Modeling of Epigenetic Data, Transcription Factor Binding Data, and Expression Data
Author(s): Guanghua Xiao*+
Companies: UT Southwestern Medical Center
Address: 5323 Harry Hines Blvd, Dallas, TX, 75390,
Keywords: integrative analysis ; epigenetic data ; drug addiction ; bayesian modeling

A large amount of genome-wide molecular profiling data has been accumulated on mRNA expression, histone modification, DNA methylation and transcription factor (TF) binding to study drug addiction. In this study, we develop a novel statistical model to joint model multiple molecular profiling data for detection of important genes associated with cocaine addiction. Here, data from ChIP-chip experiments include genome-wide DNA methylation, histone modification, TF binding data with cocaine exposure and with saline treatment. Expression data will include mRNA expression datasets with and without cocaine treatment. A Bayesian hierarchical model, combined with a logistic regression model for multiple-level responses, is used to integrate information from the multiple data sources available. Both simulation studies and real data examples show the good performance of the proposed model.

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