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

Abstract Details

Activity Number: 235
Type: Contributed
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #308247
Title: Bayesian Meta-Analysis of Genetic Association Studies: Effective Combining Evidence and Efficent Handling Missing Data
Author(s): Xiaoquan Wen*+ and Matthew Stephens
Companies: The University of Chicago and The University of Chicago
Address: Dept of Statistics, Chicago, IL, 60637,
Keywords: Meta-analysis ; Bayes Factor ; Imputation ; Genetic Association ; Hierarchical model ; Data reduction
Abstract:

To uncover association between disease and genetic factor, meta-analyses are required for combining results from multiple studies. Two important statistical issues need to be addressed: 1. A systematic way to combine evidence from heterogeneous data sources 2. Non-random missing data pattern caused by studies typing different sets of markers. We propose a Bayesian framework to perform random-effects meta-analysis. Our method yields Bayes Factors in closed-form, which is easy to compute/interpret and even has an implication on corresponding Frequentist approach. For untyped genotypes, we propose to perform genotype imputation using an accurate regularized linear genotype predictor with imputation uncertainties appropriately incorporated in downstream analysis. As a result, our model also achieves great data reduction: it only requires summary-level statistics from participating studies.


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