JSM 2015 Preliminary Program

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Activity Number: 253
Type: Contributed
Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #317480 View Presentation
Title: Estimation of Random Effects in Meta-Analysis of Gene Expression Studies
Author(s): Uma Siangphoe* and Kellie J. Archer
Companies: Virginia Commonwealth University and Virginia Commonwealth University
Keywords: gene expression ; meta-analysis ; random effects
Abstract:

Combining effect sizes from individual studies using random effects models are commonly applied in high-dimensional gene expression data. However, unknown study heterogeneity can arise from inconsistency of sample qualities and experimental conditions. A high heterogeneity of effect sizes can reduce statistical power of the models. We propose a new weighted estimate based on a Gaussian linear mixed-model to estimate and test for significance of random effects for individual genes and applied the method to perform an Alzheimer's gene expression meta-analysis. This method provided a lower minimum sum of squared error than standard methods. We also examined the strength of study heterogeneity to compare gene variation among meta-analyses. Estimation methods used included unrestricted and restricted maximum likelihood as well as robust estimation to account for outliers and skewed distributions. A permutation method was applied to generate z-statistics for both common and random effects estimates and a modified Benjamini and Hochberg's method was used to control the false discovery rate. We demonstrate our method has relatively better precision among the four methods compared.


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