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Abstract Details

Activity Number: 309
Type: Invited
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract - #303906
Title: Variable Selection in Meta-Analysis of High-Dimensional Genetic Data
Author(s): Danyu Lin*+
Companies: The University of North Carolina at Chapel Hill
Address: Department of Biostatistics, Chapel Hill, NC, 27517,
Keywords: Adaptive LASSO ; Genomewide association studies ; Random-effects models ; Selection consistency ; Summary statistics
Abstract:

Variable selection is an important task, especially in genomic studies. Because the predictors identified from a single study tend to be non-reproducible, it is desirable to select important predictors from multiple independent studies. This issue has received little attention in the statistical literature, the only existing work requiring individual-level data. We develop variable selection methods based on summary statistics under both fixed- and random-effects models. Adaptive LASSO is adopted for selection and the weights in the selection will automatically shrink the coefficients for unimportant predictors to 0. The selection consistency and asymptotic normality of the estimators are established. The empirical performance is assessed through simulation studies. An application to a set of genomewide association studies is provided.


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