This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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242
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #307837 |
Title:
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Bayesian LASSO for Genome-Wide Association Analysis
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Author(s):
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Jiahan Li* and Rongling Wu+
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Companies:
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Penn State and Penn State
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Address:
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78 University Manor West,, Hershey, PA, 7175318024,
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Keywords:
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Bayesian ;
Model Selection ;
GWAS ;
MCMC
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Abstract:
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Recently, genome-wide association studies have successfully identified genes that may affect complex traits or diseases. Although standard statistical tests for each single-nucleotide polymorphism (SNP) separately are able to capture main genetic effects, different approaches are necessary to identify multiple SNPs that influence disease risk jointly or SNP-SNP interactions. In this paper, we present a Bayesian approach which simultaneously estimates all possible genetic effects associated with all SNPs. The proposed model considers a double-exponential prior for the variances, which results in a Bayesian LASSO model that is advantageous when the number of predictors far exceeds the number of observations. By doing this, the most relevant disease genes can be identified from a huge number of SNPs. The methods are illustrated using both simulated and real data.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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