Abstract Details
Activity Number:
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350
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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International Chinese Statistical Association
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Abstract #316105
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View Presentation
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Title:
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Detecting Rare Haplotype-Environment Interaction Under Uncertainty of Gene-Environment Independence Assumption
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Author(s):
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Yuan Zhang* and Swati Biswas and Shili Lin
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Companies:
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The University of Texas at Dallas and The University of Texas at Dallas and The Ohio State University
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Keywords:
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GXE ;
G-E independence ;
LBL ;
MCMC ;
Missing Heritability ;
Rare variants
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Abstract:
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Detecting gene-environment interactions (GXE) where G is a rare haplotype variant is a challenging research problem. A common assumption made in such analyses is independence of G and E. We consider the recently proposed method based on logistic Bayesian LASSO (LBL) for detecting GXE, which assumes G-E independence - we refer to it as LBL-GXE-I. It has inflated type I errors when the assumption is violated. Here we propose a way of relaxing this assumption by modeling the haplotype frequencies as functions of E - we refer to it as LBL-GXE-D. It successfully controls type I error rates in all situations. However, LBL-GXE-D has reduced power than LBL-GXE-I when G-E independence holds. To address this problem, we propose a unified approach by employing reversible jump Markov chain Monte Carlo method - we refer to this method as LBL-GXE. Simulation studies show that LBL-GXE has power similar to that of LBL-GXE-I in case of G-E independence and at the same time has controlled type I errors in all situations. Finally, we apply LBL-GXE to a lung cancer dataset.
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Authors who are presenting talks have a * after their name.
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