JSM 2011 Online Program

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Abstract Details

Activity Number: 233
Type: Contributed
Date/Time: Monday, August 1, 2011 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #301135
Title: On Model Selection Strategies to Identify Genes Underlying Binary Traits
Author(s): Zheyang Wu*+ and Hongyu Zhao
Companies: Worcester Polytechnic Institute and Yale University
Address: 100 Institute Road, Worcester, MA, 01609,
Keywords: Model selection ; Genome-wide scan ; Statistical power ; Marker detection
Abstract:

For more fruitful discoveries of disease genes, it is important to know whether joint analysis of multiple markers is more powerful than the commonly used single-marker analysis, especially in the presence of gene-gene interactions. In studying this problem, the literature has different even conflict arguments about the power of the common model selection strategies: marginal search, exhaustive search, and forward search. In the scenario of binary trait loci detection, we provide a statistical framework to rigorously address the question by analytically calculating the power of these strategies plus a widely used two-stage screen strategy. Our approach incorporates linkage disequilibrium, random genotypes, and correlations among test statistics, which are critical characters of model selection procedures but are often ignored for simplicity in the existing literature. Two types of widely applied error controls were compared: the discovery number control and the Bonferroni type I error rate control. This study provides a fast computation tool in R as well as insights into the statistical mechanism of capturing genetic signals under different epistatic models.


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