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Activity Number: 198
Type: Invited
Date/Time: Monday, August 3, 2009 : 2:00 PM to 3:50 PM
Sponsor: SSC
Abstract - #302793
Title: Model Selection Bias in Genome-Wide Genetic Studies
Author(s): Lei Sun*+
Companies: University of Toronto
Address: 155 College Street, Toronto, ON, M5T 3M7, Canada
Keywords: genetic studies ; bootstrap ; likelihood ; bias ; resampling ; Bayesian
Abstract:

Ascertainment bias in parameter estimates (e.g., heritability, odds ratio) is prevalent in genetic studies due to selection of both significant results and competing genetic variants. I will discuss and compare two categories of bias-reduced estimators that can be applied to the original data-set: bootstrap-based methods proposed by Sun and Bull (2005) and likelihood-based by Zollner and Pritchard (2007). Both approaches reduce bias substantially but variances remain large. To further improve the accuracy and efficiency, I will present a Bayesian framework incorporating prior information. In addition, I also demonstrate the computational feasibility of the bootstrap-based methods in the context of high-throughput genotype data. This is joint work with Professors Shelley Bull and Radu Craiu, and graduate students Apostolos Dimitromanolakis, Laura Faye, Longyang Wu and Lizhen Xu.

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